example_map.Rmd

---
title: "Template to make PSRC leaflet map"
author: "PSRC Data"
date: "2023"
output:
  pdf_document: default
  html_document:
    df_print: paged
---
# How to quickly make a formatted map
This template shows you how to make a map given a numeric or categorical data item with a geographic id and a shape file with the same ID.


**Inputs required: **
a data table with a field to map and an ID, 
a geographic layer with a matching ID

**User options:**
Legend title, Legend subtitle
color ramp

### 0. Clone the repository https://github.com/psrc/psrc.map
and set working directory to directory with code map_region.R
```{r}
setwd("C:/GitHub/psrc.map/")
```

### 1. Read in mapping and helper libraries


```{r, results='hide', message=FALSE, warning=FALSE}
library(sf) # geographic data library
library(dplyr) # for working with data frames
library(psrcelmer) # to get data from Elmer
library(psrcplot) # to get color schemes
library(mapview) # for outputting the map to html or png
library(htmlwidgets) # to help with map formatting
```


### 2. Read in any other libraries or keys you need

```{r, results='hide', message=FALSE, warning=FALSE}
library(psrccensus)
Sys.getenv("CENSUS_API_KEY")
```


### 3. Define create_psrc_map function


This **map_region.R** file has a function called **create_psrc_map** to create a leaflet map for you

```{r}
# should be in your working directory
source('map_region.R')
```

### 4.  Read in Data Table with a geographic field ID
Prepare data to join with the geographic layer
Note: you will need field names to match on
This example gets ACS 5-year data for Asian Groups by Tract. For Asian Pacific Islander Month, we had observed that https://www.psrc.org/media/7650  https://www.psrc.org/media/7703 Vietnamese workers had some of the higher shares of driving to work and lower shares of teleworking. This may relate to the jobs sectors Vietnamese workers are part of. We wanted to look into where Vietnamese people live in region to better understand their commuting patterns. B2015: Asian Alone by Selected Group
```{r, results=FALSE, message=FALSE, warning=FALSE}
big_tbl <-psrccensus::get_acs_recs(geography='tract',table.names='B02015',year=2021, acs.type='acs5')
```

### 5. Clean up the data in preparation to join to the geographic layer
```{r}
# read in data table and process data to join and map
tbl <- big_tbl %>%filter(label=='Estimate!!Total:!!Vietnamese')%>% 
  dplyr::select(GEOID,estimate) %>%
  dplyr::mutate(dplyr::across(c('GEOID'), as.character))%>%
  dplyr::group_by(GEOID) %>%
  dplyr::summarise(Total=sum(estimate))
```


### 6. Read in ElmerGeo geographic layer

```{r}
tract_layer_name <- "TRACT2020_NOWATER"
lyr <- st_read_elmergeo(tract_layer_name)
```

### 7. Join to data table layer via matching IDs, choose the field to map
```{r }
lyr_data<- dplyr::left_join(lyr,tbl, by = c("geoid20"="GEOID"))
# this is the field to map
lyr_data_field<-lyr_data$Total
```


### 8. Call the function to make the map.

these are the function inputs:

lyr: sf map layer, the geographic layer including the field you want to map

lyr_data_field: numeric vector of data values to map

legend_title: string, The title in the legend

legend_subtitle: string, The title in the legend

psrc_col_pal: string list, color ramp

map_lat=47.615, map_lon=-122.257: defaults, can be overridden for centering the map

map_zoom=8.5, wgs84=4326 : defaults, can be overriden, zoom level and projection
```{r}
# need to add a white color to ensure contrast
psrc_purple_plus<-append(psrc_colors$purples_inc, "#FFFFFF", after=0)
map_it<-create_psrc_map(lyr=lyr_data, 
                        lyr_data_field=lyr_data_field,
                        legend_title= 'Vietnamese Population',
                        legend_subtitle = 'by Census Tract',
                        psrc_col_pal=psrc_purple_plus)

map_it
```

### Step 9. (optional) Save the map as html
```{r}
mapshot(map_it, url= "VietnamesePop.html")
```


### Step 10. (optional) Save the map as png
```{r}
mapshot(map_it, file= "VietnamesePop.png")

```