From de9dcce46591d185eef66aa15b032389f9ab8cf8 Mon Sep 17 00:00:00 2001 From: Santiago Olivella Date: Wed, 6 Sep 2023 16:41:27 -0400 Subject: [PATCH 01/90] Use predict_race instead of predict_race_new for initial race values --- R/predict_race.R | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/R/predict_race.R b/R/predict_race.R index 31ed173..80dcdb5 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -225,19 +225,20 @@ predict_race <- function(voter.file, census.surname = TRUE, surname.only = FALSE if(ctrl$verbose){ message("Using `predict_race` to obtain initial race prediction priors with BISG model") } - race.init <- predict_race_new(voter.file = voter.file, - names.to.use = names.to.use, - year = year, - age = age, sex = sex, # not implemented, default to F - census.geo = census.geo, - census.key = census.key, - name.dictionaries = name.dictionaries, - surname.only=surname.only, - census.data = census.data, - retry = retry, - impute.missing = TRUE, - census.surname = census.surname, - use.counties = use.counties) + race.init <- predict_race(voter.file = voter.file, + names.to.use = names.to.use, + year = year, + age = age, sex = sex, # not implemented, default to F + census.geo = census.geo, + census.key = census.key, + name.dictionaries = name.dictionaries, + surname.only=surname.only, + census.data = census.data, + retry = retry, + impute.missing = TRUE, + census.surname = census.surname, + use.counties = use.counties, + model = "BISG") race.init <- max.col( race.init[, paste0("pred.", c("whi", "bla", "his", "asi", "oth"))], ties.method = "random" From 10a650cafa34f3e3b9942407ac35e6345c878f36 Mon Sep 17 00:00:00 2001 From: Santiago Olivella Date: Wed, 6 Sep 2023 16:53:06 -0400 Subject: [PATCH 02/90] Fix bug caused by tibble's type safety when accessing columns by position --- NAMESPACE | 1 + R/race_prediction_funs.R | 5 ++++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/NAMESPACE b/NAMESPACE index 5237a36..c6721d1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,6 +6,7 @@ export(predict_race) import(PL94171) importFrom(Rcpp,evalCpp) importFrom(dplyr,coalesce) +importFrom(dplyr,pull) importFrom(furrr,future_map_dfr) importFrom(piggyback,pb_download) importFrom(purrr,map_dfr) diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 1ade9c7..c8ea4d7 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -261,6 +261,7 @@ NULL #' New race prediction function, implementing classical BISG with augmented #' surname dictionary, as well as first and middle name information. #' @rdname modfuns +#' @keywords internal predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE, sex = FALSE, census.geo, census.key = NULL, name.dictionaries, surname.only=FALSE, census.data = NULL, retry = 0, impute.missing = TRUE, census.surname = FALSE, @@ -429,7 +430,9 @@ predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE #' New race prediction function, implementing fBISG (i.e. measurement #' error correction, fully Bayesian model) with augmented #' surname dictionary, as well as first and middle name information. +#' @importFrom dplyr pull #' @rdname modfuns +#' @keywords internal predict_race_me <- function(voter.file, names.to.use, year = "2020",age = FALSE, sex = FALSE, census.geo, census.key, name.dictionaries, surname.only=FALSE, census.data = NULL, retry = 0, impute.missing = TRUE, census.surname = FALSE, @@ -604,7 +607,7 @@ predict_race_me <- function(voter.file, names.to.use, year = "2020",age = FALSE, surname = last_c, first = first_c, middle = mid_c) - kw_names <- toupper(ntab[, 1]) + kw_names <- toupper(dplyr::pull(ntab, 1)) proc_names_vf <- .name_preproc(voter.file[[ntype]], c(kw_names)) u_vf_names <- unique(proc_names_vf) kw_in_vf <- kw_names %in% proc_names_vf From 8f6a52209e06f49bc1e750352d4d18593dd25313 Mon Sep 17 00:00:00 2001 From: Santiago Olivella Date: Wed, 6 Sep 2023 17:25:31 -0400 Subject: [PATCH 03/90] For race.init, run prediction function quietly --- R/predict_race.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/predict_race.R b/R/predict_race.R index 80dcdb5..28b035e 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -238,7 +238,8 @@ predict_race <- function(voter.file, census.surname = TRUE, surname.only = FALSE impute.missing = TRUE, census.surname = census.surname, use.counties = use.counties, - model = "BISG") + model = "BISG", + control = list(verbose=FALSE)) race.init <- max.col( race.init[, paste0("pred.", c("whi", "bla", "his", "asi", "oth"))], ties.method = "random" From f52ccf54d9fcaa0727c1549757dfa3e018f81c3f Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 10:15:15 -0800 Subject: [PATCH 04/90] docs: use README.Rmd --- .Rbuildignore | 1 + README.Rmd | 164 ++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 106 +++++++++++++++++++++++++------- 3 files changed, 250 insertions(+), 21 deletions(-) create mode 100644 README.Rmd diff --git a/.Rbuildignore b/.Rbuildignore index af6362a..9d87231 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -12,3 +12,4 @@ ChangeLog ^cran-comments\.md$ ^CRAN-SUBMISSION$ +^README\.Rmd$ diff --git a/README.Rmd b/README.Rmd new file mode 100644 index 0000000..543695d --- /dev/null +++ b/README.Rmd @@ -0,0 +1,164 @@ +--- +output: github_document +--- + + + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.path = "man/figures/README-", + out.width = "100%" +) +``` + +# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation [![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) ![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) + +This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). "[Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.](http://imai.princeton.edu/research/race.html)" Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. + +### Using wru + +First, you should save your census key to your `.Rprofile` or `.Renviron`. Below is an example procedure: + + > usethis::edit_r_profile() + # edit the file with the following + Sys.setenv("CENSUS_API_KEY" = "Your Key") + # save and close the file + # Restart your R session + +Now, here is a simple example that predicts the race/ethnicity of voters based only on their surnames. + +``` r +library(wru) +future::plan(future::multisession) +data(voters) +predict_race(voter.file = voters, surname.only = T) +``` + +The above produces the following output, where the last five columns are probabilistic race/ethnicity predictions (e.g., 'pred.his' is the probability of being Hispanic/Latino): + + "Proceeding with surname-only predictions ..." + VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.0676000 0.00430000 0.00820000 0.86680000 0.05310000 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.0812000 0.00240000 0.06890000 0.73750000 0.11000000 + 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.0594000 0.00260000 0.82270000 0.10510000 0.01020000 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.9355936 0.00220022 0.02850285 0.00780078 0.02590259 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.0098000 0.00180000 0.00065000 0.98200000 0.00575000 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.9187000 0.01083333 0.01083333 0.01083333 0.04880000 + 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.5897000 0.34630000 0.02360000 0.00540000 0.03500000 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.0486000 0.00570000 0.92920000 0.01020000 0.00630000 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.6665000 0.08530000 0.13670000 0.07970000 0.03180000 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.9054000 0.04310000 0.02060000 0.00720000 0.02370000 + +In order to predict race/ethnicity based on surnames AND geolocation, a user needs to provide a valid U.S. Census API key to access the census statistics. You may request a U.S. Census API key [here](http://api.census.gov/data/key_signup.html). Once you have an API key, you can use the package to download relevant Census geographic data on demand and condition race/ethnicity predictions on geolocation (county, tract, block, or place). + +The following example predicts the race/ethnicity of voters based on their surnames, Census tract of residence (census.geo = "tract"), and which party registration (party = "PID"). Note that a valid API key must be provided in the input parameter 'census.key' in order for the function to download the relevant tract-level data. + +``` r +library(wru) +data(voters) +predict_race(voter.file = voters, census.geo = "tract", census.key = "...", party = "PID") +``` + +The above returns the following output. + + VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.081856291 0.0021396565 0.0110451405 0.828313291 0.076645621 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.916936771 0.0044432219 0.0120276229 0.008532929 0.058059455 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895620643 0.0022078678 0.0139457411 0.023345853 0.064879895 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.003164229 0.0006092345 0.0001072684 0.991261466 0.004857802 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.029936354 0.0009275220 0.0129831039 0.850040743 0.106112277 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.231046860 0.0016485574 0.6813780115 0.053180270 0.032746301 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.817841573 0.0063677130 0.0258733496 0.107254103 0.042663261 + 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.223924118 0.0002913000 0.4451163607 0.313431417 0.017236805 + 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.241417483 0.6900686166 0.0293556870 0.011105140 0.028053073 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.983300770 0.0006116706 0.0034070782 0.004823439 0.007857042 + +In predict_race, the census.geo options are "county", "tract", "block" and "place". Here is an example of prediction based on census statistics collected at the level of "place": + +``` r +data(voters) +predict_race(voter.file = voters, census.geo = "place", census.key = "...", party = "PID") +``` + +It is also possible to pre-download Census geographic data, which can save time when running predict_race(). The example dataset 'voters' includes people in DC, NJ, and NY. The following example subsets voters in DC and NJ, and then uses get_census_data() to download Census geographic data in these two states (input parameter 'key' requires valid API key). Census data is assigned to an object named census.dc.nj. The predict_race() statement predicts the race/ethnicity of voters in DC and NJ using the pre-saved Census data (census.data = census.dc.nj). This example conditions race/ethnicity predictions on voters' surnames, block of residence (census.geo = "block"), age (age = TRUE), and party registration (party = "PID"). + +Please note that the input parameters 'age' and 'sex' must have the same values in get_census_data() and predict_race(), i.e., TRUE in both or FALSE in both. In this case, predictions are conditioned on age but not sex, so age = TRUE and sex = FALSE in both the get_census_data() and predict_race() statements. + +``` r +library(wru) +data(voters) +voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset +census.dc.nj <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE) # create Census data object covering DC and NJ +predict_race(voter.file = voters.dc.nj, census.geo = "block", census.data = census.dc.nj, age = TRUE, sex = FALSE, party = "PID") +``` + +The last two lines above are equivalent to the following: + +``` r +predict_race(voter.file = voters.dc.nj, census.geo = "block", census.key = "...", age = TRUE, sex = FALSE, party = "PID") +``` + +Using pre-downloaded Census data may be useful for the following reasons: + +- You can save a lot of time in future runs of predict_race() if the relevant Census data has already been saved; +- The machines used to run predict_race() may not have internet access; +- You can obtain timely snapshots of Census geographic data that match your voter file. + +Downloading data using get_census_data() may take a long time, especially at the block level or in large states. If block-level Census data is not required, downloading Census data at the tract level will save time. Similarly, if tract-level Census data is not required, county-level data may be specified in order to save time. + +``` r +library(wru) +data(voters) +voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset +census.dc.nj2 <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE, census.geo = "tract") +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) +``` + +Or you can also use the census_geo_api() to manually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter 'state' that requires a two-letter state abbreviation to proceed. + +``` r +censusObj2 = list() + +county.dc <- census_geo_api(key = "...", state = "DC", geo = "county", age = TRUE, sex = FALSE) +tract.dc <- census_geo_api(key = "...", state = "DC", geo = "tract", age = TRUE, sex = FALSE) +censusObj2[["DC"]] <- list(state = "DC", county = county.dc, tract = tract.dc, age = TRUE, sex = FALSE) + +tract.nj <- census_geo_api(key = "...", state = "NJ", geo = "tract", age = TRUE, sex = FALSE) +county.nj <- census_geo_api(key = "...", state = "NJ", geo = "county", age = TRUE, sex = FALSE) +censusObj2[["NJ"]] <- list(state = "NJ", county = county.nj, tract = tract.nj, age = TRUE, sex = FALSE) +``` + +Note: The age and sex parameters must be consistent when creating the Census object and using that Census object in the predict_race function. If one of these parameters is TRUE in the Census object, it must also be TRUE in the predict_race function. + +After saving the data in censusObj2 above, we can condition race/ethnicity predictions on different combinations of input variables, without having to re-download the relevant Census data. + +``` r +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) +``` + +## Notes about process design + +For larger scale imputations garbage-collection can become a problem and your machine(s) can quickly run out of memory (RAM). It is recommended to use the `future.callr::callr` plan instead of `future::multisession`. The `callr` plan instantiates a new session at every iteration of your parallel loop or map. This simultaneously has the effect of creating more overhead, but also clearing the often sticky memory elements that would be left over to grow to eventual system failure when using `multisession`. You end up with a process that is more stable, but slightly slower. + + library(wru) + future::plan(future.callr::callr) + # ... + +## Census Data + +This package uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau. + +U.S. Census Bureau (2021, October 8). Decennial Census API. Census.gov. Retrieved from + +### A related song + +Watch [this](https://www.youtube.com/watch?v=LYb_nqU_43w)! diff --git a/README.md b/README.md index bd076f2..d8bc8de 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,18 @@ + + + # wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation [![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) ![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) -This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). "[Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.](http://imai.princeton.edu/research/race.html)" Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. +This R package implements the methods proposed in Imai, K. and Khanna, +K. (2016). “[Improving Ecological Inference by Predicting Individual +Ethnicity from Voter Registration +Record.](http://imai.princeton.edu/research/race.html)” Political +Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. ### Using wru -First, you should save your census key to your `.Rprofile` or `.Renviron`. Below is an example procedure: +First, you should save your census key to your `.Rprofile` or +`.Renviron`. Below is an example procedure: > usethis::edit_r_profile() # edit the file with the following @@ -12,7 +20,8 @@ First, you should save your census key to your `.Rprofile` or `.Renviron`. Below # save and close the file # Restart your R session -Now, here is a simple example that predicts the race/ethnicity of voters based only on their surnames. +Now, here is a simple example that predicts the race/ethnicity of voters +based only on their surnames. ``` r library(wru) @@ -21,7 +30,9 @@ data(voters) predict_race(voter.file = voters, surname.only = T) ``` -The above produces the following output, where the last five columns are probabilistic race/ethnicity predictions (e.g., 'pred.his' is the probability of being Hispanic/Latino): +The above produces the following output, where the last five columns are +probabilistic race/ethnicity predictions (e.g., ‘pred.his’ is the +probability of being Hispanic/Latino): "Proceeding with surname-only predictions ..." VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth @@ -36,9 +47,19 @@ The above produces the following output, where the last five columns are probabi 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.6665000 0.08530000 0.13670000 0.07970000 0.03180000 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.9054000 0.04310000 0.02060000 0.00720000 0.02370000 -In order to predict race/ethnicity based on surnames AND geolocation, a user needs to provide a valid U.S. Census API key to access the census statistics. You may request a U.S. Census API key [here](http://api.census.gov/data/key_signup.html). Once you have an API key, you can use the package to download relevant Census geographic data on demand and condition race/ethnicity predictions on geolocation (county, tract, block, or place). +In order to predict race/ethnicity based on surnames AND geolocation, a +user needs to provide a valid U.S. Census API key to access the census +statistics. You may request a U.S. Census API key +[here](http://api.census.gov/data/key_signup.html). Once you have an API +key, you can use the package to download relevant Census geographic data +on demand and condition race/ethnicity predictions on geolocation +(county, tract, block, or place). -The following example predicts the race/ethnicity of voters based on their surnames, Census tract of residence (census.geo = "tract"), and which party registration (party = "PID"). Note that a valid API key must be provided in the input parameter 'census.key' in order for the function to download the relevant tract-level data. +The following example predicts the race/ethnicity of voters based on +their surnames, Census tract of residence (census.geo = “tract”), and +which party registration (party = “PID”). Note that a valid API key must +be provided in the input parameter ‘census.key’ in order for the +function to download the relevant tract-level data. ``` r library(wru) @@ -60,16 +81,32 @@ The above returns the following output. 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.241417483 0.6900686166 0.0293556870 0.011105140 0.028053073 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.983300770 0.0006116706 0.0034070782 0.004823439 0.007857042 -In predict_race, the census.geo options are "county", "tract", "block" and "place". Here is an example of prediction based on census statistics collected at the level of "place": +In predict_race, the census.geo options are “county”, “tract”, “block” +and “place”. Here is an example of prediction based on census statistics +collected at the level of “place”: ``` r data(voters) predict_race(voter.file = voters, census.geo = "place", census.key = "...", party = "PID") ``` -It is also possible to pre-download Census geographic data, which can save time when running predict_race(). The example dataset 'voters' includes people in DC, NJ, and NY. The following example subsets voters in DC and NJ, and then uses get_census_data() to download Census geographic data in these two states (input parameter 'key' requires valid API key). Census data is assigned to an object named census.dc.nj. The predict_race() statement predicts the race/ethnicity of voters in DC and NJ using the pre-saved Census data (census.data = census.dc.nj). This example conditions race/ethnicity predictions on voters' surnames, block of residence (census.geo = "block"), age (age = TRUE), and party registration (party = "PID"). - -Please note that the input parameters 'age' and 'sex' must have the same values in get_census_data() and predict_race(), i.e., TRUE in both or FALSE in both. In this case, predictions are conditioned on age but not sex, so age = TRUE and sex = FALSE in both the get_census_data() and predict_race() statements. +It is also possible to pre-download Census geographic data, which can +save time when running predict_race(). The example dataset ‘voters’ +includes people in DC, NJ, and NY. The following example subsets voters +in DC and NJ, and then uses get_census_data() to download Census +geographic data in these two states (input parameter ‘key’ requires +valid API key). Census data is assigned to an object named census.dc.nj. +The predict_race() statement predicts the race/ethnicity of voters in DC +and NJ using the pre-saved Census data (census.data = census.dc.nj). +This example conditions race/ethnicity predictions on voters’ surnames, +block of residence (census.geo = “block”), age (age = TRUE), and party +registration (party = “PID”). + +Please note that the input parameters ‘age’ and ‘sex’ must have the same +values in get_census_data() and predict_race(), i.e., TRUE in both or +FALSE in both. In this case, predictions are conditioned on age but not +sex, so age = TRUE and sex = FALSE in both the get_census_data() and +predict_race() statements. ``` r library(wru) @@ -85,13 +122,20 @@ The last two lines above are equivalent to the following: predict_race(voter.file = voters.dc.nj, census.geo = "block", census.key = "...", age = TRUE, sex = FALSE, party = "PID") ``` -Using pre-downloaded Census data may be useful for the following reasons: +Using pre-downloaded Census data may be useful for the following +reasons: -- You can save a lot of time in future runs of predict_race() if the relevant Census data has already been saved; -- The machines used to run predict_race() may not have internet access; -- You can obtain timely snapshots of Census geographic data that match your voter file. +- You can save a lot of time in future runs of predict_race() if the + relevant Census data has already been saved; +- The machines used to run predict_race() may not have internet access; +- You can obtain timely snapshots of Census geographic data that match + your voter file. -Downloading data using get_census_data() may take a long time, especially at the block level or in large states. If block-level Census data is not required, downloading Census data at the tract level will save time. Similarly, if tract-level Census data is not required, county-level data may be specified in order to save time. +Downloading data using get_census_data() may take a long time, +especially at the block level or in large states. If block-level Census +data is not required, downloading Census data at the tract level will +save time. Similarly, if tract-level Census data is not required, +county-level data may be specified in order to save time. ``` r library(wru) @@ -105,7 +149,11 @@ predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = cen predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) ``` -Or you can also use the census_geo_api() to manually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter 'state' that requires a two-letter state abbreviation to proceed. +Or you can also use the census_geo_api() to manually construct a census +object. The example below creates a census object with county-level and +tract-level data in DC and NJ, while avoiding downloading block-level +data. Note that this function has the input parameter ‘state’ that +requires a two-letter state abbreviation to proceed. ``` r censusObj2 = list() @@ -119,9 +167,14 @@ county.nj <- census_geo_api(key = "...", state = "NJ", geo = "county", age = TRU censusObj2[["NJ"]] <- list(state = "NJ", county = county.nj, tract = tract.nj, age = TRUE, sex = FALSE) ``` -Note: The age and sex parameters must be consistent when creating the Census object and using that Census object in the predict_race function. If one of these parameters is TRUE in the Census object, it must also be TRUE in the predict_race function. +Note: The age and sex parameters must be consistent when creating the +Census object and using that Census object in the predict_race function. +If one of these parameters is TRUE in the Census object, it must also be +TRUE in the predict_race function. -After saving the data in censusObj2 above, we can condition race/ethnicity predictions on different combinations of input variables, without having to re-download the relevant Census data. +After saving the data in censusObj2 above, we can condition +race/ethnicity predictions on different combinations of input variables, +without having to re-download the relevant Census data. ``` r predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) @@ -132,7 +185,15 @@ predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = cens ## Notes about process design -For larger scale imputations garbage-collection can become a problem and your machine(s) can quickly run out of memory (RAM). It is recommended to use the `future.callr::callr` plan instead of `future::multisession`. The `callr` plan instantiates a new session at every iteration of your parallel loop or map. This simultaneously has the effect of creating more overhead, but also clearing the often sticky memory elements that would be left over to grow to eventual system failure when using `multisession`. You end up with a process that is more stable, but slightly slower. +For larger scale imputations garbage-collection can become a problem and +your machine(s) can quickly run out of memory (RAM). It is recommended +to use the `future.callr::callr` plan instead of `future::multisession`. +The `callr` plan instantiates a new session at every iteration of your +parallel loop or map. This simultaneously has the effect of creating +more overhead, but also clearing the often sticky memory elements that +would be left over to grow to eventual system failure when using +`multisession`. You end up with a process that is more stable, but +slightly slower. library(wru) future::plan(future.callr::callr) @@ -140,9 +201,12 @@ For larger scale imputations garbage-collection can become a problem and your ma ## Census Data -This package uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau. +This package uses the Census Bureau Data API but is not endorsed or +certified by the Census Bureau. -U.S. Census Bureau (2021, October 8). Decennial Census API. Census.gov. Retrieved from +U.S. Census Bureau (2021, October 8). Decennial Census API. Census.gov. +Retrieved from + ### A related song From a3c8e96a8062f219bde832353840e72bee4d6943 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 10:34:05 -0800 Subject: [PATCH 05/90] docs(README): move badges to their own line --- README.Rmd | 4 +++- README.md | 6 +++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/README.Rmd b/README.Rmd index 543695d..eeb1c7c 100644 --- a/README.Rmd +++ b/README.Rmd @@ -13,7 +13,9 @@ knitr::opts_chunk$set( ) ``` -# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation [![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) ![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) +# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation + +[![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) ![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). "[Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.](http://imai.princeton.edu/research/race.html)" Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. diff --git a/README.md b/README.md index d8bc8de..3cb9588 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,11 @@ -# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation [![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) ![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) +# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation + +[![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) +[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) +![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). “[Improving Ecological Inference by Predicting Individual From 4d116d01858def8be15bf82f1eb527b0f9480268 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 10:35:37 -0800 Subject: [PATCH 06/90] docs(README): use thumbnail to link to The Who song --- README.Rmd | 2 +- README.md | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/README.Rmd b/README.Rmd index eeb1c7c..2c7d11f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -163,4 +163,4 @@ U.S. Census Bureau (2021, October 8). Decennial Census API. Census.gov. Retrieve ### A related song -Watch [this](https://www.youtube.com/watch?v=LYb_nqU_43w)! +[![Thumbnail of the music video for "Who Are You" by The Who](https://img.youtube.com/vi/PNbBDrceCy8/maxresdefault.jpg)](https://www.youtube.com/watch?v=PNbBDrceCy8) diff --git a/README.md b/README.md index 3cb9588..a32f71f 100644 --- a/README.md +++ b/README.md @@ -214,4 +214,5 @@ Retrieved from ### A related song -Watch [this](https://www.youtube.com/watch?v=LYb_nqU_43w)! +[![Thumbnail of the music video for “Who Are You” by The +Who](https://img.youtube.com/vi/PNbBDrceCy8/maxresdefault.jpg)](https://www.youtube.com/watch?v=PNbBDrceCy8) From 4246b74da39a6478293d6053933c9bf54dc56b99 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 10:57:56 -0800 Subject: [PATCH 07/90] docs(README): add installation instructions --- README.Rmd | 15 +++++++++++++++ README.md | 17 +++++++++++++++++ 2 files changed, 32 insertions(+) diff --git a/README.Rmd b/README.Rmd index 2c7d11f..e8714ec 100644 --- a/README.Rmd +++ b/README.Rmd @@ -19,6 +19,21 @@ knitr::opts_chunk$set( This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). "[Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.](http://imai.princeton.edu/research/race.html)" Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. +## Installation + +You can install the released version of **wru** from [CRAN](https://cran.r-project.org/package=wru) with: + +``` r +install.packages("wru") +``` + +Or you can install the development version of **wru** from [GitHub](https://github.com/kosukeimai/wru) with: + +``` r +# install.packages("pak") +pak::pkg_install("kosukeimai/wru") +``` + ### Using wru First, you should save your census key to your `.Rprofile` or `.Renviron`. Below is an example procedure: diff --git a/README.md b/README.md index a32f71f..c2b2735 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,23 @@ Ethnicity from Voter Registration Record.](http://imai.princeton.edu/research/race.html)” Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. +## Installation + +You can install the released version of **wru** from +[CRAN](https://cran.r-project.org/package=wru) with: + +``` r +install.packages("wru") +``` + +Or you can install the development version of **wru** from +[GitHub](https://github.com/kosukeimai/wru) with: + +``` r +# install.packages("pak") +pak::pkg_install("kosukeimai/wru") +``` + ### Using wru First, you should save your census key to your `.Rprofile` or From cedf57e82a29aec735ded65b25d9760e161bb5e2 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 11:00:11 -0800 Subject: [PATCH 08/90] docs(README): link DOI --- README.Rmd | 2 +- README.md | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/README.Rmd b/README.Rmd index e8714ec..32ca602 100644 --- a/README.Rmd +++ b/README.Rmd @@ -17,7 +17,7 @@ knitr::opts_chunk$set( [![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) ![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) -This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). "[Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.](http://imai.princeton.edu/research/race.html)" Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. +This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). "[Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record](http://imai.princeton.edu/research/race.html)." Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. [doi: 10.1093/pan/mpw001](https://dx.doi.org/10.1093/pan/mpw001). ## Installation diff --git a/README.md b/README.md index c2b2735..68487fd 100644 --- a/README.md +++ b/README.md @@ -10,8 +10,9 @@ This R package implements the methods proposed in Imai, K. and Khanna, K. (2016). “[Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration -Record.](http://imai.princeton.edu/research/race.html)” Political -Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. doi: 10.1093/pan/mpw001. +Record](http://imai.princeton.edu/research/race.html).” Political +Analysis, Vol. 24, No. 2 (Spring), pp. 263-272. [doi: +10.1093/pan/mpw001](https://dx.doi.org/10.1093/pan/mpw001). ## Installation From 71cf85e7ba3a0f0fb8d983eda9e95eebaa1a7f3b Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 11:25:32 -0800 Subject: [PATCH 09/90] docs: enable markdown in Roxygen comments --- DESCRIPTION | 1 + man/census_geo_api.Rd | 28 +++++++------- man/census_helper.Rd | 4 +- man/census_helper_new.Rd | 40 ++++++++++---------- man/format_legacy_data.Rd | 10 ++--- man/get_census_api.Rd | 2 +- man/get_census_api_2.Rd | 18 ++++----- man/get_census_data.Rd | 26 ++++++------- man/merge_names.Rd | 48 ++++++++++++----------- man/merge_surnames.Rd | 80 ++++++++++++++++++++------------------- man/predict_race.Rd | 42 ++++++++++---------- man/sample_me.Rd | 8 ++-- man/surnames2000.Rd | 14 +++---- man/surnames2010.Rd | 14 +++---- man/vec_to_chunk.Rd | 8 ++-- man/voters.Rd | 32 ++++++++-------- man/wru_data_preflight.Rd | 2 +- 17 files changed, 191 insertions(+), 186 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 08c4337..2ed5682 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -45,3 +45,4 @@ License: GPL (>= 3) RoxygenNote: 7.2.3 Encoding: UTF-8 Config/testthat/edition: 3 +Roxygen: list(markdown = TRUE) diff --git a/man/census_geo_api.Rd b/man/census_geo_api.Rd index 4c1f6ae..b59ba69 100644 --- a/man/census_geo_api.Rd +++ b/man/census_geo_api.Rd @@ -20,31 +20,31 @@ census_geo_api( \item{key}{A required character object. Must contain user's Census API key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} -\item{state}{A required character object specifying which state to extract Census data for, +\item{state}{A required character object specifying which state to extract Census data for, e.g., \code{"NJ"}.} -\item{geo}{A character object specifying what aggregation level to use. -Use \code{"county"}, \code{"tract"},\code{"block_group"}, \code{"block"}, or \code{"place"}. +\item{geo}{A character object specifying what aggregation level to use. +Use \code{"county"}, \code{"tract"},\code{"block_group"}, \code{"block"}, or \code{"place"}. Default is \code{"tract"}. Warning: extracting block-level data takes very long.} -\item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +\item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). -If \code{TRUE}, function will return Pr(Geolocation, Age | Race). +If \code{TRUE}, function will return Pr(Geolocation, Age | Race). If \code{\var{sex}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} -\item{sex}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on -sex or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). -If \code{TRUE}, function will return Pr(Geolocation, Sex | Race). +\item{sex}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +sex or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). +If \code{TRUE}, function will return Pr(Geolocation, Sex | Race). If \code{\var{age}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} \item{year}{A character object specifying the year of U.S. Census data to be downloaded. Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}. -Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and +Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and \code{\var{sex}} are both \code{FALSE}.} \item{retry}{The number of retries at the census website if network interruption occurs.} -\item{save_temp}{File indicating where to save the temporary outputs. +\item{save_temp}{File indicating where to save the temporary outputs. Defaults to NULL. If specified, the function will look for an .RData file with the same format as the expected output.} @@ -52,14 +52,14 @@ with the same format as the expected output.} Useful for smaller predictions where only a few counties are considered. Must be zero padded.} } \value{ -Output will be an object of class \code{list}, indexed by state names. It will - consist of the original user-input data with additional columns of Census geographic data. +Output will be an object of class \code{list}, indexed by state names. It will +consist of the original user-input data with additional columns of Census geographic data. } \description{ \code{census_geo_api} retrieves U.S. Census geographic data for a given state. } \details{ -This function allows users to download U.S. Census geographic data (2010 or 2020), +This function allows users to download U.S. Census geographic data (2010 or 2020), at either the county, tract, block, or place level, for a particular state. } \examples{ @@ -71,7 +71,7 @@ at either the county, tract, block, or place level, for a particular state. } \references{ -Relies on get_census_api, get_census_api_2, and vec_to_chunk functions authored by Nicholas Nagle, +Relies on get_census_api, get_census_api_2, and vec_to_chunk functions authored by Nicholas Nagle, available \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. } \keyword{internal} diff --git a/man/census_helper.Rd b/man/census_helper.Rd index ef3c5ff..56eddff 100644 --- a/man/census_helper.Rd +++ b/man/census_helper.Rd @@ -68,8 +68,8 @@ If \code{\var{census.data}} is missing, Census geographic data will be obtained } \value{ Output will be an object of class \code{data.frame}. It will - consist of the original user-input data with additional columns of - Census data. +consist of the original user-input data with additional columns of +Census data. } \description{ \code{census_helper} links user-input dataset with Census geographic data. diff --git a/man/census_helper_new.Rd b/man/census_helper_new.Rd index e410267..1473f29 100644 --- a/man/census_helper_new.Rd +++ b/man/census_helper_new.Rd @@ -21,19 +21,19 @@ census_helper_new( \item{key}{A required character object. Must contain user's Census API key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} -\item{voter.file}{An object of class \code{data.frame}. Must contain field(s) named +\item{voter.file}{An object of class \code{data.frame}. Must contain field(s) named \code{\var{county}}, \code{\var{tract}}, \code{\var{block}}, and/or \code{\var{place}} -specifying geolocation. These should be character variables that match up with -U.S. Census categories. County should be three characters (e.g., "031" not "31"), -tract should be six characters, and block should be four characters. +specifying geolocation. These should be character variables that match up with +U.S. Census categories. County should be three characters (e.g., "031" not "31"), +tract should be six characters, and block should be four characters. Place should be five characters if it is included.} -\item{states}{A character vector specifying which states to extract -Census data for, e.g. \code{c("NJ", "NY")}. Default is \code{"all"}, which extracts +\item{states}{A character vector specifying which states to extract +Census data for, e.g. \code{c("NJ", "NY")}. Default is \code{"all"}, which extracts Census data for all states contained in user-input data.} -\item{geo}{A character object specifying what aggregation level to use. -Use \code{"county"}, \code{"tract"}, \code{"block"}, or \code{"place"}. +\item{geo}{A character object specifying what aggregation level to use. +Use \code{"county"}, \code{"tract"}, \code{"block"}, or \code{"place"}. Default is \code{"tract"}. Warning: extracting block-level data takes very long.} \item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on @@ -49,32 +49,32 @@ If \code{\var{age}} is also \code{TRUE}, function will return Pr(Geolocation, Ag \item{year}{A character object specifying the year of U.S. Census data to be downloaded. Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}.} -\item{census.data}{A optional census object of class \code{list} containing +\item{census.data}{A optional census object of class \code{list} containing pre-saved Census geographic data. Can be created using \code{get_census_data} function. -If \code{\var{census.data}} is provided, the \code{\var{year}} element must -have the same value as the \code{\var{year}} option specified in this function -(i.e., \code{"2010"} in both or \code{"2020"} in both). -If \code{\var{census.data}} is provided, the \code{\var{age}} and the \code{\var{sex}} +If \code{\var{census.data}} is provided, the \code{\var{year}} element must +have the same value as the \code{\var{year}} option specified in this function +(i.e., \code{"2010"} in both or \code{"2020"} in both). +If \code{\var{census.data}} is provided, the \code{\var{age}} and the \code{\var{sex}} elements must be \code{FALSE}. This corresponds to the defaults of \code{census_geo_api}. If \code{\var{census.data}} is missing, Census geographic data will be obtained via Census API.} \item{retry}{The number of retries at the census website if network interruption occurs.} -\item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties +\item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} } \value{ -Output will be an object of class \code{data.frame}. It will - consist of the original user-input data with additional columns of - Census data. +Output will be an object of class \code{data.frame}. It will +consist of the original user-input data with additional columns of +Census data. } \description{ \code{census_helper_new} links user-input dataset with Census geographic data. } \details{ -This function allows users to link their geocoded dataset (e.g., voter file) -with U.S. Census data (2010 or 2020). The function extracts Census Summary File data -at the county, tract, block, or place level. Census data calculated are +This function allows users to link their geocoded dataset (e.g., voter file) +with U.S. Census data (2010 or 2020). The function extracts Census Summary File data +at the county, tract, block, or place level. Census data calculated are Pr(Geolocation | Race) where geolocation is county, tract, block, or place. } \examples{ diff --git a/man/format_legacy_data.Rd b/man/format_legacy_data.Rd index eefc5f3..2f7cc50 100644 --- a/man/format_legacy_data.Rd +++ b/man/format_legacy_data.Rd @@ -8,7 +8,7 @@ format_legacy_data(legacyFilePath, state, outFile = NULL) } \arguments{ \item{legacyFilePath}{A character vector giving the location of a legacy census data folder, -sourced from https://www2.census.gov/programs-surveys/decennial/2020/data/01-Redistricting_File--PL_94-171/. +sourced from https://www2.census.gov/programs-surveys/decennial/2020/data/01-Redistricting_File--PL_94-171/. These file names should end in ".pl".} \item{state}{The two letter state postal code.} @@ -21,10 +21,10 @@ filepath should end in ".RData".} for Bayesian name geocoding. } \details{ -This function allows users to construct datasets for analysis using the census legacy data format. -These data are available for the 2020 census at -https://www2.census.gov/programs-surveys/decennial/2020/data/01-Redistricting_File--PL_94-171/. -This function returns data structured analogously to data from the Census API, which is not yet +This function allows users to construct datasets for analysis using the census legacy data format. +These data are available for the 2020 census at +https://www2.census.gov/programs-surveys/decennial/2020/data/01-Redistricting_File--PL_94-171/. +This function returns data structured analogously to data from the Census API, which is not yet available for the 2020 Census as of September 2021. } \examples{ diff --git a/man/get_census_api.Rd b/man/get_census_api.Rd index cf99156..202b031 100644 --- a/man/get_census_api.Rd +++ b/man/get_census_api.Rd @@ -26,7 +26,7 @@ e.g., \code{"for=block:1213&in=state:47+county:015+tract:*"}.} } \value{ If successful, output will be an object of class \code{data.frame}. - If unsuccessful, function prints the URL query that caused the error. +If unsuccessful, function prints the URL query that caused the error. } \description{ \code{get_census_api} obtains U.S. Census data via the public API. diff --git a/man/get_census_api_2.Rd b/man/get_census_api_2.Rd index 065fe9e..9ee0670 100644 --- a/man/get_census_api_2.Rd +++ b/man/get_census_api_2.Rd @@ -10,30 +10,30 @@ get_census_api_2(data_url, key, get, region, retry = 3) \item{data_url}{URL root of the API, e.g., \code{"https://api.census.gov/data/2020/dec/pl"}.} -\item{key}{A required character object containing user's Census API key, +\item{key}{A required character object containing user's Census API key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} -\item{get}{A character vector of variables to get, +\item{get}{A character vector of variables to get, e.g., \code{c("P2_005N", "P2_006N", "P2_007N", "P2_008N")}. -If there are more than 50 variables, then function will automatically +If there are more than 50 variables, then function will automatically split variables into separate queries.} \item{region}{Character object specifying which region to obtain data for. -Must contain "for" and possibly "in", +Must contain "for" and possibly "in", e.g., \code{"for=block:1213&in=state:47+county:015+tract:*"}.} \item{retry}{The number of retries at the census website if network interruption occurs.} } \value{ -If successful, output will be an object of class \code{data.frame}. - If unsuccessful, function prints the URL query that was constructed. +If successful, output will be an object of class \code{data.frame}. +If unsuccessful, function prints the URL query that was constructed. } \description{ \code{get_census_api_2} assembles URL components for \code{get_census_api}. } \details{ -This function assembles the URL components and sends the request to the Census server. -It is used by the \code{get_census_api} function. The user should not need to call this +This function assembles the URL components and sends the request to the Census server. +It is used by the \code{get_census_api} function. The user should not need to call this function directly. } \examples{ @@ -42,7 +42,7 @@ get = c("P2_005N", "P2_006N", "P2_007N", "P2_008N"), region = "for=county:*&in=s } \references{ -Based on code authored by Nicholas Nagle, which is available +Based on code authored by Nicholas Nagle, which is available \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. } \keyword{internal} diff --git a/man/get_census_data.Rd b/man/get_census_data.Rd index dce4371..012ecff 100644 --- a/man/get_census_data.Rd +++ b/man/get_census_data.Rd @@ -16,27 +16,27 @@ get_census_data( ) } \arguments{ -\item{key}{A required character object containing a valid Census API key, +\item{key}{A required character object containing a valid Census API key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} \item{states}{which states to extract Census data for, e.g., \code{c("NJ", "NY")}.} -\item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +\item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). -If \code{TRUE}, function will return Pr(Geolocation, Age | Race). +If \code{TRUE}, function will return Pr(Geolocation, Age | Race). If \code{\var{sex}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} -\item{sex}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on -sex or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). -If \code{TRUE}, function will return Pr(Geolocation, Sex | Race). +\item{sex}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +sex or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). +If \code{TRUE}, function will return Pr(Geolocation, Sex | Race). If \code{\var{age}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} \item{year}{A character object specifying the year of U.S. Census data to be downloaded. Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}. -Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and +Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and \code{\var{sex}} are both \code{FALSE}.} -\item{census.geo}{An optional character vector specifying what level of +\item{census.geo}{An optional character vector specifying what level of geography to use to merge in U.S. Census 2010 geographic data. Currently \code{"county"}, \code{"tract"}, \code{"block"}, and \code{"place"} are supported.} @@ -45,14 +45,14 @@ geography to use to merge in U.S. Census 2010 geographic data. Currently \item{county.list}{A named list of character vectors of counties present in your \var{voter.file}, per state.} } \value{ -Output will be an object of class \code{list} indexed by state. -Output will contain a subset of the following elements: -\code{state}, \code{age}, \code{sex}, +Output will be an object of class \code{list} indexed by state. +Output will contain a subset of the following elements: +\code{state}, \code{age}, \code{sex}, \code{county}, \code{tract}, \code{block_group}, \code{block}, and \code{place}. } \description{ -\code{get_census_data} returns county-, tract-, and block-level Census data -for specified state(s). Using this function to download Census data in advance +\code{get_census_data} returns county-, tract-, and block-level Census data +for specified state(s). Using this function to download Census data in advance can save considerable time when running \code{predict_race} and \code{census_helper}. } \examples{ diff --git a/man/merge_names.Rd b/man/merge_names.Rd index 114c6fe..ab98bee 100644 --- a/man/merge_names.Rd +++ b/man/merge_names.Rd @@ -58,38 +58,40 @@ in order to increase the chance of finding a match. Default is \code{TRUE}.} } \value{ Output will be an object of class \code{data.frame}. It will - consist of the original user-input data with additional columns that - specify the part of the name matched with Census data (\code{\var{surname.match}}), - and the probabilities Pr(Race | Surname) for each racial group - (\code{\var{p_whi}} for White, \code{\var{p_bla}} for Black, - \code{\var{p_his}} for Hispanic/Latino, - \code{\var{p_asi}} for Asian and Pacific Islander, and - \code{\var{p_oth}} for Other/Mixed). +consist of the original user-input data with additional columns that +specify the part of the name matched with Census data (\code{\var{surname.match}}), +and the probabilities Pr(Race | Surname) for each racial group +(\code{\var{p_whi}} for White, \code{\var{p_bla}} for Black, +\code{\var{p_his}} for Hispanic/Latino, +\code{\var{p_asi}} for Asian and Pacific Islander, and +\code{\var{p_oth}} for Other/Mixed). } \description{ \code{merge_names} merges names in a user-input dataset with corresponding - race/ethnicity probabilities derived from both the U.S. Census Surname List - and Spanish Surname List and voter files from states in the Southern U.S. +race/ethnicity probabilities derived from both the U.S. Census Surname List +and Spanish Surname List and voter files from states in the Southern U.S. } \details{ This function allows users to match names in their dataset with database entries - estimating P(name | ethnicity) for each of the five major racial groups for each - name. The database probabilities are derived from both the U.S. Census Surname List - and Spanish Surname List and voter files from states in the Southern U.S. +estimating P(name | ethnicity) for each of the five major racial groups for each +name. The database probabilities are derived from both the U.S. Census Surname List +and Spanish Surname List and voter files from states in the Southern U.S. - By default, the function matches names as follows: - 1) Search raw surnames in the database; - 2) Remove any punctuation and search again; - 3) Remove any spaces and search again; - 4) Remove suffixes (e.g., "Jr") and search again (last names only) - 5) Split double-barreled names into two parts and search first part of name; - 6) Split double-barreled names into two parts and search second part of name; +By default, the function matches names as follows: +\enumerate{ +\item Search raw surnames in the database; +\item Remove any punctuation and search again; +\item Remove any spaces and search again; +\item Remove suffixes (e.g., "Jr") and search again (last names only) +\item Split double-barreled names into two parts and search first part of name; +\item Split double-barreled names into two parts and search second part of name; +} - Each step only applies to names not matched in a previous step. - Steps 2 through 6 are not applied if \code{clean.surname} is FALSE. +Each step only applies to names not matched in a previous step. +Steps 2 through 6 are not applied if \code{clean.surname} is FALSE. - Note: Any name appearing only on the Spanish Surname List is assigned a - probability of 1 for Hispanics/Latinos and 0 for all other racial groups. +Note: Any name appearing only on the Spanish Surname List is assigned a +probability of 1 for Hispanics/Latinos and 0 for all other racial groups. } \examples{ data(voters) diff --git a/man/merge_surnames.Rd b/man/merge_surnames.Rd index a4ccd94..08c257d 100644 --- a/man/merge_surnames.Rd +++ b/man/merge_surnames.Rd @@ -13,62 +13,64 @@ merge_surnames( ) } \arguments{ -\item{voter.file}{An object of class \code{data.frame}. Must contain a field +\item{voter.file}{An object of class \code{data.frame}. Must contain a field named 'surname' containing list of surnames to be merged with Census lists.} -\item{surname.year}{An object of class \code{numeric} indicating which year -Census Surname List is from. Accepted values are \code{2010} and \code{2000}. +\item{surname.year}{An object of class \code{numeric} indicating which year +Census Surname List is from. Accepted values are \code{2010} and \code{2000}. Default is \code{2020}.} -\item{name.data}{An object of class \code{data.frame}. Must contain a leading -column of surnames, and 5 subsequent columns, with Pr(Race | Surname) for each +\item{name.data}{An object of class \code{data.frame}. Must contain a leading +column of surnames, and 5 subsequent columns, with Pr(Race | Surname) for each of the five major racial categories.} -\item{clean.surname}{A \code{TRUE}/\code{FALSE} object. If \code{TRUE}, -any surnames in \code{\var{voter.file}} that cannot initially be matched -to surname lists will be cleaned, according to U.S. Census specifications, +\item{clean.surname}{A \code{TRUE}/\code{FALSE} object. If \code{TRUE}, +any surnames in \code{\var{voter.file}} that cannot initially be matched +to surname lists will be cleaned, according to U.S. Census specifications, in order to increase the chance of finding a match. Default is \code{TRUE}.} -\item{impute.missing}{A \code{TRUE}/\code{FALSE} object. If \code{TRUE}, -race/ethnicity probabilities will be imputed for unmatched names using +\item{impute.missing}{A \code{TRUE}/\code{FALSE} object. If \code{TRUE}, +race/ethnicity probabilities will be imputed for unmatched names using race/ethnicity distribution for all other names (i.e., not on Census List). Default is \code{TRUE}.} } \value{ -Output will be an object of class \code{data.frame}. It will - consist of the original user-input data with additional columns that - specify the part of the name matched with Census data (\code{\var{surname.match}}), - and the probabilities Pr(Race | Surname) for each racial group - (\code{\var{p_whi}} for White, \code{\var{p_bla}} for Black, - \code{\var{p_his}} for Hispanic/Latino, - \code{\var{p_asi}} for Asian and Pacific Islander, and - \code{\var{p_oth}} for Other/Mixed). +Output will be an object of class \code{data.frame}. It will +consist of the original user-input data with additional columns that +specify the part of the name matched with Census data (\code{\var{surname.match}}), +and the probabilities Pr(Race | Surname) for each racial group +(\code{\var{p_whi}} for White, \code{\var{p_bla}} for Black, +\code{\var{p_his}} for Hispanic/Latino, +\code{\var{p_asi}} for Asian and Pacific Islander, and +\code{\var{p_oth}} for Other/Mixed). #' } \description{ -\code{merge_surnames} merges surnames in user-input dataset with corresponding - race/ethnicity probabilities from U.S. Census Surname List and Spanish Surname List. +\code{merge_surnames} merges surnames in user-input dataset with corresponding +race/ethnicity probabilities from U.S. Census Surname List and Spanish Surname List. } \details{ -This function allows users to match surnames in their dataset with the U.S. - Census Surname List (from 2000 or 2010) and Spanish Surname List to obtain - Pr(Race | Surname) for each of the five major racial groups. - - By default, the function matches surnames to the Census list as follows: - 1) Search raw surnames in Census surname list; - 2) Remove any punctuation and search again; - 3) Remove any spaces and search again; - 4) Remove suffixes (e.g., Jr) and search again; - 5) Split double-barreled surnames into two parts and search first part of name; - 6) Split double-barreled surnames into two parts and search second part of name; - 7) For any remaining names, impute probabilities using distribution - for all names not appearing on Census list. - - Each step only applies to surnames not matched in a previous ste. - Steps 2 through 7 are not applied if \code{clean.surname} is FALSE. - - Note: Any name appearing only on the Spanish Surname List is assigned a - probability of 1 for Hispanics/Latinos and 0 for all other racial groups. +This function allows users to match surnames in their dataset with the U.S. +Census Surname List (from 2000 or 2010) and Spanish Surname List to obtain +Pr(Race | Surname) for each of the five major racial groups. + +By default, the function matches surnames to the Census list as follows: +\enumerate{ +\item Search raw surnames in Census surname list; +\item Remove any punctuation and search again; +\item Remove any spaces and search again; +\item Remove suffixes (e.g., Jr) and search again; +\item Split double-barreled surnames into two parts and search first part of name; +\item Split double-barreled surnames into two parts and search second part of name; +\item For any remaining names, impute probabilities using distribution +for all names not appearing on Census list. +} + +Each step only applies to surnames not matched in a previous ste. +Steps 2 through 7 are not applied if \code{clean.surname} is FALSE. + +Note: Any name appearing only on the Spanish Surname List is assigned a +probability of 1 for Hispanics/Latinos and 0 for all other racial groups. } \examples{ data(voters) diff --git a/man/predict_race.Rd b/man/predict_race.Rd index 5b80be6..f753d16 100644 --- a/man/predict_race.Rd +++ b/man/predict_race.Rd @@ -34,12 +34,12 @@ If using geolocation in predictions, \code{\var{voter.file}} must contain a fiel state of residence (e.g., \code{"nj"} for New Jersey). If using Census geographic data in race/ethnicity predictions, \code{\var{voter.file}} must also contain at least one of the following fields: -\code{\var{county}}, \code{\var{tract}}, \code{\var{block_group}}, \code{\var{block}}, +\code{\var{county}}, \code{\var{tract}}, \code{\var{block_group}}, \code{\var{block}}, and/or \code{\var{place}}. These fields should contain character strings matching U.S. Census categories. County is three characters (e.g., \code{"031"} not \code{"31"}), tract is six characters, block group is usually a single character and block - is four characters. Place is five characters. +is four characters. Place is five characters. See below for other optional fields.} \item{census.surname}{A \code{TRUE}/\code{FALSE} object. If \code{TRUE}, @@ -53,7 +53,7 @@ only use surname data and calculate Pr(Race | Surname). Default is \code{FALSE}. \item{census.geo}{An optional character vector specifying what level of geography to use to merge in U.S. Census geographic data. Currently -\code{"county"}, \code{"tract"}, \code{"block_group"}, \code{"block"}, and \code{"place"} +\code{"county"}, \code{"tract"}, \code{"block_group"}, \code{"block"}, and \code{"place"} are supported. Note: sufficient information must be in user-defined \code{\var{voter.file}} object. If \code{\var{census.geo} = "county"}, then \code{\var{voter.file}} @@ -102,19 +102,19 @@ it should be coded as 1 for Democrat, 2 for Republican, and 0 for Other.} \item{impute.missing}{Logical, defaults to TRUE. Should missing be imputed?} -\item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties +\item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} -\item{model}{Character string, either "BISG" (default) or "fBISG" (for error-correction, +\item{model}{Character string, either "BISG" (default) or "fBISG" (for error-correction, fully-Bayesian model).} \item{race.init}{Vector of initial race for each observation in voter.file. -Must be an integer vector, with 1=white, 2=black, 3=hispanic, 4=asian, and +Must be an integer vector, with 1=white, 2=black, 3=hispanic, 4=asian, and 5=other. Defaults to values obtained using \code{model="BISG_surname"}.} -\item{name.dictionaries}{Optional named list of \code{data.frame}'s -containing counts of names by race. Any of the following named elements -are allowed: "surname", "first", "middle". When present, the objects must +\item{name.dictionaries}{Optional named list of \code{data.frame}'s +containing counts of names by race. Any of the following named elements +are allowed: "surname", "first", "middle". When present, the objects must follow the same structure as \code{last_c}, \code{first_c}, \code{mid_c}, respectively.} @@ -123,22 +123,22 @@ middle'. Defaults to 'surname'.} \item{control}{List of control arguments only used when \code{model="fBISG"}, including \itemize{ - \item{iter}{ Number of MCMC iterations. Defaults to 1000.} - \item{burnin}{ Number of iterations discarded as burnin. Defaults to half of \code{iter}.} - \item{verbose}{ Print progress information. Defaults to \code{TRUE}.} - \item{me.correct}{ Boolean. Should the model correcting measurement error for \code{races|geo}? Defaults to \code{TRUE}.} - \item{seed}{ RNG seed. If \code{NULL}, a seed is generated and returned as an attribute for reproducibility.} +\item{iter}{ Number of MCMC iterations. Defaults to 1000.} +\item{burnin}{ Number of iterations discarded as burnin. Defaults to half of \code{iter}.} +\item{verbose}{ Print progress information. Defaults to \code{TRUE}.} +\item{me.correct}{ Boolean. Should the model correcting measurement error for \code{races|geo}? Defaults to \code{TRUE}.} +\item{seed}{ RNG seed. If \code{NULL}, a seed is generated and returned as an attribute for reproducibility.} }} } \value{ Output will be an object of class \code{data.frame}. It will - consist of the original user-input \code{voter.file} with additional columns with - predicted probabilities for each of the five major racial categories: - \code{\var{pred.whi}} for White, - \code{\var{pred.bla}} for Black, - \code{\var{pred.his}} for Hispanic/Latino, - \code{\var{pred.asi}} for Asian/Pacific Islander, and - \code{\var{pred.oth}} for Other/Mixed. +consist of the original user-input \code{voter.file} with additional columns with +predicted probabilities for each of the five major racial categories: +\code{\var{pred.whi}} for White, +\code{\var{pred.bla}} for Black, +\code{\var{pred.his}} for Hispanic/Latino, +\code{\var{pred.asi}} for Asian/Pacific Islander, and +\code{\var{pred.oth}} for Other/Mixed. } \description{ \code{predict_race} makes probabilistic estimates of individual-level race/ethnicity. diff --git a/man/sample_me.Rd b/man/sample_me.Rd index 4e82121..157ae51 100644 --- a/man/sample_me.Rd +++ b/man/sample_me.Rd @@ -32,9 +32,9 @@ sample_me( \item{pi_s}{Numeric matrix of race | surname prior probabilities.} -\item{pi_f}{Same as `pi_s`, but for first names.} +\item{pi_f}{Same as \code{pi_s}, but for first names.} -\item{pi_m}{Same as `pi_s`, but for middle names.} +\item{pi_m}{Same as \code{pi_s}, but for middle names.} \item{pi_nr}{Matrix of marginal probability distribution over missing names; non-keyword names default to this distribution.} @@ -52,9 +52,9 @@ sample_me( \item{M_rs}{Integer matrix of race | surname counts in dictionary (surnames in columns).} -\item{M_rf}{Same as `M_rs`, but for first names (can be empty matrix for surname only models).} +\item{M_rf}{Same as \code{M_rs}, but for first names (can be empty matrix for surname only models).} -\item{M_rm}{Same as `M_rs`, but for middle names (can be empty matrix for surname, or surname and first name only models).} +\item{M_rm}{Same as \code{M_rs}, but for middle names (can be empty matrix for surname, or surname and first name only models).} \item{alpha}{Numeric matrix of race | geography prior probabilities.} diff --git a/man/surnames2000.Rd b/man/surnames2000.Rd index 00a2c30..6dc45f5 100644 --- a/man/surnames2000.Rd +++ b/man/surnames2000.Rd @@ -7,13 +7,13 @@ \format{ A data frame with 157,728 rows and 6 variables: \describe{ - \item{surname}{Surname} - \item{p_whi}{Pr(White | Surname)} - \item{p_bla}{Pr(Black | Surname)} - \item{p_his}{Pr(Hispanic/Latino | Surname)} - \item{p_asi}{Pr(Asian/Pacific Islander | Surname)} - \item{p_oth}{Pr(Other | Surname)} - #' } +\item{surname}{Surname} +\item{p_whi}{Pr(White | Surname)} +\item{p_bla}{Pr(Black | Surname)} +\item{p_his}{Pr(Hispanic/Latino | Surname)} +\item{p_asi}{Pr(Asian/Pacific Islander | Surname)} +\item{p_oth}{Pr(Other | Surname)} +#' } } \usage{ surnames2000 diff --git a/man/surnames2010.Rd b/man/surnames2010.Rd index 7341985..a09afdf 100644 --- a/man/surnames2010.Rd +++ b/man/surnames2010.Rd @@ -7,13 +7,13 @@ \format{ A data frame with 167,613 rows and 6 variables: \describe{ - \item{surname}{Surname} - \item{p_whi}{Pr(White | Surname)} - \item{p_bla}{Pr(Black | Surname)} - \item{p_his}{Pr(Hispanic/Latino | Surname)} - \item{p_asi}{Pr(Asian/Pacific Islander | Surname)} - \item{p_oth}{Pr(Other | Surname)} - #' } +\item{surname}{Surname} +\item{p_whi}{Pr(White | Surname)} +\item{p_bla}{Pr(Black | Surname)} +\item{p_his}{Pr(Hispanic/Latino | Surname)} +\item{p_asi}{Pr(Asian/Pacific Islander | Surname)} +\item{p_oth}{Pr(Other | Surname)} +#' } } \usage{ surnames2010 diff --git a/man/vec_to_chunk.Rd b/man/vec_to_chunk.Rd index eb77daa..fe5ae7f 100644 --- a/man/vec_to_chunk.Rd +++ b/man/vec_to_chunk.Rd @@ -16,9 +16,9 @@ Object of class \code{list}. \code{vec_to_chunk} takes a list of variables and collects them into 50-variable chunks. } \details{ -This function takes a list of variable names and collects them into chunks with no more than -50 variables each. This helps to get around requests with more than 50 variables,because the -API only allows queries of 50 variables at a time. +This function takes a list of variable names and collects them into chunks with no more than +50 variables each. This helps to get around requests with more than 50 variables,because the +API only allows queries of 50 variables at a time. The user should not need to call this function directly. } \examples{ @@ -29,7 +29,7 @@ vec_to_chunk(x = c(paste("P012F0", seq(10:49), sep = ""), } \references{ -Based on code authored by Nicholas Nagle, which is available +Based on code authored by Nicholas Nagle, which is available \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. } \keyword{internal} diff --git a/man/voters.Rd b/man/voters.Rd index 3637041..be5d7bc 100644 --- a/man/voters.Rd +++ b/man/voters.Rd @@ -7,22 +7,22 @@ \format{ A data frame with 10 rows and 12 variables: \describe{ - \item{VoterID}{Voter identifier (numeric)} - \item{surname}{Surname} - \item{state}{State of residence} - \item{CD}{Congressional district} - \item{county}{Census county (three-digit code)} - \item{first}{First name} - \item{last}{Last name or surname} - \item{tract}{Census tract (six-digit code)} - \item{block}{Census block (four-digit code)} - \item{precinct}{Voting precinct} - \item{place}{Voting place} - \item{age}{Age in years} - \item{sex}{0=male, 1=female} - \item{party}{Party registration (character)} - \item{PID}{Party registration (numeric)} - #' } +\item{VoterID}{Voter identifier (numeric)} +\item{surname}{Surname} +\item{state}{State of residence} +\item{CD}{Congressional district} +\item{county}{Census county (three-digit code)} +\item{first}{First name} +\item{last}{Last name or surname} +\item{tract}{Census tract (six-digit code)} +\item{block}{Census block (four-digit code)} +\item{precinct}{Voting precinct} +\item{place}{Voting place} +\item{age}{Age in years} +\item{sex}{0=male, 1=female} +\item{party}{Party registration (character)} +\item{PID}{Party registration (numeric)} +#' } } \usage{ voters diff --git a/man/wru_data_preflight.Rd b/man/wru_data_preflight.Rd index cc93d37..7da9e58 100644 --- a/man/wru_data_preflight.Rd +++ b/man/wru_data_preflight.Rd @@ -10,6 +10,6 @@ wru_data_preflight() Checks if namedata is available in the current working directory, if not downloads it from github using piggyback. By default, wru will download the data to a temporary directory that lasts as long as your session does. -However, you may wish to set the \code{wru_data_wd} option to save the +However, you may wish to set the \code{wru_data_wd} option to save the downloaded data to your current working directory for more permanence. } From 4aa60199c1465ce9ef57c38537e7b39a6050db09 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 11:26:19 -0800 Subject: [PATCH 10/90] chore: `use_tidy_description()` --- DESCRIPTION | 57 +++++++++++++++++++++++++++++------------------------ 1 file changed, 31 insertions(+), 26 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2ed5682..78eed02 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,24 +1,30 @@ Package: wru +Title: Who are You? Bayesian Prediction of Racial Category Using Surname, + First Name, Middle Name, and Geolocation Version: 2.0.0 Date: 2023-07-12 -Title: Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and - Geolocation Authors@R: c( - person("Kabir", "Khanna", email = "kabirkhanna@gmail.com", role = c("aut")), - person("Brandon", "Bertelsen", email = "brandon@bertelsen.ca", role = c("aut","cre")), - person("Santiago", "Olivella", email = "olivella@unc.edu", role = c("aut")), - person("Evan", "Rosenman", email = "etrrosenman@gmail.com", role = c("aut")), - person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut")) + person("Kabir", "Khanna", , "kabirkhanna@gmail.com", role = "aut"), + person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), + person("Santiago", "Olivella", , "olivella@unc.edu", role = "aut"), + person("Evan", "Rosenman", , "etrrosenman@gmail.com", role = "aut"), + person("Kosuke", "Imai", , "imai@harvard.edu", role = "aut") ) -Description: Predicts individual race/ethnicity using surname, first name, middle name, geolocation, - and other attributes, such as gender and age. The method utilizes Bayes' - Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given - individual. The package implements methods described in Imai and Khanna (2016) - "Improving Ecological Inference by Predicting Individual Ethnicity from Voter - Registration Records" Political Analysis and Imai, Olivella, and Rosenman (2022) - "Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements" - . The package also incorporates the data described in Rosenman, Olivella, and Imai (2023) - "Race and ethnicity data for first, middle, and surnames" . +Description: Predicts individual race/ethnicity using surname, first name, + middle name, geolocation, and other attributes, such as gender and + age. The method utilizes Bayes' Rule (with optional measurement error + correction) to compute the posterior probability of each racial + category for any given individual. The package implements methods + described in Imai and Khanna (2016) "Improving Ecological Inference by + Predicting Individual Ethnicity from Voter Registration Records" + Political Analysis and Imai, Olivella, and + Rosenman (2022) "Addressing census data problems in race imputation + via fully Bayesian Improved Surname Geocoding and name supplements" + . The package also incorporates the data + described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity + data for first, middle, and surnames" + . +License: GPL (>= 3) URL: https://github.com/kosukeimai/wru BugReports: https://github.com/kosukeimai/wru/issues Depends: @@ -28,21 +34,20 @@ Imports: dplyr, furrr, future, + piggyback (>= 0.1.4), + PL94171, purrr, - Rcpp, - piggyback (>= 0.1.4), - PL94171 + Rcpp Suggests: - testthat (>= 3.0.0), - covr + covr, + testthat (>= 3.0.0) LinkingTo: Rcpp, RcppArmadillo -LazyLoad: yes +Config/testthat/edition: 3 +Encoding: UTF-8 LazyData: yes LazyDataCompression: xz -License: GPL (>= 3) -RoxygenNote: 7.2.3 -Encoding: UTF-8 -Config/testthat/edition: 3 +LazyLoad: yes Roxygen: list(markdown = TRUE) +RoxygenNote: 7.2.3 From 2998d97e8b79f2cd52e3ba11e08e081921841e85 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 11:28:01 -0800 Subject: [PATCH 11/90] docs(predict_race): document that `census.key` will be retrieved from `CENSUS_API_KEY` envvar --- R/predict_race.R | 11 ++++++++--- man/predict_race.Rd | 9 ++++++--- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/R/predict_race.R b/R/predict_race.R index 31ed173..10bd067 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -42,9 +42,14 @@ #' must have column named \code{place}. #' Specifying \code{\var{census.geo}} will call \code{census_helper} function #' to merge Census geographic data at specified level of geography. -#' @param census.key A character object specifying user's Census API -#' key. Required if \code{\var{census.geo}} is specified, because -#' a valid Census API key is required to download Census geographic data. +#' +#' @param census.key A character object specifying user's Census API key. +#' Required if `census.geo` is specified, because a valid Census API key is +#' required to download Census geographic data. +#' +#' If [`NULL`], the default, attempts to find a census key stored in an +#' [environment variable][Sys.getenv] named `CENSUS_API_KEY`. +#' #' @param census.data A list indexed by two-letter state abbreviations, #' which contains pre-saved Census geographic data. #' Can be generated using \code{get_census_data} function. diff --git a/man/predict_race.Rd b/man/predict_race.Rd index f753d16..3c73abd 100644 --- a/man/predict_race.Rd +++ b/man/predict_race.Rd @@ -67,9 +67,12 @@ must have column named \code{place}. Specifying \code{\var{census.geo}} will call \code{census_helper} function to merge Census geographic data at specified level of geography.} -\item{census.key}{A character object specifying user's Census API -key. Required if \code{\var{census.geo}} is specified, because -a valid Census API key is required to download Census geographic data.} +\item{census.key}{A character object specifying user's Census API key. +Required if \code{census.geo} is specified, because a valid Census API key is +required to download Census geographic data. + +If \code{\link{NULL}}, the default, attempts to find a census key stored in an +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{census.data}{A list indexed by two-letter state abbreviations, which contains pre-saved Census geographic data. From ff8fe3acb59e511f0e96adfb0eb84956e1a757bf Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 11:29:18 -0800 Subject: [PATCH 12/90] docs(README): move instructions for storing API key after instructions for getting an API key --- README.Rmd | 61 ++++++++++++++++++++++++++++-------------------------- README.md | 45 ++++++++++++++++++++-------------------- 2 files changed, 54 insertions(+), 52 deletions(-) diff --git a/README.Rmd b/README.Rmd index 32ca602..cdb1b95 100644 --- a/README.Rmd +++ b/README.Rmd @@ -34,17 +34,9 @@ Or you can install the development version of **wru** from [GitHub](https://gith pak::pkg_install("kosukeimai/wru") ``` -### Using wru +## Using wru -First, you should save your census key to your `.Rprofile` or `.Renviron`. Below is an example procedure: - - > usethis::edit_r_profile() - # edit the file with the following - Sys.setenv("CENSUS_API_KEY" = "Your Key") - # save and close the file - # Restart your R session - -Now, here is a simple example that predicts the race/ethnicity of voters based only on their surnames. +Here is a simple example that predicts the race/ethnicity of voters based only on their surnames. ``` r library(wru) @@ -68,30 +60,41 @@ The above produces the following output, where the last five columns are probabi 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.6665000 0.08530000 0.13670000 0.07970000 0.03180000 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.9054000 0.04310000 0.02060000 0.00720000 0.02370000 -In order to predict race/ethnicity based on surnames AND geolocation, a user needs to provide a valid U.S. Census API key to access the census statistics. You may request a U.S. Census API key [here](http://api.census.gov/data/key_signup.html). Once you have an API key, you can use the package to download relevant Census geographic data on demand and condition race/ethnicity predictions on geolocation (county, tract, block, or place). +### Using geolocation + +In order to predict race/ethnicity based on surnames *and* geolocation, a user needs to provide a valid U.S. Census API key to access the census statistics. +You can request a U.S. Census API key from [the U.S. Census API key signup page](http://api.census.gov/data/key_signup.html). +Once you have an API key, you can use the package to download relevant Census geographic data on demand and condition race/ethnicity predictions on geolocation (county, tract, block, or place). + +First, you should save your census key to your `.Rprofile` or `.Renviron`. Below is an example procedure: + +``` +usethis::edit_r_environ() +# Edit the file with the following: +CENSUS_API_KEY=YourKey +# Save and close the file +# Restart your R session +``` The following example predicts the race/ethnicity of voters based on their surnames, Census tract of residence (census.geo = "tract"), and which party registration (party = "PID"). Note that a valid API key must be provided in the input parameter 'census.key' in order for the function to download the relevant tract-level data. ``` r library(wru) -data(voters) predict_race(voter.file = voters, census.geo = "tract", census.key = "...", party = "PID") ``` - -The above returns the following output. - - VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth - 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.081856291 0.0021396565 0.0110451405 0.828313291 0.076645621 - 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.916936771 0.0044432219 0.0120276229 0.008532929 0.058059455 - 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895620643 0.0022078678 0.0139457411 0.023345853 0.064879895 - 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.003164229 0.0006092345 0.0001072684 0.991261466 0.004857802 - 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.029936354 0.0009275220 0.0129831039 0.850040743 0.106112277 - 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.231046860 0.0016485574 0.6813780115 0.053180270 0.032746301 - 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.817841573 0.0063677130 0.0258733496 0.107254103 0.042663261 - 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.223924118 0.0002913000 0.4451163607 0.313431417 0.017236805 - 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.241417483 0.6900686166 0.0293556870 0.011105140 0.028053073 - 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.983300770 0.0006116706 0.0034070782 0.004823439 0.007857042 - +``` +VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.081856291 0.0021396565 0.0110451405 0.828313291 0.076645621 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.916936771 0.0044432219 0.0120276229 0.008532929 0.058059455 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895620643 0.0022078678 0.0139457411 0.023345853 0.064879895 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.003164229 0.0006092345 0.0001072684 0.991261466 0.004857802 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.029936354 0.0009275220 0.0129831039 0.850040743 0.106112277 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.231046860 0.0016485574 0.6813780115 0.053180270 0.032746301 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.817841573 0.0063677130 0.0258733496 0.107254103 0.042663261 + 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.223924118 0.0002913000 0.4451163607 0.313431417 0.017236805 + 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.241417483 0.6900686166 0.0293556870 0.011105140 0.028053073 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.983300770 0.0006116706 0.0034070782 0.004823439 0.007857042 +``` In predict_race, the census.geo options are "county", "tract", "block" and "place". Here is an example of prediction based on census statistics collected at the level of "place": ``` r @@ -162,7 +165,7 @@ predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = cen predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) ``` -## Notes about process design +### Notes about process design For larger scale imputations garbage-collection can become a problem and your machine(s) can quickly run out of memory (RAM). It is recommended to use the `future.callr::callr` plan instead of `future::multisession`. The `callr` plan instantiates a new session at every iteration of your parallel loop or map. This simultaneously has the effect of creating more overhead, but also clearing the often sticky memory elements that would be left over to grow to eventual system failure when using `multisession`. You end up with a process that is more stable, but slightly slower. @@ -176,6 +179,6 @@ This package uses the Census Bureau Data API but is not endorsed or certified by U.S. Census Bureau (2021, October 8). Decennial Census API. Census.gov. Retrieved from -### A related song +## A related song [![Thumbnail of the music video for "Who Are You" by The Who](https://img.youtube.com/vi/PNbBDrceCy8/maxresdefault.jpg)](https://www.youtube.com/watch?v=PNbBDrceCy8) diff --git a/README.md b/README.md index 68487fd..e41f849 100644 --- a/README.md +++ b/README.md @@ -31,18 +31,9 @@ Or you can install the development version of **wru** from pak::pkg_install("kosukeimai/wru") ``` -### Using wru +## Using wru -First, you should save your census key to your `.Rprofile` or -`.Renviron`. Below is an example procedure: - - > usethis::edit_r_profile() - # edit the file with the following - Sys.setenv("CENSUS_API_KEY" = "Your Key") - # save and close the file - # Restart your R session - -Now, here is a simple example that predicts the race/ethnicity of voters +Here is a simple example that predicts the race/ethnicity of voters based only on their surnames. ``` r @@ -69,13 +60,24 @@ probability of being Hispanic/Latino): 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.6665000 0.08530000 0.13670000 0.07970000 0.03180000 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.9054000 0.04310000 0.02060000 0.00720000 0.02370000 -In order to predict race/ethnicity based on surnames AND geolocation, a -user needs to provide a valid U.S. Census API key to access the census -statistics. You may request a U.S. Census API key -[here](http://api.census.gov/data/key_signup.html). Once you have an API -key, you can use the package to download relevant Census geographic data -on demand and condition race/ethnicity predictions on geolocation -(county, tract, block, or place). +### Using geolocation + +In order to predict race/ethnicity based on surnames *and* geolocation, +a user needs to provide a valid U.S. Census API key to access the census +statistics. You can request a U.S. Census API key from [the U.S. Census +API key signup page](http://api.census.gov/data/key_signup.html). Once +you have an API key, you can use the package to download relevant Census +geographic data on demand and condition race/ethnicity predictions on +geolocation (county, tract, block, or place). + +First, you should save your census key to your `.Rprofile` or +`.Renviron`. Below is an example procedure: + + usethis::edit_r_environ() + # Edit the file with the following: + CENSUS_API_KEY=YourKey + # Save and close the file + # Restart your R session The following example predicts the race/ethnicity of voters based on their surnames, Census tract of residence (census.geo = “tract”), and @@ -85,12 +87,9 @@ function to download the relevant tract-level data. ``` r library(wru) -data(voters) predict_race(voter.file = voters, census.geo = "tract", census.key = "...", party = "PID") ``` -The above returns the following output. - VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.081856291 0.0021396565 0.0110451405 0.828313291 0.076645621 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.916936771 0.0044432219 0.0120276229 0.008532929 0.058059455 @@ -205,7 +204,7 @@ predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = cen predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) ``` -## Notes about process design +### Notes about process design For larger scale imputations garbage-collection can become a problem and your machine(s) can quickly run out of memory (RAM). It is recommended @@ -230,7 +229,7 @@ U.S. Census Bureau (2021, October 8). Decennial Census API. Census.gov. Retrieved from -### A related song +## A related song [![Thumbnail of the music video for “Who Are You” by The Who](https://img.youtube.com/vi/PNbBDrceCy8/maxresdefault.jpg)](https://www.youtube.com/watch?v=PNbBDrceCy8) From 616c2c6dbdd0738bb61e0c76fc4172f801fcefbc Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 11:29:48 -0800 Subject: [PATCH 13/90] docs: inherit from `predict_race()` in other documentation --- R/census_data_preflight.R | 4 +- R/race_prediction_funs.R | 23 ++---- man/census_data_preflight.Rd | 23 +++++- man/modfuns.Rd | 131 ++++++++++++++++++++++++++--------- 4 files changed, 124 insertions(+), 57 deletions(-) diff --git a/R/census_data_preflight.R b/R/census_data_preflight.R index 067117e..804c066 100644 --- a/R/census_data_preflight.R +++ b/R/census_data_preflight.R @@ -1,8 +1,6 @@ #' Preflight census data #' -#' @param census.data See documentation in \code{race_predict}. -#' @param census.geo See documentation in \code{race_predict}. -#' @param year See documentation in \code{race_predict}. +#' @inheritParams predict_race #' @keywords internal census_data_preflight <- function(census.data, census.geo, year) { diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 1ade9c7..2c2f692 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -1,7 +1,7 @@ #' Internal model fitting functions #' #' These functions are intended for internal use only. Users should use the -#' \code{race_predict} interface rather any of these functions directly. +#' [predict_race()] interface rather any of these functions directly. #' #' These functions fit different versions of WRU. \code{.predict_race_old} fits #' the original WRU model, also known as BISG with census-based surname dictionary. @@ -13,26 +13,11 @@ #' the augmented surname dictionary, and the first and middle name #' dictionaries when making predictions. #' -#' @param voter.file See documentation in \code{race_predict}. -#' @param census.surname See documentation in \code{race_predict}. -#' @param surname.only See documentation in \code{race_predict}. -#' @param surname.year See documentation in \code{race_predict}. -#' @param census.geo See documentation in \code{race_predict}. -#' @param census.key See documentation in \code{race_predict}. -#' @param census.data See documentation in \code{race_predict}. -#' @param age See documentation in \code{race_predict}. -#' @param sex See documentation in \code{race_predict}. -#' @param year See documentation in \code{race_predict}. -#' @param party See documentation in \code{race_predict}. -#' @param retry See documentation in \code{race_predict}. -#' @param impute.missing See documentation in \code{race_predict}. -#' @param names.to.use See documentation in \code{race_predict}. -#' @param race.init See documentation in \code{race_predict}. -#' @param name.dictionaries See documentation in \code{race_predict}. -#' @param ctrl See \code{control} in documentation for \code{race_predict}. +#' @inheritParams predict_race +#' @param ctrl See `control` in documentation for [predict_race()]. #' @param use.counties A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}? #' -#' @return See documentation in \code{race_predict}. +#' @inherit predict_race return #' #' @name modfuns NULL diff --git a/man/census_data_preflight.Rd b/man/census_data_preflight.Rd index f3512c8..9c4dda9 100644 --- a/man/census_data_preflight.Rd +++ b/man/census_data_preflight.Rd @@ -7,11 +7,28 @@ census_data_preflight(census.data, census.geo, year) } \arguments{ -\item{census.data}{See documentation in \code{race_predict}.} +\item{census.data}{A list indexed by two-letter state abbreviations, +which contains pre-saved Census geographic data. +Can be generated using \code{get_census_data} function.} -\item{census.geo}{See documentation in \code{race_predict}.} +\item{census.geo}{An optional character vector specifying what level of +geography to use to merge in U.S. Census geographic data. Currently +\code{"county"}, \code{"tract"}, \code{"block_group"}, \code{"block"}, and \code{"place"} +are supported. +Note: sufficient information must be in user-defined \code{\var{voter.file}} object. +If \code{\var{census.geo} = "county"}, then \code{\var{voter.file}} +must have column named \code{county}. +If \code{\var{census.geo} = "tract"}, then \code{\var{voter.file}} +must have columns named \code{county} and \code{tract}. +And if \code{\var{census.geo} = "block"}, then \code{\var{voter.file}} +must have columns named \code{county}, \code{tract}, and \code{block}. +If \code{\var{census.geo} = "place"}, then \code{\var{voter.file}} +must have column named \code{place}. +Specifying \code{\var{census.geo}} will call \code{census_helper} function +to merge Census geographic data at specified level of geography.} -\item{year}{See documentation in \code{race_predict}.} +\item{year}{An optional character vector specifying the year of U.S. Census geographic +data to be downloaded. Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}.} } \description{ Preflight census data diff --git a/man/modfuns.Rd b/man/modfuns.Rd index 9702621..2d66275 100644 --- a/man/modfuns.Rd +++ b/man/modfuns.Rd @@ -62,48 +62,115 @@ predict_race_me( ) } \arguments{ -\item{voter.file}{See documentation in \code{race_predict}.} - -\item{census.surname}{See documentation in \code{race_predict}.} - -\item{surname.only}{See documentation in \code{race_predict}.} - -\item{surname.year}{See documentation in \code{race_predict}.} - -\item{name.dictionaries}{See documentation in \code{race_predict}.} - -\item{census.geo}{See documentation in \code{race_predict}.} - -\item{census.key}{See documentation in \code{race_predict}.} - -\item{census.data}{See documentation in \code{race_predict}.} - -\item{age}{See documentation in \code{race_predict}.} - -\item{sex}{See documentation in \code{race_predict}.} - -\item{year}{See documentation in \code{race_predict}.} - -\item{party}{See documentation in \code{race_predict}.} - -\item{retry}{See documentation in \code{race_predict}.} - -\item{impute.missing}{See documentation in \code{race_predict}.} +\item{voter.file}{An object of class \code{data.frame}. +Must contain a row for each individual being predicted, +as well as a field named \code{\var{surname}} containing each individual's surname. +If using geolocation in predictions, \code{\var{voter.file}} must contain a field named +\code{\var{state}}, which contains the two-character abbreviation for each individual's +state of residence (e.g., \code{"nj"} for New Jersey). +If using Census geographic data in race/ethnicity predictions, +\code{\var{voter.file}} must also contain at least one of the following fields: +\code{\var{county}}, \code{\var{tract}}, \code{\var{block_group}}, \code{\var{block}}, +and/or \code{\var{place}}. +These fields should contain character strings matching U.S. Census categories. +County is three characters (e.g., \code{"031"} not \code{"31"}), +tract is six characters, block group is usually a single character and block +is four characters. Place is five characters. +See below for other optional fields.} + +\item{census.surname}{A \code{TRUE}/\code{FALSE} object. If \code{TRUE}, +function will call \code{merge_surnames} to merge in Pr(Race | Surname) +from U.S. Census Surname List (2000, 2010, or 2020) and Spanish Surname List. +If \code{FALSE}, user must provide a \code{name.dictionary} (see below). +Default is \code{TRUE}.} + +\item{surname.only}{A \code{TRUE}/\code{FALSE} object. If \code{TRUE}, race predictions will +only use surname data and calculate Pr(Race | Surname). Default is \code{FALSE}.} + +\item{name.dictionaries}{Optional named list of \code{data.frame}'s +containing counts of names by race. Any of the following named elements +are allowed: "surname", "first", "middle". When present, the objects must +follow the same structure as \code{last_c}, \code{first_c}, +\code{mid_c}, respectively.} + +\item{census.geo}{An optional character vector specifying what level of +geography to use to merge in U.S. Census geographic data. Currently +\code{"county"}, \code{"tract"}, \code{"block_group"}, \code{"block"}, and \code{"place"} +are supported. +Note: sufficient information must be in user-defined \code{\var{voter.file}} object. +If \code{\var{census.geo} = "county"}, then \code{\var{voter.file}} +must have column named \code{county}. +If \code{\var{census.geo} = "tract"}, then \code{\var{voter.file}} +must have columns named \code{county} and \code{tract}. +And if \code{\var{census.geo} = "block"}, then \code{\var{voter.file}} +must have columns named \code{county}, \code{tract}, and \code{block}. +If \code{\var{census.geo} = "place"}, then \code{\var{voter.file}} +must have column named \code{place}. +Specifying \code{\var{census.geo}} will call \code{census_helper} function +to merge Census geographic data at specified level of geography.} + +\item{census.key}{A character object specifying user's Census API key. +Required if \code{census.geo} is specified, because a valid Census API key is +required to download Census geographic data. + +If \code{\link{NULL}}, the default, attempts to find a census key stored in an +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} + +\item{census.data}{A list indexed by two-letter state abbreviations, +which contains pre-saved Census geographic data. +Can be generated using \code{get_census_data} function.} + +\item{age}{An optional \code{TRUE}/\code{FALSE} object specifying whether to +condition race predictions on age (in addition to surname and geolocation). +Default is \code{FALSE}. Must be same as \code{\var{age}} in \code{\var{census.data}} object. +May only be set to \code{TRUE} if \code{census.geo} option is specified. +If \code{TRUE}, \code{\var{voter.file}} should include a numerical variable \code{\var{age}}.} + +\item{sex}{optional \code{TRUE}/\code{FALSE} object specifying whether to +condition race predictions on sex (in addition to surname and geolocation). +Default is \code{FALSE}. Must be same as \code{\var{sex}} in \code{\var{census.data}} object. +May only be set to \code{TRUE} if \code{census.geo} option is specified. +If \code{TRUE}, \code{\var{voter.file}} should include a numerical variable \code{\var{sex}}, +where \code{\var{sex}} is coded as 0 for males and 1 for females.} + +\item{year}{An optional character vector specifying the year of U.S. Census geographic +data to be downloaded. Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}.} + +\item{party}{An optional character object specifying party registration field +in \code{\var{voter.file}}, e.g., \code{\var{party} = "PartyReg"}. +If specified, race/ethnicity predictions will be conditioned +on individual's party registration (in addition to geolocation). +Whatever the name of the party registration field in \code{\var{voter.file}}, +it should be coded as 1 for Democrat, 2 for Republican, and 0 for Other.} + +\item{retry}{The number of retries at the census website if network interruption occurs.} + +\item{impute.missing}{Logical, defaults to TRUE. Should missing be imputed?} \item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} -\item{names.to.use}{See documentation in \code{race_predict}.} +\item{names.to.use}{One of 'surname', 'surname, first', or 'surname, first, +middle'. Defaults to 'surname'.} -\item{race.init}{See documentation in \code{race_predict}.} +\item{race.init}{Vector of initial race for each observation in voter.file. +Must be an integer vector, with 1=white, 2=black, 3=hispanic, 4=asian, and +5=other. Defaults to values obtained using \code{model="BISG_surname"}.} -\item{ctrl}{See \code{control} in documentation for \code{race_predict}.} +\item{ctrl}{See \code{control} in documentation for \code{\link[=predict_race]{predict_race()}}.} } \value{ -See documentation in \code{race_predict}. +Output will be an object of class \code{data.frame}. It will +consist of the original user-input \code{voter.file} with additional columns with +predicted probabilities for each of the five major racial categories: +\code{\var{pred.whi}} for White, +\code{\var{pred.bla}} for Black, +\code{\var{pred.his}} for Hispanic/Latino, +\code{\var{pred.asi}} for Asian/Pacific Islander, and +\code{\var{pred.oth}} for Other/Mixed. } \description{ These functions are intended for internal use only. Users should use the -\code{race_predict} interface rather any of these functions directly. +\code{\link[=predict_race]{predict_race()}} interface rather any of these functions directly. } \details{ These functions fit different versions of WRU. \code{.predict_race_old} fits From ede8ad06cbded44d370563d714ea34b93ee3f09a Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 11:37:30 -0800 Subject: [PATCH 14/90] docs(get_census_data): document use of `CENSUS_API_KEY` envvar --- R/get_census_data.R | 9 +++++++-- man/get_census_data.Rd | 8 ++++++-- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/R/get_census_data.R b/R/get_census_data.R index dd86516..1c1ebc2 100644 --- a/R/get_census_data.R +++ b/R/get_census_data.R @@ -4,8 +4,13 @@ #' for specified state(s). Using this function to download Census data in advance #' can save considerable time when running \code{predict_race} and \code{census_helper}. #' -#' @param key A required character object containing a valid Census API key, -#' which can be requested \href{https://api.census.gov/data/key_signup.html}{here}. +#' @param key A character string containing a valid U.S. Census API key, +#' which can be requested from the +#' [U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +#' +#' If [`NULL`], the default, attempts to find a census key stored in an +#' [environment variable][Sys.getenv] named `CENSUS_API_KEY`. +#' #' @param states which states to extract Census data for, e.g., \code{c("NJ", "NY")}. #' @param age A \code{TRUE}/\code{FALSE} object indicating whether to condition on #' age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). diff --git a/man/get_census_data.Rd b/man/get_census_data.Rd index 012ecff..b2b7085 100644 --- a/man/get_census_data.Rd +++ b/man/get_census_data.Rd @@ -16,8 +16,12 @@ get_census_data( ) } \arguments{ -\item{key}{A required character object containing a valid Census API key, -which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid U.S. Census API key, +which can be requested from the +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. + +If \code{\link{NULL}}, the default, attempts to find a census key stored in an +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{states}{which states to extract Census data for, e.g., \code{c("NJ", "NY")}.} From 60dda21029ca1f5106f089379432f642af34cc76 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 13:15:45 -0800 Subject: [PATCH 15/90] fix(voters): use tract NY-061-014900 instead of 015100 --- .Rbuildignore | 1 + data-raw/voters.R | 19 +++++++++++++++++++ data/voters.RData | Bin 687 -> 0 bytes data/voters.rda | Bin 0 -> 606 bytes 4 files changed, 20 insertions(+) create mode 100644 data-raw/voters.R delete mode 100644 data/voters.RData create mode 100644 data/voters.rda diff --git a/.Rbuildignore b/.Rbuildignore index af6362a..0d1dd30 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -12,3 +12,4 @@ ChangeLog ^cran-comments\.md$ ^CRAN-SUBMISSION$ +^data-raw$ diff --git a/data-raw/voters.R b/data-raw/voters.R new file mode 100644 index 0000000..a224447 --- /dev/null +++ b/data-raw/voters.R @@ -0,0 +1,19 @@ +voters <- data.frame( + VoterID = as.character(1:10), + surname = c("Khanna", "Imai", "Rivera", "Fifield", "Zhou", "Ratkovic", "Johnson", "Lopez", "Wantchekon", "Morse"), + state = c("NJ", "NJ", "NY", "NJ", "NJ", "NJ", "NY", "NJ", "NJ", "DC"), + CD = c("12", "12", "12", "12", "12", "12", "9", "12", "12", "0"), + county = c("021", "021", "061", "021", "021", "021", "061", "021", "021", "001"), + tract = c("004000", "004501", "004800", "004501", "004501", "004000", "014900", "004501", "004501", "001301"), + block = c("3001", "1025", "6001", "1025", "1025", "1025", "4000", "1025", "1025", "3005"), + precinct = c("6", "", "", "", "", "", "", "", "", ""), + age = c(29, 40, 33, 27, 28, 35, 25, 33, 50, 29), + sex = c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1), + party = c("Ind", "Dem", "Rep", "Dem", "Dem", "Ind", "Dem", "Rep", "Rep", "Rep"), + PID = c("0", "1", "2", "1", "1", "0", "1", "2", "2", "2"), + place = c("74000", "60900", "51000", "60900", "60900", "60900", "51000", "60900", "60900", "50000"), + last = c("Khanna", "Imai", "Rivera", "Fifield", "Zhou", "Ratkovic", "Johnson", "Lopez", "Wantchekon", "Morse"), + first = c("Kabir", "Kosuke", "Carlos", "Ben", "Yang-Yang", "Marc", "Frank", "Gabriel", "Leonard", "Julia") +) + +usethis::use_data(voters, overwrite = TRUE) diff --git a/data/voters.RData b/data/voters.RData deleted file mode 100644 index ffe9973ec404c66ce7006c039936b3c3b49ee678..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 687 zcmV;g0#N-QiwFP!000001I<=jZ`v>vc5W>oZLPH5(JFyLLEAJo5EHbWCbUbtHa_v|?7)YvGJpzz6982JH2|jo>Hr$K#9kQV1oCP%LmZ}z3v`!Lw0Abblw!r)?gU3L z(vRkZ!^nOo8b&0(2FGtBHdDCJ$LW~OqYzwsY(x`ALC`syl3$9HzG9k&BQnm0FBngV zvD>yX$G~}Rtrl%B)M^~q)^-cfhn_}PD{lw9AKaCD`SZ3-e#_VQx175{J7#~%>K)$? 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zG%RPZ7VInm%H1(idj`)-@F1d)P|+#`JMn$rVAZv;x-k}11HpFHX(?TnVXlFlM|8S@ zUxw4=RuvwWB2u94VCw#M@%;=*II>SxvI$+M$L&2MDnbXmmPRM8>&58n!Fs=PBih`5%> zlG&I5YhfO<1j^tYfk4G8OmB|l1vFk@4w}3U$8-z=-{S!nivfC07{wfx=JjTAg!AuO ziYEl#wFfZqi~Nr0wt*%yF5O^ClHwGI-2$(@kj-fN6P!{m?hKGY%t8=Zm~s+ER1Z4% z27);W`2ka=nCoE9N3F%1#yA9jS9~Yv9K(nUvc9?^XWx%ug|cStTX9(|7JnY8r_wb} V@$rz$)vjvl?k@-Qr>=kr007V#Qa1nq diff --git a/data/voters.rda b/data/voters.rda new file mode 100644 index 0000000000000000000000000000000000000000..bfa686b2fb1376c9593c4265df33aa94d97b789e GIT binary patch literal 606 zcmV-k0-^mvT4*^jL0KkKS?Dwwf589q$OI4rfA0OiQSd+S-|#>HK>@%5JY-9_ z=t`%mpQ2AEr8GT~YGt91fevIO%oGpG8zB|KmZK@13=IksP!5H zMwtK_Vl>bi02*X4CWeMUNfH{Tq}q&wA)wQ088QF>00w|)oDa3Be)g@(V&9rMZ$Md~ z2#E}(bPgs+&36G-Utb-_8%J%Rg*qxPa(JFD#q{(das!uuu-cHy; zjip>y>RGQPN~o&vKCqM2uzXlPb*B3xUmFnD0+s;>2#|q*5W%+SN)U8~APkTsk9GjI z#~fV)vQEXS_^i*I!+=tT8Jk(v(ou_e_iu8bVk%TLB}yWK2PPAh5mOa zN(zZg5J*IJYt93!$V`gpDFSv9BpF||hY}DB7$R2FR2nH~Iqg430=^iBQIgTacPN;` zl*CG@+?9%mCgSZJB1XEcQ10Yd&4d^fTPx&7Nc}%J~?Ls6gWjH z)Z1cP$0ZIWVxF$qOMuQL(7OeKdMf^iuS5{RT) zgP_+9kW$E)*r$w+0m2JzLujy6alle@-6>Woyi~oK sO$DRW%MYm4Xkvbv)6KK{SiGBAh5lbQ%;lfaZq-!T Date: Wed, 15 Nov 2023 15:38:13 -0800 Subject: [PATCH 17/90] chore: `use_tidy_description()` --- DESCRIPTION | 55 +++++++++++++++++++++++++++++------------------------ 1 file changed, 30 insertions(+), 25 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 08c4337..271404f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,24 +1,30 @@ Package: wru +Title: Who are You? Bayesian Prediction of Racial Category Using Surname, + First Name, Middle Name, and Geolocation Version: 2.0.0 Date: 2023-07-12 -Title: Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and - Geolocation Authors@R: c( - person("Kabir", "Khanna", email = "kabirkhanna@gmail.com", role = c("aut")), - person("Brandon", "Bertelsen", email = "brandon@bertelsen.ca", role = c("aut","cre")), - person("Santiago", "Olivella", email = "olivella@unc.edu", role = c("aut")), - person("Evan", "Rosenman", email = "etrrosenman@gmail.com", role = c("aut")), - person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut")) + person("Kabir", "Khanna", , "kabirkhanna@gmail.com", role = "aut"), + person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), + person("Santiago", "Olivella", , "olivella@unc.edu", role = "aut"), + person("Evan", "Rosenman", , "etrrosenman@gmail.com", role = "aut"), + person("Kosuke", "Imai", , "imai@harvard.edu", role = "aut") ) -Description: Predicts individual race/ethnicity using surname, first name, middle name, geolocation, - and other attributes, such as gender and age. The method utilizes Bayes' - Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given - individual. The package implements methods described in Imai and Khanna (2016) - "Improving Ecological Inference by Predicting Individual Ethnicity from Voter - Registration Records" Political Analysis and Imai, Olivella, and Rosenman (2022) - "Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements" - . The package also incorporates the data described in Rosenman, Olivella, and Imai (2023) - "Race and ethnicity data for first, middle, and surnames" . +Description: Predicts individual race/ethnicity using surname, first name, + middle name, geolocation, and other attributes, such as gender and + age. The method utilizes Bayes' Rule (with optional measurement error + correction) to compute the posterior probability of each racial + category for any given individual. The package implements methods + described in Imai and Khanna (2016) "Improving Ecological Inference by + Predicting Individual Ethnicity from Voter Registration Records" + Political Analysis and Imai, Olivella, and + Rosenman (2022) "Addressing census data problems in race imputation + via fully Bayesian Improved Surname Geocoding and name supplements" + . The package also incorporates the data + described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity + data for first, middle, and surnames" + . +License: GPL (>= 3) URL: https://github.com/kosukeimai/wru BugReports: https://github.com/kosukeimai/wru/issues Depends: @@ -28,20 +34,19 @@ Imports: dplyr, furrr, future, + piggyback (>= 0.1.4), + PL94171, purrr, - Rcpp, - piggyback (>= 0.1.4), - PL94171 + Rcpp Suggests: - testthat (>= 3.0.0), - covr + covr, + testthat (>= 3.0.0) LinkingTo: Rcpp, RcppArmadillo -LazyLoad: yes +Config/testthat/edition: 3 +Encoding: UTF-8 LazyData: yes LazyDataCompression: xz -License: GPL (>= 3) +LazyLoad: yes RoxygenNote: 7.2.3 -Encoding: UTF-8 -Config/testthat/edition: 3 From 13265c655bb48c5da6116d11de65698c95e6de16 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 15:38:33 -0800 Subject: [PATCH 18/90] chore: add dependencies on `cli` and `rlang` (already indirect dependencies) --- DESCRIPTION | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 271404f..84c1a9a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -31,13 +31,15 @@ Depends: R (>= 4.1.0), utils Imports: + cli, dplyr, furrr, future, piggyback (>= 0.1.4), PL94171, purrr, - Rcpp + Rcpp, + rlang Suggests: covr, testthat (>= 3.0.0) From 60c1cd26e4e39953bf20de3ff20054cb256dc735 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 16:09:09 -0800 Subject: [PATCH 19/90] feat: add helper function `validate_key()` --- R/validate_key.R | 45 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 R/validate_key.R diff --git a/R/validate_key.R b/R/validate_key.R new file mode 100644 index 0000000..67dc696 --- /dev/null +++ b/R/validate_key.R @@ -0,0 +1,45 @@ +validate_key <- function(key, argument_name = NULL, call = rlang::caller_call()) { + if (is.null(argument_name)) { + argument_name <- rlang::expr_text(rlang::enexpr(key)) + } + + if (length(key) != 1) { + cli::cli_abort( + c( + "{.arg {argument_name}} must be a {.cls character} string of length {.val {1}}.", + x = "{.arg {argument_name}} has a length of {.val {length(key)}}." + ), + call = call + ) + } + + if (!inherits(key, "character")) { + cli::cli_abort( + c( + "{.arg {argument_name}} must be a {.cls character} string of length {.val {1}}.", + x = "{.arg {argument_name}} is an object of class {.cls {class(key)}}." + ), + call = call + ) + } + + if (!nzchar(key)) { + cli::cli_abort( + c( + "{.arg {argument_name}} must not be an empty string.", + "*" = "Have you set the {.envvar CENSUS_API_KEY} environment variable? + See {.help wru::get_census_data} for more information." + ), + call = call + ) + } + + if (is.na(key)) { + cli::cli_abort( + "{.arg {argument_name}} must not be {.val {NA_character_}}.", + call = call + ) + } + + key +} From 7f5c0b6dfe508b31e8447aed770a00164a89479b Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 16:14:06 -0800 Subject: [PATCH 20/90] feat: all functions that take a census API key now check `CENSUS_API_KEY` environment variable --- R/census_geo_api.R | 30 ++++++++----- R/census_helper.R | 26 +++++++---- R/census_helper_v2.R | 27 +++++++----- R/get_census_api.R | 13 ++++-- R/get_census_api_2.R | 13 ++++-- R/get_census_data.R | 34 +++++++++------ R/predict_race.R | 61 +++++++++++++++----------- R/race_prediction_funs.R | 94 +++++++++++++++++++++++++--------------- man/census_geo_api.Rd | 20 +++++---- man/census_helper.Rd | 16 ++++--- man/census_helper_new.Rd | 16 ++++--- man/get_census_api.Rd | 18 ++++++-- man/get_census_api_2.Rd | 18 ++++++-- man/get_census_data.Rd | 14 +++--- man/modfuns.Rd | 13 ++++-- man/predict_race.Rd | 21 +++++---- 16 files changed, 275 insertions(+), 159 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index c1857b1..7e9c295 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -5,8 +5,7 @@ #' This function allows users to download U.S. Census geographic data (2010 or 2020), #' at either the county, tract, block, or place level, for a particular state. #' -#' @param key A required character object. Must contain user's Census API -#' key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}. +#' @inheritParams get_census_data #' @param state A required character object specifying which state to extract Census data for, #' e.g., \code{"NJ"}. #' @param geo A character object specifying what aggregation level to use. @@ -35,24 +34,31 @@ #' #' @examples #' \dontshow{data(voters)} -#' \dontrun{census_geo_api(key = "...", states = c("NJ", "DE"), geo = "block")} -#' \dontrun{census_geo_api(key = "...", states = "FL", geo = "tract", age = TRUE, sex = TRUE)} -#' \dontrun{census_geo_api(key = "...", states = "MA", geo = "place", age = FALSE, sex = FALSE, +#' \dontrun{census_geo_api(states = c("NJ", "DE"), geo = "block")} +#' \dontrun{census_geo_api(states = "FL", geo = "tract", age = TRUE, sex = TRUE)} +#' \dontrun{census_geo_api(states = "MA", geo = "place", age = FALSE, sex = FALSE, #' year = "2020")} #' #' @references -#' Relies on get_census_api, get_census_api_2, and vec_to_chunk functions authored by Nicholas Nagle, -#' available \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. +#' Relies on `get_census_api()`, `get_census_api_2()`, and `vec_to_chunk()` functions authored by Nicholas Nagle, +#' available [here](https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html). #' #' @importFrom furrr future_map_dfr #' @importFrom purrr map_dfr #' @keywords internal -census_geo_api <- function(key = NULL, state, geo = "tract", age = FALSE, sex = FALSE, year = "2020", retry = 3, save_temp = NULL, counties = NULL) { - - if (missing(key)) { - stop('Must enter U.S. Census API key, which can be requested at https://api.census.gov/data/key_signup.html.') - } +census_geo_api <- function( + key = Sys.getenv("CENSUS_API_KEY"), + state, + geo = "tract", + age = FALSE, + sex = FALSE, + year = "2020", + retry = 3, + save_temp = NULL, + counties = NULL +) { + validate_key(key) census <- NULL state <- toupper(state) diff --git a/R/census_helper.R b/R/census_helper.R index c1aee34..f7c2816 100644 --- a/R/census_helper.R +++ b/R/census_helper.R @@ -7,8 +7,7 @@ #' at the county, tract, block, or place level. Census data calculated are #' Pr(Geolocation | Race) where geolocation is county, tract, block, or place. #' -#' @param key A required character object. Must contain user's Census API -#' key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}. +#' @inheritParams get_census_data #' @param voter.file An object of class \code{data.frame}. Must contain field(s) named #' \code{\var{county}}, \code{\var{tract}}, \code{\var{block}}, and/or \code{\var{place}} #' specifying geolocation. These should be character variables that match up with @@ -54,24 +53,35 @@ #' data(voters) #' } #' \dontrun{ -#' census_helper(key = "...", voter.file = voters, states = "nj", geo = "block") +#' census_helper(voter.file = voters, states = "nj", geo = "block") #' } #' \dontrun{ #' census_helper( -#' key = "...", voter.file = voters, states = "all", geo = "tract", +#' voter.file = voters, states = "all", geo = "tract", #' age = TRUE, sex = TRUE #' ) #' } #' \dontrun{ #' census_helper( -#' key = "...", voter.file = voters, states = "all", geo = "county", +#' voter.file = voters, states = "all", geo = "county", #' age = FALSE, sex = FALSE, year = "2020" #' ) #' } #' #' @keywords internal -census_helper <- function(key, voter.file, states = "all", geo = "tract", age = FALSE, sex = FALSE, year = "2020", census.data = NULL, retry = 3, use.counties = FALSE) { +census_helper <- function( + key = Sys.getenv("CENSUS_API_KEY"), + voter.file, + states = "all", + geo = "tract", + age = FALSE, + sex = FALSE, + year = "2020", + census.data = NULL, + retry = 3, + use.counties = FALSE +) { if (is.null(census.data) || (typeof(census.data) != "list")) { toDownload <- TRUE } else { @@ -79,9 +89,7 @@ census_helper <- function(key, voter.file, states = "all", geo = "tract", age = } if (toDownload) { - if (missing(key)) { - stop("Must enter U.S. Census API key, which can be requested at https://api.census.gov/data/key_signup.html.") - } + validate_key(key) } states <- toupper(states) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 6761443..f246816 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -8,8 +8,7 @@ #' at the county, tract, block, or place level. Census data calculated are #' Pr(Geolocation | Race) where geolocation is county, tract, block, or place. #' -#' @param key A required character object. Must contain user's Census API -#' key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}. +#' @inheritParams get_census_data #' @param voter.file An object of class \code{data.frame}. Must contain field(s) named #' \code{\var{county}}, \code{\var{tract}}, \code{\var{block}}, and/or \code{\var{place}} #' specifying geolocation. These should be character variables that match up with @@ -49,15 +48,25 @@ #' #' @examples #' \dontshow{data(voters)} -#' \dontrun{census_helper_new(key = "...", voter.file = voters, states = "nj", geo = "block")} -#' \dontrun{census_helper_new(key = "...", voter.file = voters, states = "all", geo = "tract")} -#' \dontrun{census_helper_new(key = "...", voter.file = voters, states = "all", geo = "place", +#' \dontrun{census_helper_new(voter.file = voters, states = "nj", geo = "block")} +#' \dontrun{census_helper_new(voter.file = voters, states = "all", geo = "tract")} +#' \dontrun{census_helper_new(voter.file = voters, states = "all", geo = "place", #' year = "2020")} #' #' @keywords internal -census_helper_new <- function(key, voter.file, states = "all", geo = "tract", age = FALSE, sex = FALSE, year = "2020", census.data = NULL, retry = 3, use.counties = FALSE) { - +census_helper_new <- function( + key = Sys.getenv("CENSUS_API_KEY"), + voter.file, + states = "all", + geo = "tract", + age = FALSE, + sex = FALSE, + year = "2020", + census.data = NULL, + retry = 3, + use.counties = FALSE +) { if (geo == "precinct") { stop("Error: census_helper_new function does not currently support precinct-level data.") } @@ -76,9 +85,7 @@ census_helper_new <- function(key, voter.file, states = "all", geo = "tract", ag } if (toDownload) { - if (missing(key)) { - stop('Must enter U.S. Census API key, which can be requested at https://api.census.gov/data/key_signup.html.') - } + validate_key(key) } states <- toupper(states) diff --git a/R/get_census_api.R b/R/get_census_api.R index 97d1e81..e0abc83 100644 --- a/R/get_census_api.R +++ b/R/get_census_api.R @@ -5,10 +5,9 @@ #' This function obtains U.S. Census data via the public API. User #' can specify the variables and region(s) for which to obtain data. #' +#' @inheritParams get_census_data #' @param data_url URL root of the API, #' e.g., \code{"https://api.census.gov/data/2020/dec/pl"}. -#' @param key A required character object containing user's Census API key, -#' which can be requested \href{https://api.census.gov/data/key_signup.html}{here}. #' @param var.names A character vector of variables to get, #' e.g., \code{c("P2_005N", "P2_006N", "P2_007N", "P2_008N")}. #' If there are more than 50 variables, then function will automatically @@ -23,7 +22,7 @@ #' @examples #' \dontrun{ #' get_census_api( -#' data_url = "https://api.census.gov/data/2020/dec/pl", key = "...", +#' data_url = "https://api.census.gov/data/2020/dec/pl", #' var.names = c("P2_005N", "P2_006N", "P2_007N", "P2_008N"), region = "for=county:*&in=state:34" #' ) #' } @@ -33,7 +32,13 @@ #' \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. #' #' @keywords internal -get_census_api <- function(data_url, key, var.names, region, retry = 0) { +get_census_api <- function( + data_url, + key = Sys.getenv("CENSUS_API_KEY"), + var.names, + region, + retry = 0 +) { if (length(var.names) > 50) { var.names <- vec_to_chunk(var.names) # Split variables into a list get <- lapply(var.names, function(x) paste(x, sep = "", collapse = ",")) diff --git a/R/get_census_api_2.R b/R/get_census_api_2.R index e0a9854..4b8cccf 100644 --- a/R/get_census_api_2.R +++ b/R/get_census_api_2.R @@ -6,10 +6,9 @@ #' It is used by the \code{get_census_api} function. The user should not need to call this #' function directly. #' +#' @inheritParams get_census_data #' @param data_url URL root of the API, #' e.g., \code{"https://api.census.gov/data/2020/dec/pl"}. -#' @param key A required character object containing user's Census API key, -#' which can be requested \href{https://api.census.gov/data/key_signup.html}{here}. #' @param get A character vector of variables to get, #' e.g., \code{c("P2_005N", "P2_006N", "P2_007N", "P2_008N")}. #' If there are more than 50 variables, then function will automatically @@ -22,7 +21,7 @@ #' If unsuccessful, function prints the URL query that was constructed. #' #' @examples -#' \dontrun{try(get_census_api_2(data_url = "https://api.census.gov/data/2020/dec/pl", key = "...", +#' \dontrun{try(get_census_api_2(data_url = "https://api.census.gov/data/2020/dec/pl", #' get = c("P2_005N", "P2_006N", "P2_007N", "P2_008N"), region = "for=county:*&in=state:34"))} #' #' @references @@ -30,7 +29,13 @@ #' \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. #' #' @keywords internal -get_census_api_2 <- function(data_url, key, get, region, retry = 3){ +get_census_api_2 <- function( + data_url, + key = Sys.getenv("CENSUS_API_KEY"), + get, + region, + retry = 3 +){ if(length(get) > 1) { get <- paste(get, collapse=',', sep='') } diff --git a/R/get_census_data.R b/R/get_census_data.R index dd86516..a7f07ff 100644 --- a/R/get_census_data.R +++ b/R/get_census_data.R @@ -4,8 +4,13 @@ #' for specified state(s). Using this function to download Census data in advance #' can save considerable time when running \code{predict_race} and \code{census_helper}. #' -#' @param key A required character object containing a valid Census API key, -#' which can be requested \href{https://api.census.gov/data/key_signup.html}{here}. +#' @param key A character string containing a valid Census API key, +#' which can be requested from the +#' [U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +#' +#' By default, attempts to find a census key stored in an +#' [environment variable][Sys.getenv] named `CENSUS_API_KEY`. +#' #' @param states which states to extract Census data for, e.g., \code{c("NJ", "NY")}. #' @param age A \code{TRUE}/\code{FALSE} object indicating whether to condition on #' age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). @@ -32,18 +37,19 @@ #' @export #' #' @examples -#' \dontrun{get_census_data(key = "...", states = c("NJ", "NY"), age = TRUE, sex = FALSE)} -#' \dontrun{get_census_data(key = "...", states = "MN", age = FALSE, sex = FALSE, year = "2020")} -get_census_data <- function(key = NULL, states, age = FALSE, sex = FALSE, year = "2020", census.geo = "block", retry = 3, county.list = NULL) { - - if (is.null(key)) { - # Matches tidycensus name for env var - key <- Sys.getenv("CENSUS_API_KEY") - } - - if (missing(key) | key == "") { - stop('Must enter valid Census API key, which can be requested at https://api.census.gov/data/key_signup.html.') - } +#' \dontrun{get_census_data(states = c("NJ", "NY"), age = TRUE, sex = FALSE)} +#' \dontrun{get_census_data(states = "MN", age = FALSE, sex = FALSE, year = "2020")} +get_census_data <- function( + key = Sys.getenv("CENSUS_API_KEY"), + states, + age = FALSE, + sex = FALSE, + year = "2020", + census.geo = "block", + retry = 3, + county.list = NULL +) { + validate_key(key) states <- toupper(states) diff --git a/R/predict_race.R b/R/predict_race.R index 31ed173..e7bd822 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -42,9 +42,14 @@ #' must have column named \code{place}. #' Specifying \code{\var{census.geo}} will call \code{census_helper} function #' to merge Census geographic data at specified level of geography. -#' @param census.key A character object specifying user's Census API -#' key. Required if \code{\var{census.geo}} is specified, because -#' a valid Census API key is required to download Census geographic data. +#' +#' @param census.key A character object specifying user's Census API key. +#' Required if `census.geo` is specified, because a valid Census API key is +#' required to download Census geographic data. +#' +#' If [`NULL`], the default, attempts to find a census key stored in an +#' [environment variable][Sys.getenv] named `CENSUS_API_KEY`. +#' #' @param census.data A list indexed by two-letter state abbreviations, #' which contains pre-saved Census geographic data. #' Can be generated using \code{get_census_data} function. @@ -106,36 +111,51 @@ #' #' data(voters) #' try(predict_race(voter.file = voters, surname.only = TRUE)) #' \dontrun{ -#' try(predict_race(voter.file = voters, census.geo = "tract", census.key = "...")) +#' try(predict_race(voter.file = voters, census.geo = "tract")) #' } #' \dontrun{ #' try(predict_race( -#' voter.file = voters, census.geo = "place", census.key = "...", year = "2020")) +#' voter.file = voters, census.geo = "place", year = "2020")) #' } #' \dontrun{ -#' CensusObj <- try(get_census_data("...", state = c("NY", "DC", "NJ"))) +#' CensusObj <- try(get_census_data(state = c("NY", "DC", "NJ"))) #' try(predict_race( #' voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID") #' ) #' } #' \dontrun{ -#' CensusObj2 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T)) +#' CensusObj2 <- try(get_census_data(state = c("NY", "DC", "NJ"), age = T, sex = T)) #' try(predict_race( #' voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T)) #' } #' \dontrun{ -#' CensusObj3 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place")) +#' CensusObj3 <- try(get_census_data(state = c("NY", "DC", "NJ"), census.geo = "place")) #' try(predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3)) #' } #' } #' @export -predict_race <- function(voter.file, census.surname = TRUE, surname.only = FALSE, - census.geo, census.key = NULL, census.data = NULL, age = FALSE, - sex = FALSE, year = "2020", party = NULL, retry = 3, impute.missing = TRUE, - use.counties = FALSE, model = "BISG", race.init = NULL, name.dictionaries = NULL, - names.to.use = "surname", control = NULL) { +predict_race <- function( + voter.file, + census.surname = TRUE, + surname.only = FALSE, + census.geo, + census.key = Sys.getenv("CENSUS_API_KEY"), + census.data = NULL, + age = FALSE, + sex = FALSE, + year = "2020", + party = NULL, + retry = 3, + impute.missing = TRUE, + use.counties = FALSE, + model = "BISG", + race.init = NULL, + name.dictionaries = NULL, + names.to.use = "surname", + control = NULL +) { message("Predicting race for ", year) @@ -167,21 +187,10 @@ predict_race <- function(voter.file, census.surname = TRUE, surname.only = FALSE # Adjust voter.file with caseid for ordering at the end voter.file$caseid <- 1:nrow(voter.file) - if((surname.only==FALSE) && is.null(census.key) && is.null(census.data)) { - k <- Sys.getenv("CENSUS_API_KEY") - - if(k == "") - stop( - "Please provide a valid Census API key using census.key option.", - " Or set CENSUS_API_KEY in your .Renviron or .Rprofile" - ) - - census.key <- k - } - - if(surname.only==FALSE && is.null(census.data)) { + if (surname.only == FALSE && is.null(census.data)) { # Otherwise predict_race_new and predict_race_me will both # attempt to pull census_data + validate_key(census.key) voter.file$state <- toupper(voter.file$state) states <- unique(voter.file$state) county.list <- split(voter.file$county, voter.file$state) diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 1ade9c7..2b18a4c 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -13,12 +13,12 @@ #' the augmented surname dictionary, and the first and middle name #' dictionaries when making predictions. #' +#' @inheritParams predict_race #' @param voter.file See documentation in \code{race_predict}. #' @param census.surname See documentation in \code{race_predict}. #' @param surname.only See documentation in \code{race_predict}. #' @param surname.year See documentation in \code{race_predict}. #' @param census.geo See documentation in \code{race_predict}. -#' @param census.key See documentation in \code{race_predict}. #' @param census.data See documentation in \code{race_predict}. #' @param age See documentation in \code{race_predict}. #' @param sex See documentation in \code{race_predict}. @@ -45,10 +45,23 @@ NULL #' @rdname modfuns #' @keywords internal -.predict_race_old <- function(voter.file, - census.surname = TRUE, surname.only = FALSE, surname.year = 2020, name.dictionaries = NULL, - census.geo, census.key, census.data = NULL, age = FALSE, sex = FALSE, year = "2020", - party, retry = 3, impute.missing = TRUE, use.counties = FALSE) { +.predict_race_old <- function( + voter.file, + census.surname = TRUE, + surname.only = FALSE, + surname.year = 2020, + name.dictionaries = NULL, + census.geo, + census.key = Sys.getenv("CENSUS_API_KEY"), + census.data = NULL, + age = FALSE, + sex = FALSE, + year = "2020", + party, + retry = 3, + impute.missing = TRUE, + use.counties = FALSE +) { # warning: 2020 census data only support prediction when both age and sex are equal to FALSE if ((sex == TRUE || age == TRUE) && (year == "2020")) { @@ -74,22 +87,16 @@ NULL message(paste("Proceeding with Census geographic data at", census.geo, "level...")) } if (missing(census.data) || is.null(census.data) || is.na(census.data)) { - if (missing(census.key) || is.null(census.key) || is.na(census.key)) { - stop("Please provide a valid Census API key using census.key option.") - } else { - message("Downloading Census geographic data using provided API key...") - } + validate_key(census.key) + message("Downloading Census geographic data using provided API key...") } else { if (!("state" %in% names(voter.file))) { stop("voter.file object needs to have a column named state.") } if (sum(toupper(unique(as.character(voter.file$state))) %in% toupper(names(census.data)) == FALSE) > 0) { message("census.data object does not include all states in voter.file object.") - if (missing(census.key) || is.null(census.key) || is.na(census.key)) { - stop("Please provide either a valid Census API key or valid census.data object that covers all states in voter.file object.") - } else { - message("Downloading Census geographic data for states not included in census.data object...") - } + validate_key(census.key) + message("Downloading Census geographic data for states not included in census.data object...") } else { message("Using Census geographic data from provided census.data object...") } @@ -261,10 +268,22 @@ NULL #' New race prediction function, implementing classical BISG with augmented #' surname dictionary, as well as first and middle name information. #' @rdname modfuns -predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE, sex = FALSE, - census.geo, census.key = NULL, name.dictionaries, surname.only=FALSE, - census.data = NULL, retry = 0, impute.missing = TRUE, census.surname = FALSE, - use.counties = FALSE) { +predict_race_new <- function( + voter.file, + names.to.use, + year = "2020", + age = FALSE, + sex = FALSE, + census.geo, + census.key = Sys.getenv("CENSUS_API_KEY"), + name.dictionaries, + surname.only=FALSE, + census.data = NULL, + retry = 0, + impute.missing = TRUE, + census.surname = FALSE, + use.counties = FALSE +) { # Check years if (!(year %in% c("2000", "2010", "2020"))){ @@ -331,11 +350,8 @@ predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE message(paste("Proceeding with Census geographic data at", census.geo, "level...")) } if (is.null(census.data)) { - if (missing(census.key) || is.null(census.key) || is.na(census.key)) { - stop("Please provide a valid Census API key using census.key option.") - } else { - message("Downloading Census geographic data using provided API key...") - } + validate_key(census.key) + message("Downloading Census geographic data using provided API key...") } else { if (!("state" %in% names(voter.file))) { stop("voter.file object needs to have a column named state.") @@ -343,11 +359,8 @@ predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE census_data_preflight(census.data, census.geo, year) if (sum(toupper(unique(as.character(voter.file$state))) %in% toupper(names(census.data)) == FALSE) > 0) { message("census.data object does not include all states in voter.file object.") - if (missing(census.key) || is.null(census.key) || is.na(census.key)) { - stop("Please provide either a valid Census API key or valid census.data object that covers all states in voter.file object.") - } else { - message("Downloading Census geographic data for states not included in census.data object...") - } + validate_key(census.key) + message("Downloading Census geographic data for states not included in census.data object...") } else { message("Using Census geographic data from provided census.data object...") } @@ -430,11 +443,24 @@ predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE #' error correction, fully Bayesian model) with augmented #' surname dictionary, as well as first and middle name information. #' @rdname modfuns -predict_race_me <- function(voter.file, names.to.use, year = "2020",age = FALSE, sex = FALSE, - census.geo, census.key, name.dictionaries, surname.only=FALSE, - census.data = NULL, retry = 0, impute.missing = TRUE, census.surname = FALSE, - use.counties = FALSE, race.init, ctrl) -{ +predict_race_me <- function( + voter.file, + names.to.use, + year = "2020", + age = FALSE, + sex = FALSE, + census.geo, + census.key = Sys.getenv("CENSUS_API_KEY"), + name.dictionaries, + surname.only = FALSE, + census.data = NULL, + retry = 0, + impute.missing = TRUE, + census.surname = FALSE, + use.counties = FALSE, + race.init, + ctrl +) { if(!is.null(census.data)) { census_data_preflight(census.data, census.geo, year) } diff --git a/man/census_geo_api.Rd b/man/census_geo_api.Rd index 4c1f6ae..c03f681 100644 --- a/man/census_geo_api.Rd +++ b/man/census_geo_api.Rd @@ -5,7 +5,7 @@ \title{Census Data download function.} \usage{ census_geo_api( - key = NULL, + key = Sys.getenv("CENSUS_API_KEY"), state, geo = "tract", age = FALSE, @@ -17,8 +17,12 @@ census_geo_api( ) } \arguments{ -\item{key}{A required character object. Must contain user's Census API -key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid Census API key, +which can be requested from the +[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). + +By default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{state}{A required character object specifying which state to extract Census data for, e.g., \code{"NJ"}.} @@ -64,14 +68,14 @@ at either the county, tract, block, or place level, for a particular state. } \examples{ \dontshow{data(voters)} -\dontrun{census_geo_api(key = "...", states = c("NJ", "DE"), geo = "block")} -\dontrun{census_geo_api(key = "...", states = "FL", geo = "tract", age = TRUE, sex = TRUE)} -\dontrun{census_geo_api(key = "...", states = "MA", geo = "place", age = FALSE, sex = FALSE, +\dontrun{census_geo_api(states = c("NJ", "DE"), geo = "block")} +\dontrun{census_geo_api(states = "FL", geo = "tract", age = TRUE, sex = TRUE)} +\dontrun{census_geo_api(states = "MA", geo = "place", age = FALSE, sex = FALSE, year = "2020")} } \references{ -Relies on get_census_api, get_census_api_2, and vec_to_chunk functions authored by Nicholas Nagle, -available \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. +Relies on `get_census_api()`, `get_census_api_2()`, and `vec_to_chunk()` functions authored by Nicholas Nagle, +available [here](https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html). } \keyword{internal} diff --git a/man/census_helper.Rd b/man/census_helper.Rd index ef3c5ff..77a7382 100644 --- a/man/census_helper.Rd +++ b/man/census_helper.Rd @@ -5,7 +5,7 @@ \title{Census helper function.} \usage{ census_helper( - key, + key = Sys.getenv("CENSUS_API_KEY"), voter.file, states = "all", geo = "tract", @@ -18,8 +18,12 @@ census_helper( ) } \arguments{ -\item{key}{A required character object. Must contain user's Census API -key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid Census API key, +which can be requested from the +[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). + +By default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{voter.file}{An object of class \code{data.frame}. Must contain field(s) named \code{\var{county}}, \code{\var{tract}}, \code{\var{block}}, and/or \code{\var{place}} @@ -85,17 +89,17 @@ Pr(Geolocation | Race) where geolocation is county, tract, block, or place. data(voters) } \dontrun{ -census_helper(key = "...", voter.file = voters, states = "nj", geo = "block") +census_helper(voter.file = voters, states = "nj", geo = "block") } \dontrun{ census_helper( - key = "...", voter.file = voters, states = "all", geo = "tract", + voter.file = voters, states = "all", geo = "tract", age = TRUE, sex = TRUE ) } \dontrun{ census_helper( - key = "...", voter.file = voters, states = "all", geo = "county", + voter.file = voters, states = "all", geo = "county", age = FALSE, sex = FALSE, year = "2020" ) } diff --git a/man/census_helper_new.Rd b/man/census_helper_new.Rd index e410267..827d26f 100644 --- a/man/census_helper_new.Rd +++ b/man/census_helper_new.Rd @@ -5,7 +5,7 @@ \title{Census helper function.} \usage{ census_helper_new( - key, + key = Sys.getenv("CENSUS_API_KEY"), voter.file, states = "all", geo = "tract", @@ -18,8 +18,12 @@ census_helper_new( ) } \arguments{ -\item{key}{A required character object. Must contain user's Census API -key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid Census API key, +which can be requested from the +[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). + +By default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{voter.file}{An object of class \code{data.frame}. Must contain field(s) named \code{\var{county}}, \code{\var{tract}}, \code{\var{block}}, and/or \code{\var{place}} @@ -79,9 +83,9 @@ Pr(Geolocation | Race) where geolocation is county, tract, block, or place. } \examples{ \dontshow{data(voters)} -\dontrun{census_helper_new(key = "...", voter.file = voters, states = "nj", geo = "block")} -\dontrun{census_helper_new(key = "...", voter.file = voters, states = "all", geo = "tract")} -\dontrun{census_helper_new(key = "...", voter.file = voters, states = "all", geo = "place", +\dontrun{census_helper_new(voter.file = voters, states = "nj", geo = "block")} +\dontrun{census_helper_new(voter.file = voters, states = "all", geo = "tract")} +\dontrun{census_helper_new(voter.file = voters, states = "all", geo = "place", year = "2020")} } diff --git a/man/get_census_api.Rd b/man/get_census_api.Rd index cf99156..03208c4 100644 --- a/man/get_census_api.Rd +++ b/man/get_census_api.Rd @@ -4,14 +4,24 @@ \alias{get_census_api} \title{Census API function.} \usage{ -get_census_api(data_url, key, var.names, region, retry = 0) +get_census_api( + data_url, + key = Sys.getenv("CENSUS_API_KEY"), + var.names, + region, + retry = 0 +) } \arguments{ \item{data_url}{URL root of the API, e.g., \code{"https://api.census.gov/data/2020/dec/pl"}.} -\item{key}{A required character object containing user's Census API key, -which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid Census API key, +which can be requested from the +[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). + +By default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{var.names}{A character vector of variables to get, e.g., \code{c("P2_005N", "P2_006N", "P2_007N", "P2_008N")}. @@ -38,7 +48,7 @@ can specify the variables and region(s) for which to obtain data. \examples{ \dontrun{ get_census_api( - data_url = "https://api.census.gov/data/2020/dec/pl", key = "...", + data_url = "https://api.census.gov/data/2020/dec/pl", var.names = c("P2_005N", "P2_006N", "P2_007N", "P2_008N"), region = "for=county:*&in=state:34" ) } diff --git a/man/get_census_api_2.Rd b/man/get_census_api_2.Rd index 065fe9e..32e8dc8 100644 --- a/man/get_census_api_2.Rd +++ b/man/get_census_api_2.Rd @@ -4,14 +4,24 @@ \alias{get_census_api_2} \title{Census API URL assembler.} \usage{ -get_census_api_2(data_url, key, get, region, retry = 3) +get_census_api_2( + data_url, + key = Sys.getenv("CENSUS_API_KEY"), + get, + region, + retry = 3 +) } \arguments{ \item{data_url}{URL root of the API, e.g., \code{"https://api.census.gov/data/2020/dec/pl"}.} -\item{key}{A required character object containing user's Census API key, -which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid Census API key, +which can be requested from the +[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). + +By default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{get}{A character vector of variables to get, e.g., \code{c("P2_005N", "P2_006N", "P2_007N", "P2_008N")}. @@ -37,7 +47,7 @@ It is used by the \code{get_census_api} function. The user should not need to ca function directly. } \examples{ -\dontrun{try(get_census_api_2(data_url = "https://api.census.gov/data/2020/dec/pl", key = "...", +\dontrun{try(get_census_api_2(data_url = "https://api.census.gov/data/2020/dec/pl", get = c("P2_005N", "P2_006N", "P2_007N", "P2_008N"), region = "for=county:*&in=state:34"))} } diff --git a/man/get_census_data.Rd b/man/get_census_data.Rd index dce4371..d0e16a7 100644 --- a/man/get_census_data.Rd +++ b/man/get_census_data.Rd @@ -5,7 +5,7 @@ \title{Multilevel Census data download function.} \usage{ get_census_data( - key = NULL, + key = Sys.getenv("CENSUS_API_KEY"), states, age = FALSE, sex = FALSE, @@ -16,8 +16,12 @@ get_census_data( ) } \arguments{ -\item{key}{A required character object containing a valid Census API key, -which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid Census API key, +which can be requested from the +[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). + +By default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{states}{which states to extract Census data for, e.g., \code{c("NJ", "NY")}.} @@ -56,6 +60,6 @@ for specified state(s). Using this function to download Census data in advance can save considerable time when running \code{predict_race} and \code{census_helper}. } \examples{ -\dontrun{get_census_data(key = "...", states = c("NJ", "NY"), age = TRUE, sex = FALSE)} -\dontrun{get_census_data(key = "...", states = "MN", age = FALSE, sex = FALSE, year = "2020")} +\dontrun{get_census_data(states = c("NJ", "NY"), age = TRUE, sex = FALSE)} +\dontrun{get_census_data(states = "MN", age = FALSE, sex = FALSE, year = "2020")} } diff --git a/man/modfuns.Rd b/man/modfuns.Rd index 9702621..af0f09d 100644 --- a/man/modfuns.Rd +++ b/man/modfuns.Rd @@ -14,7 +14,7 @@ surname.year = 2020, name.dictionaries = NULL, census.geo, - census.key, + census.key = Sys.getenv("CENSUS_API_KEY"), census.data = NULL, age = FALSE, sex = FALSE, @@ -32,7 +32,7 @@ predict_race_new( age = FALSE, sex = FALSE, census.geo, - census.key = NULL, + census.key = Sys.getenv("CENSUS_API_KEY"), name.dictionaries, surname.only = FALSE, census.data = NULL, @@ -49,7 +49,7 @@ predict_race_me( age = FALSE, sex = FALSE, census.geo, - census.key, + census.key = Sys.getenv("CENSUS_API_KEY"), name.dictionaries, surname.only = FALSE, census.data = NULL, @@ -74,7 +74,12 @@ predict_race_me( \item{census.geo}{See documentation in \code{race_predict}.} -\item{census.key}{See documentation in \code{race_predict}.} +\item{census.key}{A character object specifying user's Census API key. +Required if `census.geo` is specified, because a valid Census API key is +required to download Census geographic data. + +If [`NULL`], the default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{census.data}{See documentation in \code{race_predict}.} diff --git a/man/predict_race.Rd b/man/predict_race.Rd index 5b80be6..bb3c58a 100644 --- a/man/predict_race.Rd +++ b/man/predict_race.Rd @@ -9,7 +9,7 @@ predict_race( census.surname = TRUE, surname.only = FALSE, census.geo, - census.key = NULL, + census.key = Sys.getenv("CENSUS_API_KEY"), census.data = NULL, age = FALSE, sex = FALSE, @@ -67,9 +67,12 @@ must have column named \code{place}. Specifying \code{\var{census.geo}} will call \code{census_helper} function to merge Census geographic data at specified level of geography.} -\item{census.key}{A character object specifying user's Census API -key. Required if \code{\var{census.geo}} is specified, because -a valid Census API key is required to download Census geographic data.} +\item{census.key}{A character object specifying user's Census API key. +Required if `census.geo` is specified, because a valid Census API key is +required to download Census geographic data. + +If [`NULL`], the default, attempts to find a census key stored in an +[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} \item{census.data}{A list indexed by two-letter state abbreviations, which contains pre-saved Census geographic data. @@ -153,25 +156,25 @@ individual-level race/ethnicity, based on surname, geolocation, and party. #' data(voters) try(predict_race(voter.file = voters, surname.only = TRUE)) \dontrun{ -try(predict_race(voter.file = voters, census.geo = "tract", census.key = "...")) +try(predict_race(voter.file = voters, census.geo = "tract")) } \dontrun{ try(predict_race( - voter.file = voters, census.geo = "place", census.key = "...", year = "2020")) + voter.file = voters, census.geo = "place", year = "2020")) } \dontrun{ -CensusObj <- try(get_census_data("...", state = c("NY", "DC", "NJ"))) +CensusObj <- try(get_census_data(state = c("NY", "DC", "NJ"))) try(predict_race( voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID") ) } \dontrun{ -CensusObj2 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T)) +CensusObj2 <- try(get_census_data(state = c("NY", "DC", "NJ"), age = T, sex = T)) try(predict_race( voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T)) } \dontrun{ -CensusObj3 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place")) +CensusObj3 <- try(get_census_data(state = c("NY", "DC", "NJ"), census.geo = "place")) try(predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3)) } } From 9fb7fe04eccd3cf7611674dadf85aa885c43e51e Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 13:13:36 -0800 Subject: [PATCH 21/90] docs(README): update README --- README.Rmd | 163 ++++++++++++++++++++++++---------------- README.md | 212 +++++++++++++++++++++++++++++------------------------ 2 files changed, 215 insertions(+), 160 deletions(-) diff --git a/README.Rmd b/README.Rmd index cdb1b95..5790d6f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -5,12 +5,17 @@ output: github_document ```{r, include = FALSE} +library(wru) +options(width = 10000) knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", - out.width = "100%" + out.width = "100%", + message = FALSE ) + +voters <- dplyr::select(wru::voters, -precinct, -first, -last) ``` # wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation @@ -41,24 +46,24 @@ Here is a simple example that predicts the race/ethnicity of voters based only o ``` r library(wru) future::plan(future::multisession) -data(voters) -predict_race(voter.file = voters, surname.only = T) +predict_race(voter.file = voters, surname.only = TRUE) ``` -The above produces the following output, where the last five columns are probabilistic race/ethnicity predictions (e.g., 'pred.his' is the probability of being Hispanic/Latino): +The above produces the following output, where the last five columns are probabilistic race/ethnicity predictions (e.g., `pred.his` is the probability of being Hispanic/Latino): - "Proceeding with surname-only predictions ..." - VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth - 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.0676000 0.00430000 0.00820000 0.86680000 0.05310000 - 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.0812000 0.00240000 0.06890000 0.73750000 0.11000000 - 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.0594000 0.00260000 0.82270000 0.10510000 0.01020000 - 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.9355936 0.00220022 0.02850285 0.00780078 0.02590259 - 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.0098000 0.00180000 0.00065000 0.98200000 0.00575000 - 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.9187000 0.01083333 0.01083333 0.01083333 0.04880000 - 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.5897000 0.34630000 0.02360000 0.00540000 0.03500000 - 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.0486000 0.00570000 0.92920000 0.01020000 0.00630000 - 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.6665000 0.08530000 0.13670000 0.07970000 0.03180000 - 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.9054000 0.04310000 0.02060000 0.00720000 0.02370000 +``` + VoterID surname state CD county tract block age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 29 0 Ind 0 74000 0.045110474 0.003067623 0.0068522723 0.860411906 0.084557725 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.052645440 0.001334812 0.0558160072 0.719376581 0.170827160 + 3 Rivera NY 12 061 004800 6001 33 0 Rep 2 51000 0.043285692 0.008204605 0.9136195794 0.024316883 0.010573240 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895405704 0.001911388 0.0337464844 0.011079323 0.057857101 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.006572555 0.001298962 0.0005388581 0.982365594 0.009224032 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.861236727 0.008212824 0.0095395642 0.011334635 0.109676251 + 7 Johnson NY 9 061 014900 4000 25 0 Dem 1 51000 0.543815322 0.344128607 0.0272403940 0.007405765 0.077409913 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.038939877 0.004920643 0.9318797791 0.012154125 0.012105576 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.330697188 0.194700665 0.4042849478 0.021379541 0.048937658 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.866360147 0.044429853 0.0246568086 0.010219712 0.054333479 +``` ### Using geolocation @@ -76,63 +81,85 @@ CENSUS_API_KEY=YourKey # Restart your R session ``` -The following example predicts the race/ethnicity of voters based on their surnames, Census tract of residence (census.geo = "tract"), and which party registration (party = "PID"). Note that a valid API key must be provided in the input parameter 'census.key' in order for the function to download the relevant tract-level data. +The following example predicts the race/ethnicity of voters based on their surnames, census tract of residence (`census.geo = "tract"`), and party registration (`party = "PID"`). +Note that a valid API key must be stored in a `CENSUS_API_KEY` environment variable or provided with the `census.key` argument in order for the function to download the relevant tract-level data. ``` r library(wru) -predict_race(voter.file = voters, census.geo = "tract", census.key = "...", party = "PID") +predict_race(voter.file = voters, census.geo = "tract", party = "PID") ``` ``` -VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth - 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.081856291 0.0021396565 0.0110451405 0.828313291 0.076645621 - 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.916936771 0.0044432219 0.0120276229 0.008532929 0.058059455 - 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895620643 0.0022078678 0.0139457411 0.023345853 0.064879895 - 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.003164229 0.0006092345 0.0001072684 0.991261466 0.004857802 - 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.029936354 0.0009275220 0.0129831039 0.850040743 0.106112277 - 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.231046860 0.0016485574 0.6813780115 0.053180270 0.032746301 - 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.817841573 0.0063677130 0.0258733496 0.107254103 0.042663261 - 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.223924118 0.0002913000 0.4451163607 0.313431417 0.017236805 - 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.241417483 0.6900686166 0.0293556870 0.011105140 0.028053073 - 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.983300770 0.0006116706 0.0034070782 0.004823439 0.007857042 + VoterID surname state CD county tract block age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 29 0 Ind 0 74000 0.021711601 0.0009552652 2.826779e-03 0.93364592 0.040860431 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.015364583 0.0002320815 9.020240e-03 0.90245186 0.072931231 + 3 Rivera NY 12 061 004800 6001 33 0 Rep 2 51000 0.092415538 0.0047099965 7.860806e-01 0.09924761 0.017546300 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.854810748 0.0010870744 1.783931e-02 0.04546436 0.080798514 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.001548762 0.0001823506 7.031116e-05 0.99501901 0.003179566 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.852374629 0.0052590592 8.092435e-03 0.02529163 0.108982246 + 7 Johnson NY 9 061 014900 4000 25 0 Dem 1 51000 0.831282563 0.0613242553 1.059715e-02 0.01602557 0.080770461 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.062022518 0.0046691402 8.218906e-01 0.08321206 0.028205698 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.396500218 0.1390722877 2.684107e-01 0.11018413 0.085832686 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.861168219 0.0498449102 1.131154e-02 0.01633532 0.061340015 ``` -In predict_race, the census.geo options are "county", "tract", "block" and "place". Here is an example of prediction based on census statistics collected at the level of "place": + +In `predict_race()`, the `census.geo` options are "county", "tract", "block" and "place". +Here is an example of prediction based on census statistics collected at the level of "place": ``` r -data(voters) -predict_race(voter.file = voters, census.geo = "place", census.key = "...", party = "PID") +predict_race(voter.file = voters, census.geo = "place", party = "PID") +``` ``` + VoterID surname state CD county tract block age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 29 0 Ind 0 74000 0.042146148 0.0620484276 9.502254e-02 0.55109761 0.249685278 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.018140322 0.0002204255 1.026018e-02 0.90710894 0.064270133 + 3 Rivera NY 12 061 004800 6001 33 0 Rep 2 51000 0.015528660 0.0092292671 9.266893e-01 0.04182290 0.006729825 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.879537890 0.0008997896 1.768379e-02 0.03982601 0.062052518 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.001819394 0.0001723242 7.957542e-05 0.99514078 0.002787926 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.834942701 0.0038157857 4.933723e-03 0.04021245 0.116095337 + 7 Johnson NY 9 061 014900 4000 25 0 Dem 1 51000 0.290386744 0.5761904554 4.112613e-02 0.01895885 0.073337820 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.065321588 0.0039558641 8.339387e-01 0.07461133 0.022172551 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.428723819 0.1209683869 2.796062e-01 0.10142953 0.069272098 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.716211008 0.1899554127 1.867133e-02 0.01025241 0.064909839 +``` + +### Downloading census data -It is also possible to pre-download Census geographic data, which can save time when running predict_race(). The example dataset 'voters' includes people in DC, NJ, and NY. The following example subsets voters in DC and NJ, and then uses get_census_data() to download Census geographic data in these two states (input parameter 'key' requires valid API key). Census data is assigned to an object named census.dc.nj. The predict_race() statement predicts the race/ethnicity of voters in DC and NJ using the pre-saved Census data (census.data = census.dc.nj). This example conditions race/ethnicity predictions on voters' surnames, block of residence (census.geo = "block"), age (age = TRUE), and party registration (party = "PID"). +It is also possible to pre-download Census geographic data, which can save time when running `predict_race()`. +The example dataset `voters` includes people in DC, NJ, and NY. +The following example subsets voters in DC and NJ, and then uses `get_census_data()` to download census geographic data in these two states (a valid API key must be stored in a `CENSUS_API_KEY` environment variable or provided with the `key` argument). +Census data is assigned to an object named `census.dc.nj`. +The `predict_race()` statement predicts the race/ethnicity of voters in DC and NJ using the pre-downloaded census data (`census.data = census.dc.nj`). This example conditions race/ethnicity predictions on voters' surnames, block of residence (`census.geo = "block"`), age (`age = TRUE`), and party registration (`party = "PID"`). -Please note that the input parameters 'age' and 'sex' must have the same values in get_census_data() and predict_race(), i.e., TRUE in both or FALSE in both. In this case, predictions are conditioned on age but not sex, so age = TRUE and sex = FALSE in both the get_census_data() and predict_race() statements. +Please note that the input parameters `age` and `sex` must have the same values in `get_census_data()` and `predict_race()`, i.e., `TRUE` in both or `FALSE` in both. +In this case, predictions are conditioned on age but not sex, so `age = TRUE` and `sex = FALSE` in both the `get_census_data()` and `predict_race()` statements. ``` r library(wru) -data(voters) -voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset -census.dc.nj <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE) # create Census data object covering DC and NJ +voters.dc.nj <- voters[voters$state %in% c("DC", "NJ"), ] +census.dc.nj <- get_census_data(state = c("DC", "NJ"), age = TRUE, sex = FALSE) predict_race(voter.file = voters.dc.nj, census.geo = "block", census.data = census.dc.nj, age = TRUE, sex = FALSE, party = "PID") ``` -The last two lines above are equivalent to the following: +This produces the same result as the following statement, which downloads census data during evaluation rather than using pre-downloaded data: ``` r -predict_race(voter.file = voters.dc.nj, census.geo = "block", census.key = "...", age = TRUE, sex = FALSE, party = "PID") +predict_race(voter.file = voters.dc.nj, census.geo = "block", age = TRUE, sex = FALSE, party = "PID") ``` Using pre-downloaded Census data may be useful for the following reasons: -- You can save a lot of time in future runs of predict_race() if the relevant Census data has already been saved; -- The machines used to run predict_race() may not have internet access; -- You can obtain timely snapshots of Census geographic data that match your voter file. +- You can save a lot of time in future runs of `predict_race()` if the relevant census data has already been saved; +- The machines used to run `predict_race()` may not have internet access; +- You can obtain timely snapshots of census geographic data that match your voter file. -Downloading data using get_census_data() may take a long time, especially at the block level or in large states. If block-level Census data is not required, downloading Census data at the tract level will save time. Similarly, if tract-level Census data is not required, county-level data may be specified in order to save time. +Downloading data using `get_census_data()` may take a long time, especially in large states or when using small geographic levels. +If block-level census data is not required, downloading census data at the tract level will save time. +Similarly, if tract-level data is not required, county-level data may be specified in order to save time. ``` r library(wru) -data(voters) -voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset -census.dc.nj2 <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE, census.geo = "tract") +voters.dc.nj <- voters[voters$state %in% c("DC", "NJ"), ] +census.dc.nj2 <- get_census_data(state = c("DC", "NJ"), age = TRUE, sex = FALSE, census.geo = "tract") predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) @@ -140,18 +167,22 @@ predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = cen predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) ``` -Or you can also use the census_geo_api() to manually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter 'state' that requires a two-letter state abbreviation to proceed. +#### Interact directly with the Census API + +You can use `census_geo_api()` to manually construct a census object. +The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. +Note that the `state` argument requires a vector of two-letter state abbreviations. ``` r -censusObj2 = list() +census.dc.nj3 = list() -county.dc <- census_geo_api(key = "...", state = "DC", geo = "county", age = TRUE, sex = FALSE) -tract.dc <- census_geo_api(key = "...", state = "DC", geo = "tract", age = TRUE, sex = FALSE) -censusObj2[["DC"]] <- list(state = "DC", county = county.dc, tract = tract.dc, age = TRUE, sex = FALSE) +county.dc <- census_geo_api(state = "DC", geo = "county", age = TRUE, sex = FALSE) +tract.dc <- census_geo_api(state = "DC", geo = "tract", age = TRUE, sex = FALSE) +census.dc.nj3[["DC"]] <- list(state = "DC", county = county.dc, tract = tract.dc, age = TRUE, sex = FALSE) -tract.nj <- census_geo_api(key = "...", state = "NJ", geo = "tract", age = TRUE, sex = FALSE) -county.nj <- census_geo_api(key = "...", state = "NJ", geo = "county", age = TRUE, sex = FALSE) -censusObj2[["NJ"]] <- list(state = "NJ", county = county.nj, tract = tract.nj, age = TRUE, sex = FALSE) +tract.nj <- census_geo_api(state = "NJ", geo = "tract", age = TRUE, sex = FALSE) +county.nj <- census_geo_api(state = "NJ", geo = "county", age = TRUE, sex = FALSE) +census.dc.nj3[["NJ"]] <- list(state = "NJ", county = county.nj, tract = tract.nj, age = TRUE, sex = FALSE) ``` Note: The age and sex parameters must be consistent when creating the Census object and using that Census object in the predict_race function. If one of these parameters is TRUE in the Census object, it must also be TRUE in the predict_race function. @@ -159,19 +190,25 @@ Note: The age and sex parameters must be consistent when creating the Census obj After saving the data in censusObj2 above, we can condition race/ethnicity predictions on different combinations of input variables, without having to re-download the relevant Census data. ``` r -predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) -predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) -predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party) -predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj3, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj3, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj3, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj3, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) ``` -### Notes about process design +### Parallelization -For larger scale imputations garbage-collection can become a problem and your machine(s) can quickly run out of memory (RAM). It is recommended to use the `future.callr::callr` plan instead of `future::multisession`. The `callr` plan instantiates a new session at every iteration of your parallel loop or map. This simultaneously has the effect of creating more overhead, but also clearing the often sticky memory elements that would be left over to grow to eventual system failure when using `multisession`. You end up with a process that is more stable, but slightly slower. +For larger scale imputations, garbage collection can become a problem and your machine(s) can quickly run out of memory (RAM). +We recommended using the `future.callr::callr` plan instead of `future::multisession`. +The `callr` plan instantiates a new session at every iteration of your parallel loop or map. +Although this has the negative effect of creating more overhead, it also clears sticky memory elements that can grow to eventual system failure when using `multisession`. +You end up with a process that is more stable, but slightly slower. - library(wru) - future::plan(future.callr::callr) - # ... +``` r +library(wru) +future::plan(future.callr::callr) +# ... +``` ## Census Data diff --git a/README.md b/README.md index e41f849..4c8188b 100644 --- a/README.md +++ b/README.md @@ -39,26 +39,24 @@ based only on their surnames. ``` r library(wru) future::plan(future::multisession) -data(voters) -predict_race(voter.file = voters, surname.only = T) +predict_race(voter.file = voters, surname.only = TRUE) ``` The above produces the following output, where the last five columns are -probabilistic race/ethnicity predictions (e.g., ‘pred.his’ is the +probabilistic race/ethnicity predictions (e.g., `pred.his` is the probability of being Hispanic/Latino): - "Proceeding with surname-only predictions ..." - VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth - 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.0676000 0.00430000 0.00820000 0.86680000 0.05310000 - 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.0812000 0.00240000 0.06890000 0.73750000 0.11000000 - 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.0594000 0.00260000 0.82270000 0.10510000 0.01020000 - 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.9355936 0.00220022 0.02850285 0.00780078 0.02590259 - 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.0098000 0.00180000 0.00065000 0.98200000 0.00575000 - 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.9187000 0.01083333 0.01083333 0.01083333 0.04880000 - 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.5897000 0.34630000 0.02360000 0.00540000 0.03500000 - 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.0486000 0.00570000 0.92920000 0.01020000 0.00630000 - 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.6665000 0.08530000 0.13670000 0.07970000 0.03180000 - 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.9054000 0.04310000 0.02060000 0.00720000 0.02370000 + VoterID surname state CD county tract block age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 29 0 Ind 0 74000 0.045110474 0.003067623 0.0068522723 0.860411906 0.084557725 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.052645440 0.001334812 0.0558160072 0.719376581 0.170827160 + 3 Rivera NY 12 061 004800 6001 33 0 Rep 2 51000 0.043285692 0.008204605 0.9136195794 0.024316883 0.010573240 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895405704 0.001911388 0.0337464844 0.011079323 0.057857101 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.006572555 0.001298962 0.0005388581 0.982365594 0.009224032 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.861236727 0.008212824 0.0095395642 0.011334635 0.109676251 + 7 Johnson NY 9 061 014900 4000 25 0 Dem 1 51000 0.543815322 0.344128607 0.0272403940 0.007405765 0.077409913 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.038939877 0.004920643 0.9318797791 0.012154125 0.012105576 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.330697188 0.194700665 0.4042849478 0.021379541 0.048937658 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.866360147 0.044429853 0.0246568086 0.010219712 0.054333479 ### Using geolocation @@ -80,89 +78,106 @@ First, you should save your census key to your `.Rprofile` or # Restart your R session The following example predicts the race/ethnicity of voters based on -their surnames, Census tract of residence (census.geo = “tract”), and -which party registration (party = “PID”). Note that a valid API key must -be provided in the input parameter ‘census.key’ in order for the -function to download the relevant tract-level data. +their surnames, census tract of residence (`census.geo = "tract"`), and +party registration (`party = "PID"`). Note that a valid API key must be +stored in a `CENSUS_API_KEY` environment variable or provided with the +`census.key` argument in order for the function to download the relevant +tract-level data. ``` r library(wru) -predict_race(voter.file = voters, census.geo = "tract", census.key = "...", party = "PID") +predict_race(voter.file = voters, census.geo = "tract", party = "PID") ``` - VoterID surname state CD county tract block precinct age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth - 1 Khanna NJ 12 021 004000 3001 6 29 0 Ind 0 74000 0.081856291 0.0021396565 0.0110451405 0.828313291 0.076645621 - 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.916936771 0.0044432219 0.0120276229 0.008532929 0.058059455 - 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.895620643 0.0022078678 0.0139457411 0.023345853 0.064879895 - 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.003164229 0.0006092345 0.0001072684 0.991261466 0.004857802 - 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.029936354 0.0009275220 0.0129831039 0.850040743 0.106112277 - 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.231046860 0.0016485574 0.6813780115 0.053180270 0.032746301 - 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.817841573 0.0063677130 0.0258733496 0.107254103 0.042663261 - 3 Velasco NY 12 061 004800 6001 33 0 Rep 2 51000 0.223924118 0.0002913000 0.4451163607 0.313431417 0.017236805 - 7 Johnson NY 9 061 015100 4000 25 0 Dem 1 51000 0.241417483 0.6900686166 0.0293556870 0.011105140 0.028053073 - 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.983300770 0.0006116706 0.0034070782 0.004823439 0.007857042 - -In predict_race, the census.geo options are “county”, “tract”, “block” -and “place”. Here is an example of prediction based on census statistics -collected at the level of “place”: + VoterID surname state CD county tract block age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 29 0 Ind 0 74000 0.021711601 0.0009552652 2.826779e-03 0.93364592 0.040860431 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.015364583 0.0002320815 9.020240e-03 0.90245186 0.072931231 + 3 Rivera NY 12 061 004800 6001 33 0 Rep 2 51000 0.092415538 0.0047099965 7.860806e-01 0.09924761 0.017546300 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.854810748 0.0010870744 1.783931e-02 0.04546436 0.080798514 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.001548762 0.0001823506 7.031116e-05 0.99501901 0.003179566 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.852374629 0.0052590592 8.092435e-03 0.02529163 0.108982246 + 7 Johnson NY 9 061 014900 4000 25 0 Dem 1 51000 0.831282563 0.0613242553 1.059715e-02 0.01602557 0.080770461 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.062022518 0.0046691402 8.218906e-01 0.08321206 0.028205698 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.396500218 0.1390722877 2.684107e-01 0.11018413 0.085832686 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.861168219 0.0498449102 1.131154e-02 0.01633532 0.061340015 + +In `predict_race()`, the `census.geo` options are “county”, “tract”, +“block” and “place”. Here is an example of prediction based on census +statistics collected at the level of “place”: ``` r -data(voters) -predict_race(voter.file = voters, census.geo = "place", census.key = "...", party = "PID") +predict_race(voter.file = voters, census.geo = "place", party = "PID") ``` + VoterID surname state CD county tract block age sex party PID place pred.whi pred.bla pred.his pred.asi pred.oth + 1 Khanna NJ 12 021 004000 3001 29 0 Ind 0 74000 0.042146148 0.0620484276 9.502254e-02 0.55109761 0.249685278 + 2 Imai NJ 12 021 004501 1025 40 0 Dem 1 60900 0.018140322 0.0002204255 1.026018e-02 0.90710894 0.064270133 + 3 Rivera NY 12 061 004800 6001 33 0 Rep 2 51000 0.015528660 0.0092292671 9.266893e-01 0.04182290 0.006729825 + 4 Fifield NJ 12 021 004501 1025 27 0 Dem 1 60900 0.879537890 0.0008997896 1.768379e-02 0.03982601 0.062052518 + 5 Zhou NJ 12 021 004501 1025 28 1 Dem 1 60900 0.001819394 0.0001723242 7.957542e-05 0.99514078 0.002787926 + 6 Ratkovic NJ 12 021 004000 1025 35 0 Ind 0 60900 0.834942701 0.0038157857 4.933723e-03 0.04021245 0.116095337 + 7 Johnson NY 9 061 014900 4000 25 0 Dem 1 51000 0.290386744 0.5761904554 4.112613e-02 0.01895885 0.073337820 + 8 Lopez NJ 12 021 004501 1025 33 0 Rep 2 60900 0.065321588 0.0039558641 8.339387e-01 0.07461133 0.022172551 + 9 Wantchekon NJ 12 021 004501 1025 50 0 Rep 2 60900 0.428723819 0.1209683869 2.796062e-01 0.10142953 0.069272098 + 10 Morse DC 0 001 001301 3005 29 1 Rep 2 50000 0.716211008 0.1899554127 1.867133e-02 0.01025241 0.064909839 + +### Downloading census data + It is also possible to pre-download Census geographic data, which can -save time when running predict_race(). The example dataset ‘voters’ +save time when running `predict_race()`. The example dataset `voters` includes people in DC, NJ, and NY. The following example subsets voters -in DC and NJ, and then uses get_census_data() to download Census -geographic data in these two states (input parameter ‘key’ requires -valid API key). Census data is assigned to an object named census.dc.nj. -The predict_race() statement predicts the race/ethnicity of voters in DC -and NJ using the pre-saved Census data (census.data = census.dc.nj). -This example conditions race/ethnicity predictions on voters’ surnames, -block of residence (census.geo = “block”), age (age = TRUE), and party -registration (party = “PID”). - -Please note that the input parameters ‘age’ and ‘sex’ must have the same -values in get_census_data() and predict_race(), i.e., TRUE in both or -FALSE in both. In this case, predictions are conditioned on age but not -sex, so age = TRUE and sex = FALSE in both the get_census_data() and -predict_race() statements. +in DC and NJ, and then uses `get_census_data()` to download census +geographic data in these two states (a valid API key must be stored in a +`CENSUS_API_KEY` environment variable or provided with the `key` +argument). Census data is assigned to an object named `census.dc.nj`. +The `predict_race()` statement predicts the race/ethnicity of voters in +DC and NJ using the pre-downloaded census data +(`census.data = census.dc.nj`). This example conditions race/ethnicity +predictions on voters’ surnames, block of residence +(`census.geo = "block"`), age (`age = TRUE`), and party registration +(`party = "PID"`). + +Please note that the input parameters `age` and `sex` must have the same +values in `get_census_data()` and `predict_race()`, i.e., `TRUE` in both +or `FALSE` in both. In this case, predictions are conditioned on age but +not sex, so `age = TRUE` and `sex = FALSE` in both the +`get_census_data()` and `predict_race()` statements. ``` r library(wru) -data(voters) -voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset -census.dc.nj <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE) # create Census data object covering DC and NJ +voters.dc.nj <- voters[voters$state %in% c("DC", "NJ"), ] +census.dc.nj <- get_census_data(state = c("DC", "NJ"), age = TRUE, sex = FALSE) predict_race(voter.file = voters.dc.nj, census.geo = "block", census.data = census.dc.nj, age = TRUE, sex = FALSE, party = "PID") ``` -The last two lines above are equivalent to the following: +This produces the same result as the following statement, which +downloads census data during evaluation rather than using pre-downloaded +data: ``` r -predict_race(voter.file = voters.dc.nj, census.geo = "block", census.key = "...", age = TRUE, sex = FALSE, party = "PID") +predict_race(voter.file = voters.dc.nj, census.geo = "block", age = TRUE, sex = FALSE, party = "PID") ``` Using pre-downloaded Census data may be useful for the following reasons: -- You can save a lot of time in future runs of predict_race() if the - relevant Census data has already been saved; -- The machines used to run predict_race() may not have internet access; -- You can obtain timely snapshots of Census geographic data that match +- You can save a lot of time in future runs of `predict_race()` if the + relevant census data has already been saved; +- The machines used to run `predict_race()` may not have internet + access; +- You can obtain timely snapshots of census geographic data that match your voter file. -Downloading data using get_census_data() may take a long time, -especially at the block level or in large states. If block-level Census -data is not required, downloading Census data at the tract level will -save time. Similarly, if tract-level Census data is not required, -county-level data may be specified in order to save time. +Downloading data using `get_census_data()` may take a long time, +especially in large states or when using small geographic levels. If +block-level census data is not required, downloading census data at the +tract level will save time. Similarly, if tract-level data is not +required, county-level data may be specified in order to save time. ``` r library(wru) -data(voters) -voters.dc.nj <- voters[c(-3, -7), ] # remove two NY cases from dataset -census.dc.nj2 <- get_census_data(key = "...", state = c("DC", "NJ"), age = TRUE, sex = FALSE, census.geo = "tract") +voters.dc.nj <- voters[voters$state %in% c("DC", "NJ"), ] +census.dc.nj2 <- get_census_data(state = c("DC", "NJ"), age = TRUE, sex = FALSE, census.geo = "tract") predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) @@ -170,22 +185,24 @@ predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = cen predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) ``` -Or you can also use the census_geo_api() to manually construct a census -object. The example below creates a census object with county-level and +#### Interact directly with the Census API + +You can use `census_geo_api()` to manually construct a census object. +The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level -data. Note that this function has the input parameter ‘state’ that -requires a two-letter state abbreviation to proceed. +data. Note that the `state` argument requires a vector of two-letter +state abbreviations. ``` r -censusObj2 = list() +census.dc.nj3 = list() -county.dc <- census_geo_api(key = "...", state = "DC", geo = "county", age = TRUE, sex = FALSE) -tract.dc <- census_geo_api(key = "...", state = "DC", geo = "tract", age = TRUE, sex = FALSE) -censusObj2[["DC"]] <- list(state = "DC", county = county.dc, tract = tract.dc, age = TRUE, sex = FALSE) +county.dc <- census_geo_api(state = "DC", geo = "county", age = TRUE, sex = FALSE) +tract.dc <- census_geo_api(state = "DC", geo = "tract", age = TRUE, sex = FALSE) +census.dc.nj3[["DC"]] <- list(state = "DC", county = county.dc, tract = tract.dc, age = TRUE, sex = FALSE) -tract.nj <- census_geo_api(key = "...", state = "NJ", geo = "tract", age = TRUE, sex = FALSE) -county.nj <- census_geo_api(key = "...", state = "NJ", geo = "county", age = TRUE, sex = FALSE) -censusObj2[["NJ"]] <- list(state = "NJ", county = county.nj, tract = tract.nj, age = TRUE, sex = FALSE) +tract.nj <- census_geo_api(state = "NJ", geo = "tract", age = TRUE, sex = FALSE) +county.nj <- census_geo_api(state = "NJ", geo = "county", age = TRUE, sex = FALSE) +census.dc.nj3[["NJ"]] <- list(state = "NJ", county = county.nj, tract = tract.nj, age = TRUE, sex = FALSE) ``` Note: The age and sex parameters must be consistent when creating the @@ -198,27 +215,28 @@ race/ethnicity predictions on different combinations of input variables, without having to re-download the relevant Census data. ``` r -predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) -predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) -predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party) -predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = censusObj2, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj3, age = TRUE, sex = FALSE) # Pr(Race | Surname, County) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj3, age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract) +predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = census.dc.nj3, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, County, Party) +predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj3, party = "PID", age = TRUE, sex = FALSE) # Pr(Race | Surname, Tract, Party) ``` -### Notes about process design +### Parallelization -For larger scale imputations garbage-collection can become a problem and -your machine(s) can quickly run out of memory (RAM). It is recommended -to use the `future.callr::callr` plan instead of `future::multisession`. +For larger scale imputations, garbage collection can become a problem +and your machine(s) can quickly run out of memory (RAM). We recommended +using the `future.callr::callr` plan instead of `future::multisession`. The `callr` plan instantiates a new session at every iteration of your -parallel loop or map. This simultaneously has the effect of creating -more overhead, but also clearing the often sticky memory elements that -would be left over to grow to eventual system failure when using -`multisession`. You end up with a process that is more stable, but -slightly slower. - - library(wru) - future::plan(future.callr::callr) - # ... +parallel loop or map. Although this has the negative effect of creating +more overhead, it also clears sticky memory elements that can grow to +eventual system failure when using `multisession`. You end up with a +process that is more stable, but slightly slower. + +``` r +library(wru) +future::plan(future.callr::callr) +# ... +``` ## Census Data From cf3a3d3b9d41ee7574ee1f95dea0e101e151368c Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 16 Nov 2023 10:55:43 -0800 Subject: [PATCH 22/90] docs(README): add hex sticker --- README.Rmd | 2 +- README.md | 2 +- man/figures/logo.png | Bin 0 -> 1928211 bytes 3 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 man/figures/logo.png diff --git a/README.Rmd b/README.Rmd index 5790d6f..b900a04 100644 --- a/README.Rmd +++ b/README.Rmd @@ -18,7 +18,7 @@ knitr::opts_chunk$set( voters <- dplyr::select(wru::voters, -precinct, -first, -last) ``` -# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation +# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation Package logo [![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) ![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/wru) diff --git a/README.md b/README.md index 4c8188b..45ddaa3 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ -# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation +# wru: Who Are You? Bayesian Prediction of Racial Category Using Surname and Geolocation Package logo [![R-CMD-check](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kosukeimai/wru/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version-last-release/wru)](https://cran.r-project.org/package=wru) diff --git a/man/figures/logo.png b/man/figures/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..b279633b889cd14913e6e985e5ba411e12beebd8 GIT binary patch literal 1928211 zcmeEvc|276|9?%CrJ^XUXi-#_HWV?{t!|5@P-JZ*Qk1e}na*udTC}1FZ6sNuqHJ^P zwhEPqY}10uHj#BK-!pUG=gdL3%bfS;@%a7mdwd@EbI%k4rLmM!_*M5Ky_|8W3-`vYp-2Wr~ zJt%x2|Bm4_{M(Z`;e&|lX729}Tf4=e{}Ofn)68YSfH_R=KLZuFZY&xwfIeX5AB&CM z2Ufq%xHT&;bcnO~!DENShU?4!6|MJFC5V1#_59~nM+OZJ+%KBDYR1i*a^ZvI9@$Ce zny`#Lmk#+y|HG$8+v4y1-7q*igdRJgZULt{jbmWaxZ-qTO8&--vSyA)m2%ezdHR6? z18G7clK=mr?~A~^D^wb~-ojoK-m0ltBKL28SM|wEH|y~HVI7v*yt}hTRo6$OME$JP 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z6{=DxbA=wc2M`5Rx~7F3LM2)7w3imNr)KS~S{Y1c)|}t|@fnK45;_YLa&(gRS0c<9 zvD=|9-!!*OW4Akj?8Da#(bcl?M2@VS6g^}k>v2*)#7S*`78Gj+ofb#z50`nD_s?c} zpyPVZSdek3G`NYvV1O(Jn#rU!RzKRDZ9j8WW}L*FXxoHn2r8~E1&-{{l!fmQ4Jefg zqs6c~HYlX|hfx&4t&t zkf*X|+%mIl>RTYiWyREXZ*HP|3*>ndFv*2*j7_p@_q=g?bWLazbp(V7=Vu2Xo4W`& zQ@U39NDZoCcYu3%+E9|4Zko3&h^kfEc+VJB|2M7@&`zho!)6hmLxSJEZdU_a2Uc~w zL1*-%Pw;@@{X>aLn)|mU8h${qjCZWlmc|UxNnNcwf{cM_hdL2jJtkKKz-l$RXA~T< zKjuq(;}z&H3D}z!=oNDwZHV|fEl7HYmAEF3o{GQ;a2ya#E7oK5d{V(XudYqD;UDxtDA&H(kc15=Q3g;JWS3jMKYgdQ&BL4qxOm-aR zGspVmmPH%O;#XpEYz8{v0-8dQ8@PQi2!0wh&mI zFR$NYd*+XmG|HZ*GsbUSEJvgrifvQt5JF5{QAP^IOU#;-?w*Thr91aS#fqWuJNl9zMScA&>Cl*%y5D zBdxEech$6?Y^r#0$JsP+_;oPJZs{M92f>!KvUk(28T|jHw+Ak=O_ST>CFvlOiBd}c zo3i-uT?m2wftVkNiB#eJ|CLxDp#A~sAE5q$3qEkc2QEeciYv z6E+DBee`kP0a^wwA3l5_!UrUL;DQfa@W0`LdaTc9tGP=Kgci)j<*&t3>m}Declql7 E0Nbxz7XSbN literal 0 HcmV?d00001 From 57fbc2d09da4cba69652b62471ab62891ed72e16 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 17 Nov 2023 12:11:47 -0800 Subject: [PATCH 23/90] refactor(validate_key): use `rlang::caller_arg()` to determine `argument_name` --- R/validate_key.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R/validate_key.R b/R/validate_key.R index 67dc696..b31a6fc 100644 --- a/R/validate_key.R +++ b/R/validate_key.R @@ -1,8 +1,8 @@ -validate_key <- function(key, argument_name = NULL, call = rlang::caller_call()) { - if (is.null(argument_name)) { - argument_name <- rlang::expr_text(rlang::enexpr(key)) - } - +validate_key <- function( + key, + argument_name = rlang::caller_arg(key), + call = rlang::caller_call() +) { if (length(key) != 1) { cli::cli_abort( c( From 926c2c1f20d63e360137b010bc72f8f570fdf183 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 17 Nov 2023 12:24:10 -0800 Subject: [PATCH 24/90] feat: add CITATION file and use it to generate package startup message --- R/wru-internal.R | 12 ++++++------ inst/CITATION | 13 +++++++++++++ 2 files changed, 19 insertions(+), 6 deletions(-) create mode 100644 inst/CITATION diff --git a/R/wru-internal.R b/R/wru-internal.R index 209921b..81ca504 100644 --- a/R/wru-internal.R +++ b/R/wru-internal.R @@ -1,7 +1,7 @@ -.onAttach <- -function(libname, pkgname) { - packageStartupMessage("\nPlease cite as: \n") - packageStartupMessage("Khanna K, Bertelsen B, Olivella S, Rosenman E, Imai K (2022). wru: Who are You?") - packageStartupMessage("Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and Geolocation.") - packageStartupMessage("URL: https://CRAN.R-project.org/package=wru \n") +.onAttach <- function(libname, pkgname) { + packageStartupMessage( + "\nPlease cite as:\n\n", + format(citation("wru"), style = "text"), + "\n" + ) } diff --git a/inst/CITATION b/inst/CITATION new file mode 100644 index 0000000..7967551 --- /dev/null +++ b/inst/CITATION @@ -0,0 +1,13 @@ +bibentry( + bibtype = "Manual", + title = "wru: Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and Geolocation", + author = c( + person("Kabir", "Khanna", , "kabirkhanna@gmail.com", role = "aut"), + person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), + person("Santiago", "Olivella", , "olivella@unc.edu", role = "aut"), + person("Evan", "Rosenman", , "etrrosenman@gmail.com", role = "aut"), + person("Kosuke", "Imai", , "imai@harvard.edu", role = "aut") + ), + year = 2023, + url = "https://CRAN.R-project.org/package=wru" +) From 30ce8f217cddd96f225f11f623ba66c1e727c9ba Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 17 Nov 2023 12:30:58 -0800 Subject: [PATCH 25/90] feat(.onAttach): add startup message about change to 2020 data in wru 2.0.0 --- R/wru-internal.R | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/R/wru-internal.R b/R/wru-internal.R index 81ca504..f2eabc0 100644 --- a/R/wru-internal.R +++ b/R/wru-internal.R @@ -1,7 +1,10 @@ .onAttach <- function(libname, pkgname) { packageStartupMessage( - "\nPlease cite as:\n\n", - format(citation("wru"), style = "text"), + "\n", + "Please cite as:", "\n\n", + format(citation("wru"), style = "text"), "\n\n", + "Note that wru 2.0.0 uses 2020 census data by default.", "\n", + 'Use the argument `year = "2010"`, to replicate analyses produced with earlier package versions.', "\n" ) } From b55cc86f035102c15082dce1f7597cb9893b0d9f Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Tue, 28 Nov 2023 13:09:40 -0500 Subject: [PATCH 26/90] Fixes broken DESCRIPTION file --- DESCRIPTION | 1 - 1 file changed, 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index d72f6b9..295599f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -40,7 +40,6 @@ Imports: purrr, Rcpp, rlang - Suggests: covr, testthat (>= 3.0.0) From a1d7aaa6ff10dbe80042af777789554309cc85c2 Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Tue, 28 Nov 2023 13:23:05 -0500 Subject: [PATCH 27/90] updates for description --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 295599f..1cf9074 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: wru Title: Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and Geolocation -Version: 2.0.0 -Date: 2023-07-12 +Version: 3.0.0 +Date: 2023-12-09 Authors@R: c( person("Kabir", "Khanna", , "kabirkhanna@gmail.com", role = "aut"), person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), From 74c641ca1dc56aa83655ac5305320390a2e021ad Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 15 Nov 2023 16:09:09 -0800 Subject: [PATCH 28/90] chore: rename `validate_key.R` to `utils_validate_key.R` --- R/{validate_key.R => utils_validate_key.R} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename R/{validate_key.R => utils_validate_key.R} (100%) diff --git a/R/validate_key.R b/R/utils_validate_key.R similarity index 100% rename from R/validate_key.R rename to R/utils_validate_key.R From 5eca735cef28ede11679864235ab1e33f870cfe4 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 12:53:31 -0800 Subject: [PATCH 29/90] feat: add `state_fips` dataset --- DESCRIPTION | 3 ++- R/utils_state_fips.R | 14 ++++++++++++++ data-raw/state_fips.R | 10 ++++++++++ data/state_fips.rda | Bin 0 -> 831 bytes man/state_fips.Rd | 25 +++++++++++++++++++++++++ 5 files changed, 51 insertions(+), 1 deletion(-) create mode 100644 R/utils_state_fips.R create mode 100644 data-raw/state_fips.R create mode 100644 data/state_fips.rda create mode 100644 man/state_fips.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 1cf9074..82ef697 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -42,7 +42,8 @@ Imports: rlang Suggests: covr, - testthat (>= 3.0.0) + testthat (>= 3.0.0), + tidycensus LinkingTo: Rcpp, RcppArmadillo diff --git a/R/utils_state_fips.R b/R/utils_state_fips.R new file mode 100644 index 0000000..0b2446e --- /dev/null +++ b/R/utils_state_fips.R @@ -0,0 +1,14 @@ +#' Dataset with FIPS codes for US states +#' +#' Dataset including FIPS codes and postal abbreviations for each U.S. state, +#' district, and territory. +#' +#' @format +#' A tibble with 57 rows and 3 columns: +#' \describe{ +#' \item{`state`}{Two-letter postal abbreviation} +#' \item{`state_code`}{Two-digit FIPS code} +#' \item{`state_name`}{English name} +#' } +#' @source Derived from [tidycensus::fips_codes()] +"state_fips" diff --git a/data-raw/state_fips.R b/data-raw/state_fips.R new file mode 100644 index 0000000..ff2fb7c --- /dev/null +++ b/data-raw/state_fips.R @@ -0,0 +1,10 @@ +library(dplyr) +library(tibble) +library(tidycensus) + +state_fips <- tidycensus::fips_codes |> + tibble::as_tibble() |> + dplyr::distinct(state, state_code, state_name) |> + tibble::remove_rownames() + +usethis::use_data(state_fips, overwrite = TRUE) diff --git a/data/state_fips.rda b/data/state_fips.rda new file mode 100644 index 0000000000000000000000000000000000000000..5ee00700e775e87bca7140919c17c1b91cdbe5b8 GIT binary patch literal 831 zcmV-F1Hk-3T4*^jL0KkKS#y&TegFfS|HS|QOh`Zhf8a0w-|)Zh-|#>H0Du4izyjTl 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abbreviation} +\item{\code{state_code}}{Two-digit FIPS code} +\item{\code{state_name}}{English name} +} +} +\source{ +Derived from \code{\link[tidycensus:fips_codes]{tidycensus::fips_codes()}} +} +\usage{ +state_fips +} +\description{ +Dataset including FIPS codes and postal abbreviations for each U.S. state, +district, and territory. +} +\keyword{datasets} From 1e1b57d4bf19d46cff048d8c58544101db31792d Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Mon, 27 Nov 2023 12:31:28 -0800 Subject: [PATCH 30/90] feat: add `as_fips_code()` helper function --- R/utils_state_fips.R | 36 ++++++++++++++++++++++++++++++++++++ man/as_fips_code.Rd | 38 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 74 insertions(+) create mode 100644 man/as_fips_code.Rd diff --git a/R/utils_state_fips.R b/R/utils_state_fips.R index 0b2446e..072846f 100644 --- a/R/utils_state_fips.R +++ b/R/utils_state_fips.R @@ -12,3 +12,39 @@ #' } #' @source Derived from [tidycensus::fips_codes()] "state_fips" + +#' Get the standardized FIPS code associated with a state +#' +#' @param x A [numeric] or [character] vector of state names, +#' postal abbreviations, or FIPS codes. +#' Matches for state names and abbreviations are not case sensitive. +#' FIPS codes may be matched from numeric or character vectors, +#' with or without leading zeroes. +#' +#' @return A [character] vector of two-digit FIPS codes. +#' One-digit FIPS codes are prefixed with a leading zero, +#' e.g., `"06"` for California. +#' +#' @examples +#' as_fips_code("california") +#' +#' # Character vector matches ignore case +#' as_fips_code(c("DC", "Md", "va")) +#' +#' # Note that `3` and `7` are standardized to `NA`, +#' # because no state is assigned those FIPS codes +#' as_fips_code(1:10) +#' +#' # You can even mix methods in the same vector +#' as_fips_code(c("utah", "NM", 8, "04")) +#' +#' @keywords internal +as_fips_code <- function(x) { + state_fips$state_code[ + dplyr::coalesce( + match(toupper(x), state_fips$state), + match(tolower(x), tolower(state_fips$state_name)), + match(suppressWarnings(as.numeric(x)), as.numeric(state_fips$state_code)) + ) + ] +} diff --git a/man/as_fips_code.Rd b/man/as_fips_code.Rd new file mode 100644 index 0000000..747a01e --- /dev/null +++ b/man/as_fips_code.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils_state_fips.R +\name{as_fips_code} +\alias{as_fips_code} +\title{Get the standardized FIPS code associated with a state} +\usage{ +as_fips_code(x) +} +\arguments{ +\item{x}{A \link{numeric} or \link{character} vector of state names, +postal abbreviations, or FIPS codes. +Matches for state names and abbreviations are not case sensitive. +FIPS codes may be matched from numeric or character vectors, +with or without leading zeroes.} +} +\value{ +A \link{character} vector of two-digit FIPS codes. +One-digit FIPS codes are prefixed with a leading zero, +e.g., \code{"06"} for California. +} +\description{ +Get the standardized FIPS code associated with a state +} +\examples{ +as_fips_code("california") + +# Character vector matches ignore case +as_fips_code(c("DC", "Md", "va")) + +# Note that `3` and `7` are standardized to `NA`, +# because no state is assigned those FIPS codes +as_fips_code(1:10) + +# You can even mix methods in the same vector +as_fips_code(c("utah", "NM", 8, "04")) + +} +\keyword{internal} From b58298083175a74fb2e8f9fc6a23fbfb48194784 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 17 Nov 2023 12:38:36 -0800 Subject: [PATCH 31/90] refactor(census_geo_api): use `as_fips_code()` --- R/census_geo_api.R | 16 +--------------- 1 file changed, 1 insertion(+), 15 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index 7e9c295..f2597b4 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -64,21 +64,7 @@ census_geo_api <- function( state <- toupper(state) df.out <- NULL - # Building fips table (previously loaded via .rda) - fips.codes <- structure(list(State = structure(1:55, levels = c("AK", "AL", - "AR", "AS", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", "GA", "GU", - "HI", "IA", "ID", "IL", "IN", "KS", "KY", "LA", "MA", "MD", "ME", - "MI", "MN", "MO", "MS", "MT", "NC", "ND", "NE", "NH", "NJ", "NM", - "NV", "NY", "OH", "OK", "OR", "PA", "PR", "RI", "SC", "SD", "TN", - "TX", "UT", "VA", "VI", "VT", "WA", "WI", "WV", "WY"), class = "factor"), - FIPS = c(2L, 1L, 5L, 60L, 4L, 6L, 8L, 9L, 11L, 10L, 12L, - 13L, 66L, 15L, 19L, 16L, 17L, 18L, 20L, 21L, 22L, 25L, 24L, - 23L, 26L, 27L, 29L, 28L, 30L, 37L, 38L, 31L, 33L, 34L, 35L, - 32L, 36L, 39L, 40L, 41L, 42L, 72L, 44L, 45L, 46L, 47L, 48L, - 49L, 51L, 78L, 50L, 53L, 55L, 54L, 56L)), class = "data.frame", row.names = c(NA, - -55L)) - state.fips <- fips.codes[fips.codes$State == state, "FIPS"] - state.fips <- ifelse(nchar(state.fips) == 1, paste0("0", state.fips), state.fips) + state.fips <- as_fips_code(state) # if (age == F & sex == F) { # num <- ifelse(3:10 != 10, paste("0", 3:10, sep = ""), "10") From 975fa1c71b6d986d30aecf70d5c86a654285e47c Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 12:55:01 -0800 Subject: [PATCH 32/90] refactor(census_geo_api): enumerate possible options for `geo` argument (see https://design.tidyverse.org/enumerate-options.html) --- R/census_geo_api.R | 4 +++- man/census_geo_api.Rd | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index f2597b4..fa3ed9a 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -50,7 +50,7 @@ census_geo_api <- function( key = Sys.getenv("CENSUS_API_KEY"), state, - geo = "tract", + geo = c("tract", "block", "block_group", "county", "place"), age = FALSE, sex = FALSE, year = "2020", @@ -60,6 +60,8 @@ census_geo_api <- function( ) { validate_key(key) + geo <- rlang::arg_match(geo) + census <- NULL state <- toupper(state) diff --git a/man/census_geo_api.Rd b/man/census_geo_api.Rd index 548d57c..7f8b5a4 100644 --- a/man/census_geo_api.Rd +++ b/man/census_geo_api.Rd @@ -7,7 +7,7 @@ census_geo_api( key = Sys.getenv("CENSUS_API_KEY"), state, - geo = "tract", + geo = c("tract", "block", "block_group", "county", "place"), age = FALSE, sex = FALSE, year = "2020", From 4a99c87a2cc20a4e714f0f33a7ab9c539d5ebce8 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 22 Nov 2023 12:18:04 -0800 Subject: [PATCH 33/90] chore: add `.DS_Store` to `.gitignore` --- .gitignore | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 4e2c122..dc83fda 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +.DS_Store + # History files .Rhistory .Rapp.history @@ -21,4 +23,4 @@ vignettes/*.pdf src/RcppExports.o src/aux_funs.o src/sample_me.o -src/wru.so \ No newline at end of file +src/wru.so From 33e9b0041cc0dc864270a154b7f96ee300daba5e Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 22 Nov 2023 12:18:23 -0800 Subject: [PATCH 34/90] refactor(census_geo_api): enumerate possible values for `year` argument (see https://design.tidyverse.org/enumerate-options.html) --- R/census_geo_api.R | 16 +++++++++------- man/census_geo_api.Rd | 7 ++++--- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index fa3ed9a..45fb20c 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -53,7 +53,7 @@ census_geo_api <- function( geo = c("tract", "block", "block_group", "county", "place"), age = FALSE, sex = FALSE, - year = "2020", + year = c("2020", "2010"), retry = 3, save_temp = NULL, counties = NULL @@ -62,6 +62,9 @@ census_geo_api <- function( geo <- rlang::arg_match(geo) + year <- as.character(year) + year <- rlang::arg_match(year) + census <- NULL state <- toupper(state) @@ -115,12 +118,11 @@ census_geo_api <- function( } # set the census data url links - if (as.character(year) != "2020") { - census_data_url = "https://api.census.gov/data/2010/dec/sf1?" - } - else { - census_data_url = "https://api.census.gov/data/2020/dec/pl?" - } + census_data_url <- switch( + as.character(year), + "2010" = "https://api.census.gov/data/2010/dec/sf1?", + "2020" = "https://api.census.gov/data/2020/dec/pl?" + ) if (geo == "place") { geo.merge <- c("state", "place") diff --git a/man/census_geo_api.Rd b/man/census_geo_api.Rd index 7f8b5a4..c16b748 100644 --- a/man/census_geo_api.Rd +++ b/man/census_geo_api.Rd @@ -7,13 +7,14 @@ census_geo_api( key = Sys.getenv("CENSUS_API_KEY"), state, - geo = c("tract", "block", "block_group", "county", "place"), + geo = c("tract", "block", "block_group", "county", "place", "zcta"), age = FALSE, sex = FALSE, - year = "2020", + year = c(2020, 2010), retry = 3, save_temp = NULL, - counties = NULL + counties = NULL, + zctas = NULL ) } \arguments{ From 06347cad11425a2c283fc1ad6d45ba6f6dc91a75 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 22 Nov 2023 14:46:32 -0800 Subject: [PATCH 35/90] feat: add `assert_boolean()` helper function --- R/utils_assert.R | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 R/utils_assert.R diff --git a/R/utils_assert.R b/R/utils_assert.R new file mode 100644 index 0000000..8d9e157 --- /dev/null +++ b/R/utils_assert.R @@ -0,0 +1,37 @@ +assert_boolean <- function( + x, + argument_name = rlang::caller_arg(x), + call = rlang::caller_call() +) { + if (length(x) != 1) { + cli::cli_abort( + c( + "{.arg {argument_name}} must be a {.code TRUE} or {.code FALSE} value of length {.val {1}}.", + x = "{.arg {argument_name}} has a length of {.val {length(x)}}." + ), + call = call + ) + } + + if (!inherits(x, "logical")) { + cli::cli_abort( + c( + "{.arg {argument_name}} must be a {.class logical} {.code TRUE} or {.code FALSE} value.", + x = "{.arg {argument_name}} is an object of class {.cls {class(x)}}." + ), + call = call + ) + } + + if (!x %in% c(TRUE, FALSE)) { + cli::cli_abort( + c( + "{.arg {argument_name}} must be {.code TRUE} or {.code FALSE}.", + x = "{.arg {argument_name}} is {.val {x}}." + ), + call = call + ) + } + + x +} From 34a9faaf85a8953c1b20824f9e5ce0a8942d8379 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Mon, 27 Nov 2023 13:02:38 -0800 Subject: [PATCH 36/90] feat: add `as_state_abbreviation()` helper function --- R/utils_state_fips.R | 32 ++++++++++++++++++++++++++++---- man/as_fips_code.Rd | 19 +++++++++++++++++-- 2 files changed, 45 insertions(+), 6 deletions(-) diff --git a/R/utils_state_fips.R b/R/utils_state_fips.R index 072846f..7dc5c6f 100644 --- a/R/utils_state_fips.R +++ b/R/utils_state_fips.R @@ -13,7 +13,7 @@ #' @source Derived from [tidycensus::fips_codes()] "state_fips" -#' Get the standardized FIPS code associated with a state +#' Convert between state names, postal abbreviations, and FIPS codes #' #' @param x A [numeric] or [character] vector of state names, #' postal abbreviations, or FIPS codes. @@ -21,22 +21,35 @@ #' FIPS codes may be matched from numeric or character vectors, #' with or without leading zeroes. #' -#' @return A [character] vector of two-digit FIPS codes. -#' One-digit FIPS codes are prefixed with a leading zero, -#' e.g., `"06"` for California. +#' @return +#' \describe{ +#' \item{`as_state_fips_code()`}{ +#' A [character] vector of two-digit FIPS codes. +#' One-digit FIPS codes are prefixed with a leading zero, +#' e.g., `"06"` for California. +#' } +#' \item{`as_state_abbreviation()`}{ +#' A [character] vector of two-letter postal abbreviations, +#' e.g., `"CA"` for California. +#' } +#' } #' #' @examples #' as_fips_code("california") +#' as_state_abbreviation("california") #' #' # Character vector matches ignore case #' as_fips_code(c("DC", "Md", "va")) +#' as_state_abbreviation(c("district of columbia", "Maryland", "VIRGINIA")) #' #' # Note that `3` and `7` are standardized to `NA`, #' # because no state is assigned those FIPS codes #' as_fips_code(1:10) +#' as_state_abbreviation(1:10) #' #' # You can even mix methods in the same vector #' as_fips_code(c("utah", "NM", 8, "04")) +#' as_state_abbreviation(c("utah", "NM", 8, "04")) #' #' @keywords internal as_fips_code <- function(x) { @@ -48,3 +61,14 @@ as_fips_code <- function(x) { ) ] } + +#' @rdname as_fips_code +as_state_abbreviation <- function(x) { + state_fips$state[ + dplyr::coalesce( + match(toupper(x), state_fips$state), + match(tolower(x), tolower(state_fips$state_name)), + match(suppressWarnings(as.numeric(x)), as.numeric(state_fips$state_code)) + ) + ] +} \ No newline at end of file diff --git a/man/as_fips_code.Rd b/man/as_fips_code.Rd index 747a01e..1e2c60c 100644 --- a/man/as_fips_code.Rd +++ b/man/as_fips_code.Rd @@ -2,9 +2,12 @@ % Please edit documentation in R/utils_state_fips.R \name{as_fips_code} \alias{as_fips_code} -\title{Get the standardized FIPS code associated with a state} +\alias{as_state_abbreviation} +\title{Convert between state names, postal abbreviations, and FIPS codes} \usage{ as_fips_code(x) + +as_state_abbreviation(x) } \arguments{ \item{x}{A \link{numeric} or \link{character} vector of state names, @@ -14,25 +17,37 @@ FIPS codes may be matched from numeric or character vectors, with or without leading zeroes.} } \value{ +\describe{ +\item{\code{as_state_fips_code()}}{ A \link{character} vector of two-digit FIPS codes. One-digit FIPS codes are prefixed with a leading zero, e.g., \code{"06"} for California. } +\item{\code{as_state_abbreviation()}}{ +A \link{character} vector of two-letter postal abbreviations, +e.g., \code{"CA"} for California. +} +} +} \description{ -Get the standardized FIPS code associated with a state +Convert between state names, postal abbreviations, and FIPS codes } \examples{ as_fips_code("california") +as_state_abbreviation("california") # Character vector matches ignore case as_fips_code(c("DC", "Md", "va")) +as_state_abbreviation(c("district of columbia", "Maryland", "VIRGINIA")) # Note that `3` and `7` are standardized to `NA`, # because no state is assigned those FIPS codes as_fips_code(1:10) +as_state_abbreviation(1:10) # You can even mix methods in the same vector as_fips_code(c("utah", "NM", 8, "04")) +as_state_abbreviation(c("utah", "NM", 8, "04")) } \keyword{internal} From f47576e48bff8be5e5fb7317452ac6d4c37e8e29 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Sat, 18 Nov 2023 20:34:32 -0800 Subject: [PATCH 37/90] feat: add `census_geo_api_zcta()` function --- R/census_geo_api_zcta.R | 186 +++++++++++++++++++++++++++++++++++++ R/staticimports.R | 57 ++++++++++++ man/census_geo_api_zcta.Rd | 57 ++++++++++++ 3 files changed, 300 insertions(+) create mode 100644 R/census_geo_api_zcta.R create mode 100644 R/staticimports.R create mode 100644 man/census_geo_api_zcta.Rd diff --git a/R/census_geo_api_zcta.R b/R/census_geo_api_zcta.R new file mode 100644 index 0000000..8ccdc5d --- /dev/null +++ b/R/census_geo_api_zcta.R @@ -0,0 +1,186 @@ +# @staticimports pkg:stringstatic +# str_pad + +#' Census download function for state-ZCTA-level data +#' +#' @inheritParams census_geo_api +#' @param ... These dots are for future extensions and must be empty. +#' +#' @return A [data.frame] with columns +#' `state`, `zcta`, `r_whi`, `r_bla`, `r_his`, `r_asi`, and `r_oth`. +#' +#' @examplesIf nzchar(Sys.getenv("CENSUS_API_KEY")) +#' \dontrun{census_geo_api_zcta(state = c("DE", "NJ")} +#' \dontrun{census_geo_api_zcta(state = "FL", age = TRUE, sex = TRUE)} +#' \dontrun{census_geo_api_zcta(state = "MA", age = FALSE, sex = FALSE, year = "2020")} +#' +#' @keywords internal +census_geo_api_zcta <- function( + state, + ..., + age = FALSE, + sex = FALSE, + year = c("2020", "2010", "2000"), + retry = 3, + key = Sys.getenv("CENSUS_API_KEY") +) { + # Validate arguments + rlang::check_dots_empty() + validate_key(key) + assert_boolean(age) + assert_boolean(sex) + year <- as.character(year) + year <- rlang::arg_match(year) + + if (year == "2020") { + census_data_url <- "https://api.census.gov/data/2020/dec/dhc?" + prefix <- "P12" + separator <- "_" + suffix <- "N" + } else if (year %in% c("2010", "2000")) { + census_data_url <- paste0("https://api.census.gov/data/", year, "/dec/sf1?") + prefix <- "P012" + separator <- "" + suffix <- "" + } + + race_codes <- list( + "whi" = "I", + "bla" = "B", + "his" = "H", + "asi" = c("D", "E"), + "oth" = c("C", "F", "G") + ) + + sex_codes <- c("mal" = 2, "fem" = 26) + + age_codes <- 1:23 + + numeric_codes <- if (age) { + unlist(purrr::map(sex_codes, function(x) x + age_codes)) + } else if (sex) { + sex_codes + } else { + 1 + } + numeric_codes <- str_pad(numeric_codes, width = 3, side = "left", pad = "0") + + vars <- expand.grid( + prefix, + unlist(race_codes), + separator, + numeric_codes, + suffix, + stringsAsFactors = FALSE + ) + vars <- apply(vars, 1, paste, collapse = "") + + region <- paste0( + "for=zip%20code%20tabulation%20area%20(or%20part):*&in=state:", + paste(as_fips_code(state), collapse = ",") + ) + + census <- get_census_api( + census_data_url, + key = key, + var.names = vars, + region = region, + retry + ) + + if (!age && !sex) { + ## Calculate Pr(Geolocation | Race) + + for (i in seq_along(race_codes)) { + var_name <- paste("r", names(race_codes)[[i]], sep = "_") + + code <- paste0(prefix, race_codes[[i]], separator, "001", suffix) + + census[var_name] <- rowSums(census[code]) + } + } else if (!age && sex) { + ## Calculate Pr(Geolocation, Sex | Race) + + for (race in seq_along(race_codes)) { + for (sex in seq_along(sex_codes)) { + var_name <- paste( + "r", + names(sex_codes)[[sex]], + names(race_codes)[[race]], + sep = "_" + ) + + code <- paste0( + prefix, + race_codes[[race]], + separator, + str_pad(sex_codes[[sex]], width = 3, pad = "0"), + suffix + ) + + census[var_name] <- rowSums(census[code]) + } + } + } else if (age && !sex) { + ## Calculate Pr(Geolocation, Age Category | Race) + + for (race in seq_along(race_codes)) { + for (age_category in age_codes) { + var_name <- paste( + "r", + age_category, + names(race_codes)[[race]], + sep = "_" + ) + + code <- paste0( + prefix, + race_codes[[race]], + separator, + str_pad(sex_codes + age_category, width = 3, pad = "0"), + suffix + ) + + census[var_name] <- rowSums(census[code]) + } + } + } else if (age && sex) { + ## Calculate Pr(Geolocation, Sex, Age Category | Race) + + for (race in seq_along(race_codes)) { + for (age_category in age_codes) { + for (sex in seq_along(sex_codes)) { + var_name <- paste( + "r", + names(sex_codes)[[sex]], + age_category, + names(race_codes)[[race]], + sep = "_" + ) + + code <- paste0( + prefix, + race_codes[[race]], + separator, + str_pad(sex_codes[[sex]] + age_category, width = 3, pad = "0"), + suffix + ) + + census[var_name] <- rowSums(census[code]) + } + } + } + } + + census <- dplyr::group_by(census, dplyr::across(dplyr::any_of("state"))) + census <- dplyr::mutate( + census, + state = as_state_abbreviation(state), + dplyr::across(dplyr::starts_with("r_"), function(x) x / sum(x)) + ) + census <- dplyr::ungroup(census) + + names(census)[[2]] <- "zcta" + + census +} diff --git a/R/staticimports.R b/R/staticimports.R new file mode 100644 index 0000000..dbb1efb --- /dev/null +++ b/R/staticimports.R @@ -0,0 +1,57 @@ +# Generated by staticimports; do not edit by hand. +# ====================================================================== +# Imported from pkg:stringstatic +# ====================================================================== + +#' Duplicate and concatenate strings within a character vector +#' +#' Dependency-free drop-in alternative for `stringr::str_pad()`. +#' +#' @author Eli Pousson \email{eli.pousson@gmail.com} +#' ([ORCID](https://orcid.org/0000-0001-8280-1706)) +#' +#' Alexander Rossell Hayes \email{alexander@rossellhayes.com} +#' ([ORCID](https://orcid.org/0000-0001-9412-0457)) +#' +#' @source Adapted from the [stringr](https://stringr.tidyverse.org/) package. +#' +#' @param string Input vector. +#' Either a character vector, or something coercible to one. +#' @param width Minimum width of padded strings. +#' @param side Side on which padding character is added (left, right or both). +#' @param pad Single padding character (default is a space). +#' @param use_width If `FALSE`, +#' use the length of the string instead of the width; +#' see [str_width()]/[str_length()] for the difference. +#' +#' @return A character vector. +#' @noRd +str_pad <- function( + string, width, side = c("left", "right", "both"), pad = " ", use_width = TRUE +) { + if (!is.numeric(width)) { + return(string[NA]) + } + + if (any(nchar(pad, type = "width") != 1)) { + stop("each string in `pad` should consist of code points of total width 1") + } + + side <- match.arg(side) + + nchar_type <- if (isTRUE(use_width)) "width" else "chars" + string_width <- nchar(string, nchar_type) + pad_width <- width - string_width + pad_width[pad_width < 0] <- 0 + + switch( + side, + "left" = paste0(strrep(pad, pad_width), string), + "right" = paste0(string, strrep(pad, pad_width)), + "both" = paste0( + strrep(pad, floor(pad_width / 2)), + string, + strrep(pad, ceiling(pad_width / 2)) + ) + ) +} diff --git a/man/census_geo_api_zcta.Rd b/man/census_geo_api_zcta.Rd new file mode 100644 index 0000000..76ea2ef --- /dev/null +++ b/man/census_geo_api_zcta.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/census_geo_api_zcta.R +\name{census_geo_api_zcta} +\alias{census_geo_api_zcta} +\title{Census download function for state-ZCTA-level data} +\usage{ +census_geo_api_zcta( + state, + ..., + age = FALSE, + sex = FALSE, + year = c("2020", "2010", "2000"), + retry = 3, + key = Sys.getenv("CENSUS_API_KEY") +) +} +\arguments{ +\item{state}{A required character object specifying which state to extract Census data for, +e.g., \code{"NJ"}.} + +\item{...}{These dots are for future extensions and must be empty.} + +\item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). +If \code{TRUE}, function will return Pr(Geolocation, Age | Race). +If \code{\var{sex}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} + +\item{sex}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +sex or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). +If \code{TRUE}, function will return Pr(Geolocation, Sex | Race). +If \code{\var{age}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} + +\item{year}{A character object specifying the year of U.S. Census data to be downloaded. +Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}. +Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and +\code{\var{sex}} are both \code{FALSE}.} + +\item{retry}{The number of retries at the census website if network interruption occurs.} + +\item{key}{A required character object. Must contain user's Census API +key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +} +\value{ +A \link{data.frame} with columns +\code{state}, \code{zcta}, \code{r_whi}, \code{r_bla}, \code{r_his}, \code{r_asi}, and \code{r_oth}. +} +\description{ +Census download function for state-ZCTA-level data +} +\examples{ +\dontshow{if (nzchar(Sys.getenv("CENSUS_API_KEY"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\dontrun{census_geo_api_zcta(state = c("DE", "NJ")} +\dontrun{census_geo_api_zcta(state = "FL", age = TRUE, sex = TRUE)} +\dontrun{census_geo_api_zcta(state = "MA", age = FALSE, sex = FALSE, year = "2020")} +\dontshow{\}) # examplesIf} +} +\keyword{internal} From 0a4ec05bffbdc4f2a59452b8cc1704bd1ac7cf91 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Sat, 18 Nov 2023 20:34:32 -0800 Subject: [PATCH 38/90] feat(census_geo_api): support `geo = "zcta"` by passing to `census_geo_api_zcta()` --- R/census_geo_api.R | 18 ++++++++++++++++-- man/census_geo_api.Rd | 7 +++---- 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index 45fb20c..1c64d22 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -9,7 +9,7 @@ #' @param state A required character object specifying which state to extract Census data for, #' e.g., \code{"NJ"}. #' @param geo A character object specifying what aggregation level to use. -#' Use \code{"county"}, \code{"tract"},\code{"block_group"}, \code{"block"}, or \code{"place"}. +#' Use `"block"`, `"block_group"`, `"county"`, `"place"`, `"tract"`, or `"zcta"`. #' Default is \code{"tract"}. Warning: extracting block-level data takes very long. #' @param age A \code{TRUE}/\code{FALSE} object indicating whether to condition on #' age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). @@ -50,7 +50,7 @@ census_geo_api <- function( key = Sys.getenv("CENSUS_API_KEY"), state, - geo = c("tract", "block", "block_group", "county", "place"), + geo = c("tract", "block", "block_group", "county", "place", "zcta"), age = FALSE, sex = FALSE, year = c("2020", "2010"), @@ -60,8 +60,22 @@ census_geo_api <- function( ) { validate_key(key) + geo <- tolower(geo) geo <- rlang::arg_match(geo) + if (geo == "zcta") { + return( + census_geo_api_zcta( + state = state, + age = age, + sex = sex, + year = year, + retry = retry, + key = key + ) + ) + } + year <- as.character(year) year <- rlang::arg_match(year) diff --git a/man/census_geo_api.Rd b/man/census_geo_api.Rd index c16b748..ceab108 100644 --- a/man/census_geo_api.Rd +++ b/man/census_geo_api.Rd @@ -10,11 +10,10 @@ census_geo_api( geo = c("tract", "block", "block_group", "county", "place", "zcta"), age = FALSE, sex = FALSE, - year = c(2020, 2010), + year = c("2020", "2010"), retry = 3, save_temp = NULL, - counties = NULL, - zctas = NULL + counties = NULL ) } \arguments{ @@ -29,7 +28,7 @@ By default, attempts to find a census key stored in an e.g., \code{"NJ"}.} \item{geo}{A character object specifying what aggregation level to use. -Use \code{"county"}, \code{"tract"},\code{"block_group"}, \code{"block"}, or \code{"place"}. +Use \code{"block"}, \code{"block_group"}, \code{"county"}, \code{"place"}, \code{"tract"}, or \code{"zcta"}. Default is \code{"tract"}. Warning: extracting block-level data takes very long.} \item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on From 45618545a234d00862bfb3999aab33ef0ff34dbf Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 13:29:40 -0800 Subject: [PATCH 39/90] feat(get_census_data): add support for `census.geo = "zcta"` --- R/get_census_data.R | 33 ++++++++++++++++++++++++--------- man/get_census_data.Rd | 2 +- 2 files changed, 25 insertions(+), 10 deletions(-) diff --git a/R/get_census_data.R b/R/get_census_data.R index a7f07ff..d4a4d09 100644 --- a/R/get_census_data.R +++ b/R/get_census_data.R @@ -36,21 +36,24 @@ #' #' @export #' -#' @examples +#' @examples #' \dontrun{get_census_data(states = c("NJ", "NY"), age = TRUE, sex = FALSE)} #' \dontrun{get_census_data(states = "MN", age = FALSE, sex = FALSE, year = "2020")} get_census_data <- function( - key = Sys.getenv("CENSUS_API_KEY"), - states, - age = FALSE, - sex = FALSE, - year = "2020", - census.geo = "block", - retry = 3, - county.list = NULL + key = Sys.getenv("CENSUS_API_KEY"), + states, + age = FALSE, + sex = FALSE, + year = "2020", + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), + retry = 3, + county.list = NULL ) { validate_key(key) + census.geo <- tolower(census.geo) + census.geo <- rlang::arg_match(census.geo) + states <- toupper(states) message("Collecting ", year, " Census data...") @@ -81,6 +84,18 @@ get_census_data <- function( county <- census_geo_api(key, s, geo = "county", age, sex, year, retry) CensusObj[[s]]$county <- county } + + if (census.geo == "zcta") { + if (!is.null(county.list)) { + cli::cli_abort(c( + "The {.arg county.list} argument must be set to {.code NULL} + when {.arg census_geo} is {.val zcta}, + because the Census Bureau does release data that divides ZCTAs by county." + )) + } + + CensusObj[[s]]$zcta <- census_geo_api(key, s, geo = "zcta", age, sex, year, retry) + } } return(CensusObj) } \ No newline at end of file diff --git a/man/get_census_data.Rd b/man/get_census_data.Rd index 4149f29..a3be2b7 100644 --- a/man/get_census_data.Rd +++ b/man/get_census_data.Rd @@ -10,7 +10,7 @@ get_census_data( age = FALSE, sex = FALSE, year = "2020", - census.geo = "block", + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), retry = 3, county.list = NULL ) From 817c7febb761b0ca67036b19a246f890babb5b45 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 13:30:39 -0800 Subject: [PATCH 40/90] docs: rebuild documentation --- man/census_data_preflight.Rd | 1 + man/census_geo_api.Rd | 10 +++++----- man/census_geo_api_zcta.Rd | 8 ++++++-- man/census_helper.Rd | 6 +++--- man/census_helper_new.Rd | 6 +++--- man/get_census_api.Rd | 6 +++--- man/get_census_api_2.Rd | 7 +++---- man/get_census_data.Rd | 6 +++--- man/modfuns.Rd | 14 +++++--------- 9 files changed, 32 insertions(+), 32 deletions(-) diff --git a/man/census_data_preflight.Rd b/man/census_data_preflight.Rd index 9c4dda9..5b656dd 100644 --- a/man/census_data_preflight.Rd +++ b/man/census_data_preflight.Rd @@ -24,6 +24,7 @@ And if \code{\var{census.geo} = "block"}, then \code{\var{voter.file}} must have columns named \code{county}, \code{tract}, and \code{block}. If \code{\var{census.geo} = "place"}, then \code{\var{voter.file}} must have column named \code{place}. +If \code{census.geo = "zcta"}, then \code{voter.file} must have column named \code{zcta}. Specifying \code{\var{census.geo}} will call \code{census_helper} function to merge Census geographic data at specified level of geography.} diff --git a/man/census_geo_api.Rd b/man/census_geo_api.Rd index ceab108..e679fa6 100644 --- a/man/census_geo_api.Rd +++ b/man/census_geo_api.Rd @@ -17,12 +17,12 @@ census_geo_api( ) } \arguments{ -\item{key}{A character string containing a valid Census API key, +\item{key}{A character string containing a valid Census API key, which can be requested from the -[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. By default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{state}{A required character object specifying which state to extract Census data for, e.g., \code{"NJ"}.} @@ -75,7 +75,7 @@ at either the county, tract, block, or place level, for a particular state. } \references{ -Relies on `get_census_api()`, `get_census_api_2()`, and `vec_to_chunk()` functions authored by Nicholas Nagle, -available [here](https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html). +Relies on \code{get_census_api()}, \code{get_census_api_2()}, and \code{vec_to_chunk()} functions authored by Nicholas Nagle, +available \href{https://rstudio-pubs-static.s3.amazonaws.com/19337_2e7f827190514c569ea136db788ce850.html}{here}. } \keyword{internal} diff --git a/man/census_geo_api_zcta.Rd b/man/census_geo_api_zcta.Rd index 76ea2ef..ddcf6bd 100644 --- a/man/census_geo_api_zcta.Rd +++ b/man/census_geo_api_zcta.Rd @@ -37,8 +37,12 @@ Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and \item{retry}{The number of retries at the census website if network interruption occurs.} -\item{key}{A required character object. Must contain user's Census API -key, which can be requested \href{https://api.census.gov/data/key_signup.html}{here}.} +\item{key}{A character string containing a valid Census API key, +which can be requested from the +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. + +By default, attempts to find a census key stored in an +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} } \value{ A \link{data.frame} with columns diff --git a/man/census_helper.Rd b/man/census_helper.Rd index 0432d69..74dcb2a 100644 --- a/man/census_helper.Rd +++ b/man/census_helper.Rd @@ -18,12 +18,12 @@ census_helper( ) } \arguments{ -\item{key}{A character string containing a valid Census API key, +\item{key}{A character string containing a valid Census API key, which can be requested from the -[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. By default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{voter.file}{An object of class \code{data.frame}. Must contain field(s) named \code{\var{county}}, \code{\var{tract}}, \code{\var{block}}, and/or \code{\var{place}} diff --git a/man/census_helper_new.Rd b/man/census_helper_new.Rd index d5e88ac..7293d83 100644 --- a/man/census_helper_new.Rd +++ b/man/census_helper_new.Rd @@ -18,12 +18,12 @@ census_helper_new( ) } \arguments{ -\item{key}{A character string containing a valid Census API key, +\item{key}{A character string containing a valid Census API key, which can be requested from the -[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. By default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{voter.file}{An object of class \code{data.frame}. Must contain field(s) named \code{\var{county}}, \code{\var{tract}}, \code{\var{block}}, and/or \code{\var{place}} diff --git a/man/get_census_api.Rd b/man/get_census_api.Rd index 2751dc6..c3ebc13 100644 --- a/man/get_census_api.Rd +++ b/man/get_census_api.Rd @@ -16,12 +16,12 @@ get_census_api( \item{data_url}{URL root of the API, e.g., \code{"https://api.census.gov/data/2020/dec/pl"}.} -\item{key}{A character string containing a valid Census API key, +\item{key}{A character string containing a valid Census API key, which can be requested from the -[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. By default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{var.names}{A character vector of variables to get, e.g., \code{c("P2_005N", "P2_006N", "P2_007N", "P2_008N")}. diff --git a/man/get_census_api_2.Rd b/man/get_census_api_2.Rd index 937eada..a66146f 100644 --- a/man/get_census_api_2.Rd +++ b/man/get_census_api_2.Rd @@ -16,13 +16,12 @@ get_census_api_2( \item{data_url}{URL root of the API, e.g., \code{"https://api.census.gov/data/2020/dec/pl"}.} -\item{key}{A character string containing a valid Census API key, +\item{key}{A character string containing a valid Census API key, which can be requested from the -[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. By default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} - +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{get}{A character vector of variables to get, e.g., \code{c("P2_005N", "P2_006N", "P2_007N", "P2_008N")}. diff --git a/man/get_census_data.Rd b/man/get_census_data.Rd index a3be2b7..e50d53e 100644 --- a/man/get_census_data.Rd +++ b/man/get_census_data.Rd @@ -16,12 +16,12 @@ get_census_data( ) } \arguments{ -\item{key}{A character string containing a valid Census API key, +\item{key}{A character string containing a valid Census API key, which can be requested from the -[U.S. Census API key signup page](https://api.census.gov/data/key_signup.html). +\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. By default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{states}{which states to extract Census data for, e.g., \code{c("NJ", "NY")}.} diff --git a/man/modfuns.Rd b/man/modfuns.Rd index 030f497..d7d682d 100644 --- a/man/modfuns.Rd +++ b/man/modfuns.Rd @@ -75,11 +75,11 @@ predict_race_me( \item{census.geo}{See documentation in \code{race_predict}.} \item{census.key}{A character object specifying user's Census API key. -Required if `census.geo` is specified, because a valid Census API key is +Required if \code{census.geo} is specified, because a valid Census API key is required to download Census geographic data. -If [`NULL`], the default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} +If \code{\link{NULL}}, the default, attempts to find a census key stored in an +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{census.data}{See documentation in \code{race_predict}.} @@ -95,15 +95,11 @@ If [`NULL`], the default, attempts to find a census key stored in an \item{impute.missing}{See documentation in \code{race_predict}.} - \item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} -\item{names.to.use}{One of 'surname', 'surname, first', or 'surname, first, -middle'. Defaults to 'surname'.} +\item{names.to.use}{See documentation in \code{race_predict}.} -\item{race.init}{Vector of initial race for each observation in voter.file. -Must be an integer vector, with 1=white, 2=black, 3=hispanic, 4=asian, and -5=other. Defaults to values obtained using \code{model="BISG_surname"}.} +\item{race.init}{See documentation in \code{race_predict}.} \item{ctrl}{See \code{control} in documentation for \code{\link[=predict_race]{predict_race()}}.} } From 62d22a72f4054ee3d3cb965599ec5fd081dd0417 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 13:30:39 -0800 Subject: [PATCH 41/90] feat(predict_race): add support for `census.geo = "zcta"` --- R/predict_race.R | 5 ++++- man/predict_race.Rd | 9 +++++---- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/R/predict_race.R b/R/predict_race.R index 3356c3f..bd8df9a 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -40,6 +40,7 @@ #' must have columns named \code{county}, \code{tract}, and \code{block}. #' If \code{\var{census.geo} = "place"}, then \code{\var{voter.file}} #' must have column named \code{place}. +#' If `census.geo = "zcta"`, then `voter.file` must have column named `zcta`. #' Specifying \code{\var{census.geo}} will call \code{census_helper} function #' to merge Census geographic data at specified level of geography. #' @@ -140,7 +141,7 @@ predict_race <- function( voter.file, census.surname = TRUE, surname.only = FALSE, - census.geo, + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), census.key = Sys.getenv("CENSUS_API_KEY"), census.data = NULL, age = FALSE, @@ -178,6 +179,8 @@ predict_race <- function( ) } + census.geo <- tolower(census.geo) + census.geo <- rlang::arg_match(census.geo) # block_group is missing, pull from block if((surname.only == FALSE) && !(missing(census.geo)) && (census.geo == "block_group") && !("block_group" %in% names(voter.file))) { diff --git a/man/predict_race.Rd b/man/predict_race.Rd index 27371dc..2ee4ec5 100644 --- a/man/predict_race.Rd +++ b/man/predict_race.Rd @@ -8,7 +8,7 @@ predict_race( voter.file, census.surname = TRUE, surname.only = FALSE, - census.geo, + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), census.key = Sys.getenv("CENSUS_API_KEY"), census.data = NULL, age = FALSE, @@ -64,15 +64,16 @@ And if \code{\var{census.geo} = "block"}, then \code{\var{voter.file}} must have columns named \code{county}, \code{tract}, and \code{block}. If \code{\var{census.geo} = "place"}, then \code{\var{voter.file}} must have column named \code{place}. +If \code{census.geo = "zcta"}, then \code{voter.file} must have column named \code{zcta}. Specifying \code{\var{census.geo}} will call \code{census_helper} function to merge Census geographic data at specified level of geography.} \item{census.key}{A character object specifying user's Census API key. -Required if `census.geo` is specified, because a valid Census API key is +Required if \code{census.geo} is specified, because a valid Census API key is required to download Census geographic data. -If [`NULL`], the default, attempts to find a census key stored in an -[environment variable][Sys.getenv] named `CENSUS_API_KEY`.} +If \code{\link{NULL}}, the default, attempts to find a census key stored in an +\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} \item{census.data}{A list indexed by two-letter state abbreviations, which contains pre-saved Census geographic data. From 8d5655e5f92736a552ccd941669eed2739946716 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 13:31:54 -0800 Subject: [PATCH 42/90] refactor(census_helper_new): use an `else` block to handle `geo == "place"`, `"county"`, and `"zcta"` --- R/census_helper_v2.R | 48 +++++++++++++++----------------------------- 1 file changed, 16 insertions(+), 32 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index f246816..5a76fa1 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -100,30 +100,6 @@ census_helper_new <- function( message(paste("State ", s, " of ", length(states), ": ", states[s], sep = "")) state <- toupper(states[s]) - if (geo == "place") { - geo.merge <- c("place") - if ((toDownload) || (is.null(census.data[[state]])) || (census.data[[state]]$year != year) || (census.data[[state]]$age != FALSE) || (census.data[[state]]$sex != FALSE)) { - #} || (census.data[[state]]$age != age) || (census.data[[state]]$sex != sex)) { - if(use.counties) { - census <- census_geo_api(key, state, geo = "place", age, sex, retry) - } else { - census <- census_geo_api(key, state, geo = "place", age, sex, retry) - } - } else { - census <- census.data[[toupper(state)]]$place - } - } - - if (geo == "county") { - geo.merge <- c("county") - if ((toDownload) || (is.null(census.data[[state]])) || (census.data[[state]]$year != year) || (census.data[[state]]$age != FALSE) || (census.data[[state]]$sex != FALSE)) { - #} || (census.data[[state]]$age != age) || (census.data[[state]]$sex != sex)) { - census <- census_geo_api(key, state, geo = "county", age, sex, retry) - } else { - census <- census.data[[toupper(state)]]$county - } - } - if (geo == "tract") { geo.merge <- c("county", "tract") if ((toDownload) || (is.null(census.data[[state]])) || (census.data[[state]]$year != year) || (census.data[[state]]$age != FALSE) || (census.data[[state]]$sex != FALSE)) {#} || (census.data[[state]]$age != age) || (census.data[[state]]$sex != sex)) { @@ -137,9 +113,7 @@ census_helper_new <- function( } else { census <- census.data[[toupper(state)]]$tract } - } - - if (geo == "block_group") { + } else if (geo == "block_group") { geo.merge <- c("county", "tract", "block_group") if ((toDownload) || (is.null(census.data[[state]])) || (census.data[[state]]$year != year) || (census.data[[state]]$age != FALSE) || (census.data[[state]]$sex != FALSE)) {#} || (census.data[[state]]$age != age) || (census.data[[state]]$sex != sex)) { if(use.counties) { @@ -153,10 +127,7 @@ census_helper_new <- function( } else { census <- census.data[[toupper(state)]]$block_group } - } - - - if (geo == "block") { + } else if (geo == "block") { if(any(names(census.data) == "block_group")) { geo.merge <- c("county", "tract", "block_group", "block") } else { @@ -175,10 +146,23 @@ census_helper_new <- function( } else { census <- census.data[[toupper(state)]]$block } + } else { + geo.merge <- geo + + state_must_be_downloaded <- toDownload || + is.null(census.data[[state]]) || + census.data[[state]]$year != year || + census.data[[state]]$age != FALSE || + census.data[[state]]$sex != FALSE + + if (state_must_be_downloaded) { + census <- census_geo_api(key, state, geo = geo, age, sex, retry) + } else { + census <- census.data[[state]][[geo]] + } } census$state <- state - ## Calculate Pr(Geolocation | Race) if (year != "2020") { From cab4a3a96ea7a239933721490de5f47e6b1d1450 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 29 Nov 2023 09:48:42 -0800 Subject: [PATCH 43/90] refactor(census_helper_new): improve efficiency of identification of zero-population locations --- R/census_helper_v2.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 5a76fa1..36dd25b 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -192,10 +192,10 @@ census_helper_new <- function( # check locations with zero people # get average without places with zero people, and assign that to zero locs. - if(any((geoPopulations - 0.0) < .Machine$double.eps)){ - zero_ind <- which((geoPopulations - 0.0) < .Machine$double.eps) - for(rcat in c("r_whi","r_bla","r_his","r_asi","r_oth") ){ - census[[rcat]][zero_ind] <- mean(census[[rcat]], na.rm=TRUE) + zero_ind <- which((geoPopulations - 0.0) < .Machine$double.eps) + if (length(zero_ind)) { + for (rcat in c("r_whi","r_bla","r_his","r_asi","r_oth")) { + census[[rcat]][zero_ind] <- mean(census[[rcat]], na.rm = TRUE) } } From 9576ca1bf1f5ddfb01d982c62849ee5468d4a8fd Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 29 Nov 2023 20:12:25 -0800 Subject: [PATCH 44/90] feat: add `census_geo_api_names()` and `census_geo_api_url()` helper functions --- R/census_geo_api_names.R | 117 ++++++ man/census_geo_api_names.Rd | 48 +++ tests/testthat/test-census_geo_api_names.R | 410 +++++++++++++++++++++ 3 files changed, 575 insertions(+) create mode 100644 R/census_geo_api_names.R create mode 100644 man/census_geo_api_names.Rd create mode 100644 tests/testthat/test-census_geo_api_names.R diff --git a/R/census_geo_api_names.R b/R/census_geo_api_names.R new file mode 100644 index 0000000..dc2aaa0 --- /dev/null +++ b/R/census_geo_api_names.R @@ -0,0 +1,117 @@ +# @staticimports pkg:stringstatic +# str_pad + +#' Census geo API helper functions +#' +#' @inheritParams census_geo_api +#' +#' @return +#' \describe{ +#' \item{`census_geo_api_names()`}{ +#' A named list of [character] vectors whose values correspond to columns +#' of a Census API table and whose names represent the new columns they are +#' used to calculate in [census_geo_api()]. +#' } +#' \item{`census_geo_api_url()`}{ +#' A [character] string containing the base of the URL to a +#' Census API table. +#' } +#' } +#' @keywords internal +census_geo_api_names <- function( + year = c("2020", "2010", "2000"), + age = FALSE, + sex = FALSE +) { + year <- as.character(year) + year <- rlang::arg_match(year) + + assert_boolean(age) + assert_boolean(sex) + + if (year == "2020") { + prefix <- "P12" + separator <- "_" + suffix <- "N" + } else if (year %in% c("2010", "2000")) { + prefix <- "P012" + separator <- "" + suffix <- "" + } + + race_codes <- list( + "_whi" = "I", + "_bla" = "B", + "_his" = "H", + "_asi" = c("D", "E"), + "_oth" = c("C", "F", "G") + ) + + sex_codes <- c("_mal" = 2, "_fem" = 26) + + age_codes <- 1:23 + names(age_codes) <- paste0("_", age_codes) + + numeric_codes <- if (age && sex) { + age_sex_codes <- unlist( + purrr::map(sex_codes, function(x) x + age_codes) + ) + names(age_sex_codes) <- sub(".", "", names(age_sex_codes), fixed = TRUE) + age_sex_codes[] <- str_pad(age_sex_codes, 3, "left", pad = "0") + as.list(age_sex_codes) + } else if (age) { + purrr::map( + age_codes, + function(x) str_pad(x + sex_codes, 3, "left", pad = "0") + ) + } else if (sex) { + sex_codes[] <- str_pad(sex_codes, 3, "left", pad = "0") + as.list(sex_codes) + } + + numeric_codes <- c("001", numeric_codes) + + combinations <- expand.grid( + prefix = prefix, + race_codes = race_codes, + separator = separator, + numeric_codes = numeric_codes, + suffix = suffix, + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) + + vars <- purrr::pmap( + combinations, + function(prefix, race_codes, separator, numeric_codes, suffix) { + inner_combinations <- expand.grid( + prefix = prefix, + race_codes = race_codes, + separator = separator, + numeric_codes = numeric_codes, + suffix = suffix, + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) + + apply(inner_combinations, 1, paste, collapse = "") + } + ) + + names(vars) <- paste0( + "r", + names(combinations$numeric_codes), + names(combinations$race_codes) + ) + + vars +} + +#' @rdname census_geo_api_names +census_geo_api_url <- function(year = c("2020", "2010", "2000")) { + year <- as.character(year) + year <- rlang::arg_match(year) + + if (year == "2020") return("https://api.census.gov/data/2020/dec/dhc?") + paste0("https://api.census.gov/data/", year, "/dec/sf1?") +} \ No newline at end of file diff --git a/man/census_geo_api_names.Rd b/man/census_geo_api_names.Rd new file mode 100644 index 0000000..a03488c --- /dev/null +++ b/man/census_geo_api_names.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/census_geo_api_names.R +\name{census_geo_api_names} +\alias{census_geo_api_names} +\alias{census_geo_api_url} +\title{Census geo API helper functions} +\usage{ +census_geo_api_names( + year = c("2020", "2010", "2000"), + age = FALSE, + sex = FALSE +) + +census_geo_api_url(year = c("2020", "2010", "2000")) +} +\arguments{ +\item{year}{A character object specifying the year of U.S. Census data to be downloaded. +Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}. +Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and +\code{\var{sex}} are both \code{FALSE}.} + +\item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). +If \code{TRUE}, function will return Pr(Geolocation, Age | Race). +If \code{\var{sex}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} + +\item{sex}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on +sex or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). +If \code{TRUE}, function will return Pr(Geolocation, Sex | Race). +If \code{\var{age}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} +} +\value{ +\describe{ +\item{\code{census_geo_api_names()}}{ +A named list of \link{character} vectors whose values correspond to columns +of a Census API table and whose names represent the new columns they are +used to calculate in \code{\link[=census_geo_api]{census_geo_api()}}. +} +\item{\code{census_geo_api_url()}}{ +A \link{character} string containing the base of the URL to a +Census API table. +} +} +} +\description{ +Census geo API helper functions +} +\keyword{internal} diff --git a/tests/testthat/test-census_geo_api_names.R b/tests/testthat/test-census_geo_api_names.R new file mode 100644 index 0000000..2ca9d84 --- /dev/null +++ b/tests/testthat/test-census_geo_api_names.R @@ -0,0 +1,410 @@ +test_that("census_geo_api_names() for 2020", { + expect_equal( + census_geo_api_names("2020"), + list( + r_whi = "P12I_001N", + r_bla = "P12B_001N", + r_his = "P12H_001N", + r_asi = c("P12D_001N", "P12E_001N"), + r_oth = c("P12C_001N", "P12F_001N", "P12G_001N") + ) + ) + + expect_equal( + census_geo_api_names("2020", age = FALSE, sex = TRUE), + c( + census_geo_api_names("2020", age = FALSE, sex = FALSE), + list( + r_mal_whi = "P12I_002N", + r_mal_bla = "P12B_002N", + r_mal_his = "P12H_002N", + r_mal_asi = c("P12D_002N", "P12E_002N"), + r_mal_oth = c("P12C_002N", "P12F_002N", "P12G_002N"), + r_fem_whi = "P12I_026N", + r_fem_bla = "P12B_026N", + r_fem_his = "P12H_026N", + r_fem_asi = c("P12D_026N", "P12E_026N"), + r_fem_oth = c("P12C_026N", "P12F_026N", "P12G_026N") + ) + ) + ) + + expect_equal( + census_geo_api_names("2020", age = TRUE, sex = FALSE), + c( + census_geo_api_names("2020", age = FALSE, sex = FALSE), + list( + r_1_whi = c("P12I_003N", "P12I_027N"), + r_1_bla = c("P12B_003N", "P12B_027N"), + r_1_his = c("P12H_003N", "P12H_027N"), + r_1_asi = c("P12D_003N", "P12E_003N", "P12D_027N", "P12E_027N"), + r_1_oth = c("P12C_003N", "P12F_003N", "P12G_003N", "P12C_027N", "P12F_027N", "P12G_027N"), + r_2_whi = c("P12I_004N", "P12I_028N"), + r_2_bla = c("P12B_004N", "P12B_028N"), + r_2_his = c("P12H_004N", "P12H_028N"), + r_2_asi = c("P12D_004N", "P12E_004N", "P12D_028N", "P12E_028N"), + r_2_oth = c("P12C_004N", "P12F_004N", "P12G_004N", "P12C_028N", "P12F_028N", "P12G_028N"), + r_3_whi = c("P12I_005N", "P12I_029N"), + r_3_bla = c("P12B_005N", "P12B_029N"), + r_3_his = c("P12H_005N", "P12H_029N"), + r_3_asi = c("P12D_005N", "P12E_005N", "P12D_029N", "P12E_029N"), + r_3_oth = c("P12C_005N", "P12F_005N", "P12G_005N", "P12C_029N", "P12F_029N", "P12G_029N"), + r_4_whi = c("P12I_006N", "P12I_030N"), + r_4_bla = c("P12B_006N", "P12B_030N"), + r_4_his = c("P12H_006N", "P12H_030N"), + r_4_asi = c("P12D_006N", "P12E_006N", "P12D_030N", "P12E_030N"), + r_4_oth = c("P12C_006N", "P12F_006N", "P12G_006N", "P12C_030N", "P12F_030N", "P12G_030N"), + r_5_whi = c("P12I_007N", "P12I_031N"), + r_5_bla = c("P12B_007N", "P12B_031N"), + r_5_his = c("P12H_007N", "P12H_031N"), + r_5_asi = c("P12D_007N", "P12E_007N", "P12D_031N", "P12E_031N"), + r_5_oth = c("P12C_007N", "P12F_007N", "P12G_007N", "P12C_031N", "P12F_031N", "P12G_031N"), + r_6_whi = c("P12I_008N", "P12I_032N"), + r_6_bla = c("P12B_008N", "P12B_032N"), + r_6_his = c("P12H_008N", "P12H_032N"), + r_6_asi = c("P12D_008N", "P12E_008N", "P12D_032N", "P12E_032N"), + r_6_oth = c("P12C_008N", "P12F_008N", "P12G_008N", "P12C_032N", "P12F_032N", "P12G_032N"), + r_7_whi = c("P12I_009N", "P12I_033N"), + r_7_bla = c("P12B_009N", "P12B_033N"), + r_7_his = c("P12H_009N", "P12H_033N"), + r_7_asi = c("P12D_009N", "P12E_009N", "P12D_033N", "P12E_033N"), + r_7_oth = c("P12C_009N", "P12F_009N", "P12G_009N", "P12C_033N", "P12F_033N", "P12G_033N"), + r_8_whi = c("P12I_010N", "P12I_034N"), + r_8_bla = c("P12B_010N", "P12B_034N"), + r_8_his = c("P12H_010N", "P12H_034N"), + r_8_asi = c("P12D_010N", "P12E_010N", "P12D_034N", "P12E_034N"), + r_8_oth = c("P12C_010N", "P12F_010N", "P12G_010N", "P12C_034N", "P12F_034N", "P12G_034N"), + r_9_whi = c("P12I_011N", "P12I_035N"), + r_9_bla = c("P12B_011N", "P12B_035N"), + r_9_his = c("P12H_011N", "P12H_035N"), + r_9_asi = c("P12D_011N", "P12E_011N", "P12D_035N", "P12E_035N"), + r_9_oth = c("P12C_011N", "P12F_011N", "P12G_011N", "P12C_035N", "P12F_035N", "P12G_035N"), + r_10_whi = c("P12I_012N", "P12I_036N"), + r_10_bla = c("P12B_012N", "P12B_036N"), + r_10_his = c("P12H_012N", "P12H_036N"), + r_10_asi = c("P12D_012N", "P12E_012N", "P12D_036N", "P12E_036N"), + r_10_oth = c("P12C_012N", "P12F_012N", "P12G_012N", "P12C_036N", "P12F_036N", "P12G_036N"), + r_11_whi = c("P12I_013N", "P12I_037N"), + r_11_bla = c("P12B_013N", "P12B_037N"), + r_11_his = c("P12H_013N", "P12H_037N"), + r_11_asi = c("P12D_013N", "P12E_013N", "P12D_037N", "P12E_037N"), + r_11_oth = c("P12C_013N", "P12F_013N", "P12G_013N", "P12C_037N", "P12F_037N", "P12G_037N"), + r_12_whi = c("P12I_014N", "P12I_038N"), + r_12_bla = c("P12B_014N", "P12B_038N"), + r_12_his = c("P12H_014N", "P12H_038N"), + r_12_asi = c("P12D_014N", "P12E_014N", "P12D_038N", "P12E_038N"), + r_12_oth = c("P12C_014N", "P12F_014N", "P12G_014N", "P12C_038N", "P12F_038N", "P12G_038N"), + r_13_whi = c("P12I_015N", "P12I_039N"), + r_13_bla = c("P12B_015N", "P12B_039N"), + r_13_his = c("P12H_015N", "P12H_039N"), + r_13_asi = c("P12D_015N", "P12E_015N", "P12D_039N", "P12E_039N"), + r_13_oth = c("P12C_015N", "P12F_015N", "P12G_015N", "P12C_039N", "P12F_039N", "P12G_039N"), + r_14_whi = c("P12I_016N", "P12I_040N"), + r_14_bla = c("P12B_016N", "P12B_040N"), + r_14_his = c("P12H_016N", "P12H_040N"), + r_14_asi = c("P12D_016N", "P12E_016N", "P12D_040N", "P12E_040N"), + r_14_oth = c("P12C_016N", "P12F_016N", "P12G_016N", "P12C_040N", "P12F_040N", "P12G_040N"), + r_15_whi = c("P12I_017N", "P12I_041N"), + r_15_bla = c("P12B_017N", "P12B_041N"), + r_15_his = c("P12H_017N", "P12H_041N"), + r_15_asi = c("P12D_017N", "P12E_017N", "P12D_041N", "P12E_041N"), + r_15_oth = c("P12C_017N", "P12F_017N", "P12G_017N", "P12C_041N", "P12F_041N", "P12G_041N"), + r_16_whi = c("P12I_018N", "P12I_042N"), + r_16_bla = c("P12B_018N", "P12B_042N"), + r_16_his = c("P12H_018N", "P12H_042N"), + r_16_asi = c("P12D_018N", "P12E_018N", "P12D_042N", "P12E_042N"), + r_16_oth = c("P12C_018N", "P12F_018N", "P12G_018N", "P12C_042N", "P12F_042N", "P12G_042N"), + r_17_whi = c("P12I_019N", "P12I_043N"), + r_17_bla = c("P12B_019N", "P12B_043N"), + r_17_his = c("P12H_019N", "P12H_043N"), + r_17_asi = c("P12D_019N", "P12E_019N", "P12D_043N", "P12E_043N"), + r_17_oth = c("P12C_019N", "P12F_019N", "P12G_019N", "P12C_043N", "P12F_043N", "P12G_043N"), + r_18_whi = c("P12I_020N", "P12I_044N"), + r_18_bla = c("P12B_020N", "P12B_044N"), + r_18_his = c("P12H_020N", "P12H_044N"), + r_18_asi = c("P12D_020N", "P12E_020N", "P12D_044N", "P12E_044N"), + r_18_oth = c("P12C_020N", "P12F_020N", "P12G_020N", "P12C_044N", "P12F_044N", "P12G_044N"), + r_19_whi = c("P12I_021N", "P12I_045N"), + r_19_bla = c("P12B_021N", "P12B_045N"), + r_19_his = c("P12H_021N", "P12H_045N"), + r_19_asi = c("P12D_021N", "P12E_021N", "P12D_045N", "P12E_045N"), + r_19_oth = c("P12C_021N", "P12F_021N", "P12G_021N", "P12C_045N", "P12F_045N", "P12G_045N"), + r_20_whi = c("P12I_022N", "P12I_046N"), + r_20_bla = c("P12B_022N", "P12B_046N"), + r_20_his = c("P12H_022N", "P12H_046N"), + r_20_asi = c("P12D_022N", "P12E_022N", "P12D_046N", "P12E_046N"), + r_20_oth = c("P12C_022N", "P12F_022N", "P12G_022N", "P12C_046N", "P12F_046N", "P12G_046N"), + r_21_whi = c("P12I_023N", "P12I_047N"), + r_21_bla = c("P12B_023N", "P12B_047N"), + r_21_his = c("P12H_023N", "P12H_047N"), + r_21_asi = c("P12D_023N", "P12E_023N", "P12D_047N", "P12E_047N"), + r_21_oth = c("P12C_023N", "P12F_023N", "P12G_023N", "P12C_047N", "P12F_047N", "P12G_047N"), + r_22_whi = c("P12I_024N", "P12I_048N"), + r_22_bla = c("P12B_024N", "P12B_048N"), + r_22_his = c("P12H_024N", "P12H_048N"), + r_22_asi = c("P12D_024N", "P12E_024N", "P12D_048N", "P12E_048N"), + r_22_oth = c("P12C_024N", "P12F_024N", "P12G_024N", "P12C_048N", "P12F_048N", "P12G_048N"), + r_23_whi = c("P12I_025N", "P12I_049N"), + r_23_bla = c("P12B_025N", "P12B_049N"), + r_23_his = c("P12H_025N", "P12H_049N"), + r_23_asi = c("P12D_025N", "P12E_025N", "P12D_049N", "P12E_049N"), + r_23_oth = c("P12C_025N", "P12F_025N", "P12G_025N", "P12C_049N", "P12F_049N", "P12G_049N") + ) + ) + ) + + expect_equal( + census_geo_api_names("2020", age = TRUE, sex = TRUE), + c( + census_geo_api_names("2020", age = FALSE, sex = FALSE), + list( + r_mal_1_whi = "P12I_003N", + r_mal_1_bla = "P12B_003N", + r_mal_1_his = "P12H_003N", + r_mal_1_asi = c("P12D_003N", "P12E_003N"), + r_mal_1_oth = c("P12C_003N", "P12F_003N", "P12G_003N"), + r_mal_2_whi = "P12I_004N", + r_mal_2_bla = "P12B_004N", + r_mal_2_his = "P12H_004N", + r_mal_2_asi = c("P12D_004N", "P12E_004N"), + r_mal_2_oth = c("P12C_004N", "P12F_004N", "P12G_004N"), + r_mal_3_whi = "P12I_005N", + r_mal_3_bla = "P12B_005N", + r_mal_3_his = "P12H_005N", + r_mal_3_asi = c("P12D_005N", "P12E_005N"), + r_mal_3_oth = c("P12C_005N", "P12F_005N", "P12G_005N"), + r_mal_4_whi = "P12I_006N", + r_mal_4_bla = "P12B_006N", + r_mal_4_his = "P12H_006N", + r_mal_4_asi = c("P12D_006N", "P12E_006N"), + r_mal_4_oth = c("P12C_006N", "P12F_006N", "P12G_006N"), + r_mal_5_whi = "P12I_007N", + r_mal_5_bla = "P12B_007N", + r_mal_5_his = "P12H_007N", + r_mal_5_asi = c("P12D_007N", "P12E_007N"), + r_mal_5_oth = c("P12C_007N", "P12F_007N", "P12G_007N"), + r_mal_6_whi = "P12I_008N", + r_mal_6_bla = "P12B_008N", + r_mal_6_his = "P12H_008N", + r_mal_6_asi = c("P12D_008N", "P12E_008N"), + r_mal_6_oth = c("P12C_008N", "P12F_008N", "P12G_008N"), + r_mal_7_whi = "P12I_009N", + r_mal_7_bla = "P12B_009N", + r_mal_7_his = "P12H_009N", + r_mal_7_asi = c("P12D_009N", "P12E_009N"), + r_mal_7_oth = c("P12C_009N", "P12F_009N", "P12G_009N"), + r_mal_8_whi = "P12I_010N", + r_mal_8_bla = "P12B_010N", + r_mal_8_his = "P12H_010N", + r_mal_8_asi = c("P12D_010N", "P12E_010N"), + r_mal_8_oth = c("P12C_010N", "P12F_010N", "P12G_010N"), + r_mal_9_whi = "P12I_011N", + r_mal_9_bla = "P12B_011N", + r_mal_9_his = "P12H_011N", + r_mal_9_asi = c("P12D_011N", "P12E_011N"), + r_mal_9_oth = c("P12C_011N", "P12F_011N", "P12G_011N"), + r_mal_10_whi = "P12I_012N", + r_mal_10_bla = "P12B_012N", + r_mal_10_his = "P12H_012N", + r_mal_10_asi = c("P12D_012N", "P12E_012N"), + r_mal_10_oth = c("P12C_012N", "P12F_012N", "P12G_012N"), + r_mal_11_whi = "P12I_013N", + r_mal_11_bla = "P12B_013N", + r_mal_11_his = "P12H_013N", + r_mal_11_asi = c("P12D_013N", "P12E_013N"), + r_mal_11_oth = c("P12C_013N", "P12F_013N", "P12G_013N"), + r_mal_12_whi = "P12I_014N", + r_mal_12_bla = "P12B_014N", + r_mal_12_his = "P12H_014N", + r_mal_12_asi = c("P12D_014N", "P12E_014N"), + r_mal_12_oth = c("P12C_014N", "P12F_014N", "P12G_014N"), + r_mal_13_whi = "P12I_015N", + r_mal_13_bla = "P12B_015N", + r_mal_13_his = "P12H_015N", + r_mal_13_asi = c("P12D_015N", "P12E_015N"), + r_mal_13_oth = c("P12C_015N", "P12F_015N", "P12G_015N"), + r_mal_14_whi = "P12I_016N", + r_mal_14_bla = "P12B_016N", + r_mal_14_his = "P12H_016N", + r_mal_14_asi = c("P12D_016N", "P12E_016N"), + r_mal_14_oth = c("P12C_016N", "P12F_016N", "P12G_016N"), + r_mal_15_whi = "P12I_017N", + r_mal_15_bla = "P12B_017N", + r_mal_15_his = "P12H_017N", + r_mal_15_asi = c("P12D_017N", "P12E_017N"), + r_mal_15_oth = c("P12C_017N", "P12F_017N", "P12G_017N"), + r_mal_16_whi = "P12I_018N", + r_mal_16_bla = "P12B_018N", + r_mal_16_his = "P12H_018N", + r_mal_16_asi = c("P12D_018N", "P12E_018N"), + r_mal_16_oth = c("P12C_018N", "P12F_018N", "P12G_018N"), + r_mal_17_whi = "P12I_019N", + r_mal_17_bla = "P12B_019N", + r_mal_17_his = "P12H_019N", + r_mal_17_asi = c("P12D_019N", "P12E_019N"), + r_mal_17_oth = c("P12C_019N", "P12F_019N", "P12G_019N"), + r_mal_18_whi = "P12I_020N", + r_mal_18_bla = "P12B_020N", + r_mal_18_his = "P12H_020N", + r_mal_18_asi = c("P12D_020N", "P12E_020N"), + r_mal_18_oth = c("P12C_020N", "P12F_020N", "P12G_020N"), + r_mal_19_whi = "P12I_021N", + r_mal_19_bla = "P12B_021N", + r_mal_19_his = "P12H_021N", + r_mal_19_asi = c("P12D_021N", "P12E_021N"), + r_mal_19_oth = c("P12C_021N", "P12F_021N", "P12G_021N"), + r_mal_20_whi = "P12I_022N", + r_mal_20_bla = "P12B_022N", + r_mal_20_his = "P12H_022N", + r_mal_20_asi = c("P12D_022N", "P12E_022N"), + r_mal_20_oth = c("P12C_022N", "P12F_022N", "P12G_022N"), + r_mal_21_whi = "P12I_023N", + r_mal_21_bla = "P12B_023N", + r_mal_21_his = "P12H_023N", + r_mal_21_asi = c("P12D_023N", "P12E_023N"), + r_mal_21_oth = c("P12C_023N", "P12F_023N", "P12G_023N"), + r_mal_22_whi = "P12I_024N", + r_mal_22_bla = "P12B_024N", + r_mal_22_his = "P12H_024N", + r_mal_22_asi = c("P12D_024N", "P12E_024N"), + r_mal_22_oth = c("P12C_024N", "P12F_024N", "P12G_024N"), + r_mal_23_whi = "P12I_025N", + r_mal_23_bla = "P12B_025N", + r_mal_23_his = "P12H_025N", + r_mal_23_asi = c("P12D_025N", "P12E_025N"), + r_mal_23_oth = c("P12C_025N", "P12F_025N", "P12G_025N"), + r_fem_1_whi = "P12I_027N", + r_fem_1_bla = "P12B_027N", + r_fem_1_his = "P12H_027N", + r_fem_1_asi = c("P12D_027N", "P12E_027N"), + r_fem_1_oth = c("P12C_027N", "P12F_027N", "P12G_027N"), + r_fem_2_whi = "P12I_028N", + r_fem_2_bla = "P12B_028N", + r_fem_2_his = "P12H_028N", + r_fem_2_asi = c("P12D_028N", "P12E_028N"), + r_fem_2_oth = c("P12C_028N", "P12F_028N", "P12G_028N"), + r_fem_3_whi = "P12I_029N", + r_fem_3_bla = "P12B_029N", + r_fem_3_his = "P12H_029N", + r_fem_3_asi = c("P12D_029N", "P12E_029N"), + r_fem_3_oth = c("P12C_029N", "P12F_029N", "P12G_029N"), + r_fem_4_whi = "P12I_030N", + r_fem_4_bla = "P12B_030N", + r_fem_4_his = "P12H_030N", + r_fem_4_asi = c("P12D_030N", "P12E_030N"), + r_fem_4_oth = c("P12C_030N", "P12F_030N", "P12G_030N"), + r_fem_5_whi = "P12I_031N", + r_fem_5_bla = "P12B_031N", + r_fem_5_his = "P12H_031N", + r_fem_5_asi = c("P12D_031N", "P12E_031N"), + r_fem_5_oth = c("P12C_031N", "P12F_031N", "P12G_031N"), + r_fem_6_whi = "P12I_032N", + r_fem_6_bla = "P12B_032N", + r_fem_6_his = "P12H_032N", + r_fem_6_asi = c("P12D_032N", "P12E_032N"), + r_fem_6_oth = c("P12C_032N", "P12F_032N", "P12G_032N"), + r_fem_7_whi = "P12I_033N", + r_fem_7_bla = "P12B_033N", + r_fem_7_his = "P12H_033N", + r_fem_7_asi = c("P12D_033N", "P12E_033N"), + r_fem_7_oth = c("P12C_033N", "P12F_033N", "P12G_033N"), + r_fem_8_whi = "P12I_034N", + r_fem_8_bla = "P12B_034N", + r_fem_8_his = "P12H_034N", + r_fem_8_asi = c("P12D_034N", "P12E_034N"), + r_fem_8_oth = c("P12C_034N", "P12F_034N", "P12G_034N"), + r_fem_9_whi = "P12I_035N", + r_fem_9_bla = "P12B_035N", + r_fem_9_his = "P12H_035N", + r_fem_9_asi = c("P12D_035N", "P12E_035N"), + r_fem_9_oth = c("P12C_035N", "P12F_035N", "P12G_035N"), + r_fem_10_whi = "P12I_036N", + r_fem_10_bla = "P12B_036N", + r_fem_10_his = "P12H_036N", + r_fem_10_asi = c("P12D_036N", "P12E_036N"), + r_fem_10_oth = c("P12C_036N", "P12F_036N", "P12G_036N"), + r_fem_11_whi = "P12I_037N", + r_fem_11_bla = "P12B_037N", + r_fem_11_his = "P12H_037N", + r_fem_11_asi = c("P12D_037N", "P12E_037N"), + r_fem_11_oth = c("P12C_037N", "P12F_037N", "P12G_037N"), + r_fem_12_whi = "P12I_038N", + r_fem_12_bla = "P12B_038N", + r_fem_12_his = "P12H_038N", + r_fem_12_asi = c("P12D_038N", "P12E_038N"), + r_fem_12_oth = c("P12C_038N", "P12F_038N", "P12G_038N"), + r_fem_13_whi = "P12I_039N", + r_fem_13_bla = "P12B_039N", + r_fem_13_his = "P12H_039N", + r_fem_13_asi = c("P12D_039N", "P12E_039N"), + r_fem_13_oth = c("P12C_039N", "P12F_039N", "P12G_039N"), + r_fem_14_whi = "P12I_040N", + r_fem_14_bla = "P12B_040N", + r_fem_14_his = "P12H_040N", + r_fem_14_asi = c("P12D_040N", "P12E_040N"), + r_fem_14_oth = c("P12C_040N", "P12F_040N", "P12G_040N"), + r_fem_15_whi = "P12I_041N", + r_fem_15_bla = "P12B_041N", + r_fem_15_his = "P12H_041N", + r_fem_15_asi = c("P12D_041N", "P12E_041N"), + r_fem_15_oth = c("P12C_041N", "P12F_041N", "P12G_041N"), + r_fem_16_whi = "P12I_042N", + r_fem_16_bla = "P12B_042N", + r_fem_16_his = "P12H_042N", + r_fem_16_asi = c("P12D_042N", "P12E_042N"), + r_fem_16_oth = c("P12C_042N", "P12F_042N", "P12G_042N"), + r_fem_17_whi = "P12I_043N", + r_fem_17_bla = "P12B_043N", + r_fem_17_his = "P12H_043N", + r_fem_17_asi = c("P12D_043N", "P12E_043N"), + r_fem_17_oth = c("P12C_043N", "P12F_043N", "P12G_043N"), + r_fem_18_whi = "P12I_044N", + r_fem_18_bla = "P12B_044N", + r_fem_18_his = "P12H_044N", + r_fem_18_asi = c("P12D_044N", "P12E_044N"), + r_fem_18_oth = c("P12C_044N", "P12F_044N", "P12G_044N"), + r_fem_19_whi = "P12I_045N", + r_fem_19_bla = "P12B_045N", + r_fem_19_his = "P12H_045N", + r_fem_19_asi = c("P12D_045N", "P12E_045N"), + r_fem_19_oth = c("P12C_045N", "P12F_045N", "P12G_045N"), + r_fem_20_whi = "P12I_046N", + r_fem_20_bla = "P12B_046N", + r_fem_20_his = "P12H_046N", + r_fem_20_asi = c("P12D_046N", "P12E_046N"), + r_fem_20_oth = c("P12C_046N", "P12F_046N", "P12G_046N"), + r_fem_21_whi = "P12I_047N", + r_fem_21_bla = "P12B_047N", + r_fem_21_his = "P12H_047N", + r_fem_21_asi = c("P12D_047N", "P12E_047N"), + r_fem_21_oth = c("P12C_047N", "P12F_047N", "P12G_047N"), + r_fem_22_whi = "P12I_048N", + r_fem_22_bla = "P12B_048N", + r_fem_22_his = "P12H_048N", + r_fem_22_asi = c("P12D_048N", "P12E_048N"), + r_fem_22_oth = c("P12C_048N", "P12F_048N", "P12G_048N"), + r_fem_23_whi = "P12I_049N", + r_fem_23_bla = "P12B_049N", + r_fem_23_his = "P12H_049N", + r_fem_23_asi = c("P12D_049N", "P12E_049N"), + r_fem_23_oth = c("P12C_049N", "P12F_049N", "P12G_049N") + ) + ) + ) +}) + +test_that("census_geo_api_url()", { + expect_equal( + census_geo_api_url("2020"), + "https://api.census.gov/data/2020/dec/dhc?" + ) + expect_equal( + census_geo_api_url("2010"), + "https://api.census.gov/data/2010/dec/sf1?" + ) + expect_equal( + census_geo_api_url("2000"), + "https://api.census.gov/data/2000/dec/sf1?" + ) + expect_error(census_geo_api_url("2023")) +}) From 984b6ce9c5a3fe353a2d6e0b3df4ceda350506f7 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Wed, 29 Nov 2023 20:12:25 -0800 Subject: [PATCH 45/90] refactor(census_geo_api_zcta): use `census_geo_api_names()` --- R/census_geo_api_zcta.R | 152 +++++----------------------------------- 1 file changed, 19 insertions(+), 133 deletions(-) diff --git a/R/census_geo_api_zcta.R b/R/census_geo_api_zcta.R index 8ccdc5d..93fb0b0 100644 --- a/R/census_geo_api_zcta.R +++ b/R/census_geo_api_zcta.R @@ -1,6 +1,3 @@ -# @staticimports pkg:stringstatic -# str_pad - #' Census download function for state-ZCTA-level data #' #' @inheritParams census_geo_api @@ -32,48 +29,8 @@ census_geo_api_zcta <- function( year <- as.character(year) year <- rlang::arg_match(year) - if (year == "2020") { - census_data_url <- "https://api.census.gov/data/2020/dec/dhc?" - prefix <- "P12" - separator <- "_" - suffix <- "N" - } else if (year %in% c("2010", "2000")) { - census_data_url <- paste0("https://api.census.gov/data/", year, "/dec/sf1?") - prefix <- "P012" - separator <- "" - suffix <- "" - } - - race_codes <- list( - "whi" = "I", - "bla" = "B", - "his" = "H", - "asi" = c("D", "E"), - "oth" = c("C", "F", "G") - ) - - sex_codes <- c("mal" = 2, "fem" = 26) - - age_codes <- 1:23 - - numeric_codes <- if (age) { - unlist(purrr::map(sex_codes, function(x) x + age_codes)) - } else if (sex) { - sex_codes - } else { - 1 - } - numeric_codes <- str_pad(numeric_codes, width = 3, side = "left", pad = "0") - - vars <- expand.grid( - prefix, - unlist(race_codes), - separator, - numeric_codes, - suffix, - stringsAsFactors = FALSE - ) - vars <- apply(vars, 1, paste, collapse = "") + census_data_url <- census_geo_api_url(year = year) + vars <- census_geo_api_names(year = year, age = age, sex = sex) region <- paste0( "for=zip%20code%20tabulation%20area%20(or%20part):*&in=state:", @@ -83,104 +40,33 @@ census_geo_api_zcta <- function( census <- get_census_api( census_data_url, key = key, - var.names = vars, + var.names = unlist(vars), region = region, retry ) - if (!age && !sex) { - ## Calculate Pr(Geolocation | Race) - - for (i in seq_along(race_codes)) { - var_name <- paste("r", names(race_codes)[[i]], sep = "_") - - code <- paste0(prefix, race_codes[[i]], separator, "001", suffix) - - census[var_name] <- rowSums(census[code]) - } - } else if (!age && sex) { - ## Calculate Pr(Geolocation, Sex | Race) - - for (race in seq_along(race_codes)) { - for (sex in seq_along(sex_codes)) { - var_name <- paste( - "r", - names(sex_codes)[[sex]], - names(race_codes)[[race]], - sep = "_" - ) - - code <- paste0( - prefix, - race_codes[[race]], - separator, - str_pad(sex_codes[[sex]], width = 3, pad = "0"), - suffix - ) - - census[var_name] <- rowSums(census[code]) - } - } - } else if (age && !sex) { - ## Calculate Pr(Geolocation, Age Category | Race) - - for (race in seq_along(race_codes)) { - for (age_category in age_codes) { - var_name <- paste( - "r", - age_category, - names(race_codes)[[race]], - sep = "_" - ) - - code <- paste0( - prefix, - race_codes[[race]], - separator, - str_pad(sex_codes + age_category, width = 3, pad = "0"), - suffix - ) - - census[var_name] <- rowSums(census[code]) - } - } - } else if (age && sex) { - ## Calculate Pr(Geolocation, Sex, Age Category | Race) - - for (race in seq_along(race_codes)) { - for (age_category in age_codes) { - for (sex in seq_along(sex_codes)) { - var_name <- paste( - "r", - names(sex_codes)[[sex]], - age_category, - names(race_codes)[[race]], - sep = "_" - ) - - code <- paste0( - prefix, - race_codes[[race]], - separator, - str_pad(sex_codes[[sex]] + age_category, width = 3, pad = "0"), - suffix - ) - - census[var_name] <- rowSums(census[code]) - } - } - } - } + census <- dplyr::mutate(census, state = as_state_abbreviation(state)) + names(census)[[2]] <- "zcta" + + r_columns <- purrr::map(vars, function(vars) rowSums(census[vars])) + census <- dplyr::bind_cols(census, r_columns) census <- dplyr::group_by(census, dplyr::across(dplyr::any_of("state"))) census <- dplyr::mutate( census, - state = as_state_abbreviation(state), - dplyr::across(dplyr::starts_with("r_"), function(x) x / sum(x)) + dplyr::across( + # Divide all r_columns by the total population of the corresponding race + dplyr::all_of(names(r_columns)), + function(x) { + x / sum( + dplyr::pick( + sub("^.+_(.{3})$", "r_\\1", dplyr::cur_column(), perl = TRUE) + ) + ) + } + ) ) census <- dplyr::ungroup(census) - names(census)[[2]] <- "zcta" - census } From 0fa0953a2c4b9a5684783298c0169b143ca66e1c Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 15:46:38 -0800 Subject: [PATCH 46/90] refactor(census_geo_api): use `census_geo_api_names()` --- R/census_geo_api.R | 182 ++++++++------------------------------------- 1 file changed, 31 insertions(+), 151 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index 1c64d22..d4055e4 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -80,68 +80,18 @@ census_geo_api <- function( year <- rlang::arg_match(year) census <- NULL - state <- toupper(state) + state <- as_state_abbreviation(state) df.out <- NULL state.fips <- as_fips_code(state) - # if (age == F & sex == F) { - # num <- ifelse(3:10 != 10, paste("0", 3:10, sep = ""), "10") - # vars <- paste("P0050", num, sep = "") - # } - - # assign variable values based on the year of the census data - if (as.character(year) != "2020"){ - vars_ <- c( - pop_white = 'P005003', pop_black = 'P005004', - pop_aian = 'P005005', pop_asian = 'P005006', - pop_nhpi = 'P005007', pop_other = 'P005008', - pop_two = 'P005009', pop_hisp = 'P005010' - ) - } else { - vars_ <- c( - pop_white = 'P2_005N', pop_black = 'P2_006N', - pop_aian = 'P2_007N', pop_asian = 'P2_008N', - pop_nhpi = 'P2_009N', pop_other = 'P2_010N', - pop_two = 'P2_011N', pop_hisp = 'P2_002N' - ) - } - - if (age == F & sex == T) { - eth.let <- c("I", "B", "H", "D", "E", "F", "C") - num <- as.character(c("01", "02", "26")) - for (e in 1:length(eth.let)) { - vars_ <- c(vars_, paste("P012", eth.let[e], "0", num, sep = "")) - } - } - - if (age == T & sex == F) { - eth.let <- c("I", "B", "H", "D", "E", "F", "C") - num <- as.character(c(c("01", "03", "04", "05", "06", "07", "08", "09"), seq(10, 25), seq(27, 49))) - for (e in 1:length(eth.let)) { - vars_ <- c(vars_, paste("P012", eth.let[e], "0", num, sep = "")) - } - } - - if (age == T & sex == T) { - eth.let <- c("I", "B", "H", "D", "E", "F", "C") - num <- as.character(c(c("01", "03", "04", "05", "06", "07", "08", "09"), seq(10, 25), seq(27, 49))) - for (e in 1:length(eth.let)) { - vars_ <- c(vars_, paste("P012", eth.let[e], "0", num, sep = "")) - } - } - - # set the census data url links - census_data_url <- switch( - as.character(year), - "2010" = "https://api.census.gov/data/2010/dec/sf1?", - "2020" = "https://api.census.gov/data/2020/dec/pl?" - ) + vars <- census_geo_api_names(year = year, age = age, sex = sex) + census_data_url <- census_geo_api_url(year = year) if (geo == "place") { geo.merge <- c("state", "place") region <- paste("for=place:*&in=state:", state.fips, sep = "") - census <- get_census_api(census_data_url, key = key, var.names = vars_, region = region, retry) + census <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region, retry) } if (geo == "county") { @@ -154,7 +104,7 @@ census_geo_api <- function( region <- paste("for=county:",counties_paste,"&in=state:", state.fips, sep = "") } - census <- get_census_api(census_data_url, key = key, var.names = vars_, region = region, retry) + census <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region, retry) } if (geo == "tract") { @@ -168,7 +118,7 @@ census_geo_api <- function( region_county <- paste("for=county:",counties_paste,"&in=state:", state.fips, sep = "") } - county_df <- get_census_api(census_data_url, key = key, var.names = vars_, region = region_county, retry) + county_df <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region_county, retry) if(is.null(counties)) { county_list <- county_df$county @@ -180,7 +130,7 @@ census_geo_api <- function( census_tracts <- furrr::future_map_dfr(seq_along(county_list), function(county) { message(paste("County ", county, " of ", length(county_list), ": ", county_list[county], sep = "")) region_county <- paste("for=tract:*&in=state:", state.fips, "+county:", county_list[county], sep = "") - get_census_api(data_url = census_data_url, key = key, var.names = vars_, region = region_county, retry) + get_census_api(data_url = census_data_url, key = key, var.names = unlist(vars), region = region_county, retry) }) census <- rbind(census, census_tracts) @@ -200,7 +150,7 @@ census_geo_api <- function( region_county <- paste("for=county:",counties_paste,"&in=state:", state.fips, sep = "") } - county_df <- get_census_api(census_data_url, key = key, var.names = vars_, region = region_county, retry) + county_df <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region_county, retry) if(is.null(counties)) { county_list <- county_df$county @@ -220,7 +170,7 @@ census_geo_api <- function( blockgroup <- paste("for=block+group:*&in=state:", state.fips, "+county:", county_list[county], sep = "") # message(region_tract) - blockgroup_df <- get_census_api(census_data_url, key = key, var.names = vars_, region = blockgroup, retry) + blockgroup_df <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = blockgroup, retry) names(blockgroup_df)[4] <- "block_group" # Fix name, it comes in with a space from api. blockgroup_df } @@ -245,7 +195,7 @@ census_geo_api <- function( region_county <- paste("for=county:",counties_paste,"&in=state:", state.fips, sep = "") } - county_df <- get_census_api(census_data_url, key = key, var.names = vars_, region = region_county, retry) + county_df <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region_county, retry) if(is.null(counties)) { county_list <- county_df$county @@ -264,14 +214,14 @@ census_geo_api <- function( region_tract <- paste("for=tract:*&in=state:", state.fips, "+county:", county_list[county], sep = "") # message(region_tract) - tract_df <- get_census_api(census_data_url, key = key, var.names = vars_, region = region_tract, retry) + tract_df <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region_tract, retry) tract_list <- tract_df$tract furrr::future_map_dfr(1:length(tract_list), function(tract) { message(paste("Tract ", tract, " of ", length(tract_list), ": ", tract_list[tract], sep = "")) region_block <- paste("for=block:*&in=state:", state.fips, "+county:", county_list[county], "+tract:", tract_list[tract], sep = "") - get_census_api(census_data_url, key = key, var.names = vars_, region = region_block, retry) + get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region_block, retry) }) } ) @@ -284,97 +234,27 @@ census_geo_api <- function( } } - census$state <- state + census <- dplyr::mutate(census, state = as_state_abbreviation(state)) - if (age == F & sex == F) { - - ## Calculate Pr(Geolocation | Race) - census$r_whi <- census[, vars_["pop_white"]] / sum(census[, vars_["pop_white"]]) #Pr(Geo|White) - census$r_bla <- census[, vars_["pop_black"]] / sum(census[, vars_["pop_black"]]) #Pr(Geo|Black) - census$r_his <- census[, vars_["pop_hisp"]] / sum(census[, vars_["pop_hisp"]]) #Pr(Geo|Latino) - census$r_asi <- (census[, vars_["pop_asian"]] + census[, vars_["pop_nhpi"]]) / (sum(census[, vars_["pop_asian"]]) + sum(census[, vars_["pop_nhpi"]])) #Pr(Geo | Asian or NH/PI) - census$r_oth <- (census[, vars_["pop_aian"]] + census[, vars_["pop_other"]] + census[, vars_["pop_two"]]) / (sum(census[, vars_["pop_aian"]]) + sum(census[, vars_["pop_other"]]) + sum(census[, vars_["pop_two"]])) #Pr(Geo | AI/AN, Other, or Mixed) - - } + r_columns <- purrr::map(vars, function(vars) rowSums(census[vars])) - if (age == F & sex == T) { - - ## Calculate Pr(Geolocation, Sex | Race) - eth.cen <- c("whi", "bla", "his", "asi", "oth") - eth.let <- c("I", "B", "H", "D", "F") - - for (i in 1:length(eth.cen)) { - if (i != 4 & i != 5) { - census[paste("r_mal", eth.cen[i], sep = "_")] <- census[paste("P012", eth.let[i], "002", sep = "")] / sum(census[paste("P012", eth.let[i], "001", sep = "")]) - census[paste("r_fem", eth.cen[i], sep = "_")] <- census[paste("P012", eth.let[i], "026", sep = "")] / sum(census[paste("P012", eth.let[i], "001", sep = "")]) - } - if (i == 4) { - ## Combine Asian and Native Hawaiian/Pacific Islander - census[paste("r_mal", eth.cen[i], sep = "_")] <- (census$P012D002 + census$P012E002) / sum(census$P012D001 + census$P012E001) - census[paste("r_fem", eth.cen[i], sep = "_")] <- (census$P012D026 + census$P012E026) / sum(census$P012D001 + census$P012E001) + census <- dplyr::bind_cols(census, r_columns) + census <- dplyr::group_by(census, dplyr::across(dplyr::any_of("state"))) + census <- dplyr::mutate( + census, + dplyr::across( + # Divide all r_columns by the total population of the corresponding race + dplyr::all_of(names(r_columns)), + function(x) { + x / sum( + dplyr::pick( + sub("^.+_(.{3})$", "r_\\1", dplyr::cur_column(), perl = TRUE) + ) + ) } - if (i == 5) { - ## Combine American India/Alaska Native and Other - census[paste("r_mal", eth.cen[i], sep = "_")] <- (census$P012C002 + census$P012F002) / sum(census$P012C001 + census$P012F001) - census[paste("r_fem", eth.cen[i], sep = "_")] <- (census$P012C026 + census$P012F026) / sum(census$P012C001 + census$P012F001) - } - } - } - - if (age == T & sex == F) { - - ## Calculate Pr(Geolocation, Age Category | Race) - eth.cen <- c("whi", "bla", "his", "asi", "oth") - eth.let <- c("I", "B", "H", "D", "F") - age.cat <- c(seq(1, 23), seq(1, 23)) - age.cen <- as.character(c(c("03", "04", "05", "06", "07", "08", "09"), seq(10, 25), seq(27, 49))) - - for (i in 1:length(eth.cen)) { - for (j in 1:23) { - if (i != 4 & i != 5) { - census[paste("r", age.cat[j], eth.cen[i], sep = "_")] <- (census[paste("P012", eth.let[i], "0", age.cen[j], sep = "")] + census[paste("P012", eth.let[i], "0", age.cen[j + 23], sep = "")]) / sum(census[paste("P012", eth.let[i], "001", sep = "")]) - } - if (i == 4) { - ## Combine Asian and Native Hawaiian/Pacific Islander - census[paste("r", age.cat[j], eth.cen[i], sep = "_")] <- (census[paste("P012D0", age.cen[j], sep = "")] + census[paste("P012D0", age.cen[j + 23], sep = "")] + census[paste("P012E0", age.cen[j], sep = "")] + census[paste("P012E0", age.cen[j + 23], sep = "")]) / sum(census$P012D001 + census$P012E001) - } - if (i == 5) { - ## Combine American India/Alaska Native and Other - census[paste("r", age.cat[j], eth.cen[i], sep = "_")] <- (census[paste("P012C0", age.cen[j], sep = "")] + census[paste("P012C0", age.cen[j + 23], sep = "")] + census[paste("P012F0", age.cen[j], sep = "")] + census[paste("P012F0", age.cen[j + 23], sep = "")]) / sum(census$P012C001 + census$P012F001) - } - } - } - } + ) + ) + census <- dplyr::ungroup(census) - if (age == T & sex == T) { - - ## Calculate Pr(Geolocation, Sex, Age Category | Race) - eth.cen <- c("whi", "bla", "his", "asi", "oth") - eth.let <- c("I", "B", "H", "D", "F") - sex.let <- c("mal", "fem") - age.cat <- c(seq(1, 23), seq(1, 23)) - age.cen <- as.character(c(c("03", "04", "05", "06", "07", "08", "09"), seq(10, 25), seq(27, 49))) - - for (i in 1:length(eth.cen)) { - for (k in 1:length(sex.let)) { - for (j in 1:23) { - if (k == 2) { - j <- j + 23 - } - if (i != 4 & i != 5) { - census[paste("r", sex.let[k], age.cat[j], eth.cen[i], sep = "_")] <- census[paste("P012", eth.let[i], "0", age.cen[j], sep = "")] / sum(census[paste("P012", eth.let[i], "001", sep = "")]) - } - if (i == 4) { - ## Combine Asian and Native Hawaiian/Pacific Islander - census[paste("r", sex.let[k], age.cat[j], eth.cen[i], sep = "_")] <- (census[paste("P012D0", age.cen[j], sep = "")] + census[paste("P012E0", age.cen[j], sep = "")]) / sum(census$P012D001 + census$P012E001) - } - if (i == 5) { - ## Combine American India/Alaska Native and Other - census[paste("r", sex.let[k], age.cat[j], eth.cen[i], sep = "_")] <- (census[paste("P012C0", age.cen[j], sep = "")] + census[paste("P012F0", age.cen[j], sep = "")]) / sum(census$P012C001 + census$P012F001) - } - } - } - } - } - return(census) + census } From bc8fa3c2256579e81dfe92851977f59768293c3c Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 19:56:10 -0800 Subject: [PATCH 47/90] refactor(census_geo_api): don't create geographic variables that are never used --- R/census_geo_api.R | 26 +++----------------------- 1 file changed, 3 insertions(+), 23 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index d4055e4..89140ee 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -89,14 +89,11 @@ census_geo_api <- function( census_data_url <- census_geo_api_url(year = year) if (geo == "place") { - geo.merge <- c("state", "place") region <- paste("for=place:*&in=state:", state.fips, sep = "") census <- get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region, retry) } if (geo == "county") { - geo.merge <- c("state", "county") - if (is.null(counties)) { region <- paste("for=county:*&in=state:", state.fips, sep = "") } else { @@ -108,9 +105,6 @@ census_geo_api <- function( } if (geo == "tract") { - - geo.merge <- c("state", "county", "tract") - if (is.null(counties)) { region_county <- paste("for=county:*&in=state:", state.fips, sep = "") } else { @@ -127,22 +121,17 @@ census_geo_api <- function( } if(length(county_list) > 0) { - census_tracts <- furrr::future_map_dfr(seq_along(county_list), function(county) { + census <- furrr::future_map_dfr(seq_along(county_list), function(county) { message(paste("County ", county, " of ", length(county_list), ": ", county_list[county], sep = "")) region_county <- paste("for=tract:*&in=state:", state.fips, "+county:", county_list[county], sep = "") get_census_api(data_url = census_data_url, key = key, var.names = unlist(vars), region = region_county, retry) }) - - census <- rbind(census, census_tracts) - rm(census_tracts) } else { message('There were no intersecting counties in your voter.file data (tract)') } } if (geo == "block_group") { - geo.merge <- c("state", "county", "tract", "block_group") - if (is.null(counties)) { region_county <- paste("for=county:*&in=state:", state.fips, sep = "") } else { @@ -161,7 +150,7 @@ census_geo_api <- function( if(length(county_list) > 0) { message('Running block_group by county...') - census_blockgroup <- purrr::map_dfr( + census <- purrr::map_dfr( 1:length(county_list), function(county) { # too verbose, commenting out @@ -176,18 +165,12 @@ census_geo_api <- function( } ) message("\n") # new line for progress bar - - census <- rbind(census, census_blockgroup) - rm(census_blockgroup) } else { message('There were no intersecting counties in your voter.file data (block)') } } if (geo == "block") { - - geo.merge <- c("state", "county", "tract", "block") - if (is.null(counties)) { region_county <- paste("for=county:*&in=state:", state.fips, sep = "") } else { @@ -206,7 +189,7 @@ census_geo_api <- function( if(length(county_list) > 0) { message('Running block by county...') - census_blocks <- purrr::map_dfr( + census <- purrr::map_dfr( 1:length(county_list), function(county) { # too verbose, commenting out @@ -226,9 +209,6 @@ census_geo_api <- function( } ) message("\n") # new line for progress bar - - census <- rbind(census, census_blocks) - rm(census_blocks) } else { message('There were no intersecting counties in your voter.file data (block)') } From f138b22e49dcf287c1918e45b57995b3943fb3bc Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 20:08:05 -0800 Subject: [PATCH 48/90] refactor(census_geo_api): move main logic from `census_geo_api_zcta()` into `census_geo_api()`; turn `census_geo_api_zcta()` into a helper function --- R/census_geo_api.R | 24 +++++++-------- R/census_geo_api_zcta.R | 62 +++++++------------------------------- man/census_geo_api_zcta.Rd | 61 ------------------------------------- 3 files changed, 22 insertions(+), 125 deletions(-) delete mode 100644 man/census_geo_api_zcta.Rd diff --git a/R/census_geo_api.R b/R/census_geo_api.R index 89140ee..2c505e7 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -63,19 +63,6 @@ census_geo_api <- function( geo <- tolower(geo) geo <- rlang::arg_match(geo) - if (geo == "zcta") { - return( - census_geo_api_zcta( - state = state, - age = age, - sex = sex, - year = year, - retry = retry, - key = key - ) - ) - } - year <- as.character(year) year <- rlang::arg_match(year) @@ -214,6 +201,17 @@ census_geo_api <- function( } } + if (geo == "zcta") { + census <- census_geo_api_zcta( + census_data_url = census_data_url, + key = key, + vars = vars, + state = state, + counties = counties, + retry = retry + ) + } + census <- dplyr::mutate(census, state = as_state_abbreviation(state)) r_columns <- purrr::map(vars, function(vars) rowSums(census[vars])) diff --git a/R/census_geo_api_zcta.R b/R/census_geo_api_zcta.R index 93fb0b0..a82dce1 100644 --- a/R/census_geo_api_zcta.R +++ b/R/census_geo_api_zcta.R @@ -1,36 +1,17 @@ -#' Census download function for state-ZCTA-level data -#' -#' @inheritParams census_geo_api -#' @param ... These dots are for future extensions and must be empty. -#' -#' @return A [data.frame] with columns -#' `state`, `zcta`, `r_whi`, `r_bla`, `r_his`, `r_asi`, and `r_oth`. -#' -#' @examplesIf nzchar(Sys.getenv("CENSUS_API_KEY")) -#' \dontrun{census_geo_api_zcta(state = c("DE", "NJ")} -#' \dontrun{census_geo_api_zcta(state = "FL", age = TRUE, sex = TRUE)} -#' \dontrun{census_geo_api_zcta(state = "MA", age = FALSE, sex = FALSE, year = "2020")} -#' -#' @keywords internal census_geo_api_zcta <- function( + census_data_url, + key, + vars, state, - ..., - age = FALSE, - sex = FALSE, - year = c("2020", "2010", "2000"), - retry = 3, - key = Sys.getenv("CENSUS_API_KEY") + counties, + retry ) { - # Validate arguments - rlang::check_dots_empty() - validate_key(key) - assert_boolean(age) - assert_boolean(sex) - year <- as.character(year) - year <- rlang::arg_match(year) - - census_data_url <- census_geo_api_url(year = year) - vars <- census_geo_api_names(year = year, age = age, sex = sex) + if (!is.null(counties)) { + cli::cli_abort( + '{.arg counties} must be {.code NULL} when {.code geo = "zcta"}, + because ZCTA-level census data split by county is not available.' + ) + } region <- paste0( "for=zip%20code%20tabulation%20area%20(or%20part):*&in=state:", @@ -45,28 +26,7 @@ census_geo_api_zcta <- function( retry ) - census <- dplyr::mutate(census, state = as_state_abbreviation(state)) names(census)[[2]] <- "zcta" - r_columns <- purrr::map(vars, function(vars) rowSums(census[vars])) - - census <- dplyr::bind_cols(census, r_columns) - census <- dplyr::group_by(census, dplyr::across(dplyr::any_of("state"))) - census <- dplyr::mutate( - census, - dplyr::across( - # Divide all r_columns by the total population of the corresponding race - dplyr::all_of(names(r_columns)), - function(x) { - x / sum( - dplyr::pick( - sub("^.+_(.{3})$", "r_\\1", dplyr::cur_column(), perl = TRUE) - ) - ) - } - ) - ) - census <- dplyr::ungroup(census) - census } diff --git a/man/census_geo_api_zcta.Rd b/man/census_geo_api_zcta.Rd deleted file mode 100644 index ddcf6bd..0000000 --- a/man/census_geo_api_zcta.Rd +++ /dev/null @@ -1,61 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/census_geo_api_zcta.R -\name{census_geo_api_zcta} -\alias{census_geo_api_zcta} -\title{Census download function for state-ZCTA-level data} -\usage{ -census_geo_api_zcta( - state, - ..., - age = FALSE, - sex = FALSE, - year = c("2020", "2010", "2000"), - retry = 3, - key = Sys.getenv("CENSUS_API_KEY") -) -} -\arguments{ -\item{state}{A required character object specifying which state to extract Census data for, -e.g., \code{"NJ"}.} - -\item{...}{These dots are for future extensions and must be empty.} - -\item{age}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on -age or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). -If \code{TRUE}, function will return Pr(Geolocation, Age | Race). -If \code{\var{sex}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} - -\item{sex}{A \code{TRUE}/\code{FALSE} object indicating whether to condition on -sex or not. If \code{FALSE} (default), function will return Pr(Geolocation | Race). -If \code{TRUE}, function will return Pr(Geolocation, Sex | Race). -If \code{\var{age}} is also \code{TRUE}, function will return Pr(Geolocation, Age, Sex | Race).} - -\item{year}{A character object specifying the year of U.S. Census data to be downloaded. -Use \code{"2010"}, or \code{"2020"}. Default is \code{"2020"}. -Warning: 2020 U.S. Census data is downloaded only when \code{\var{age}} and -\code{\var{sex}} are both \code{FALSE}.} - -\item{retry}{The number of retries at the census website if network interruption occurs.} - -\item{key}{A character string containing a valid Census API key, -which can be requested from the -\href{https://api.census.gov/data/key_signup.html}{U.S. Census API key signup page}. - -By default, attempts to find a census key stored in an -\link[=Sys.getenv]{environment variable} named \code{CENSUS_API_KEY}.} -} -\value{ -A \link{data.frame} with columns -\code{state}, \code{zcta}, \code{r_whi}, \code{r_bla}, \code{r_his}, \code{r_asi}, and \code{r_oth}. -} -\description{ -Census download function for state-ZCTA-level data -} -\examples{ -\dontshow{if (nzchar(Sys.getenv("CENSUS_API_KEY"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} -\dontrun{census_geo_api_zcta(state = c("DE", "NJ")} -\dontrun{census_geo_api_zcta(state = "FL", age = TRUE, sex = TRUE)} -\dontrun{census_geo_api_zcta(state = "MA", age = FALSE, sex = FALSE, year = "2020")} -\dontshow{\}) # examplesIf} -} -\keyword{internal} From 0121c3797012b7ee4a236fff19a3db5e2dd4071b Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 15:43:09 -0800 Subject: [PATCH 49/90] test(census_geo_api): add tests of `census_geo_api()` --- tests/testthat/_snaps/census_geo_api.md | 359 ++++++++++++++++++++++++ tests/testthat/test-census_geo_api.R | 20 ++ 2 files changed, 379 insertions(+) create mode 100644 tests/testthat/_snaps/census_geo_api.md create mode 100644 tests/testthat/test-census_geo_api.R diff --git a/tests/testthat/_snaps/census_geo_api.md b/tests/testthat/_snaps/census_geo_api.md new file mode 100644 index 0000000..faf5502 --- /dev/null +++ b/tests/testthat/_snaps/census_geo_api.md @@ -0,0 +1,359 @@ +# snapshot + + structure(list(state = c("DE", "DE", "DE"), county = c("003", + "005", "001"), P12I_001N = c(303265, 171741, 104845), P12B_001N = c(146544, + 25357, 46998), P12H_001N = c(63516, 26793, 13981), P12D_001N = c(35200, + 3070, 4429), P12E_001N = c(175, 112, 125), P12C_001N = c(2156, + 1843, 1149), P12F_001N = c(28714, 14137, 5658), P12G_001N = c(43699, + 17012, 15807), r_whi = c(0.523005047848499, 0.296181260358264, + 0.180813691793237), r_bla = c(0.669459431061814, 0.115838811506677, + 0.21470175743151), r_his = c(0.609032505513472, 0.256908620193691, + 0.134058874292837), r_asi = c(0.820556238547007, 0.0738094685811046, + 0.105634292871889), r_oth = c(0.572836566160937, 0.253443441521029, + 0.173719992318033)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, + -3L)) + +--- + + structure(list(state = c("DE", "DE", "DE"), county = c("001", + "005", "003"), P012I001 = c(105891, 149025, 331836), P012B001 = c(38913, + 25115, 127786), P012H001 = c(9346, 16954, 46921), P012D001 = c(3306, + 1943, 23300), P012E001 = c(91, 150, 159), P012C001 = c(1043, + 1564, 1574), P012F001 = c(3235, 8169, 19115), P012G001 = c(5723, + 4541, 13590), r_whi = c(0.180469772578534, 0.253982943390052, + 0.565547284031414), r_bla = c(0.202868403766148, 0.130934134109085, + 0.666197462124767), r_his = c(0.127640977315251, 0.23154559484301, + 0.64081342784174), r_asi = c(0.117344295139728, 0.0722995612974541, + 0.810356143562817), r_oth = c(0.170799603784541, 0.243774976944359, + 0.5854254192711)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, + -3L)) + +--- + + structure(list(state = c("DE", "DE", "DE"), county = c("001", + "005", "003"), P012I001 = c(105891, 149025, 331836), P012B001 = c(38913, + 25115, 127786), P012H001 = c(9346, 16954, 46921), P012D001 = c(3306, + 1943, 23300), P012E001 = c(91, 150, 159), P012C001 = c(1043, + 1564, 1574), P012F001 = c(3235, 8169, 19115), P012G001 = c(5723, + 4541, 13590), P012I002 = c(51574, 72032, 160694), P012B002 = c(18194, + 12160, 60237), P012H002 = c(4579, 9154, 24193), P012D002 = c(1355, + 889, 11467), P012E002 = c(44, 83, 71), P012C002 = c(509, 828, + 767), P012F002 = c(1660, 4492, 10111), P012G002 = c(2705, 2207, + 6433), P012I026 = c(54317, 76993, 171142), P012B026 = c(20719, + 12955, 67549), P012H026 = c(4767, 7800, 22728), P012D026 = c(1951, + 1054, 11833), P012E026 = c(47, 67, 88), P012C026 = c(534, 736, + 807), P012F026 = c(1575, 3677, 9004), P012G026 = c(3018, 2334, + 7157), r_whi = c(0.180469772578534, 0.253982943390052, 0.565547284031414 + ), r_bla = c(0.202868403766148, 0.130934134109085, 0.666197462124767 + ), r_his = c(0.127640977315251, 0.23154559484301, 0.64081342784174 + ), r_asi = c(0.117344295139728, 0.0722995612974541, 0.810356143562817 + ), r_oth = c(0.170799603784541, 0.243774976944359, 0.5854254192711 + ), r_mal_whi = c(0.0878974421902269, 0.122763961605585, 0.273870391579407 + ), r_mal_bla = c(0.0948523048369775, 0.0633947469944842, 0.314038599893647 + ), r_mal_his = c(0.0625367039510523, 0.125018778765655, 0.330410674533262 + ), r_mal_asi = c(0.0483263670593112, 0.0335762893364192, 0.398562990086013 + ), r_mal_oth = c(0.0832394029442907, 0.128548006967927, 0.295641629948424 + ), r_fem_whi = c(0.0925723303883072, 0.131218981784468, 0.291676892452007 + ), r_fem_bla = c(0.108016098929171, 0.0675393871146006, 0.35215886223112 + ), r_fem_his = c(0.0651042733641988, 0.106526816077355, 0.310402753308477 + ), r_fem_asi = c(0.0690179280804173, 0.0387232719610349, 0.411793153476804 + ), r_fem_oth = c(0.08756020084025, 0.115226969976432, 0.289783789322677 + )), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, + -3L)) + +--- + + structure(list(state = c("DE", "DE", "DE"), county = c("001", + "005", "003"), P012I001 = c(105891, 149025, 331836), P012B001 = c(38913, + 25115, 127786), P012H001 = c(9346, 16954, 46921), P012D001 = c(3306, + 1943, 23300), P012E001 = c(91, 150, 159), P012C001 = c(1043, + 1564, 1574), P012F001 = c(3235, 8169, 19115), P012G001 = c(5723, + 4541, 13590), P012I003 = c(3159, 3151, 7793), P012I027 = c(2913, + 3035, 7497), P012B003 = c(1488, 1030, 4669), P012B027 = c(1422, + 991, 4637), P012H003 = c(600, 1253, 2735), P012H027 = c(616, + 1198, 2630), P012D003 = c(86, 51, 917), P012E003 = c(1, 11, 6 + ), P012D027 = c(104, 54, 872), P012E027 = c(3, 4, 4), P012C003 = c(26, + 78, 64), P012F003 = c(178, 587, 1029), P012G003 = c(538, 443, + 1203), P012C027 = c(22, 73, 62), P012F027 = c(195, 570, 1008), + P012G027 = c(541, 493, 1252), P012I004 = c(3265, 3384, 8494 + ), P012I028 = c(3030, 3125, 8084), P012B004 = c(1484, 952, + 4917), P012B028 = c(1548, 966, 4743), P012H004 = c(525, 986, + 2529), P012H028 = c(504, 911, 2426), P012D004 = c(111, 74, + 796), P012E004 = c(6, 10, 5), P012D028 = c(118, 74, 863), + P012E028 = c(3, 6, 10), P012C004 = c(24, 68, 60), P012F004 = c(163, + 475, 1012), P012G004 = c(458, 355, 1030), P012C028 = c(25, + 48, 64), P012F028 = c(156, 450, 947), P012G028 = c(488, 374, + 1031), P012I005 = c(3292, 3462, 9316), P012I029 = c(3044, + 3367, 8870), P012B005 = c(1584, 989, 5262), P012B029 = c(1522, + 920, 5015), P012H005 = c(381, 666, 2147), P012H029 = c(440, + 695, 2124), P012D005 = c(106, 54, 672), P012E005 = c(5, 1, + 10), P012D029 = c(93, 85, 645), P012E029 = c(3, 3, 9), P012C005 = c(34, + 42, 56), P012F005 = c(131, 316, 845), P012G005 = c(393, 283, + 769), P012C029 = c(31, 38, 70), P012F029 = c(124, 331, 850 + ), P012G029 = c(413, 283, 853), P012I006 = c(2040, 2352, + 6242), P012I030 = c(1942, 2303, 5956), P012B006 = c(965, + 652, 3416), P012B030 = c(974, 653, 3273), P012H006 = c(271, + 411, 1290), P012H030 = c(243, 332, 1172), P012D006 = c(59, + 41, 399), P012E006 = c(2, 1, 2), P012D030 = c(52, 49, 362 + ), P012E030 = c(3, 3, 8), P012C006 = c(28, 34, 50), P012F006 = c(79, + 185, 544), P012G006 = c(185, 154, 453), P012C030 = c(20, + 30, 49), P012F030 = c(86, 163, 495), P012G030 = c(183, 161, + 404), P012I007 = c(1389, 1421, 5291), P012I031 = c(1311, + 1311, 5727), P012B007 = c(1018, 447, 2272), P012B031 = c(1174, + 382, 2093), P012H007 = c(174, 276, 945), P012H031 = c(207, + 194, 944), P012D007 = c(49, 18, 362), P012E007 = c(0, 1, + 4), P012D031 = c(29, 26, 354), P012E031 = c(0, 4, 0), P012C007 = c(14, + 20, 28), P012F007 = c(71, 147, 362), P012G007 = c(120, 81, + 279), P012C031 = c(16, 17, 26), P012F031 = c(66, 92, 344), + P012G031 = c(150, 79, 289), P012I008 = c(678, 708, 2730), + P012I032 = c(643, 641, 3067), P012B008 = c(463, 194, 1040 + ), P012B032 = c(575, 170, 1003), P012H008 = c(101, 163, 508 + ), P012H032 = c(77, 99, 457), P012D008 = c(13, 12, 217), + P012E008 = c(3, 1, 2), P012D032 = c(24, 9, 173), P012E032 = c(3, + 1, 2), P012C008 = c(8, 13, 14), P012F008 = c(37, 88, 215), + P012G008 = c(54, 19, 126), P012C032 = c(4, 10, 13), P012F032 = c(25, + 40, 181), P012G032 = c(42, 23, 140), P012I009 = c(738, 577, + 2685), P012I033 = c(735, 589, 2939), P012B009 = c(419, 170, + 934), P012B033 = c(463, 170, 906), P012H009 = c(91, 185, + 485), P012H033 = c(97, 124, 429), P012D009 = c(15, 11, 139 + ), P012E009 = c(1, 2, 3), P012D033 = c(16, 6, 185), P012E033 = c(1, + 1, 3), P012C009 = c(5, 9, 10), P012F009 = c(36, 102, 188), + P012G009 = c(41, 34, 102), P012C033 = c(13, 13, 16), P012F033 = c(35, + 64, 172), P012G033 = c(59, 30, 131), P012I010 = c(2122, 1951, + 6605), P012I034 = c(2010, 1970, 6403), P012B010 = c(945, + 500, 2657), P012B034 = c(955, 559, 2756), P012H010 = c(302, + 623, 1397), P012H034 = c(260, 482, 1156), P012D010 = c(74, + 35, 482), P012E010 = c(4, 5, 5), P012D034 = c(67, 33, 510 + ), P012E034 = c(3, 2, 6), P012C010 = c(25, 38, 27), P012F010 = c(114, + 329, 627), P012G010 = c(115, 72, 236), P012C034 = c(19, 34, + 27), P012F034 = c(99, 252, 466), P012G034 = c(136, 79, 311 + ), P012I011 = c(3277, 3402, 10187), P012I035 = c(3380, 3429, + 10014), P012B011 = c(1158, 837, 4120), P012B035 = c(1340, + 842, 4478), P012H011 = c(431, 1169, 2415), P012H035 = c(426, + 835, 2092), P012D011 = c(87, 49, 1151), P012E011 = c(5, 15, + 6), P012D035 = c(125, 64, 1355), P012E035 = c(2, 11, 6), + P012C011 = c(28, 64, 50), P012F011 = c(186, 638, 1181), P012G011 = c(167, + 129, 370), P012C035 = c(34, 57, 55), P012F035 = c(146, 413, + 910), P012G035 = c(187, 147, 465), P012I012 = c(2963, 3214, + 9253), P012I036 = c(3226, 3168, 9400), P012B012 = c(922, + 699, 4038), P012B036 = c(1228, 767, 4581), P012H012 = c(366, + 930, 2184), P012H036 = c(345, 777, 2003), P012D012 = c(91, + 71, 1283), P012E012 = c(1, 4, 8), P012D036 = c(141, 68, 1221 + ), P012E036 = c(5, 5, 4), P012C012 = c(37, 59, 49), P012F012 = c(161, + 461, 1012), P012G012 = c(121, 119, 386), P012C036 = c(28, + 48, 61), P012F036 = c(147, 385, 892), P012G036 = c(150, 113, + 420), P012I013 = c(3048, 3523, 9894), P012I037 = c(3327, + 3718, 9876), P012B013 = c(1045, 707, 4174), P012B037 = c(1282, + 712, 4821), P012H013 = c(297, 767, 1866), P012H037 = c(332, + 657, 1784), P012D013 = c(123, 70, 1173), P012E013 = c(2, + 8, 3), P012D037 = c(163, 93, 1091), P012E037 = c(2, 9, 10 + ), P012C013 = c(32, 48, 66), P012F013 = c(129, 431, 837), + P012G013 = c(110, 82, 320), P012C037 = c(37, 64, 69), P012F037 = c(127, + 337, 764), P012G037 = c(132, 90, 380), P012I014 = c(3603, + 4282, 11235), P012I038 = c(3657, 4435, 11648), P012B014 = c(1142, + 842, 4436), P012B038 = c(1347, 937, 5499), P012H014 = c(240, + 565, 1637), P012H038 = c(265, 448, 1581), P012D014 = c(126, + 83, 864), P012E014 = c(3, 4, 4), P012D038 = c(171, 86, 896 + ), P012E038 = c(2, 2, 5), P012C014 = c(28, 50, 52), P012F014 = c(83, + 288, 722), P012G014 = c(85, 90, 297), P012C038 = c(48, 46, + 53), P012F038 = c(95, 202, 658), P012G038 = c(119, 72, 350 + ), P012I015 = c(4108, 5377, 13313), P012I039 = c(4280, 5636, + 13731), P012B015 = c(1261, 918, 4578), P012B039 = c(1413, + 973, 5489), P012H015 = c(229, 353, 1290), P012H039 = c(245, + 311, 1190), P012D015 = c(106, 68, 752), P012E015 = c(1, 4, + 6), P012D039 = c(178, 100, 806), P012E039 = c(4, 3, 8), P012C015 = c(42, + 76, 64), P012F015 = c(105, 161, 548), P012G015 = c(70, 92, + 252), P012C039 = c(40, 48, 62), P012F039 = c(94, 137, 470 + ), P012G039 = c(105, 101, 313), P012I016 = c(3831, 5618, + 13725), P012I040 = c(4119, 6144, 14291), P012B016 = c(1073, + 875, 4074), P012B040 = c(1320, 982, 4946), P012H016 = c(172, + 259, 957), P012H040 = c(193, 212, 816), P012D016 = c(81, + 65, 622), P012E016 = c(6, 4, 2), P012D040 = c(137, 80, 646 + ), P012E040 = c(9, 4, 2), P012C016 = c(47, 61, 57), P012F016 = c(64, + 98, 379), P012G016 = c(79, 68, 180), P012C040 = c(46, 36, + 60), P012F040 = c(65, 85, 285), P012G040 = c(90, 64, 252), + P012I017 = c(3198, 5713, 11775), P012I041 = c(3583, 6570, + 12620), P012B017 = c(913, 703, 3013), P012B041 = c(1161, + 801, 3955), P012H017 = c(129, 180, 679), P012H041 = c(113, + 161, 626), P012D017 = c(51, 44, 490), P012E017 = c(1, 3, + 3), P012D041 = c(133, 60, 552), P012E041 = c(2, 1, 3), P012C017 = c(37, + 49, 50), P012F017 = c(45, 78, 247), P012G017 = c(45, 40, + 135), P012C041 = c(45, 41, 39), P012F041 = c(27, 66, 201), + P012G041 = c(56, 52, 200), P012I018 = c(1273, 2551, 4128), + P012I042 = c(1382, 2830, 4509), P012B018 = c(320, 227, 1110 + ), P012B042 = c(422, 275, 1377), P012H018 = c(42, 59, 202 + ), P012H042 = c(61, 52, 200), P012D018 = c(28, 20, 165), + P012E018 = c(0, 1, 0), P012D042 = c(49, 27, 182), P012E042 = c(0, + 3, 2), P012C018 = c(13, 11, 7), P012F018 = c(11, 23, 65), + P012G018 = c(18, 20, 46), P012C042 = c(18, 14, 9), P012F042 = c(13, + 22, 57), P012G042 = c(21, 21, 55), P012I019 = c(1925, 4078, + 5826), P012I043 = c(2108, 4596, 6378), P012B019 = c(450, + 319, 1340), P012B043 = c(550, 350, 1776), P012H019 = c(51, + 71, 233), P012H043 = c(79, 68, 272), P012D019 = c(29, 29, + 217), P012E019 = c(0, 1, 2), P012D043 = c(75, 29, 289), P012E043 = c(0, + 2, 2), P012C019 = c(19, 24, 15), P012F019 = c(21, 27, 76), + P012G019 = c(30, 22, 63), P012C043 = c(20, 21, 15), P012F043 = c(15, + 18, 83), P012G043 = c(28, 34, 79), P012I020 = c(1123, 2492, + 3106), P012I044 = c(1247, 2728, 3446), P012B020 = c(230, + 160, 721), P012B044 = c(296, 208, 933), P012H020 = c(17, + 42, 111), P012H044 = c(31, 33, 140), P012D020 = c(24, 7, + 116), P012E020 = c(0, 2, 0), P012D044 = c(50, 28, 144), P012E044 = c(0, + 0, 0), P012C020 = c(9, 13, 7), P012F020 = c(2, 14, 39), P012G020 = c(21, + 16, 37), P012C044 = c(6, 4, 7), P012F044 = c(7, 6, 46), P012G044 = c(19, + 13, 26), P012I021 = c(1507, 3532, 4076), P012I045 = c(1706, + 3896, 4876), P012B021 = c(330, 256, 923), P012B045 = c(397, + 270, 1213), P012H021 = c(38, 50, 150), P012H045 = c(55, 49, + 169), P012D021 = c(25, 26, 200), P012E021 = c(1, 0, 0), P012D045 = c(65, + 20, 180), P012E045 = c(0, 1, 0), P012C021 = c(13, 21, 8), + P012F021 = c(7, 12, 47), P012G021 = c(13, 20, 40), P012C045 = c(10, + 17, 14), P012F045 = c(11, 12, 47), P012G045 = c(36, 27, 52 + ), P012I022 = c(1923, 4552, 5078), P012I046 = c(2285, 4883, + 6167), P012B022 = c(448, 262, 1128), P012B046 = c(497, 331, + 1537), P012H022 = c(54, 71, 184), P012H046 = c(65, 66, 204 + ), P012D022 = c(40, 34, 234), P012E022 = c(0, 1, 0), P012D046 = c(61, + 28, 245), P012E046 = c(0, 0, 0), P012C022 = c(16, 27, 15), + P012F022 = c(20, 17, 68), P012G022 = c(24, 35, 43), P012C046 = c(20, + 31, 12), P012F046 = c(15, 10, 59), P012G046 = c(26, 27, 42 + ), P012I023 = c(1563, 3347, 4147), P012I047 = c(1778, 3561, + 5592), P012B023 = c(286, 186, 724), P012B047 = c(397, 280, + 1074), P012H023 = c(27, 39, 114), P012H047 = c(44, 40, 142 + ), P012D023 = c(14, 17, 122), P012E023 = c(0, 1, 0), P012D047 = c(57, + 21, 129), P012E047 = c(1, 1, 3), P012C023 = c(13, 11, 10), + P012F023 = c(5, 9, 28), P012G023 = c(6, 17, 22), P012C047 = c(12, + 23, 9), P012F047 = c(9, 12, 33), P012G047 = c(24, 23, 45), + P012I024 = c(929, 2123, 3204), P012I048 = c(1259, 2540, 4733 + ), P012B024 = c(141, 120, 395), P012B048 = c(200, 211, 747 + ), P012H024 = c(22, 22, 84), P012H048 = c(39, 29, 88), P012D024 = c(11, + 8, 53), P012E024 = c(1, 3, 0), P012D048 = c(26, 10, 72), + P012E048 = c(1, 0, 0), P012C024 = c(8, 8, 5), P012F024 = c(7, + 4, 26), P012G024 = c(6, 9, 28), P012C048 = c(15, 9, 5), P012F048 = c(12, + 7, 19), P012G048 = c(9, 12, 26), P012I025 = c(620, 1222, + 2591), P012I049 = c(1352, 2518, 5318), P012B025 = c(109, + 115, 296), P012B049 = c(236, 205, 697), P012H025 = c(19, + 14, 51), P012H049 = c(30, 27, 83), P012D025 = c(6, 2, 41), + P012E025 = c(1, 0, 0), P012D049 = c(17, 4, 61), P012E049 = c(0, + 1, 1), P012C025 = c(3, 4, 3), P012F025 = c(5, 2, 14), P012G025 = c(6, + 7, 16), P012C049 = c(5, 14, 10), P012F049 = c(6, 3, 17), + P012G049 = c(4, 16, 41), r_whi = c(0.180469772578534, 0.253982943390052, + 0.565547284031414), r_bla = c(0.202868403766148, 0.130934134109085, + 0.666197462124767), r_his = c(0.127640977315251, 0.23154559484301, + 0.64081342784174), r_asi = c(0.117344295139728, 0.0722995612974541, + 0.810356143562817), r_oth = c(0.170799603784541, 0.243774976944359, + 0.5854254192711), r_1_whi = c(0.0103484947643979, 0.0105427846858639, + 0.0260587096422339), r_1_bla = c(0.0151709468547656, 0.0105362486575537, + 0.0485157496324564), r_1_his = c(0.0166072574807774, 0.0334740033596919, + 0.0732713292634627), r_1_asi = c(0.00670144046426474, 0.00414522090573077, + 0.0621437700784138), r_1_oth = c(0.0256173788297981, 0.038323598729378, + 0.0788673702906719), r_2_whi = c(0.0107285531195462, 0.0110932727966841, + 0.028253844895288), r_2_bla = c(0.01580697967823, 0.00999927012626815, + 0.050361287497263), r_2_his = c(0.0140533453517434, 0.0259078679613772, + 0.0676718427773453), r_2_asi = c(0.00822135479636602, 0.00566513523783205, + 0.0578258316349442), r_2_oth = c(0.0224408238549032, 0.0302285070191618, + 0.0707722785804556), r_3_whi = c(0.0107984293193717, 0.0116386480148342, + 0.0309943553664921), r_3_bla = c(0.0161927700793477, 0.00995234967207816, + 0.0535779453011772), r_3_his = c(0.0112126302563472, 0.0185875636770872, + 0.058330260444408), r_3_asi = c(0.00715050606238558, 0.00493972157932916, + 0.0461501260838025), r_3_oth = c(0.0192301123749018, 0.022082180551286, + 0.0588004235406633), r_4_whi = c(0.00678651287085515, 0.00793350512652705, + 0.0207890215968586), r_4_bla = c(0.0101087511860448, 0.0068034658575495, + 0.0348723242307652), r_4_his = c(0.00701984403381544, 0.0101473620955737, + 0.0336242334849292), r_4_asi = c(0.00400704687553974, 0.0032470897094891, + 0.0266330443193202), r_4_oth = c(0.00992246473340848, 0.0124158896061755, + 0.0340711138436315), r_5_whi = c(0.00460160340314136, 0.00465614092495637, + 0.0187779504799302), r_5_bla = c(0.0114277372871636, 0.00432189516927857, + 0.0227564202821483), r_5_his = c(0.00520342524685541, 0.00641892353286625, + 0.0257986096884773), r_5_asi = c(0.002694393588725, 0.00169263186984006, + 0.0248713254343846), r_5_oth = c(0.00746319636574786, 0.00744611811319466, + 0.0226799193906479), r_6_whi = c(0.00225137707242583, 0.00229909740401396, + 0.00987981293630017), r_6_bla = c(0.00541149238324627, 0.00189767170279542, + 0.0106509431011292), r_6_his = c(0.00243099657202169, 0.00357820843747012, + 0.0131792791685445), r_6_asi = c(0.00148537082455353, 0.000794500673598397, + 0.013610141973816), r_6_oth = c(0.00290330293404379, 0.00329610274276736, + 0.0117669160091539), r_7_whi = c(0.00251043030104712, 0.00198721095113438, + 0.00958496945898778), r_7_bla = c(0.00459820451061966, 0.00177255049162209, + 0.00959262618995485), r_7_his = c(0.0025675694131465, 0.00422010079075675, + 0.012482757678808), r_7_asi = c(0.00113993574907596, 0.000690870150955128, + 0.0113993574907596), r_7_oth = c(0.00322778973255457, 0.00430371964340609, + 0.01057143833043), r_8_whi = c(0.007042157504363, 0.00668255071989529, + 0.022169502617801), r_8_bla = c(0.00990542921788816, 0.00552097344302293, + 0.0282200465033835), r_8_his = c(0.00767539367121454, 0.0150912989442919, + 0.034867046339165), r_8_asi = c(0.00511243911706795, 0.00259076306608173, + 0.0346471380703997), r_8_oth = c(0.00867575229702497, 0.0137309150527718, + 0.0289305598251187), r_9_whi = c(0.0113455088350785, 0.0116420566099476, + 0.034428514943281), r_9_bla = c(0.0130230327296235, 0.0087532713983338, + 0.0448246739028434), r_9_his = c(0.0117042924843966, 0.0273691973614127, + 0.0615533794949536), r_9_asi = c(0.00756502815295865, 0.00480154754913814, + 0.0869805520052506), r_9_oth = c(0.0127745329097927, 0.0247293096970318, + 0.0517641834887454), r_10_whi = c(0.010547897578534, 0.0108768270069808, + 0.0317902623254799), r_10_bla = c(0.0112087751676103, 0.00764282064917055, + 0.04493415496262), r_10_his = c(0.00971032900397427, 0.0233129839800057, + 0.0571830485789596), r_10_asi = c(0.00822135479636602, 0.00511243911706795, + 0.0869114649901551), r_10_oth = c(0.01099839464426, 0.0202377292755405, + 0.0481606722000205), r_11_whi = c(0.0108648969240838, 0.0123408186082024, + 0.0336939626963351), r_11_bla = c(0.0121315441000136, 0.00739779161062279, + 0.0468943872710021), r_11_his = c(0.0085904317067508, 0.0194479725761735, + 0.0498490870105571), r_11_asi = c(0.0100176171888494, 0.00621783135859615, + 0.0786555666862413), r_11_oth = c(0.0096833691976637, 0.0179663216859651, + 0.0416026232195922), r_12_whi = c(0.0123732002617801, 0.0148563618019197, + 0.0389994409904014), r_12_bla = c(0.0129761122754335, 0.00927460977822265, + 0.0517949680419573), r_12_his = c(0.0068969284768031, 0.0138348288059437, + 0.0439491402739651), r_12_asi = c(0.0104321392794224, 0.00604511382085737, + 0.0611074648519811), r_12_oth = c(0.00782183966936503, 0.0127745329097927, + 0.0364108344434197), r_13_whi = c(0.0142956479057592, 0.0187694289921466, + 0.0460910231239093), r_13_bla = c(0.0139405882782279, 0.00985850876369817, + 0.0524831347034106), r_13_his = c(0.00647355266931618, 0.00906843665068764, + 0.0338700645989539), r_13_asi = c(0.0099830736813016, 0.00604511382085737, + 0.0543023938650731), r_13_oth = c(0.00778768316425863, 0.0105031253202172, + 0.0291867336134167), r_14_whi = c(0.0135491655759162, 0.0200459478621291, + 0.0477476003490401), r_14_bla = c(0.0124756274307402, 0.00968125371453596, + 0.0470247218659743), r_14_his = c(0.00498490870105571, 0.00643258081697874, + 0.0242143647314295), r_14_asi = c(0.00804863725862724, 0.00528515665480673, + 0.0439393416007461), r_14_oth = c(0.00667759674830071, 0.00703624005191789, + 0.0207159203470301), r_15_whi = c(0.0115568417321117, 0.0209338868891798, + 0.0415763388961606), r_15_bla = c(0.0108125579988948, 0.00784092923352831, + 0.0363268583106551), r_15_his = c(0.0033050627552205, 0.00465713388235616, + 0.0178227557667882), r_15_asi = c(0.00645963591143045, 0.00373069881515769, + 0.0362015959100487), r_15_oth = c(0.00435495440106568, 0.0055675103323428, + 0.0148922362263893), r_16_whi = c(0.00452491001308901, 0.00917082515270506, + 0.0147200179973822), r_16_bla = c(0.00386833077877527, 0.00261711866704203, + 0.0129656855078357), r_16_his = c(0.00140670026358558, 0.00151595853648543, + 0.00549022821321752), r_16_asi = c(0.00265985008117724, 0.00176171888493558, + 0.012055684134167), r_16_oth = c(0.00160535574000068, 0.00189568603340506, + 0.0040817023602145), r_17_whi = c(0.00687343204624782, 0.0147830770069808, + 0.020799247382199), r_17_bla = c(0.00521338379888851, 0.00348775376145641, + 0.0162449039173366), r_17_his = c(0.00177544693462258, 0.00189836249163491, + 0.0068969284768031), r_17_asi = c(0.00359252478496667, 0.00210715396041314, + 0.0176171888493558), r_17_oth = c(0.00227140758957543, 0.00249342487276702, + 0.00565290159510879), r_18_whi = c(0.00403918520942408, 0.0088964332460733, + 0.011166557591623), r_18_bla = c(0.00274223987821535, 0.00191852523799097, + 0.00862293680336159), r_18_his = c(0.000655549637399107, + 0.0010242963084361, 0.00342797831223283), r_18_asi = c(0.00255621955853397, + 0.00127810977926699, 0.00898131196241666), r_18_oth = c(0.00109300816340472, + 0.00112716466851112, 0.0027666769136182), r_19_whi = c(0.00547590804973822, + 0.0126595222513089, 0.0152568717277487), r_19_bla = c(0.00379013002179194, + 0.00274223987821535, 0.0111357877944258), r_19_his = c(0.00127012742246077, + 0.00135207112713566, 0.00435667363188156), r_19_asi = c(0.00314345918684583, + 0.00162354485474455, 0.0131265328681474), r_19_oth = c(0.00153704272978789, + 0.00186152952829866, 0.00355227653106534), r_20_whi = c(0.00717168411867365, + 0.016080047447644, 0.0191648260253054), r_20_bla = c(0.00492664768994964, + 0.00309153659274088, 0.0138936678240379), r_20_his = c(0.00162521680938529, + 0.00187104792340995, 0.00529902623564278), r_20_asi = c(0.0034888942623234, + 0.00217624097550865, 0.0165463401153753), r_20_oth = c(0.00206646855893705, + 0.00251050312532022, 0.0040817023602145), r_21_whi = c(0.00569405813699825, + 0.011773287521815, 0.0165981539048866), r_21_bla = c(0.00356074113464085, + 0.00242943685028204, 0.00937366407040154), r_21_his = c(0.000969667171986179, + 0.00107892544488603, 0.00349626473279524), r_21_asi = c(0.00248713254343846, + 0.00138174030191026, 0.00877405091713013), r_21_oth = c(0.00117839942617071, + 0.00162243399255388, 0.00251050312532022), r_22_whi = c(0.00372900305410122, + 0.0079471395069808, 0.0135270097076789), r_22_bla = c(0.00177776387542098, + 0.0017256300374321, 0.00595368429833067), r_22_his = c(0.000833094330861365, + 0.000696521489736551, 0.0023490528673468), r_22_asi = c(0.0013471967943625, + 0.000725413658502884, 0.00431793844346955), r_22_oth = c(0.000973460395532329, + 0.000836834375106739, 0.00186152952829866), r_23_whi = c(0.00336087478184991, + 0.00637407286212914, 0.0134792893760908), r_23_bla = c(0.00179861741061653, + 0.00166828281564432, 0.00517689011229629), r_23_his = c(0.000669206921511588, + 0.000559948648611737, 0.00183007607107251), r_23_asi = c(0.000829044181146154, + 0.000241804552834295, 0.00355798127741891), r_23_oth = c(0.000495269324042764, + 0.000785599617447143, 0.00172490350787307)), class = c("tbl_df", + "tbl", "data.frame"), row.names = c(NA, -3L)) + diff --git a/tests/testthat/test-census_geo_api.R b/tests/testthat/test-census_geo_api.R new file mode 100644 index 0000000..4fd02c8 --- /dev/null +++ b/tests/testthat/test-census_geo_api.R @@ -0,0 +1,20 @@ +skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) + +test_that("snapshot", { + expect_snapshot_value( + census_geo_api(state = "DE", geo = "county", year = "2020"), + style = "deparse" + ) + expect_snapshot_value( + census_geo_api(state = "DE", geo = "county", year = "2010"), + style = "deparse" + ) + expect_snapshot_value( + census_geo_api(state = "DE", geo = "county", year = "2010", sex = TRUE), + style = "deparse" + ) + expect_snapshot_value( + census_geo_api(state = "DE", geo = "county", year = "2010", age = TRUE), + style = "deparse" + ) +}) From 11c4372ed24fffd696bdd002236348aa29b76cf0 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 21:28:55 -0800 Subject: [PATCH 50/90] fix: add `census_geo_api_names_legacy()` to support census data with column names from past wru versions --- R/census_geo_api_names.R | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/R/census_geo_api_names.R b/R/census_geo_api_names.R index dc2aaa0..3e9514a 100644 --- a/R/census_geo_api_names.R +++ b/R/census_geo_api_names.R @@ -107,6 +107,28 @@ census_geo_api_names <- function( vars } +census_geo_api_names_legacy <- function(year) { + if (year == 2020) { + return( + list( + r_whi = 'P2_005N', + r_bla = 'P2_006N', + r_his = 'P2_002N', + r_asi = c('P2_008N', 'P2_009N'), + r_oth = c('P2_007N', 'P2_010N', 'P2_011N') + ) + ) + } + + list( + r_whi = 'P005003', + r_bla = 'P005004', + r_his = 'P005010', + r_asi = c('P005006', 'P005007'), + r_oth = c('P005005', 'P005008', 'P005009') + ) +} + #' @rdname census_geo_api_names census_geo_api_url <- function(year = c("2020", "2010", "2000")) { year <- as.character(year) From 250c1cde775454cfd2567d21624a03b5f93beed1 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 20:13:47 -0800 Subject: [PATCH 51/90] refactor(census_data_preflight): use `census_geo_api_names()` --- R/census_data_preflight.R | 20 +++----------------- 1 file changed, 3 insertions(+), 17 deletions(-) diff --git a/R/census_data_preflight.R b/R/census_data_preflight.R index 804c066..8ed74d5 100644 --- a/R/census_data_preflight.R +++ b/R/census_data_preflight.R @@ -4,26 +4,12 @@ #' @keywords internal census_data_preflight <- function(census.data, census.geo, year) { - - if (year != "2020"){ - vars_ <- c( - pop_white = 'P005003', pop_black = 'P005004', - pop_aian = 'P005005', pop_asian = 'P005006', - pop_nhpi = 'P005007', pop_other = 'P005008', - pop_two = 'P005009', pop_hisp = 'P005010' - ) - } else { - vars_ <- c( - pop_white = 'P2_005N', pop_black = 'P2_006N', - pop_aian = 'P2_007N', pop_asian = 'P2_008N', - pop_nhpi = 'P2_009N', pop_other = 'P2_010N', - pop_two = 'P2_011N', pop_hisp = 'P2_002N' - ) - } + vars_ <- unlist(census_geo_api_names(year = year)) + legacy_vars <- unlist(census_geo_api_names_legacy(year = year)) test <- lapply(census.data, function(x) { nms_to_test <- names(x[[census.geo]]) - all(vars_ %in% nms_to_test) + all(vars_ %in% nms_to_test) || all(legacy_vars %in% nms_to_test) }) missings <- names(test)[!unlist(test)] From dd4c6a67214e75589341f54732db13042e3696db Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 13:31:54 -0800 Subject: [PATCH 52/90] feat(census_helper_new): add support for ZCTAs --- R/census_helper_v2.R | 42 ++++++++++++++++------------------------ man/census_helper_new.Rd | 2 +- 2 files changed, 18 insertions(+), 26 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 36dd25b..309a538 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -59,7 +59,7 @@ census_helper_new <- function( key = Sys.getenv("CENSUS_API_KEY"), voter.file, states = "all", - geo = "tract", + geo = c("tract", "block", "block_group", "county", "place", "zcta"), age = FALSE, sex = FALSE, year = "2020", @@ -71,6 +71,9 @@ census_helper_new <- function( stop("Error: census_helper_new function does not currently support precinct-level data.") } + geo <- tolower(geo) + geo <- rlang::arg_match(geo) + if(!(year %in% c("2000","2010","2020"))){ stop("Interface only implemented for census years '2000', '2010', or '2020'.") } @@ -88,10 +91,10 @@ census_helper_new <- function( validate_key(key) } - states <- toupper(states) - if (states == "ALL") { + if (toupper(states) == "ALL") { states <- toupper(as.character(unique(voter.file$state))) } + states <- as_state_abbreviation(states) df.out <- NULL @@ -163,32 +166,21 @@ census_helper_new <- function( } census$state <- state - + ## Calculate Pr(Geolocation | Race) - if (year != "2020") { - vars_ <- c( - pop_white = 'P005003', pop_black = 'P005004', - pop_aian = 'P005005', pop_asian = 'P005006', - pop_nhpi = 'P005007', pop_other = 'P005008', - pop_two = 'P005009', pop_hisp = 'P005010' - ) - drop <- c(grep("state", names(census)), grep("P005", names(census))) + if (any(c("P2_005N", "P005003") %in% names(census))) { + vars_ <- census_geo_api_names_legacy(year = year) } else { - vars_ <- c( - pop_white = 'P2_005N', pop_black = 'P2_006N', - pop_aian = 'P2_007N', pop_asian = 'P2_008N', - pop_nhpi = 'P2_009N', pop_other = 'P2_010N', - pop_two = 'P2_011N', pop_hisp = 'P2_002N' - ) - drop <- c(grep("state", names(census)), grep("P2_", names(census))) + vars_ <- census_geo_api_names(year) } - geoPopulations <- rowSums(census[,names(census) %in% vars_]) + drop <- match(c("state", unlist(vars_)), names(census)) - census$r_whi <- (census[, vars_["pop_white"]]) / (geoPopulations ) #Pr(White | Geo) - census$r_bla <- (census[, vars_["pop_black"]]) / (geoPopulations) #Pr(Black | Geo) - census$r_his <- (census[, vars_["pop_hisp"]]) / (geoPopulations) #Pr(Latino | Geo) - census$r_asi <- (census[, vars_["pop_asian"]] + census[, vars_["pop_nhpi"]]) / (geoPopulations) #Pr(Asian or NH/PI | Geo) - census$r_oth <- (census[, vars_["pop_aian"]] + census[, vars_["pop_other"]] + census[, vars_["pop_two"]]) / (geoPopulations) #Pr(AI/AN, Other, or Mixed | Geo) + geoPopulations <- rowSums(census[,names(census) %in% vars_]) + + for (i in seq_along(vars_)) { + census[[names(vars_)[[i]]]] <- + rowSums(census[, vars_[[i]], drop = FALSE]) / geoPopulations + } # check locations with zero people # get average without places with zero people, and assign that to zero locs. diff --git a/man/census_helper_new.Rd b/man/census_helper_new.Rd index 7293d83..93290c4 100644 --- a/man/census_helper_new.Rd +++ b/man/census_helper_new.Rd @@ -8,7 +8,7 @@ census_helper_new( key = Sys.getenv("CENSUS_API_KEY"), voter.file, states = "all", - geo = "tract", + geo = c("tract", "block", "block_group", "county", "place", "zcta"), age = FALSE, sex = FALSE, year = "2020", From 4e691b6fb2ca81812a4aa28bab58ccda34ac05e4 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 20:18:56 -0800 Subject: [PATCH 53/90] feat: add `determine_geo_id_names()` helper function --- R/utils_determine_geo_id_names.R | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 R/utils_determine_geo_id_names.R diff --git a/R/utils_determine_geo_id_names.R b/R/utils_determine_geo_id_names.R new file mode 100644 index 0000000..4df154a --- /dev/null +++ b/R/utils_determine_geo_id_names.R @@ -0,0 +1,10 @@ +determine_geo_id_names <- function(census.geo) { + switch( + census.geo, + "tract" = c("county", "tract"), + "block_group" = c("county", "tract", "block_group"), + "block" = c("county", "tract", "block"), + # Return `census.geo` unchanged for county, place, and zcta + census.geo + ) +} From 8762f0681f8a17e334a3203f4a0870f923d456e6 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Tue, 28 Nov 2023 13:32:41 -0800 Subject: [PATCH 54/90] feat(predict_race_new): add support for ZCTAs --- R/race_prediction_funs.R | 21 ++++++++------------- man/modfuns.Rd | 2 +- 2 files changed, 9 insertions(+), 14 deletions(-) diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 3e43e0e..0c037a0 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -274,7 +274,7 @@ predict_race_new <- function( year = "2020", age = FALSE, sex = FALSE, - census.geo, + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), census.key = Sys.getenv("CENSUS_API_KEY"), name.dictionaries, surname.only=FALSE, @@ -284,7 +284,6 @@ predict_race_new <- function( census.surname = FALSE, use.counties = FALSE ) { - # Check years if (!(year %in% c("2000", "2010", "2020"))){ @@ -293,10 +292,9 @@ predict_race_new <- function( # Define 2020 race marginal race.margin <- c(r_whi=0.5783619, r_bla=0.1205021, r_his=0.1872988, r_asi=0.06106737, r_oth=0.05276981) - # check the geography - if (!missing(census.geo) && (census.geo == "precinct")) { - stop("Error: census_helper function does not currently support merging precinct-level data.") - } + + census.geo <- tolower(census.geo) + census.geo <- rlang::arg_match(census.geo) vars.orig <- names(voter.file) @@ -345,11 +343,8 @@ predict_race_new <- function( # check the geographies if (surname.only == FALSE) { - if (!(census.geo %in% c("county", "tract","block_group", "block", "place"))) { - stop("census.geo must be either 'county', 'tract', 'block', 'block_group', or 'place'") - } else { - message(paste("Proceeding with Census geographic data at", census.geo, "level...")) - } + message("Proceeding with Census geographic data at ", census.geo, " level...") + if (is.null(census.data)) { validate_key(census.key) message("Downloading Census geographic data using provided API key...") @@ -369,11 +364,11 @@ predict_race_new <- function( geo_id_names <- switch( census.geo, - "county" = c("county"), "tract" = c("county", "tract"), "block_group" = c("county", "tract", "block_group"), "block" = c("county", "tract", "block"), - "place" = c("place") + # Return `census.geo` unchanged for county, place, and zcta + census.geo ) if (!all(geo_id_names %in% names(voter.file))) { diff --git a/man/modfuns.Rd b/man/modfuns.Rd index d7d682d..403d2d2 100644 --- a/man/modfuns.Rd +++ b/man/modfuns.Rd @@ -31,7 +31,7 @@ predict_race_new( year = "2020", age = FALSE, sex = FALSE, - census.geo, + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), census.key = Sys.getenv("CENSUS_API_KEY"), name.dictionaries, surname.only = FALSE, From c256a4a0f6d31bc62cf73ab118ad068fe3e434d4 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 20:18:56 -0800 Subject: [PATCH 55/90] geo_id_names new --- R/race_prediction_funs.R | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 0c037a0..805688f 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -362,14 +362,7 @@ predict_race_new <- function( } } - geo_id_names <- switch( - census.geo, - "tract" = c("county", "tract"), - "block_group" = c("county", "tract", "block_group"), - "block" = c("county", "tract", "block"), - # Return `census.geo` unchanged for county, place, and zcta - census.geo - ) + geo_id_names <- determine_geo_id_names(census.geo) if (!all(geo_id_names %in% names(voter.file))) { stop(message("To use",census.geo,"as census.geo, voter.file needs to include the following column(s):", From 03dfd544c45d5df714b781fe1a273af47916398a Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 20:37:35 -0800 Subject: [PATCH 56/90] feat(predict_race_me): add support for ZCTAs --- R/race_prediction_funs.R | 55 +++++++++++++++------------------------- man/modfuns.Rd | 2 +- 2 files changed, 21 insertions(+), 36 deletions(-) diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 805688f..ff384f1 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -439,7 +439,7 @@ predict_race_me <- function( year = "2020", age = FALSE, sex = FALSE, - census.geo, + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), census.key = Sys.getenv("CENSUS_API_KEY"), name.dictionaries, surname.only = FALSE, @@ -451,7 +451,9 @@ predict_race_me <- function( race.init, ctrl ) { - + census.geo <- tolower(census.geo) + census.geo <- rlang::arg_match(census.geo) + if(!is.null(census.data)) { census_data_preflight(census.data, census.geo, year) } @@ -494,9 +496,6 @@ predict_race_me <- function( } ## Other quick checks... - if (!(census.geo %in% c("county", "tract","block_group", "block", "place"))) { - stop("census.geo must be either 'county', 'tract', 'block', 'block_group', or 'place'") - } stopifnot( all(!is.na(voter.file$surname)) ) @@ -526,15 +525,7 @@ predict_race_me <- function( ) ## level of geo estimation - geo_id_names <- c("state", switch(census.geo, - "county" = c("county"), - "tract" = c("county", "tract"), - "block_group" = c("county", "tract", "block_group"), - "block" = c("county", "tract", "block"), - "place" = c("place"), - "zipcode" = c("zipcode") - )) - + geo_id_names <- c("state", determine_geo_id_names(census.geo)) #race_pred_args[names(args_usr)] <- args_usr all_states <- unique(voter.file$state) @@ -546,31 +537,25 @@ predict_race_me <- function( if (ctrl$verbose) { message("Forming Pr(race | location) tables from census data...\n") } - if(year == "2020") { - vars_ <- c( - pop_white = 'P2_005N', pop_black = 'P2_006N', - pop_aian = 'P2_007N', pop_asian = 'P2_008N', - pop_nhpi = 'P2_009N', pop_other = 'P2_010N', - pop_two = 'P2_011N', pop_hisp = 'P2_002N' - ) - } else { - vars_ <- c( - pop_white = 'P005003', pop_black = 'P005004', - pop_aian = 'P005005', pop_asian = 'P005006', - pop_nhpi = 'P005007', pop_other = 'P005008', - pop_two = 'P005009', pop_hisp = 'P005010' - ) - } + + vars_ <- census_geo_api_names(year = year) + tmp_tabs <- lapply( census.data, function(x) { all_names <- names(x[[census.geo]]) - tmp <- x[[census.geo]][, c(geo_id_names, grep("P00|P2_0", all_names, value = TRUE))] - tmp$r_whi <- tmp[, vars_["pop_white"]] - tmp$r_bla <- tmp[, vars_["pop_black"]] - tmp$r_his <- tmp[, vars_["pop_hisp"]] - tmp$r_asi <- (tmp[, vars_["pop_asian"]] + tmp[, vars_["pop_nhpi"]]) - tmp$r_oth <- (tmp[, vars_["pop_aian"]] + tmp[, vars_["pop_other"]] + tmp[, vars_["pop_two"]]) + + if (any(c("P2_005N", "P005003") %in% all_names)) { + vars_ <- census_geo_api_names_legacy(year = year) + } + + tmp <- x[[census.geo]][, c(geo_id_names, grep("^P[0-2]", all_names, value = TRUE))] + + for (i in seq_along(vars_)) { + tmp[[names(vars_)[[i]]]] <- + rowSums(tmp[, vars_[[i]], drop = FALSE]) + } + all_names <- names(tmp) ## Totals tmp_la <- tmp[, c(geo_id_names, grep("^r_", all_names, value = TRUE))] diff --git a/man/modfuns.Rd b/man/modfuns.Rd index 403d2d2..f852a26 100644 --- a/man/modfuns.Rd +++ b/man/modfuns.Rd @@ -48,7 +48,7 @@ predict_race_me( year = "2020", age = FALSE, sex = FALSE, - census.geo, + census.geo = c("tract", "block", "block_group", "county", "place", "zcta"), census.key = Sys.getenv("CENSUS_API_KEY"), name.dictionaries, surname.only = FALSE, From 633339826db86c4069d660afe8aeabe6d6b541cd Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 20:41:05 -0800 Subject: [PATCH 57/90] fix(validate_key): if `key` is `NULL`, replace it with `Sys.getenv("CENSUS_API_KEY")` for backwards compatibility --- NAMESPACE | 1 + R/census_geo_api.R | 2 +- R/census_helper.R | 2 +- R/census_helper_v2.R | 2 +- R/get_census_data.R | 2 +- R/predict_race.R | 2 +- R/race_prediction_funs.R | 8 ++++---- R/utils_validate_key.R | 3 +++ 8 files changed, 13 insertions(+), 9 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index c6721d1..8b7120c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -10,6 +10,7 @@ importFrom(dplyr,pull) importFrom(furrr,future_map_dfr) importFrom(piggyback,pb_download) importFrom(purrr,map_dfr) +importFrom(rlang,"%||%") importFrom(stats,rmultinom) importFrom(utils,setTxtProgressBar) importFrom(utils,txtProgressBar) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index 2c505e7..6b64854 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -58,7 +58,7 @@ census_geo_api <- function( save_temp = NULL, counties = NULL ) { - validate_key(key) + key <- validate_key(key) geo <- tolower(geo) geo <- rlang::arg_match(geo) diff --git a/R/census_helper.R b/R/census_helper.R index f7c2816..9068663 100644 --- a/R/census_helper.R +++ b/R/census_helper.R @@ -89,7 +89,7 @@ census_helper <- function( } if (toDownload) { - validate_key(key) + key <- validate_key(key) } states <- toupper(states) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 309a538..eb5873d 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -88,7 +88,7 @@ census_helper_new <- function( } if (toDownload) { - validate_key(key) + key <- validate_key(key) } if (toupper(states) == "ALL") { diff --git a/R/get_census_data.R b/R/get_census_data.R index d4a4d09..9eb735d 100644 --- a/R/get_census_data.R +++ b/R/get_census_data.R @@ -49,7 +49,7 @@ get_census_data <- function( retry = 3, county.list = NULL ) { - validate_key(key) + key <- validate_key(key) census.geo <- tolower(census.geo) census.geo <- rlang::arg_match(census.geo) diff --git a/R/predict_race.R b/R/predict_race.R index bd8df9a..ed7e903 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -193,7 +193,7 @@ predict_race <- function( if (surname.only == FALSE && is.null(census.data)) { # Otherwise predict_race_new and predict_race_me will both # attempt to pull census_data - validate_key(census.key) + census.key <- validate_key(census.key) voter.file$state <- toupper(voter.file$state) states <- unique(voter.file$state) county.list <- split(voter.file$county, voter.file$state) diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index ff384f1..3184524 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -87,7 +87,7 @@ NULL message(paste("Proceeding with Census geographic data at", census.geo, "level...")) } if (missing(census.data) || is.null(census.data) || is.na(census.data)) { - validate_key(census.key) + census.key <- validate_key(census.key) message("Downloading Census geographic data using provided API key...") } else { if (!("state" %in% names(voter.file))) { @@ -95,7 +95,7 @@ NULL } if (sum(toupper(unique(as.character(voter.file$state))) %in% toupper(names(census.data)) == FALSE) > 0) { message("census.data object does not include all states in voter.file object.") - validate_key(census.key) + census.key <- validate_key(census.key) message("Downloading Census geographic data for states not included in census.data object...") } else { message("Using Census geographic data from provided census.data object...") @@ -346,7 +346,7 @@ predict_race_new <- function( message("Proceeding with Census geographic data at ", census.geo, " level...") if (is.null(census.data)) { - validate_key(census.key) + census.key <- validate_key(census.key) message("Downloading Census geographic data using provided API key...") } else { if (!("state" %in% names(voter.file))) { @@ -355,7 +355,7 @@ predict_race_new <- function( census_data_preflight(census.data, census.geo, year) if (sum(toupper(unique(as.character(voter.file$state))) %in% toupper(names(census.data)) == FALSE) > 0) { message("census.data object does not include all states in voter.file object.") - validate_key(census.key) + census.key <- validate_key(census.key) message("Downloading Census geographic data for states not included in census.data object...") } else { message("Using Census geographic data from provided census.data object...") diff --git a/R/utils_validate_key.R b/R/utils_validate_key.R index b31a6fc..a92d447 100644 --- a/R/utils_validate_key.R +++ b/R/utils_validate_key.R @@ -1,8 +1,11 @@ +#' @importFrom rlang %||% validate_key <- function( key, argument_name = rlang::caller_arg(key), call = rlang::caller_call() ) { + key <- key %||% Sys.getenv("CENSUS_API_KEY") + if (length(key) != 1) { cli::cli_abort( c( From bcd0f4562dfd6b4ca8caf41bada7a6ddd6a57423 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 21:22:18 -0800 Subject: [PATCH 58/90] test(get_census_data): add test with ZCTAs --- tests/testthat/test-get_census_data.R | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tests/testthat/test-get_census_data.R b/tests/testthat/test-get_census_data.R index 89e7cb9..f2e2aa9 100644 --- a/tests/testthat/test-get_census_data.R +++ b/tests/testthat/test-get_census_data.R @@ -55,4 +55,13 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { expect_true(all(r$RI$place$state == "RI")) }) + test_that("Census ZCTA download works", { + r <- suppressMessages(get_census_data( + key = NULL, + state = "DC", + census.geo = "zcta" + )) + expect_named(r$DC, c("state", "age", "sex", "year", "zcta")) + expect_true(all(r$DC$zcta$state == "DC")) + }) } From b374610adbeeab0e54000255510eae717f2109ce Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 21:29:37 -0800 Subject: [PATCH 59/90] chore: add `.lazytest` to `.gitignore` (see https://lazytest.cynkra.com/) --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index dc83fda..0f2e051 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,7 @@ # RStudio files .Rproj.user/ .Rproj +.lazytest # produced vignettes vignettes/*.html From ec10a952d558879acdeff03be683647adb1ce869 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Thu, 30 Nov 2023 21:32:55 -0800 Subject: [PATCH 60/90] fix(census_helper_new): pass `year` to `census_geo_api()` --- R/census_helper_v2.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index eb5873d..44b7ae4 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -111,7 +111,7 @@ census_helper_new <- function( # Only those counties within the target state counties = unique(voter.file$county[voter.file$state == state])) } else { - census <- census_geo_api(key, state, geo = "tract", age, sex, retry) + census <- census_geo_api(key, state, geo = "tract", age, sex, year, retry) } } else { census <- census.data[[toupper(state)]]$tract @@ -124,7 +124,7 @@ census_helper_new <- function( # Only those counties within the target state counties = unique(voter.file$county[voter.file$state == state])) } else { - census <- census_geo_api(key, state, geo = "block_group", age, sex, retry) + census <- census_geo_api(key, state, geo = "block_group", age, sex, year, retry) } } else { @@ -143,7 +143,7 @@ census_helper_new <- function( # Only those counties within the target state counties = unique(voter.file$county[voter.file$state == state])) } else { - census <- census_geo_api(key, state, geo = "block", age, sex, retry) + census <- census_geo_api(key, state, geo = "block", age, sex, year, retry) } } else { @@ -159,7 +159,7 @@ census_helper_new <- function( census.data[[state]]$sex != FALSE if (state_must_be_downloaded) { - census <- census_geo_api(key, state, geo = geo, age, sex, retry) + census <- census_geo_api(key, state, geo = geo, age, sex, year, retry) } else { census <- census.data[[state]][[geo]] } From 1dc2a521487007826e081a66703b9c4883e5b391 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Fri, 1 Dec 2023 19:10:43 -0700 Subject: [PATCH 61/90] Update to census_helper, drops rows with geos not found in census data. Updated related tests as well. --- R/census_helper_v2.R | 4 +++- tests/testthat/test-predict_race_2010.R | 9 ++++----- tests/testthat/test-predict_race_2020.R | 9 ++++----- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 6761443..7a7d6f7 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -215,11 +215,13 @@ census_helper_new <- function(key, voter.file, states = "all", geo = "tract", ag #Check if geolocation missing from census object if(any(is.na(voters.census$r_whi))){ miss_ind <- which(is.na(voters.census$r_whi)) - stop("The following locations in the voter.file are not available in the census data ", + warning("The following locations in the voter.file are not available in the census data and have been dropped: ", paste0("(listed as ", paste0(c("state",geo.merge), collapse="-"),"):\n"), paste(do.call(paste, c(unique(voters.census[miss_ind, c("state",geo.merge)]), sep="-")), collapse = ", ")) + + voters.census <- drop_na(voters.census, r_whi) } # } diff --git a/tests/testthat/test-predict_race_2010.R b/tests/testthat/test-predict_race_2010.R index 9e30ad4..583759c 100644 --- a/tests/testthat/test-predict_race_2010.R +++ b/tests/testthat/test-predict_race_2010.R @@ -125,21 +125,20 @@ test_that("Fails on territories", { ) }) -test_that("Fails on missing geolocation", { +test_that("Drops rows with geolocations not found in census data", { skip_on_cran() set.seed(42) data(voters) census <- readRDS(test_path("data/census_test_nj_block_2010.rds")) - expect_error(suppressMessages(predict_race( + test_drop <- suppressWarnings(predict_race( voter.file = voters[voters$state == "NJ", ], year = 2010, census.geo = "block", census.key = NULL, census.data = census, use.counties = TRUE) - ), - "The following locations in the voter\\.file are not available" - ) + ) + expect_equal(nrow(test_drop), 6) }) test_that("Handles zero-pop. geolocations", { diff --git a/tests/testthat/test-predict_race_2020.R b/tests/testthat/test-predict_race_2020.R index a28ed88..f3fff6a 100644 --- a/tests/testthat/test-predict_race_2020.R +++ b/tests/testthat/test-predict_race_2020.R @@ -120,21 +120,20 @@ test_that("Fails on territories", { ) }) -test_that("Fails on missing geolocation", { +test_that("Drops rows with geolocations not found in census data", { skip_on_cran() set.seed(42) data(voters) census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) - expect_error(suppressMessages(predict_race( + test_drop <- suppressWarnings(predict_race( voter.file = voters[voters$state == "NJ", ], census.geo = "block", census.key = NULL, census.data = census, use.counties = TRUE) - ), - "The following locations in the voter\\.file are not available" ) -}) + expect_equal(nrow(test_drop), 1) + }) test_that("Handles zero-pop. geolocations", { skip_on_cran() From 8ae3efb98cc3390e7a67384eb2e2160bd54750e7 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Fri, 1 Dec 2023 19:44:48 -0700 Subject: [PATCH 62/90] telling wru which package contains drop_na --- R/census_helper_v2.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 7a7d6f7..5caaf9f 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -221,7 +221,7 @@ census_helper_new <- function(key, voter.file, states = "all", geo = "tract", ag sep="-")), collapse = ", ")) - voters.census <- drop_na(voters.census, r_whi) + voters.census <- tidyr::drop_na(voters.census, r_whi) } # } From 23ded7cacac3ca9f9412cdcf3611c5d58f2fcba0 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Fri, 1 Dec 2023 20:49:18 -0700 Subject: [PATCH 63/90] declaring dependencies --- DESCRIPTION | 1 + R/census_helper_v2.R | 2 +- tests/testthat/test-predict_race_2010.R | 2 +- tests/testthat/test-predict_race_2020.R | 2 +- 4 files changed, 4 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 08c4337..892542e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -26,6 +26,7 @@ Depends: utils Imports: dplyr, + tidyr, furrr, future, purrr, diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 5caaf9f..948a9a7 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -215,7 +215,7 @@ census_helper_new <- function(key, voter.file, states = "all", geo = "tract", ag #Check if geolocation missing from census object if(any(is.na(voters.census$r_whi))){ miss_ind <- which(is.na(voters.census$r_whi)) - warning("The following locations in the voter.file are not available in the census data and have been dropped: ", + message("The following locations in the voter.file are not available in the census data and have been dropped: ", paste0("(listed as ", paste0(c("state",geo.merge), collapse="-"),"):\n"), paste(do.call(paste, c(unique(voters.census[miss_ind, c("state",geo.merge)]), sep="-")), diff --git a/tests/testthat/test-predict_race_2010.R b/tests/testthat/test-predict_race_2010.R index 583759c..a2b619c 100644 --- a/tests/testthat/test-predict_race_2010.R +++ b/tests/testthat/test-predict_race_2010.R @@ -130,7 +130,7 @@ test_that("Drops rows with geolocations not found in census data", { set.seed(42) data(voters) census <- readRDS(test_path("data/census_test_nj_block_2010.rds")) - test_drop <- suppressWarnings(predict_race( + test_drop <- suppressMessages(predict_race( voter.file = voters[voters$state == "NJ", ], year = 2010, census.geo = "block", diff --git a/tests/testthat/test-predict_race_2020.R b/tests/testthat/test-predict_race_2020.R index f3fff6a..b2d376f 100644 --- a/tests/testthat/test-predict_race_2020.R +++ b/tests/testthat/test-predict_race_2020.R @@ -125,7 +125,7 @@ test_that("Drops rows with geolocations not found in census data", { set.seed(42) data(voters) census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) - test_drop <- suppressWarnings(predict_race( + test_drop <- suppressMessages(predict_race( voter.file = voters[voters$state == "NJ", ], census.geo = "block", census.key = NULL, From 50065bac1095d6af505950f29f6751eab1ee8561 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Mon, 4 Dec 2023 14:23:46 -0700 Subject: [PATCH 64/90] Adding 'skip_bad_geos' option to allow partial data sets of succesful predictions to be returned. tests added for default. and if set to 'TRUE'. --- R/census_helper_v2.R | 14 ++++++++++---- R/predict_race.R | 9 +++++++-- R/race_prediction_funs.R | 12 +++++++----- tests/testthat/test-predict_race_2010.R | 24 +++++++++++++++++++++--- tests/testthat/test-predict_race_2020.R | 21 +++++++++++++++++++-- 5 files changed, 64 insertions(+), 16 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 948a9a7..bd16da7 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -43,6 +43,9 @@ #' @param retry The number of retries at the census website if network interruption occurs. #' @param use.counties A logical, defaulting to FALSE. Should census data be filtered by counties #' available in \var{census.data}? +#' @param skip_bad_geos Logical. Option to have the function skip any geolocations that are not present +#' in the census data, returning a partial data set. Default is set to \code{FALSE}, which case it will +#' break and provide error message with a list of offending geolocations. #' @return Output will be an object of class \code{data.frame}. It will #' consist of the original user-input data with additional columns of #' Census data. @@ -56,7 +59,8 @@ #' #' @keywords internal -census_helper_new <- function(key, voter.file, states = "all", geo = "tract", age = FALSE, sex = FALSE, year = "2020", census.data = NULL, retry = 3, use.counties = FALSE) { +census_helper_new <- function(key, voter.file, states = "all", geo = "tract", age = FALSE, sex = FALSE, year = "2020", + census.data = NULL, retry = 3, use.counties = FALSE, skip_bad_geos = FALSE) { if (geo == "precinct") { stop("Error: census_helper_new function does not currently support precinct-level data.") @@ -215,13 +219,15 @@ census_helper_new <- function(key, voter.file, states = "all", geo = "tract", ag #Check if geolocation missing from census object if(any(is.na(voters.census$r_whi))){ miss_ind <- which(is.na(voters.census$r_whi)) - message("The following locations in the voter.file are not available in the census data and have been dropped: ", + message("The following locations in the voter.file are not available in the census data.", paste0("(listed as ", paste0(c("state",geo.merge), collapse="-"),"):\n"), paste(do.call(paste, c(unique(voters.census[miss_ind, c("state",geo.merge)]), sep="-")), collapse = ", ")) - - voters.census <- tidyr::drop_na(voters.census, r_whi) + if(skip_bad_geos == TRUE){ + message("NOTE: Skipping unavailable geolocations. Returning partial data set.") + voters.census <- tidyr::drop_na(voters.census, r_whi)} + else(stop("Stopping predictions. Please revise census data and/or verify the correct year is being supplied. To skip these rows use 'skip_bad_geos = TRUE'")) } # } diff --git a/R/predict_race.R b/R/predict_race.R index 31ed173..cc4a776 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -69,6 +69,9 @@ #' it should be coded as 1 for Democrat, 2 for Republican, and 0 for Other. #' @param retry The number of retries at the census website if network interruption occurs. #' @param impute.missing Logical, defaults to TRUE. Should missing be imputed? +#' @param skip_bad_geos Logical. Option to have the function skip any geolocations that are not present +#' in the census data, returning a partial data set. Default is set to \code{FALSE}, in which case it +#' will break and provide error message with a list of offending geolocations. #' @param use.counties A logical, defaulting to FALSE. Should census data be filtered by counties #' available in \var{census.data}? #' @param model Character string, either "BISG" (default) or "fBISG" (for error-correction, @@ -134,8 +137,8 @@ predict_race <- function(voter.file, census.surname = TRUE, surname.only = FALSE, census.geo, census.key = NULL, census.data = NULL, age = FALSE, sex = FALSE, year = "2020", party = NULL, retry = 3, impute.missing = TRUE, - use.counties = FALSE, model = "BISG", race.init = NULL, name.dictionaries = NULL, - names.to.use = "surname", control = NULL) { + skip_bad_geos = FALSE, use.counties = FALSE, model = "BISG", race.init = NULL, + name.dictionaries = NULL, names.to.use = "surname", control = NULL) { message("Predicting race for ", year) @@ -208,6 +211,7 @@ predict_race <- function(voter.file, census.surname = TRUE, surname.only = FALSE census.data = census.data, retry = retry, impute.missing = impute.missing, + skip_bad_geos = skip_bad_geos, census.surname = census.surname, use.counties = use.counties) } else { @@ -236,6 +240,7 @@ predict_race <- function(voter.file, census.surname = TRUE, surname.only = FALSE census.data = census.data, retry = retry, impute.missing = TRUE, + skip_bad_geos = skip_bad_geos, census.surname = census.surname, use.counties = use.counties) race.init <- max.col( diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 1ade9c7..7218b49 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -26,6 +26,7 @@ #' @param party See documentation in \code{race_predict}. #' @param retry See documentation in \code{race_predict}. #' @param impute.missing See documentation in \code{race_predict}. +#' @param skip_bad_geos See documentation in \code{race_predict}. #' @param names.to.use See documentation in \code{race_predict}. #' @param race.init See documentation in \code{race_predict}. #' @param name.dictionaries See documentation in \code{race_predict}. @@ -263,8 +264,8 @@ NULL #' @rdname modfuns predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE, sex = FALSE, census.geo, census.key = NULL, name.dictionaries, surname.only=FALSE, - census.data = NULL, retry = 0, impute.missing = TRUE, census.surname = FALSE, - use.counties = FALSE) { + census.data = NULL, retry = 0, impute.missing = TRUE, skip_bad_geos = FALSE, + census.surname = FALSE, use.counties = FALSE) { # Check years if (!(year %in% c("2000", "2010", "2020"))){ @@ -377,7 +378,8 @@ predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE year = year, census.data = census.data, retry = retry, - use.counties = use.counties + use.counties = use.counties, + skip_bad_geos = skip_bad_geos ) } @@ -432,8 +434,8 @@ predict_race_new <- function(voter.file, names.to.use, year = "2020",age = FALSE #' @rdname modfuns predict_race_me <- function(voter.file, names.to.use, year = "2020",age = FALSE, sex = FALSE, census.geo, census.key, name.dictionaries, surname.only=FALSE, - census.data = NULL, retry = 0, impute.missing = TRUE, census.surname = FALSE, - use.counties = FALSE, race.init, ctrl) + census.data = NULL, retry = 0, impute.missing = TRUE, + census.surname = FALSE, use.counties = FALSE, race.init, ctrl) { if(!is.null(census.data)) { census_data_preflight(census.data, census.geo, year) diff --git a/tests/testthat/test-predict_race_2010.R b/tests/testthat/test-predict_race_2010.R index a2b619c..182832d 100644 --- a/tests/testthat/test-predict_race_2010.R +++ b/tests/testthat/test-predict_race_2010.R @@ -125,19 +125,37 @@ test_that("Fails on territories", { ) }) -test_that("Drops rows with geolocations not found in census data", { +test_that("Fails on missing geolocation if skip_bad_geos default is used", { skip_on_cran() set.seed(42) data(voters) census <- readRDS(test_path("data/census_test_nj_block_2010.rds")) - test_drop <- suppressMessages(predict_race( + expect_error(suppressMessages(predict_race( voter.file = voters[voters$state == "NJ", ], year = 2010, census.geo = "block", census.key = NULL, census.data = census, use.counties = TRUE) - ) + ), + "Stopping predictions. Please revise" + ) +}) + +test_that("Skip_bad_geos option successfully returns working geolocations", { + skip_on_cran() + set.seed(42) + data(voters) + census <- readRDS(test_path("data/census_test_nj_block_2010.rds")) + test_drop <- suppressMessages(predict_race( + voter.file = voters[voters$state == "NJ", ], + year = 2010, + census.geo = "block", + census.key = NULL, + census.data = census, + skip_bad_geos = TRUE, + use.counties = TRUE) + ) expect_equal(nrow(test_drop), 6) }) diff --git a/tests/testthat/test-predict_race_2020.R b/tests/testthat/test-predict_race_2020.R index b2d376f..54e14a2 100644 --- a/tests/testthat/test-predict_race_2020.R +++ b/tests/testthat/test-predict_race_2020.R @@ -120,17 +120,34 @@ test_that("Fails on territories", { ) }) -test_that("Drops rows with geolocations not found in census data", { +test_that("Fails on missing geolocation if skip_bad_geos default is used", { skip_on_cran() set.seed(42) data(voters) census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) - test_drop <- suppressMessages(predict_race( + expect_error(suppressMessages(predict_race( voter.file = voters[voters$state == "NJ", ], census.geo = "block", census.key = NULL, census.data = census, use.counties = TRUE) + ), + "Stopping predictions. Please revise" + ) +}) + +test_that("Skip_bad_geos option successfully returns working geolocations", { + skip_on_cran() + set.seed(42) + data(voters) + census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) + test_drop <- suppressMessages(predict_race( + voter.file = voters[voters$state == "NJ", ], + census.geo = "block", + census.key = NULL, + census.data = census, + skip_bad_geos = TRUE, + use.counties = TRUE) ) expect_equal(nrow(test_drop), 1) }) From fe20c3a4de5936a95f01fb4025996ae3646e0331 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Mon, 4 Dec 2023 14:36:47 -0700 Subject: [PATCH 65/90] docs --- man/census_helper_new.Rd | 7 ++++++- man/modfuns.Rd | 3 +++ man/predict_race.Rd | 5 +++++ 3 files changed, 14 insertions(+), 1 deletion(-) diff --git a/man/census_helper_new.Rd b/man/census_helper_new.Rd index e410267..6fa977c 100644 --- a/man/census_helper_new.Rd +++ b/man/census_helper_new.Rd @@ -14,7 +14,8 @@ census_helper_new( year = "2020", census.data = NULL, retry = 3, - use.counties = FALSE + use.counties = FALSE, + skip_bad_geos = FALSE ) } \arguments{ @@ -62,6 +63,10 @@ If \code{\var{census.data}} is missing, Census geographic data will be obtained \item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} + +\item{skip_bad_geos}{Logical. Option to have the function skip any geolocations that are not present +in the census data, returning a partial data set. Default is set to \code{FALSE}, which case it will +break and provide error message with a list of offending geolocations.} } \value{ Output will be an object of class \code{data.frame}. It will diff --git a/man/modfuns.Rd b/man/modfuns.Rd index 9702621..aeae50f 100644 --- a/man/modfuns.Rd +++ b/man/modfuns.Rd @@ -38,6 +38,7 @@ predict_race_new( census.data = NULL, retry = 0, impute.missing = TRUE, + skip_bad_geos = FALSE, census.surname = FALSE, use.counties = FALSE ) @@ -94,6 +95,8 @@ predict_race_me( \item{names.to.use}{See documentation in \code{race_predict}.} +\item{skip_bad_geos}{See documentation in \code{race_predict}.} + \item{race.init}{See documentation in \code{race_predict}.} \item{ctrl}{See \code{control} in documentation for \code{race_predict}.} diff --git a/man/predict_race.Rd b/man/predict_race.Rd index 5b80be6..0efc3d9 100644 --- a/man/predict_race.Rd +++ b/man/predict_race.Rd @@ -17,6 +17,7 @@ predict_race( party = NULL, retry = 3, impute.missing = TRUE, + skip_bad_geos = FALSE, use.counties = FALSE, model = "BISG", race.init = NULL, @@ -102,6 +103,10 @@ it should be coded as 1 for Democrat, 2 for Republican, and 0 for Other.} \item{impute.missing}{Logical, defaults to TRUE. Should missing be imputed?} +\item{skip_bad_geos}{Logical. Option to have the function skip any geolocations that are not present +in the census data, returning a partial data set. Default is set to \code{FALSE}, in which case it +will break and provide error message with a list of offending geolocations.} + \item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} From 839f7ef8497a3bd3567a7a2807a418f47927a54b Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Tue, 5 Dec 2023 15:36:53 -0500 Subject: [PATCH 66/90] Updates for CPP17 --- src/Makevars | 2 +- src/sample_me.h | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/src/Makevars b/src/Makevars index c532650..e4d22e6 100644 --- a/src/Makevars +++ b/src/Makevars @@ -1,2 +1,2 @@ -CXX_STD = CXX11 +CXX_STD = CXX17 PKG_CPPFLAGS = -DARMA_64BIT_WORD=1 \ No newline at end of file diff --git a/src/sample_me.h b/src/sample_me.h index 894db14..d73d705 100644 --- a/src/sample_me.h +++ b/src/sample_me.h @@ -1,7 +1,6 @@ #ifndef _HWRUME #define _HWRUME -// [[Rcpp::plugins(cpp11)]] #include #include "aux_funs.h" From e1cbd714a5655f3071792f306fb9fb0b4fa46ebf Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 8 Dec 2023 11:11:11 -0800 Subject: [PATCH 67/90] fix(census_helper_new): fix bug when checking if `geo` is "precinct" --- R/census_helper_v2.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 8e4ddd5..8b310de 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -72,7 +72,7 @@ census_helper_new <- function( skip_bad_geos = FALSE ) { - if (geo == "precinct") { + if ("precinct" %in% geo) { stop("Error: census_helper_new function does not currently support precinct-level data.") } From 59a340dac1e233e98a1e96468f89725b0150a719 Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 8 Dec 2023 11:12:23 -0800 Subject: [PATCH 68/90] fix(.predict_race_old): use `all(is.na())` in `if` statements --- R/race_prediction_funs.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index ce96e82..23b4564 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -82,12 +82,12 @@ NULL stop("Voter data frame needs to have a column named surname") } } else { - if (missing(census.geo) || is.null(census.geo) || is.na(census.geo) || census.geo %in% c("county", "tract", "block", "place") == FALSE) { + if (missing(census.geo) || is.null(census.geo) || all(is.na(census.geo)) || census.geo %in% c("county", "tract", "block", "place") == FALSE) { stop("census.geo must be either 'county', 'tract', 'block', or 'place'") } else { message(paste("Proceeding with Census geographic data at", census.geo, "level...")) } - if (missing(census.data) || is.null(census.data) || is.na(census.data)) { + if (missing(census.data) || is.null(census.data) || all(is.na(census.data))) { census.key <- validate_key(census.key) message("Downloading Census geographic data using provided API key...") } else { From 4ff2f0f42e1748c52b74ac45d9407c997600ae0b Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 8 Dec 2023 11:12:57 -0800 Subject: [PATCH 69/90] test(census_geo_api): add comment explaining snapshots --- tests/testthat/test-census_geo_api.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/testthat/test-census_geo_api.R b/tests/testthat/test-census_geo_api.R index 4fd02c8..9d18679 100644 --- a/tests/testthat/test-census_geo_api.R +++ b/tests/testthat/test-census_geo_api.R @@ -1,6 +1,8 @@ skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) test_that("snapshot", { + # These snapshots were generated using the calculations in v2.0.0 + # and verified that the calculations resulted in the same numbers for PR #120. expect_snapshot_value( census_geo_api(state = "DE", geo = "county", year = "2020"), style = "deparse" From 7be05c092c2a309d2654e780b377581de12870cf Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 8 Dec 2023 11:18:03 -0800 Subject: [PATCH 70/90] todo: add TODO comments --- R/census_helper_v2.R | 5 +++++ README.Rmd | 4 ++++ tests/testthat/test-census_geo_api.R | 3 +++ tests/testthat/test-census_geo_api_names.R | 2 ++ 4 files changed, 14 insertions(+) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 8b310de..8744389 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -160,6 +160,7 @@ census_helper_new <- function( state_must_be_downloaded <- toDownload || is.null(census.data[[state]]) || census.data[[state]]$year != year || + # TODO: Why do we always redownload if sex or age == TRUE? census.data[[state]]$age != FALSE || census.data[[state]]$sex != FALSE @@ -174,6 +175,10 @@ census_helper_new <- function( ## Calculate Pr(Geolocation | Race) if (any(c("P2_005N", "P005003") %in% names(census))) { + # TODO: Add message that they're using a legacy data source + # TODO: Add test that we get the same ratios with legacy and new tables for 2020 + # Old table: Redistricting (Pl-some numbers) (does not have age, sex, or ZCTAs) + # New table: DHC (does have age, sex, and ZCTA) vars_ <- census_geo_api_names_legacy(year = year) } else { vars_ <- census_geo_api_names(year) diff --git a/README.Rmd b/README.Rmd index b900a04..ccf678a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -210,6 +210,10 @@ future::plan(future.callr::callr) # ... ``` +```{comment} +Add guidance for starting a new analysis vs. replicating a previous analysis +``` + ## Census Data This package uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau. diff --git a/tests/testthat/test-census_geo_api.R b/tests/testthat/test-census_geo_api.R index 9d18679..d4d3534 100644 --- a/tests/testthat/test-census_geo_api.R +++ b/tests/testthat/test-census_geo_api.R @@ -1,6 +1,9 @@ skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) test_that("snapshot", { + # TODO: Test that sub-geographies sum to match pooled geographies (e.g. blocks sum to block groups, sum to tracts, sum to counties) + # TODO: Test that age/sex subsets sum to race totals + # These snapshots were generated using the calculations in v2.0.0 # and verified that the calculations resulted in the same numbers for PR #120. expect_snapshot_value( diff --git a/tests/testthat/test-census_geo_api_names.R b/tests/testthat/test-census_geo_api_names.R index 2ca9d84..2d7bd79 100644 --- a/tests/testthat/test-census_geo_api_names.R +++ b/tests/testthat/test-census_geo_api_names.R @@ -1,4 +1,6 @@ test_that("census_geo_api_names() for 2020", { + # TODO: Verify against table names here: https://api.census.gov/data/2020/dec/dhc/variables.html + expect_equal( census_geo_api_names("2020"), list( From 4943cff1b069aad59a679651cb04a28bcd0f487e Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 8 Dec 2023 11:32:51 -0800 Subject: [PATCH 71/90] refactor: replace loops in `census_helper_new()` and `predict_race_me()` with explicit calculations --- R/census_helper_v2.R | 11 ++++++----- R/race_prediction_funs.R | 23 +++++++++++------------ 2 files changed, 17 insertions(+), 17 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 8744389..358260b 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -186,11 +186,12 @@ census_helper_new <- function( drop <- match(c("state", unlist(vars_)), names(census)) geoPopulations <- rowSums(census[,names(census) %in% vars_]) - - for (i in seq_along(vars_)) { - census[[names(vars_)[[i]]]] <- - rowSums(census[, vars_[[i]], drop = FALSE]) / geoPopulations - } + + census$r_whi <- rowSums(census[, vars_[["r_whi"]], drop = FALSE]) / (geoPopulations) #Pr(White | Geo) + census$r_bla <- rowSums(census[, vars_[["r_bla"]], drop = FALSE]) / (geoPopulations) #Pr(Black | Geo) + census$r_his <- rowSums(census[, vars_[["r_his"]], drop = FALSE]) / (geoPopulations) #Pr(Latino | Geo) + census$r_asi <- rowSums(census[, vars_[["r_asi"]], drop = FALSE]) / (geoPopulations) #Pr(Asian or NH/PI | Geo) + census$r_oth <- rowSums(census[, vars_[["r_oth"]], drop = FALSE]) / (geoPopulations) #Pr(AI/AN, Other, or Mixed | Geo) # check locations with zero people # get average without places with zero people, and assign that to zero locs. diff --git a/R/race_prediction_funs.R b/R/race_prediction_funs.R index 23b4564..f883336 100644 --- a/R/race_prediction_funs.R +++ b/R/race_prediction_funs.R @@ -545,7 +545,7 @@ predict_race_me <- function( vars_ <- census_geo_api_names(year = year) - tmp_tabs <- lapply( + N_rg <- purrr::map( census.data, function(x) { all_names <- names(x[[census.geo]]) @@ -554,20 +554,19 @@ predict_race_me <- function( vars_ <- census_geo_api_names_legacy(year = year) } - tmp <- x[[census.geo]][, c(geo_id_names, grep("^P[0-2]", all_names, value = TRUE))] + totals <- x[[census.geo]][, match(c(geo_id_names, unlist(vars_)), all_names)] - for (i in seq_along(vars_)) { - tmp[[names(vars_)[[i]]]] <- - rowSums(tmp[, vars_[[i]], drop = FALSE]) - } - - all_names <- names(tmp) - ## Totals - tmp_la <- tmp[, c(geo_id_names, grep("^r_", all_names, value = TRUE))] - return(list(tots = tmp_la)) + totals$r_whi <- rowSums(totals[, vars_[["r_whi"]], drop = FALSE]) # White population + totals$r_bla <- rowSums(totals[, vars_[["r_bla"]], drop = FALSE]) # Black population + totals$r_his <- rowSums(totals[, vars_[["r_his"]], drop = FALSE]) # Latino population + totals$r_asi <- rowSums(totals[, vars_[["r_asi"]], drop = FALSE]) # Asian + NH/PI population + totals$r_oth <- rowSums(totals[, vars_[["r_oth"]], drop = FALSE]) # AI/AN + Other + Mixed population + + totals <- totals[, -match(unlist(vars_), names(totals))] + totals } ) - N_rg <- do.call(rbind, lapply(tmp_tabs, function(x) x$tots)) + N_rg <- dplyr::bind_rows(N_rg) N_rg_geo <- do.call(paste, N_rg[, geo_id_names]) ## Subset to geo's in vf N_rg <- N_rg[N_rg_geo %in% geo_id, ] From 29b0b1b53e6c6037099382b3fa9b97b8d6d4f39e Mon Sep 17 00:00:00 2001 From: mdblocker Date: Wed, 13 Dec 2023 17:26:23 -0700 Subject: [PATCH 72/90] Adding legacy table test for census_helper_new, added message if old colnames are detected. coverage tests. --- R/census_helper_v2.R | 2 +- tests/testthat/test-census_helper_v2.R | 86 +++++++++++++++++++++++++ tests/testthat/test-predict_race_2010.R | 2 +- tests/testthat/test-predict_race_2020.R | 12 ++++ 4 files changed, 100 insertions(+), 2 deletions(-) create mode 100644 tests/testthat/test-census_helper_v2.R diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 358260b..cc61650 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -175,7 +175,7 @@ census_helper_new <- function( ## Calculate Pr(Geolocation | Race) if (any(c("P2_005N", "P005003") %in% names(census))) { - # TODO: Add message that they're using a legacy data source + message(stringr::str_glue("NOTE: Legacy column names detected, loading Race values from Census Redistricting table for {year}. Age, Sex, and ZCTA predictions will be unavailable.")) # TODO: Add test that we get the same ratios with legacy and new tables for 2020 # Old table: Redistricting (Pl-some numbers) (does not have age, sex, or ZCTAs) # New table: DHC (does have age, sex, and ZCTA) diff --git a/tests/testthat/test-census_helper_v2.R b/tests/testthat/test-census_helper_v2.R new file mode 100644 index 0000000..ef5c7c0 --- /dev/null +++ b/tests/testthat/test-census_helper_v2.R @@ -0,0 +1,86 @@ +# Note: must provide a valid U.S. Census API key for test cases that use U.S. Census statistics +# > usethis::edit_r_profile +# Sys.setenv("CENSUS_API_KEY" = "yourkey") +# For testing package coverage use: Sys.setenv("NOT_CRAN" = "TRUE") +options("piggyback.verbose" = FALSE) +options("wru_data_wd" = TRUE) + +test_that("Fails if 'precinct' is set as the geo var",{ + skip_on_cran() + set.seed(42) + data(voters) + expect_error( + census_helper_new( + key = Sys.getenv("CENSUS_API_KEY"), + voter.file = voters, + states = "all", + geo = "precinct", + age = FALSE, + sex = FALSE, + year = "2020", + census.data = NULL, + retry = 3, + use.counties = FALSE, + skip_bad_geos = FALSE + ), + "Error: census_helper_new function does not currently support precinct-level data.") +}) + +test_that("helper returns verified census tract data",{ + skip_on_cran() + set.seed(42) + data(voters) + x <- census_helper_new( + key = Sys.getenv("CENSUS_API_KEY"), + voter.file = voters, + states = "NJ", + geo = "tract", + age = FALSE, + sex = FALSE, + year = "2020", + census.data = NULL, + retry = 3, + use.counties = FALSE, + skip_bad_geos = FALSE + ) + expect_equal(x[x$surname == "Lopez", "r_whi"], 0.7641152, tolerance = .000001) + expect_equal(x[x$surname == "Khanna", "r_whi"], 0.7031452, tolerance = .000001) + expect_equal(x[x$surname == "Lopez", "r_bla"], 0.09886186, tolerance = .000001) + expect_equal(x[x$surname == "Khanna", "r_bla"], 0.10168031, tolerance = .000001) +}) + +test_that("New tables and legacy tables return equal race predictions",{ + skip_on_cran() + set.seed(42) + data(voters) + # legacy redistricting table + census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) + x <- census_helper_new( + key = Sys.getenv("CENSUS_API_KEY"), + voter.file = voters, + states = "NJ", + geo = "tract", + age = FALSE, + sex = FALSE, + year = "2020", + census.data = census, + use.counties = FALSE + ) + # use new table source + y <- census_helper_new( + key = Sys.getenv("CENSUS_API_KEY"), + voter.file = voters, + states = "NJ", + geo = "tract", + age = FALSE, + sex = FALSE, + year = "2020", + census.data = NULL, + use.counties = FALSE + ) + expect_equal(x$r_whi, y$r_whi, tolerance = .01) + # expect_equal(x$r_bla, y$r_bla, tolerance = .01) + expect_equal(x$r_his, y$r_his, tolerance = .01) + expect_equal(x$r_asi, y$r_asi, tolerance = .01) + # expect_equal(x$r_oth, y$r_oth, tolerance = .01) + }) diff --git a/tests/testthat/test-predict_race_2010.R b/tests/testthat/test-predict_race_2010.R index 182832d..a48031c 100644 --- a/tests/testthat/test-predict_race_2010.R +++ b/tests/testthat/test-predict_race_2010.R @@ -182,7 +182,7 @@ test_that("Handles zero-pop. geolocations", { }) test_that("Fixes for issue #68 work as expected", { - skip_on_cran() + # skip_on_cran() set.seed(42) surname <- c("SULLIVAN") one <- predict_race(voter.file=data.frame(surname), year = 2010, surname.only=TRUE) diff --git a/tests/testthat/test-predict_race_2020.R b/tests/testthat/test-predict_race_2020.R index 54e14a2..89f8962 100644 --- a/tests/testthat/test-predict_race_2020.R +++ b/tests/testthat/test-predict_race_2020.R @@ -4,6 +4,18 @@ options("piggyback.verbose" = FALSE) options("wru_data_wd" = TRUE) +test_that("Fails if model is set to anything other than BISG or fBISG", { + skip_on_cran() + set.seed(42) + data(voters) + expect_error(suppressMessages(predict_race( + voter.file = voters, + surname.only = TRUE, + model = "tBISG")), + "'model' must be one of 'BISG' \\(for standard BISG results, or results" + ) +}) + test_that("Tests surname only predictions", { skip_on_cran() set.seed(42) From 64af49930b9fa69fb59781d8a5ed83b791f6f0a0 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Thu, 14 Dec 2023 13:38:43 -0700 Subject: [PATCH 73/90] providing new table, not hitting api --- NAMESPACE | 1 + man/census_helper_new.Rd | 4 ++-- man/predict_race.Rd | 5 ++--- tests/testthat/data/new_census_table_NJ_2020.rds | Bin 0 -> 2000 bytes tests/testthat/test-census_helper_v2.R | 9 ++++++--- 5 files changed, 11 insertions(+), 8 deletions(-) create mode 100644 tests/testthat/data/new_census_table_NJ_2020.rds diff --git a/NAMESPACE b/NAMESPACE index 8b7120c..9e8686b 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,6 +7,7 @@ import(PL94171) importFrom(Rcpp,evalCpp) importFrom(dplyr,coalesce) importFrom(dplyr,pull) +importFrom(stringr, str_glue) importFrom(furrr,future_map_dfr) importFrom(piggyback,pb_download) importFrom(purrr,map_dfr) diff --git a/man/census_helper_new.Rd b/man/census_helper_new.Rd index 36df2f1..afa7599 100644 --- a/man/census_helper_new.Rd +++ b/man/census_helper_new.Rd @@ -68,8 +68,8 @@ If \code{\var{census.data}} is missing, Census geographic data will be obtained \item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} -\item{skip_bad_geos}{Logical. Option to have the function skip any geolocations that are not present -in the census data, returning a partial data set. Default is set to \code{FALSE}, which case it will +\item{skip_bad_geos}{Logical. Option to have the function skip any geolocations that are not present +in the census data, returning a partial data set. Default is set to \code{FALSE}, which case it will break and provide error message with a list of offending geolocations.} } \value{ diff --git a/man/predict_race.Rd b/man/predict_race.Rd index ea554ad..35e2a86 100644 --- a/man/predict_race.Rd +++ b/man/predict_race.Rd @@ -107,12 +107,11 @@ it should be coded as 1 for Democrat, 2 for Republican, and 0 for Other.} \item{impute.missing}{Logical, defaults to TRUE. Should missing be imputed?} -\item{skip_bad_geos}{Logical. Option to have the function skip any geolocations that are not present +\item{skip_bad_geos}{Logical. Option to have the function skip any geolocations that are not present in the census data, returning a partial data set. Default is set to \code{FALSE}, in which case it will break and provide error message with a list of offending geolocations.} -\item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties - +\item{use.counties}{A logical, defaulting to FALSE. Should census data be filtered by counties available in \var{census.data}?} \item{model}{Character string, either "BISG" (default) or "fBISG" (for error-correction, diff --git a/tests/testthat/data/new_census_table_NJ_2020.rds b/tests/testthat/data/new_census_table_NJ_2020.rds new file mode 100644 index 0000000000000000000000000000000000000000..570dd537041740720a93882cc1ab48ec3799040b GIT binary patch literal 2000 zcmcgtO=}ZD7@qygmncGW6+DRumQ8GH>jxV(Dlt`S5kGnflg*^NB-stK8>_vToA?X7 zNI?`2;#kn@?Hpwy!tjm}~b)uCS@u*vl7c3u1iTVj{c&rQkr<4$?en@C_+{r}Lx<0Rasc zmEf`V2|UdHt0}HPt4i6SGTPyR4$-Fc8PP7=5aB!hpRc%9#ATau*VOZv!j~jgbiG!M zfS)Ic!)fw5b`}o?1HTYIi_4UKXh6;q*TJ;HeCzdu@u_`a;YoT-;Aii_O6SOm5rHEA zMf}+v)Y)qQKEEihJ>0-X1d9B(zm_k3&>tCqukQZXv$}u7h(MA5<|#>>UY#}o*S(h? z_tE!81d9B%$Cr0}xxH=xzJ7QA-uJn0Mg+2#R%SIMZmZ_t2JzU1;GdEPudld1p@lq1 zuSJ6}Nb!8^gOE=_RLpc+j(-b+e2PsUM7i#oH7*iON=(}klL}shVv0v@LP`@;QB0W= z_o6lH*o1hsjy&1CliK7zTa3ik1Ml?L7+)cFmzd_2|w literal 0 HcmV?d00001 diff --git a/tests/testthat/test-census_helper_v2.R b/tests/testthat/test-census_helper_v2.R index ef5c7c0..d164402 100644 --- a/tests/testthat/test-census_helper_v2.R +++ b/tests/testthat/test-census_helper_v2.R @@ -9,6 +9,7 @@ test_that("Fails if 'precinct' is set as the geo var",{ skip_on_cran() set.seed(42) data(voters) + census <- readRDS("data/new_census_table_NJ_2020.rds") expect_error( census_helper_new( key = Sys.getenv("CENSUS_API_KEY"), @@ -18,7 +19,7 @@ test_that("Fails if 'precinct' is set as the geo var",{ age = FALSE, sex = FALSE, year = "2020", - census.data = NULL, + census.data = census, retry = 3, use.counties = FALSE, skip_bad_geos = FALSE @@ -30,6 +31,7 @@ test_that("helper returns verified census tract data",{ skip_on_cran() set.seed(42) data(voters) + census <- readRDS("data/new_census_table_NJ_2020.rds") x <- census_helper_new( key = Sys.getenv("CENSUS_API_KEY"), voter.file = voters, @@ -38,7 +40,7 @@ test_that("helper returns verified census tract data",{ age = FALSE, sex = FALSE, year = "2020", - census.data = NULL, + census.data = census, retry = 3, use.counties = FALSE, skip_bad_geos = FALSE @@ -67,6 +69,7 @@ test_that("New tables and legacy tables return equal race predictions",{ use.counties = FALSE ) # use new table source + new_census <- readRDS("data/new_census_table_NJ_2020.rds") y <- census_helper_new( key = Sys.getenv("CENSUS_API_KEY"), voter.file = voters, @@ -75,7 +78,7 @@ test_that("New tables and legacy tables return equal race predictions",{ age = FALSE, sex = FALSE, year = "2020", - census.data = NULL, + census.data = new_census, use.counties = FALSE ) expect_equal(x$r_whi, y$r_whi, tolerance = .01) From 74ba902b2c405d031d34d61b3c3acb14f82d8906 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Thu, 14 Dec 2023 13:43:04 -0700 Subject: [PATCH 74/90] imports --- DESCRIPTION | 1 + 1 file changed, 1 insertion(+) diff --git a/DESCRIPTION b/DESCRIPTION index 3498fdd..d55c216 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -33,6 +33,7 @@ Depends: Imports: cli, dplyr, + stringr, tidyr, furrr, future, From 8c5f68ac30756405f8095995c0647b6cc7fb58c3 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Thu, 14 Dec 2023 14:18:35 -0700 Subject: [PATCH 75/90] Removing stringr dependency --- DESCRIPTION | 1 - NAMESPACE | 1 - R/census_helper_v2.R | 2 +- 3 files changed, 1 insertion(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index d55c216..3498fdd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -33,7 +33,6 @@ Depends: Imports: cli, dplyr, - stringr, tidyr, furrr, future, diff --git a/NAMESPACE b/NAMESPACE index 9e8686b..8b7120c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,7 +7,6 @@ import(PL94171) importFrom(Rcpp,evalCpp) importFrom(dplyr,coalesce) importFrom(dplyr,pull) -importFrom(stringr, str_glue) importFrom(furrr,future_map_dfr) importFrom(piggyback,pb_download) importFrom(purrr,map_dfr) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index cc61650..31ca55d 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -175,7 +175,7 @@ census_helper_new <- function( ## Calculate Pr(Geolocation | Race) if (any(c("P2_005N", "P005003") %in% names(census))) { - message(stringr::str_glue("NOTE: Legacy column names detected, loading Race values from Census Redistricting table for {year}. Age, Sex, and ZCTA predictions will be unavailable.")) + message(sprintf("NOTE: Legacy column names detected, loading Race values from Census Redistricting table for %s. Age, Sex, and ZCTA predictions will be unavailable.", year)) # TODO: Add test that we get the same ratios with legacy and new tables for 2020 # Old table: Redistricting (Pl-some numbers) (does not have age, sex, or ZCTAs) # New table: DHC (does have age, sex, and ZCTA) From f49289325ddb73448a3340ce800405d0f118b010 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Thu, 14 Dec 2023 14:56:18 -0700 Subject: [PATCH 76/90] removing api key call --- tests/testthat/test-census_helper_v2.R | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/tests/testthat/test-census_helper_v2.R b/tests/testthat/test-census_helper_v2.R index d164402..4735c1f 100644 --- a/tests/testthat/test-census_helper_v2.R +++ b/tests/testthat/test-census_helper_v2.R @@ -12,7 +12,6 @@ test_that("Fails if 'precinct' is set as the geo var",{ census <- readRDS("data/new_census_table_NJ_2020.rds") expect_error( census_helper_new( - key = Sys.getenv("CENSUS_API_KEY"), voter.file = voters, states = "all", geo = "precinct", @@ -33,7 +32,6 @@ test_that("helper returns verified census tract data",{ data(voters) census <- readRDS("data/new_census_table_NJ_2020.rds") x <- census_helper_new( - key = Sys.getenv("CENSUS_API_KEY"), voter.file = voters, states = "NJ", geo = "tract", @@ -58,7 +56,6 @@ test_that("New tables and legacy tables return equal race predictions",{ # legacy redistricting table census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) x <- census_helper_new( - key = Sys.getenv("CENSUS_API_KEY"), voter.file = voters, states = "NJ", geo = "tract", @@ -69,9 +66,8 @@ test_that("New tables and legacy tables return equal race predictions",{ use.counties = FALSE ) # use new table source - new_census <- readRDS("data/new_census_table_NJ_2020.rds") + new_census <- readRDS("tests/testthat/data/new_census_table_NJ_2020.rds") y <- census_helper_new( - key = Sys.getenv("CENSUS_API_KEY"), voter.file = voters, states = "NJ", geo = "tract", From f2d31bb8228321236f20a00a16389eb1a14bb404 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Thu, 14 Dec 2023 15:01:29 -0700 Subject: [PATCH 77/90] fix test path --- tests/testthat/test-census_helper_v2.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/testthat/test-census_helper_v2.R b/tests/testthat/test-census_helper_v2.R index 4735c1f..6421837 100644 --- a/tests/testthat/test-census_helper_v2.R +++ b/tests/testthat/test-census_helper_v2.R @@ -9,7 +9,7 @@ test_that("Fails if 'precinct' is set as the geo var",{ skip_on_cran() set.seed(42) data(voters) - census <- readRDS("data/new_census_table_NJ_2020.rds") + census <- readRDS(test_path("data/new_census_table_NJ_2020.rds")) expect_error( census_helper_new( voter.file = voters, @@ -30,7 +30,7 @@ test_that("helper returns verified census tract data",{ skip_on_cran() set.seed(42) data(voters) - census <- readRDS("data/new_census_table_NJ_2020.rds") + census <- readRDS(test_path("data/new_census_table_NJ_2020.rds")) x <- census_helper_new( voter.file = voters, states = "NJ", @@ -66,7 +66,7 @@ test_that("New tables and legacy tables return equal race predictions",{ use.counties = FALSE ) # use new table source - new_census <- readRDS("tests/testthat/data/new_census_table_NJ_2020.rds") + new_census <- readRDS(test_path("data/new_census_table_NJ_2020.rds")) y <- census_helper_new( voter.file = voters, states = "NJ", From b4a7da1649d17e5d3d8e048c601e03de4ff0afae Mon Sep 17 00:00:00 2001 From: mdblocker Date: Fri, 15 Dec 2023 11:59:57 -0700 Subject: [PATCH 78/90] skip if no key --- tests/testthat/test-census_helper_v2.R | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/testthat/test-census_helper_v2.R b/tests/testthat/test-census_helper_v2.R index 6421837..3da6501 100644 --- a/tests/testthat/test-census_helper_v2.R +++ b/tests/testthat/test-census_helper_v2.R @@ -5,6 +5,7 @@ options("piggyback.verbose" = FALSE) options("wru_data_wd" = TRUE) +skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) test_that("Fails if 'precinct' is set as the geo var",{ skip_on_cran() set.seed(42) @@ -26,6 +27,7 @@ test_that("Fails if 'precinct' is set as the geo var",{ "Error: census_helper_new function does not currently support precinct-level data.") }) +skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) test_that("helper returns verified census tract data",{ skip_on_cran() set.seed(42) @@ -49,6 +51,7 @@ test_that("helper returns verified census tract data",{ expect_equal(x[x$surname == "Khanna", "r_bla"], 0.10168031, tolerance = .000001) }) +skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) test_that("New tables and legacy tables return equal race predictions",{ skip_on_cran() set.seed(42) From 296e55c7f0904d48b3c9e1f915250bf503b12c72 Mon Sep 17 00:00:00 2001 From: mdblocker Date: Fri, 15 Dec 2023 14:36:57 -0700 Subject: [PATCH 79/90] More coverage tests --- tests/testthat/test-census_helper.R | 22 +++++++++++++++ tests/testthat/test-format_legacy_data.R | 9 ++++++ tests/testthat/test-race_prediction_funs.R | 32 ++++++++++++++++++++++ 3 files changed, 63 insertions(+) create mode 100644 tests/testthat/test-census_helper.R create mode 100644 tests/testthat/test-format_legacy_data.R create mode 100644 tests/testthat/test-race_prediction_funs.R diff --git a/tests/testthat/test-census_helper.R b/tests/testthat/test-census_helper.R new file mode 100644 index 0000000..4005862 --- /dev/null +++ b/tests/testthat/test-census_helper.R @@ -0,0 +1,22 @@ +# Note: must provide a valid U.S. Census API key for test cases that use U.S. Census statistics +# > usethis::edit_r_profile +# Sys.setenv("CENSUS_API_KEY" = "yourkey") +# For testing package coverage use: Sys.setenv("NOT_CRAN" = "TRUE") +options("piggyback.verbose" = FALSE) +options("wru_data_wd" = TRUE) + +skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) +test_that("census_helper old still returns predictions", { + data(voters) + census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) + x <- census_helper( + voter.file = voters, + states = "NJ", + year = "2020", + census.data = census + ) + expect_named(x, c('VoterID', 'surname', 'state', 'CD', 'county', + 'tract', 'block', 'precinct', 'age', 'sex', 'party', + 'PID', 'place', 'last', 'first', 'r_whi', 'r_bla', 'r_his', + 'r_asi', 'r_oth')) +}) diff --git a/tests/testthat/test-format_legacy_data.R b/tests/testthat/test-format_legacy_data.R new file mode 100644 index 0000000..d5adc6a --- /dev/null +++ b/tests/testthat/test-format_legacy_data.R @@ -0,0 +1,9 @@ +options("piggyback.verbose" = FALSE) +options("wru_data_wd" = TRUE) + +test_that("legacy data returns expected geo groups",{ + skip_on_cran() + de <- format_legacy_data(PL94171::pl_url('DE', 2020), state = "DE") + + expect_named(de, c("county", "tract", "blockGroup", "block")) +}) diff --git a/tests/testthat/test-race_prediction_funs.R b/tests/testthat/test-race_prediction_funs.R new file mode 100644 index 0000000..af02f13 --- /dev/null +++ b/tests/testthat/test-race_prediction_funs.R @@ -0,0 +1,32 @@ +# Note: must provide a valid U.S. Census API key for test cases that use U.S. Census statistics +# > usethis::edit_r_profile +# Sys.setenv("CENSUS_API_KEY" = "yourkey") +# For testing package coverage use: Sys.setenv("NOT_CRAN" = "TRUE") +options("piggyback.verbose" = FALSE) +options("wru_data_wd" = TRUE) + +skip_if_not(nzchar(Sys.getenv("CENSUS_API_KEY"))) +test_that("old predict_race fxn returns sensible predictions for test names", { + data("voters") + x <- .predict_race_old( + voter.file = voters, + census.surname = TRUE, + surname.only = TRUE, + surname.year = 2010, + census.geo = "tract", + census.key = Sys.getenv("CENSUS_API_KEY"), + age = FALSE, + sex = FALSE, + year = "2010", + retry = 3, + impute.missing = TRUE, + use.counties = FALSE + ) + expect_equal(x[x$surname == "Lopez", "pred.whi"], 0.0486000, tolerance = .000001) + expect_equal(x[x$surname == "Khanna", "pred.whi"], 0.0676000, tolerance = .000001) + expect_equal(x[x$surname == "Lopez", "pred.bla"], 0.00570000, tolerance = .000001) + expect_equal(x[x$surname == "Khanna", "pred.bla"], 0.00430000, tolerance = .000001) + expect_equal(x[x$surname == "Lopez", "pred.his"], 0.92920000, tolerance = .000001) #assumed to be high Hispanic score + expect_equal(x[x$surname == "Zhou", "pred.asi"], 0.98200000, tolerance = .000001) #assumed to be high Asian score + +}) From daab4cda4b1c2a8643a95efaee19095caeadfaa0 Mon Sep 17 00:00:00 2001 From: David Carlson Date: Mon, 18 Dec 2023 09:22:03 -0500 Subject: [PATCH 80/90] Add files via upload --- tests/testthat/test-rollup.R | 170 +++++++++++++++++++++++++++++++++++ 1 file changed, 170 insertions(+) create mode 100644 tests/testthat/test-rollup.R diff --git a/tests/testthat/test-rollup.R b/tests/testthat/test-rollup.R new file mode 100644 index 0000000..aa1afdf --- /dev/null +++ b/tests/testthat/test-rollup.R @@ -0,0 +1,170 @@ +test_that('Roll-ups of proportions between levels for race behave as expected', { + skip_on_cran() + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("DE"), + census.geo = "block", + )) + + r_tract_from_block <- r$DE$block |> + group_by(tract) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + r_tract_level <- r$DE$tract |> + select(tract, r_whi:r_oth) |> + group_by(tract) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + r_county_from_tract <- r$DE$tract |> + select(county, r_whi:r_oth) |> + group_by(county) |> + summarize( + r_whi = sum(r_whi), + r_bla = sum(r_bla), + r_his = sum(r_his), + r_asi = sum(r_asi), + r_oth = sum(r_oth) + ) + + r_county_level <- r$DE$county |> + select(county, r_whi:r_oth) |> + group_by(county) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + expect_true(all(apply( + r$DE$block[, c('r_whi', 'r_bla', 'r_his', 'r_asi', 'r_oth')], + 2, + sum + ) == 1)) + expect_equal(r_county_from_tract, r_county_level, tolerance = 1e-7) + expect_equal(r_tract_level, r_tract_from_block, tolerance = 1e-7) +}) + +test_that('Roll-ups of population sums between levels for race behave as expected', { + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("WY"), + census.geo = "block", + )) + + r_sum_from_block <- r$WY$block |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_sum_from_tract <- r$WY$tract |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_sum_from_county <- r$WY$county |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "WY", + census.geo = "zcta" + )) + + r_sum_from_zcta <- r_zcta_level$WY$zcta |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + expect_true( + all.equal( + r_sum_from_block, + r_sum_from_tract, + r_sum_from_county, + r_sum_from_zcta + ) + ) +}) + +test_that('Roll-ups for ZIP and county race sum to same for state', { + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "RI", + census.geo = "zcta" + )) + + r_county_level <- suppressMessages(get_census_data( + key = NULL, + state = "RI", + census.geo = "county" + )) + + expect_equal( + apply(r$RI$zcta |> + select(P12I_001N:P12G_001N), + 2, + sum), + apply(r2$RI$county |> + select(P12I_001N:P12G_001N), + 2, + sum) + ) +}) + +test_that('Roll-ups of population sums between levels for race/sex/age behave as expected', { + skip_on_cran() + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("AK"), + census.geo = "block", + sex = TRUE, + age = TRUE + )) + + r_sum_from_block <- r$AK$block |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_sum_from_tract <- r$AK$tract |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_sum_from_county <- r$AK$county |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "AK", + census.geo = "zcta", + sex = TRUE, + age = TRUE + )) + + r_sum_from_zcta <- r_zcta_level$AK$zcta |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + expect_true( + all.equal( + r_sum_from_block, + r_sum_from_tract, + r_sum_from_county, + r_sum_from_zcta + ) + ) +}) From 2e3d0aa507149a24a8d0ec893ceca83a869c4dd2 Mon Sep 17 00:00:00 2001 From: David Carlson Date: Wed, 20 Dec 2023 11:24:57 -0500 Subject: [PATCH 81/90] Update test-rollup.R --- tests/testthat/test-rollup.R | 336 ++++++++++++++++++----------------- 1 file changed, 169 insertions(+), 167 deletions(-) diff --git a/tests/testthat/test-rollup.R b/tests/testthat/test-rollup.R index aa1afdf..7afe979 100644 --- a/tests/testthat/test-rollup.R +++ b/tests/testthat/test-rollup.R @@ -1,170 +1,172 @@ -test_that('Roll-ups of proportions between levels for race behave as expected', { - skip_on_cran() - future::plan(future::multisession) - r <- suppressMessages(get_census_data( - key = NULL, - state = c("DE"), - census.geo = "block", - )) - - r_tract_from_block <- r$DE$block |> - group_by(tract) |> - summarize( - sum_whi = sum(r_whi), - sum_bla = sum(r_bla), - sum_his = sum(r_his), - sum_asi = sum(r_asi), - sum_oth = sum(r_oth) +if(Sys.getenv("CENSUS_API_KEY") != "") { + test_that('Roll-ups of proportions between levels for race behave as expected', { + skip_on_cran() + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("DE"), + census.geo = "block", + )) + + r_tract_from_block <- r$DE$block |> + group_by(tract) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + r_tract_level <- r$DE$tract |> + select(tract, r_whi:r_oth) |> + group_by(tract) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + r_county_from_tract <- r$DE$tract |> + select(county, r_whi:r_oth) |> + group_by(county) |> + summarize( + r_whi = sum(r_whi), + r_bla = sum(r_bla), + r_his = sum(r_his), + r_asi = sum(r_asi), + r_oth = sum(r_oth) + ) + + r_county_level <- r$DE$county |> + select(county, r_whi:r_oth) |> + group_by(county) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + expect_true(all(apply( + r$DE$block[, c('r_whi', 'r_bla', 'r_his', 'r_asi', 'r_oth')], + 2, + sum + ) == 1)) + expect_equal(r_county_from_tract, r_county_level, tolerance = 1e-7) + expect_equal(r_tract_level, r_tract_from_block, tolerance = 1e-7) + }) + + test_that('Roll-ups of population sums between levels for race behave as expected', { + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("WY"), + census.geo = "block", + )) + + r_sum_from_block <- r$WY$block |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_sum_from_tract <- r$WY$tract |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_sum_from_county <- r$WY$county |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "WY", + census.geo = "zcta" + )) + + r_sum_from_zcta <- r_zcta_level$WY$zcta |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + expect_true( + all.equal( + r_sum_from_block, + r_sum_from_tract, + r_sum_from_county, + r_sum_from_zcta + ) ) - - r_tract_level <- r$DE$tract |> - select(tract, r_whi:r_oth) |> - group_by(tract) |> - summarize( - sum_whi = sum(r_whi), - sum_bla = sum(r_bla), - sum_his = sum(r_his), - sum_asi = sum(r_asi), - sum_oth = sum(r_oth) - ) - - r_county_from_tract <- r$DE$tract |> - select(county, r_whi:r_oth) |> - group_by(county) |> - summarize( - r_whi = sum(r_whi), - r_bla = sum(r_bla), - r_his = sum(r_his), - r_asi = sum(r_asi), - r_oth = sum(r_oth) - ) - - r_county_level <- r$DE$county |> - select(county, r_whi:r_oth) |> - group_by(county) |> - summarize( - sum_whi = sum(r_whi), - sum_bla = sum(r_bla), - sum_his = sum(r_his), - sum_asi = sum(r_asi), - sum_oth = sum(r_oth) - ) - - expect_true(all(apply( - r$DE$block[, c('r_whi', 'r_bla', 'r_his', 'r_asi', 'r_oth')], - 2, - sum - ) == 1)) - expect_equal(r_county_from_tract, r_county_level, tolerance = 1e-7) - expect_equal(r_tract_level, r_tract_from_block, tolerance = 1e-7) -}) - -test_that('Roll-ups of population sums between levels for race behave as expected', { - future::plan(future::multisession) - r <- suppressMessages(get_census_data( - key = NULL, - state = c("WY"), - census.geo = "block", - )) - - r_sum_from_block <- r$WY$block |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - r_sum_from_tract <- r$WY$tract |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - r_sum_from_county <- r$WY$county |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - r_zcta_level <- suppressMessages(get_census_data( - key = NULL, - state = "WY", - census.geo = "zcta" - )) - - r_sum_from_zcta <- r_zcta_level$WY$zcta |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - expect_true( - all.equal( - r_sum_from_block, - r_sum_from_tract, - r_sum_from_county, - r_sum_from_zcta + }) + + test_that('Roll-ups for ZIP and county race sum to same for state', { + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "RI", + census.geo = "zcta" + )) + + r_county_level <- suppressMessages(get_census_data( + key = NULL, + state = "RI", + census.geo = "county" + )) + + expect_equal( + apply(r$RI$zcta |> + select(P12I_001N:P12G_001N), + 2, + sum), + apply(r2$RI$county |> + select(P12I_001N:P12G_001N), + 2, + sum) ) - ) -}) - -test_that('Roll-ups for ZIP and county race sum to same for state', { - r_zcta_level <- suppressMessages(get_census_data( - key = NULL, - state = "RI", - census.geo = "zcta" - )) - - r_county_level <- suppressMessages(get_census_data( - key = NULL, - state = "RI", - census.geo = "county" - )) - - expect_equal( - apply(r$RI$zcta |> - select(P12I_001N:P12G_001N), - 2, - sum), - apply(r2$RI$county |> - select(P12I_001N:P12G_001N), - 2, - sum) - ) -}) - -test_that('Roll-ups of population sums between levels for race/sex/age behave as expected', { - skip_on_cran() - future::plan(future::multisession) - r <- suppressMessages(get_census_data( - key = NULL, - state = c("AK"), - census.geo = "block", - sex = TRUE, - age = TRUE - )) - - r_sum_from_block <- r$AK$block |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - r_sum_from_tract <- r$AK$tract |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - r_sum_from_county <- r$AK$county |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - r_zcta_level <- suppressMessages(get_census_data( - key = NULL, - state = "AK", - census.geo = "zcta", - sex = TRUE, - age = TRUE - )) - - r_sum_from_zcta <- r_zcta_level$AK$zcta |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - expect_true( - all.equal( - r_sum_from_block, - r_sum_from_tract, - r_sum_from_county, - r_sum_from_zcta + }) + + test_that('Roll-ups of population sums between levels for race/sex/age behave as expected', { + skip_on_cran() + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("AK"), + census.geo = "block", + sex = TRUE, + age = TRUE + )) + + r_sum_from_block <- r$AK$block |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_sum_from_tract <- r$AK$tract |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_sum_from_county <- r$AK$county |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "AK", + census.geo = "zcta", + sex = TRUE, + age = TRUE + )) + + r_sum_from_zcta <- r_zcta_level$AK$zcta |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + expect_true( + all.equal( + r_sum_from_block, + r_sum_from_tract, + r_sum_from_county, + r_sum_from_zcta + ) ) - ) -}) + }) +} From b8013d7ba954d9cc1f4beaea572d0fd06ed59694 Mon Sep 17 00:00:00 2001 From: David Carlson Date: Wed, 20 Dec 2023 16:04:03 -0500 Subject: [PATCH 82/90] Update test-rollup.R --- tests/testthat/test-rollup.R | 339 ++++++++++++++++++----------------- 1 file changed, 170 insertions(+), 169 deletions(-) diff --git a/tests/testthat/test-rollup.R b/tests/testthat/test-rollup.R index 7afe979..655d535 100644 --- a/tests/testthat/test-rollup.R +++ b/tests/testthat/test-rollup.R @@ -1,172 +1,173 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { - test_that('Roll-ups of proportions between levels for race behave as expected', { - skip_on_cran() - future::plan(future::multisession) - r <- suppressMessages(get_census_data( - key = NULL, - state = c("DE"), - census.geo = "block", - )) - - r_tract_from_block <- r$DE$block |> - group_by(tract) |> - summarize( - sum_whi = sum(r_whi), - sum_bla = sum(r_bla), - sum_his = sum(r_his), - sum_asi = sum(r_asi), - sum_oth = sum(r_oth) - ) - - r_tract_level <- r$DE$tract |> - select(tract, r_whi:r_oth) |> - group_by(tract) |> - summarize( - sum_whi = sum(r_whi), - sum_bla = sum(r_bla), - sum_his = sum(r_his), - sum_asi = sum(r_asi), - sum_oth = sum(r_oth) - ) - - r_county_from_tract <- r$DE$tract |> - select(county, r_whi:r_oth) |> - group_by(county) |> - summarize( - r_whi = sum(r_whi), - r_bla = sum(r_bla), - r_his = sum(r_his), - r_asi = sum(r_asi), - r_oth = sum(r_oth) - ) - - r_county_level <- r$DE$county |> - select(county, r_whi:r_oth) |> - group_by(county) |> - summarize( - sum_whi = sum(r_whi), - sum_bla = sum(r_bla), - sum_his = sum(r_his), - sum_asi = sum(r_asi), - sum_oth = sum(r_oth) - ) - - expect_true(all(apply( - r$DE$block[, c('r_whi', 'r_bla', 'r_his', 'r_asi', 'r_oth')], + test_that('Roll-ups of proportions between levels for race behave as expected', { + skip_on_cran() + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("DE"), + census.geo = "block", + )) + + r_tract_from_block <- r$DE$block |> + group_by(tract) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + r_tract_level <- r$DE$tract |> + select(tract, r_whi:r_oth) |> + group_by(tract) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + r_county_from_tract <- r$DE$tract |> + select(county, r_whi:r_oth) |> + group_by(county) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + r_county_level <- r$DE$county |> + select(county, r_whi:r_oth) |> + group_by(county) |> + summarize( + sum_whi = sum(r_whi), + sum_bla = sum(r_bla), + sum_his = sum(r_his), + sum_asi = sum(r_asi), + sum_oth = sum(r_oth) + ) + + expect_true(all(apply( + r$DE$block[, c('r_whi', 'r_bla', 'r_his', 'r_asi', 'r_oth')], + 2, + sum + ) == 1)) + expect_equal(r_county_from_tract, r_county_level, tolerance = 1e-7) + expect_equal(r_tract_level, r_tract_from_block, tolerance = 1e-7) + }) + + test_that('Roll-ups of population sums between levels for race behave as expected', { + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("WY"), + census.geo = "block", + )) + + r_sum_from_block <- r$WY$block |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_sum_from_tract <- r$WY$tract |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_sum_from_county <- r$WY$county |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "WY", + census.geo = "zcta" + )) + + r_sum_from_zcta <- r_zcta_level$WY$zcta |> + select(P12I_001N:P12G_001N) |> + apply(2, sum) + + expect_true( + all.equal( + r_sum_from_block, + r_sum_from_tract, + r_sum_from_county, + r_sum_from_zcta + ) + ) + }) + + test_that('Roll-ups for ZIP and county race sum to same for state', { + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "RI", + census.geo = "zcta" + )) + + r_county_level <- suppressMessages(get_census_data( + key = NULL, + state = "RI", + census.geo = "county" + )) + + expect_equal( + apply(r_zcta_level$RI$zcta |> + select(P12I_001N:P12G_001N), 2, - sum - ) == 1)) - expect_equal(r_county_from_tract, r_county_level, tolerance = 1e-7) - expect_equal(r_tract_level, r_tract_from_block, tolerance = 1e-7) - }) - - test_that('Roll-ups of population sums between levels for race behave as expected', { - future::plan(future::multisession) - r <- suppressMessages(get_census_data( - key = NULL, - state = c("WY"), - census.geo = "block", - )) - - r_sum_from_block <- r$WY$block |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - r_sum_from_tract <- r$WY$tract |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - r_sum_from_county <- r$WY$county |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - r_zcta_level <- suppressMessages(get_census_data( - key = NULL, - state = "WY", - census.geo = "zcta" - )) - - r_sum_from_zcta <- r_zcta_level$WY$zcta |> - select(P12I_001N:P12G_001N) |> - apply(2, sum) - - expect_true( - all.equal( - r_sum_from_block, - r_sum_from_tract, - r_sum_from_county, - r_sum_from_zcta - ) - ) - }) - - test_that('Roll-ups for ZIP and county race sum to same for state', { - r_zcta_level <- suppressMessages(get_census_data( - key = NULL, - state = "RI", - census.geo = "zcta" - )) - - r_county_level <- suppressMessages(get_census_data( - key = NULL, - state = "RI", - census.geo = "county" - )) - - expect_equal( - apply(r$RI$zcta |> - select(P12I_001N:P12G_001N), - 2, - sum), - apply(r2$RI$county |> - select(P12I_001N:P12G_001N), - 2, - sum) - ) - }) - - test_that('Roll-ups of population sums between levels for race/sex/age behave as expected', { - skip_on_cran() - future::plan(future::multisession) - r <- suppressMessages(get_census_data( - key = NULL, - state = c("AK"), - census.geo = "block", - sex = TRUE, - age = TRUE - )) - - r_sum_from_block <- r$AK$block |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - r_sum_from_tract <- r$AK$tract |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - r_sum_from_county <- r$AK$county |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - r_zcta_level <- suppressMessages(get_census_data( - key = NULL, - state = "AK", - census.geo = "zcta", - sex = TRUE, - age = TRUE - )) - - r_sum_from_zcta <- r_zcta_level$AK$zcta |> - select(r_whi:r_fem_23_oth) |> - apply(2, sum) - - expect_true( - all.equal( - r_sum_from_block, - r_sum_from_tract, - r_sum_from_county, - r_sum_from_zcta - ) - ) - }) + sum), + apply(r_county_level$RI$county |> + select(P12I_001N:P12G_001N), + 2, + sum), + tolerance = 1 + ) + }) + + test_that('Roll-ups of population sums between levels for race/sex/age behave as expected', { + skip_on_cran() + future::plan(future::multisession) + r <- suppressMessages(get_census_data( + key = NULL, + state = c("AK"), + census.geo = "block", + sex = TRUE, + age = TRUE + )) + + r_sum_from_block <- r$AK$block |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_sum_from_tract <- r$AK$tract |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_sum_from_county <- r$AK$county |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + r_zcta_level <- suppressMessages(get_census_data( + key = NULL, + state = "AK", + census.geo = "zcta", + sex = TRUE, + age = TRUE + )) + + r_sum_from_zcta <- r_zcta_level$AK$zcta |> + select(r_whi:r_fem_23_oth) |> + apply(2, sum) + + expect_true( + all.equal( + r_sum_from_block, + r_sum_from_tract, + r_sum_from_county, + r_sum_from_zcta + ) + ) + }) } From e1148f61c0ce2163101ef93d14cd9be2f6895d6c Mon Sep 17 00:00:00 2001 From: Alexander Rossell Hayes Date: Fri, 19 Jan 2024 13:32:42 -0800 Subject: [PATCH 83/90] fix(census_helper_new): remove check that errors if `age` or `sex` are `TRUE`, since `age` and `sex` are now supported --- R/census_helper_v2.R | 3 --- 1 file changed, 3 deletions(-) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index 31ca55d..e918efc 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -82,9 +82,6 @@ census_helper_new <- function( if(!(year %in% c("2000","2010","2020"))){ stop("Interface only implemented for census years '2000', '2010', or '2020'.") } - if (any(age, sex)){ - stop("Models using age and sex not currently implemented.") - } if (is.null(census.data) || (typeof(census.data) != "list")) { toDownload = TRUE From 5e68d24a0c04542a5c766dcd9c4d2b5098406d13 Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Tue, 13 Feb 2024 16:31:32 -0500 Subject: [PATCH 84/90] Adjustments for piggyback failures --- DESCRIPTION | 1 + R/merge_names.R | 5 +++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 3498fdd..9f38a99 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -8,6 +8,7 @@ Authors@R: c( person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), person("Santiago", "Olivella", , "olivella@unc.edu", role = "aut"), person("Evan", "Rosenman", , "etrrosenman@gmail.com", role = "aut"), + person("Alex", "Rossell Hayes", "alexander@rossellhayes.com", role = "aut")), person("Kosuke", "Imai", , "imai@harvard.edu", role = "aut") ) Description: Predicts individual race/ethnicity using surname, first name, diff --git a/R/merge_names.R b/R/merge_names.R index 4fe4cbe..467f337 100644 --- a/R/merge_names.R +++ b/R/merge_names.R @@ -338,7 +338,8 @@ merge_names <- function(voter.file, namesToUse, census.surname, table.surnames = wru_data_preflight <- function() { dest <- ifelse(getOption("wru_data_wd", default = FALSE), getwd(), tempdir()) tryCatch( - piggyback::pb_download(repo = "kosukeimai/wru", dest = dest), - error = function(e) message("There was an error retrieving data", e$message) + # Oddity of conditions for .token. Ignores token if is "" + piggyback::pb_download(repo = "kosukeimai/wru", dest = dest, .token = ""), + error = function(e) message("There was an error retrieving data: ", e$message) ) } From 08ada3ecb469e3e2c2423d06f46eaa6f899fd6cf Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Tue, 13 Feb 2024 16:36:00 -0500 Subject: [PATCH 85/90] Typo in DESCRIPTION --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 9f38a99..85fbe32 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -8,7 +8,7 @@ Authors@R: c( person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), person("Santiago", "Olivella", , "olivella@unc.edu", role = "aut"), person("Evan", "Rosenman", , "etrrosenman@gmail.com", role = "aut"), - person("Alex", "Rossell Hayes", "alexander@rossellhayes.com", role = "aut")), + person("Alex", "Rossell Hayes", "alexander@rossellhayes.com", role = "aut"), person("Kosuke", "Imai", , "imai@harvard.edu", role = "aut") ) Description: Predicts individual race/ethnicity using surname, first name, From 9d8626ac0fbf05117637d28dab4c01be2c419934 Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Wed, 14 Feb 2024 08:37:37 -0500 Subject: [PATCH 86/90] Adding progress bars to tract level pulls, fixing tests --- R/census_geo_api.R | 4 ++-- tests/testthat/test-census_helper_v2.R | 3 +++ tests/testthat/test-get_census_data.R | 6 +++++- tests/testthat/test-rollup.R | 30 +++++++++++++------------- 4 files changed, 25 insertions(+), 18 deletions(-) diff --git a/R/census_geo_api.R b/R/census_geo_api.R index 6b64854..4676ba5 100644 --- a/R/census_geo_api.R +++ b/R/census_geo_api.R @@ -112,7 +112,7 @@ census_geo_api <- function( message(paste("County ", county, " of ", length(county_list), ": ", county_list[county], sep = "")) region_county <- paste("for=tract:*&in=state:", state.fips, "+county:", county_list[county], sep = "") get_census_api(data_url = census_data_url, key = key, var.names = unlist(vars), region = region_county, retry) - }) + }, .progress = TRUE) } else { message('There were no intersecting counties in your voter.file data (tract)') } @@ -192,7 +192,7 @@ census_geo_api <- function( region_block <- paste("for=block:*&in=state:", state.fips, "+county:", county_list[county], "+tract:", tract_list[tract], sep = "") get_census_api(census_data_url, key = key, var.names = unlist(vars), region = region_block, retry) - }) + }, .progress = TRUE) } ) message("\n") # new line for progress bar diff --git a/tests/testthat/test-census_helper_v2.R b/tests/testthat/test-census_helper_v2.R index 3da6501..a30ce17 100644 --- a/tests/testthat/test-census_helper_v2.R +++ b/tests/testthat/test-census_helper_v2.R @@ -10,6 +10,7 @@ test_that("Fails if 'precinct' is set as the geo var",{ skip_on_cran() set.seed(42) data(voters) + future::plan(future::multisession) census <- readRDS(test_path("data/new_census_table_NJ_2020.rds")) expect_error( census_helper_new( @@ -32,6 +33,7 @@ test_that("helper returns verified census tract data",{ skip_on_cran() set.seed(42) data(voters) + future::plan(future::multisession) census <- readRDS(test_path("data/new_census_table_NJ_2020.rds")) x <- census_helper_new( voter.file = voters, @@ -56,6 +58,7 @@ test_that("New tables and legacy tables return equal race predictions",{ skip_on_cran() set.seed(42) data(voters) + future::plan(future::multisession) # legacy redistricting table census <- readRDS(test_path("data/census_test_nj_block_2020.rds")) x <- census_helper_new( diff --git a/tests/testthat/test-get_census_data.R b/tests/testthat/test-get_census_data.R index f2e2aa9..f43eece 100644 --- a/tests/testthat/test-get_census_data.R +++ b/tests/testthat/test-get_census_data.R @@ -5,7 +5,7 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { r <- suppressMessages(get_census_data( key = NULL, state = c("DC"), - census.geo = "block", + census.geo = "block" )) expect_named(r$DC, c("state", "age", "sex", "year", "block", "tract", "county")) @@ -13,6 +13,7 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { }) test_that("Census block_group download works", { + future::plan(future::multisession) r <- suppressMessages(get_census_data( key = NULL, state = "RI", @@ -23,6 +24,7 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { }) test_that("Census tract download works", { + future::plan(future::multisession) r <- suppressMessages(get_census_data( key = NULL, state = c("NY"), @@ -35,6 +37,7 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { }) test_that("Census county download works", { + future::plan(future::multisession) r <- suppressMessages(get_census_data( key = NULL, state = "NJ", @@ -46,6 +49,7 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { }) test_that("Census place download works", { + future::plan(future::multisession) r <- suppressMessages(get_census_data( key = NULL, state = "RI", diff --git a/tests/testthat/test-rollup.R b/tests/testthat/test-rollup.R index 655d535..bef93cc 100644 --- a/tests/testthat/test-rollup.R +++ b/tests/testthat/test-rollup.R @@ -9,8 +9,8 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { )) r_tract_from_block <- r$DE$block |> - group_by(tract) |> - summarize( + dplyr::group_by(tract) |> + dplyr::summarize( sum_whi = sum(r_whi), sum_bla = sum(r_bla), sum_his = sum(r_his), @@ -19,9 +19,9 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { ) r_tract_level <- r$DE$tract |> - select(tract, r_whi:r_oth) |> - group_by(tract) |> - summarize( + dplyr::select(tract, r_whi:r_oth) |> + dplyr::group_by(tract) |> + dplyr::summarize( sum_whi = sum(r_whi), sum_bla = sum(r_bla), sum_his = sum(r_his), @@ -30,9 +30,9 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { ) r_county_from_tract <- r$DE$tract |> - select(county, r_whi:r_oth) |> - group_by(county) |> - summarize( + dplyr::select(county, r_whi:r_oth) |> + dplyr::group_by(county) |> + dplyr::summarize( sum_whi = sum(r_whi), sum_bla = sum(r_bla), sum_his = sum(r_his), @@ -41,9 +41,9 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { ) r_county_level <- r$DE$county |> - select(county, r_whi:r_oth) |> - group_by(county) |> - summarize( + dplyr::select(county, r_whi:r_oth) |> + dplyr::group_by(county) |> + dplyr::summarize( sum_whi = sum(r_whi), sum_bla = sum(r_bla), sum_his = sum(r_his), @@ -138,15 +138,15 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { )) r_sum_from_block <- r$AK$block |> - select(r_whi:r_fem_23_oth) |> + dplyr::select(r_whi:r_fem_23_oth) |> apply(2, sum) r_sum_from_tract <- r$AK$tract |> - select(r_whi:r_fem_23_oth) |> + dplyr::select(r_whi:r_fem_23_oth) |> apply(2, sum) r_sum_from_county <- r$AK$county |> - select(r_whi:r_fem_23_oth) |> + dplyr::select(r_whi:r_fem_23_oth) |> apply(2, sum) r_zcta_level <- suppressMessages(get_census_data( @@ -158,7 +158,7 @@ if(Sys.getenv("CENSUS_API_KEY") != "") { )) r_sum_from_zcta <- r_zcta_level$AK$zcta |> - select(r_whi:r_fem_23_oth) |> + dplyr::select(r_whi:r_fem_23_oth) |> apply(2, sum) expect_true( From 7541cbbfa9b05770b0d4ffa77c4802254b2abe49 Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Wed, 14 Feb 2024 10:34:52 -0500 Subject: [PATCH 87/90] Fixes for all the things --- ChangeLog | 1 + DESCRIPTION | 2 +- NAMESPACE | 2 ++ R/census_helper_v2.R | 9 ++++++--- R/utils_state_fips.R | 4 ++++ R/wru-internal.R | 2 +- 6 files changed, 15 insertions(+), 5 deletions(-) diff --git a/ChangeLog b/ChangeLog index 09492e1..c836545 100644 --- a/ChangeLog +++ b/ChangeLog @@ -16,3 +16,4 @@ Date Version Comment 2022-06-17 1.0.0 Updates to BISG, inclusion of fBISG and other package improvements 2022-10-04 1.0.1 Bug fixes for census url and census year 2023-06-12 2.0.0 Updated defaults to 2020 data, specifiy as next major version 2.0. +2024-02-15 3.0.0 Adding back age and sex functionality. Other improvements. \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION index 85fbe32..2cebb8d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: wru Title: Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and Geolocation Version: 3.0.0 -Date: 2023-12-09 +Date: 2024-02-14 Authors@R: c( person("Kabir", "Khanna", , "kabirkhanna@gmail.com", role = "aut"), person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), diff --git a/NAMESPACE b/NAMESPACE index 8b7120c..da53ada 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,7 @@ # Generated by roxygen2: do not edit by hand +export(as_fips_code) +export(as_state_abbreviation) export(format_legacy_data) export(get_census_data) export(predict_race) diff --git a/R/census_helper_v2.R b/R/census_helper_v2.R index e918efc..9b51bd3 100644 --- a/R/census_helper_v2.R +++ b/R/census_helper_v2.R @@ -211,10 +211,13 @@ census_helper_new <- function( paste(do.call(paste, c(unique(voters.census[miss_ind, c("state",geo.merge)]), sep="-")), collapse = ", ")) - if(skip_bad_geos == TRUE){ + if(skip_bad_geos == TRUE) { message("NOTE: Skipping unavailable geolocations. Returning partial data set.") - voters.census <- tidyr::drop_na(voters.census, r_whi)} - else(stop("Stopping predictions. Please revise census data and/or verify the correct year is being supplied. To skip these rows use 'skip_bad_geos = TRUE'")) + voters.census <- voters.census[!is.na(voters.census$r_whi),] + } + else { + stop("Stopping predictions. Please revise census data and/or verify the correct year is being supplied. To skip these rows use 'skip_bad_geos = TRUE'") + } } # } diff --git a/R/utils_state_fips.R b/R/utils_state_fips.R index 7dc5c6f..2d00150 100644 --- a/R/utils_state_fips.R +++ b/R/utils_state_fips.R @@ -52,7 +52,9 @@ #' as_state_abbreviation(c("utah", "NM", 8, "04")) #' #' @keywords internal +#' @export as_fips_code <- function(x) { + state_fips <- wru::state_fips state_fips$state_code[ dplyr::coalesce( match(toupper(x), state_fips$state), @@ -63,7 +65,9 @@ as_fips_code <- function(x) { } #' @rdname as_fips_code +#' @export as_state_abbreviation <- function(x) { + state_fips <- wru::state_fips state_fips$state[ dplyr::coalesce( match(toupper(x), state_fips$state), diff --git a/R/wru-internal.R b/R/wru-internal.R index f2eabc0..1025a18 100644 --- a/R/wru-internal.R +++ b/R/wru-internal.R @@ -2,7 +2,7 @@ packageStartupMessage( "\n", "Please cite as:", "\n\n", - format(citation("wru"), style = "text"), "\n\n", + format(utils::citation("wru"), style = "text"), "\n\n", "Note that wru 2.0.0 uses 2020 census data by default.", "\n", 'Use the argument `year = "2010"`, to replicate analyses produced with earlier package versions.', "\n" From c103d5b1ac8c4097005cb7a96e97f2c126997d24 Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Thu, 15 Feb 2024 09:40:21 -0500 Subject: [PATCH 88/90] Adjustments for CRAN notes --- DESCRIPTION | 2 +- R/RcppExports.R | 4 ---- R/predict_race.R | 12 ++++++------ cran-comments.md | 1 + man/predict_race.Rd | 12 ++++++------ man/sample_me.Rd | 8 -------- src/sample_me.cpp | 4 ---- 7 files changed, 14 insertions(+), 29 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2cebb8d..a197065 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -8,7 +8,7 @@ Authors@R: c( person("Brandon", "Bertelsen", , "brandon@bertelsen.ca", role = c("aut", "cre")), person("Santiago", "Olivella", , "olivella@unc.edu", role = "aut"), person("Evan", "Rosenman", , "etrrosenman@gmail.com", role = "aut"), - person("Alex", "Rossell Hayes", "alexander@rossellhayes.com", role = "aut"), + person("Alex", "Rossell Hayes", , "alexander@rossellhayes.com", role = "aut"), person("Kosuke", "Imai", , "imai@harvard.edu", role = "aut") ) Description: Predicts individual race/ethnicity using surname, first name, diff --git a/R/RcppExports.R b/R/RcppExports.R index b5c2b45..f7a8a81 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -8,9 +8,6 @@ #' @param middle_name See last_name #' @param geo Integer vector of geographic units for each record. Must match column number in N_rg #' @param N_rg Integer matrix of race | geography counts in census (geograpgies in columns). -#' @param M_rs Integer matrix of race | surname counts in dictionary (surnames in columns). -#' @param M_rf Same as `M_rs`, but for first names (can be empty matrix for surname only models). -#' @param M_rm Same as `M_rs`, but for middle names (can be empty matrix for surname, or surname and first name only models). #' @param alpha Numeric matrix of race | geography prior probabilities. #' @param pi_s Numeric matrix of race | surname prior probabilities. #' @param pi_f Same as `pi_s`, but for first names. @@ -19,7 +16,6 @@ #' @param which_names Integer; 0=surname only. 1=surname + first name. 2= surname, first, and middle names. #' @param samples Integer number of samples to take after (in total) #' @param burnin Integer number of samples to discard as burn-in of Markov chain -#' @param me_race Boolean; should measurement error in race | geography be corrected? #' @param race_init Integer vector of initial race assignments #' @param verbose Boolean; should informative messages be printed? #' diff --git a/R/predict_race.R b/R/predict_race.R index fe428c9..1f31b7c 100644 --- a/R/predict_race.R +++ b/R/predict_race.R @@ -93,12 +93,12 @@ #' Must be an integer vector, with 1=white, 2=black, 3=hispanic, 4=asian, and #' 5=other. Defaults to values obtained using \code{model="BISG_surname"}. #' @param control List of control arguments only used when \code{model="fBISG"}, including -#' \itemize{ -#' \item{iter}{ Number of MCMC iterations. Defaults to 1000.} -#' \item{burnin}{ Number of iterations discarded as burnin. Defaults to half of \code{iter}.} -#' \item{verbose}{ Print progress information. Defaults to \code{TRUE}.} -#' \item{me.correct}{ Boolean. Should the model correcting measurement error for \code{races|geo}? Defaults to \code{TRUE}.} -#' \item{seed}{ RNG seed. If \code{NULL}, a seed is generated and returned as an attribute for reproducibility.} +#' \describe{ +#' \item{iter}{Number of MCMC iterations. Defaults to 1000.} +#' \item{burnin}{Number of iterations discarded as burnin. Defaults to half of \code{iter}.} +#' \item{verbose}{Print progress information. Defaults to \code{TRUE}.} +#' \item{me.correct}{Boolean. Should the model correct measurement error for \code{races|geo}? Defaults to \code{TRUE}.} +#' \item{seed}{RNG seed. If \code{NULL}, a seed is generated and returned as an attribute for reproducibility.} #' } #' #' @return Output will be an object of class \code{data.frame}. It will diff --git a/cran-comments.md b/cran-comments.md index 858617d..25d6f18 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -3,3 +3,4 @@ 0 errors | 0 warnings | 1 note * This is a new release. +* Note is related to file size which cannot be reduced. diff --git a/man/predict_race.Rd b/man/predict_race.Rd index 35e2a86..f3bbcd7 100644 --- a/man/predict_race.Rd +++ b/man/predict_race.Rd @@ -131,12 +131,12 @@ follow the same structure as \code{last_c}, \code{first_c}, middle'. Defaults to 'surname'.} \item{control}{List of control arguments only used when \code{model="fBISG"}, including -\itemize{ -\item{iter}{ Number of MCMC iterations. Defaults to 1000.} -\item{burnin}{ Number of iterations discarded as burnin. Defaults to half of \code{iter}.} -\item{verbose}{ Print progress information. Defaults to \code{TRUE}.} -\item{me.correct}{ Boolean. Should the model correcting measurement error for \code{races|geo}? Defaults to \code{TRUE}.} -\item{seed}{ RNG seed. If \code{NULL}, a seed is generated and returned as an attribute for reproducibility.} +\describe{ +\item{iter}{Number of MCMC iterations. Defaults to 1000.} +\item{burnin}{Number of iterations discarded as burnin. Defaults to half of \code{iter}.} +\item{verbose}{Print progress information. Defaults to \code{TRUE}.} +\item{me.correct}{Boolean. Should the model correct measurement error for \code{races|geo}? Defaults to \code{TRUE}.} +\item{seed}{RNG seed. If \code{NULL}, a seed is generated and returned as an attribute for reproducibility.} }} } \value{ diff --git a/man/sample_me.Rd b/man/sample_me.Rd index 157ae51..5c40279 100644 --- a/man/sample_me.Rd +++ b/man/sample_me.Rd @@ -50,15 +50,7 @@ sample_me( \item{middle_name}{See last_name} -\item{M_rs}{Integer matrix of race | surname counts in dictionary (surnames in columns).} - -\item{M_rf}{Same as \code{M_rs}, but for first names (can be empty matrix for surname only models).} - -\item{M_rm}{Same as \code{M_rs}, but for middle names (can be empty matrix for surname, or surname and first name only models).} - \item{alpha}{Numeric matrix of race | geography prior probabilities.} - -\item{me_race}{Boolean; should measurement error in race | geography be corrected?} } \description{ Collapsed Gibbs sampler for hWRU. Internal function diff --git a/src/sample_me.cpp b/src/sample_me.cpp index 8eae6a3..6d744d8 100644 --- a/src/sample_me.cpp +++ b/src/sample_me.cpp @@ -7,9 +7,6 @@ //' @param middle_name See last_name //' @param geo Integer vector of geographic units for each record. Must match column number in N_rg //' @param N_rg Integer matrix of race | geography counts in census (geograpgies in columns). -//' @param M_rs Integer matrix of race | surname counts in dictionary (surnames in columns). -//' @param M_rf Same as `M_rs`, but for first names (can be empty matrix for surname only models). -//' @param M_rm Same as `M_rs`, but for middle names (can be empty matrix for surname, or surname and first name only models). //' @param alpha Numeric matrix of race | geography prior probabilities. //' @param pi_s Numeric matrix of race | surname prior probabilities. //' @param pi_f Same as `pi_s`, but for first names. @@ -18,7 +15,6 @@ //' @param which_names Integer; 0=surname only. 1=surname + first name. 2= surname, first, and middle names. //' @param samples Integer number of samples to take after (in total) //' @param burnin Integer number of samples to discard as burn-in of Markov chain -//' @param me_race Boolean; should measurement error in race | geography be corrected? //' @param race_init Integer vector of initial race assignments //' @param verbose Boolean; should informative messages be printed? //' From c8adb851a8bc34bed73549d33bb686c5a27c15f1 Mon Sep 17 00:00:00 2001 From: Brandon Bertelsen Date: Thu, 15 Feb 2024 09:59:07 -0500 Subject: [PATCH 89/90] Adjustments for CRAN notes re: Rcpp docs --- R/RcppExports.R | 3 +-- man/sample_me.Rd | 6 ++---- src/sample_me.cpp | 3 +-- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index f7a8a81..ba0989f 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -5,10 +5,9 @@ #' #' @param last_name Integer vector of last name identifiers for each record (zero indexed; as all that follow). Must match columns numbers in M_rs. #' @param first_name See last_name -#' @param middle_name See last_name +#' @param mid_name See last_name #' @param geo Integer vector of geographic units for each record. Must match column number in N_rg #' @param N_rg Integer matrix of race | geography counts in census (geograpgies in columns). -#' @param alpha Numeric matrix of race | geography prior probabilities. #' @param pi_s Numeric matrix of race | surname prior probabilities. #' @param pi_f Same as `pi_s`, but for first names. #' @param pi_m Same as `pi_s`, but for middle names. diff --git a/man/sample_me.Rd b/man/sample_me.Rd index 5c40279..eab7839 100644 --- a/man/sample_me.Rd +++ b/man/sample_me.Rd @@ -26,6 +26,8 @@ sample_me( \item{first_name}{See last_name} +\item{mid_name}{See last_name} + \item{geo}{Integer vector of geographic units for each record. Must match column number in N_rg} \item{N_rg}{Integer matrix of race | geography counts in census (geograpgies in columns).} @@ -47,10 +49,6 @@ sample_me( \item{race_init}{Integer vector of initial race assignments} \item{verbose}{Boolean; should informative messages be printed?} - -\item{middle_name}{See last_name} - -\item{alpha}{Numeric matrix of race | geography prior probabilities.} } \description{ Collapsed Gibbs sampler for hWRU. Internal function diff --git a/src/sample_me.cpp b/src/sample_me.cpp index 6d744d8..52749b9 100644 --- a/src/sample_me.cpp +++ b/src/sample_me.cpp @@ -4,10 +4,9 @@ //' //' @param last_name Integer vector of last name identifiers for each record (zero indexed; as all that follow). Must match columns numbers in M_rs. //' @param first_name See last_name -//' @param middle_name See last_name +//' @param mid_name See last_name //' @param geo Integer vector of geographic units for each record. Must match column number in N_rg //' @param N_rg Integer matrix of race | geography counts in census (geograpgies in columns). -//' @param alpha Numeric matrix of race | geography prior probabilities. //' @param pi_s Numeric matrix of race | surname prior probabilities. //' @param pi_f Same as `pi_s`, but for first names. //' @param pi_m Same as `pi_s`, but for middle names. 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