Ready for CRAN update

.Rbuildignore

@@ -87,7 +87,6 @@ test-out.xml
 ^pkgdown$
 .lintr
 inst/extdata/toxEval35_endpoint_summary_DLV.xlsx
-inst/shiny/www/footer.html
 vignettes/basicWorkflow.Rmd
 vignettes/shinyApp.Rmd
 vignettes/sidebar.png

.gitlab-ci.yml

@@ -1,4 +1,4 @@
-image: ${CI_DEPENDENCY_PROXY_GROUP_IMAGE_PREFIX}/rocker/geospatial:latest
+image: code.usgs.gov:5001/water/wsc/umid/docker_images:latest
 
 stages:
   - getready
@@ -35,8 +35,8 @@ getready:
     - mkdir -p $R_LIBS_USER
     - mkdir -p $APT_CACHE
     - echo "options(Ncpus=$(nproc --all), repos=c(CRAN='$CRAN'))" >> $R_PROFILE
-    - Rscript -e "install.packages(c('devtools', 'connectapi'))"
-    - Rscript -e "install.packages(c('tibble', 'vctrs', 'dplyr', 'tidyr'))"
+    - Rscript -e "install.packages(c( 'connectapi'))"
+    - Rscript -e "devtools::install_deps()"
   cache:
     paths:
      - $R_LIBS_USER

CRAN_reminders.txt

@@ -1,5 +1,8 @@
 CRAN submissions:
   1. Turn off DT tables in basicWorkflow
   2. Turn dontrun to donttest in explore_endpoints
+    (nevermind...removed the example altogether)
   3. Remove badge links from readme
+    (badges made it in last update, so maybe they are OK)
   4. Remove most extraneous links from readme (especially installation)
+5. Figure out what do to with inst/shiny/www/footer.html

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: toxEval
 Type: Package
 Title: Exploring Biological Relevance of Environmental Chemistry Observations
-Version: 1.3.1
+Version: 1.3.2
 Authors@R: c(person("Laura", "DeCicco", 
                      role = c("aut","cre"),
                      email = "[email protected]",
@@ -25,7 +25,7 @@ Authors@R: c(person("Laura", "DeCicco",
               person("Dalma", "Martinovic", 
                      role = "rev", 
                      comment = "Reviewed for USGS"))
-Description: Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell et al. (2017) <doi:10.1021/acs.est.7b01613>.
+Description: Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) <doi:10.1021/acs.est.7b01613>.
 License: CC0
 Copyright: This software is in the public domain because it contains materials
     that originally came from the United States Geological Survey, an agency of
@@ -61,3 +61,4 @@ VignetteBuilder: knitr
 BuildVignettes: true
 LazyLoad: yes
 RoxygenNote: 7.3.1
+

NAMESPACE

@@ -40,6 +40,4 @@ importFrom(magrittr,"%>%")
 importFrom(shinyAce,aceEditor)
 importFrom(shinyAce,updateAceEditor)
 importFrom(shinycssloaders,withSpinner)
-importFrom(stats,median)
-importFrom(stats,quantile)
 importFrom(tools,file_ext)

NEWS

@@ -2,6 +2,7 @@ toxEval 1.3.1
 ===========
 * Made "Chemical" a required column in the Chemical tab. Now all plot names will key off that column instead of the names listed in tox_chemicals.
 * Updated documentation to remove some notes.
+* Removed dplyr from NAMESPACE.
 
 toxEval 1.3.0
 ===========

R/endpoint_hits.R

@@ -29,7 +29,6 @@
 #' @return data frame with one row per endpoint that had a hit (based on the
 #' hit_threshold). The columns are based on the category.
 #' @rdname endpoint_hits_DT
-#' @importFrom stats median
 #' @examples
 #' # This is the example workflow:
 #' path_to_tox <- system.file("extdata", package = "toxEval")

R/filter_endPoint_info.R

@@ -11,9 +11,8 @@
 #'
 #' The default category ('groupCol') is 'intended_target_family'. Depending
 #' on the study, other categories may be more relevant. The best resource on these
-#' groupings is the "ToxCast Assay Annotation Data User Guide" directly from
-#' EPA \url{https://www.epa.gov/chemical-research/toxcast-assay-annotation-data-user-guide}.
-#' Following that link, it defines "intended_target_family" as "the target family of the
+#' groupings is the "ToxCast Assay Annotation Data User Guide".
+#' It defines "intended_target_family" as "the target family of the
 #' objective target for the assay". Much more detail can be discovered in that documentation.
 #'
 #' @param ep Data frame containing Endpoint information from ToxCast

R/get_chemical_summary.R

@@ -152,7 +152,7 @@ orderClass <- function(graphData) {
   orderClass_df <- graphData %>%
     dplyr::mutate(logEAR = log(meanEAR)) %>%
     dplyr::group_by(chnm, Class) %>%
-    dplyr::summarise(median = quantile(logEAR[logEAR != 0], 0.5, na.rm = TRUE)) %>%
+    dplyr::summarise(median = stats::quantile(logEAR[logEAR != 0], 0.5, na.rm = TRUE)) %>%
     dplyr::group_by(Class) %>%
     dplyr::summarise(max_med = max(median, na.rm = TRUE)) %>%
     dplyr::arrange(dplyr::desc(max_med))
@@ -168,7 +168,7 @@ orderChem <- function(graphData, orderClass_df) {
   orderChem_df <- graphData %>%
     dplyr::mutate(logEAR = log(meanEAR)) %>%
     dplyr::group_by(chnm, Class) %>%
-    dplyr::summarise(median = quantile(logEAR[logEAR != 0], 0.5, na.rm = TRUE)) %>%
+    dplyr::summarise(median = stats::quantile(logEAR[logEAR != 0], 0.5, na.rm = TRUE)) %>%
     dplyr::ungroup() %>%
     dplyr::mutate(Class = factor(Class, levels = rev(as.character(orderClass_df$Class))))
 
@@ -187,7 +187,7 @@ orderEP <- function(graphData) {
   orderEP_df <- graphData %>%
     dplyr::mutate(logEAR = log(meanEAR)) %>%
     dplyr::group_by(endPoint) %>%
-    dplyr::summarise(median = quantile(logEAR[logEAR != 0], 0.5, na.rm = TRUE)) %>%
+    dplyr::summarise(median = stats::quantile(logEAR[logEAR != 0], 0.5, na.rm = TRUE)) %>%
     dplyr::ungroup()
 
   orderEP_df$median[is.na(orderEP_df$median)] <- min(orderEP_df$median, na.rm = TRUE) - 1

R/hits_by_groupings.R

@@ -23,7 +23,6 @@
 #' @export
 #' @return data frame with one row per category, and one column per Biological grouping.
 #' @rdname hits_by_groupings_DT
-#' @importFrom stats median
 #' @examples
 #' # This is the example workflow:
 #' path_to_tox <- system.file("extdata", package = "toxEval")

R/hits_summary.R

@@ -29,7 +29,6 @@
 #' @rdname hits_summary_DT
 #' @return data frame with with one row per unique site/category combination. The columns
 #' are site, category, Samples with Hits, and Number of Samples.
-#' @importFrom stats median
 #' @examples
 #' # This is the example workflow:
 #' path_to_tox <- system.file("extdata", package = "toxEval")

R/makeMap.R

@@ -21,7 +21,6 @@
 #' Short Name, dec_lon, and dec_lat.
 #' @export
 #' @rdname make_tox_map
-#' @importFrom stats quantile
 #' @examples
 #' # This is the example workflow:
 #' path_to_tox <- system.file("extdata", package = "toxEval")
@@ -63,9 +62,9 @@ make_tox_map <- function(chemical_summary,
   if (length(siteToFind) == 1) {
     mapData <- dplyr::filter(chem_site, SiteID == siteToFind) %>%
       dplyr::mutate(
-        nSamples = median(mapData$count),
-        meanMax = median(mapData$meanMax),
-        sizes = median(mapData$sizes)
+        nSamples = stats::median(mapData$count),
+        meanMax = stats::median(mapData$meanMax),
+        sizes = stats::median(mapData$sizes)
       )
   }
   map <- leaflet::leaflet(height = "500px", data = mapData) %>%
@@ -174,7 +173,7 @@ map_tox_data <- function(chemical_summary,
   counts <- mapData$count
 
   if (length(siteToFind) > 1) {
-    leg_vals <- unique(as.numeric(quantile(mapData$meanMax, probs = c(0, 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, .99, 1), na.rm = TRUE)))
+    leg_vals <- unique(as.numeric(stats::quantile(mapData$meanMax, probs = c(0, 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, .99, 1), na.rm = TRUE)))
     pal <- leaflet::colorBin(col_types, mapData$meanMax, bins = leg_vals)
     rad <- 3 * seq(1, 4, length.out = 16)
 
@@ -184,7 +183,7 @@ map_tox_data <- function(chemical_summary,
       mapData$sizes <- rad[as.numeric(cut(mapData$count, breaks = 16))]
     }
   } else {
-    leg_vals <- unique(as.numeric(quantile(c(0, mapData$meanMax), probs = c(0, 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, .99, 1), na.rm = TRUE)))
+    leg_vals <- unique(as.numeric(stats::quantile(c(0, mapData$meanMax), probs = c(0, 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, .99, 1), na.rm = TRUE)))
     pal <- leaflet::colorBin(col_types, c(0, mapData$meanMax), bins = leg_vals)
     mapData$sizes <- 3
   }

R/plot_chemical_boxplots.R

@@ -84,7 +84,7 @@ plot_chemical_boxplots <- function(chemical_summary, ...,
     orderColsBy <- chemical_summary %>%
       dplyr::mutate(logEAR = log(EAR)) %>%
       dplyr::group_by(chnm, Class, ...) %>%
-      dplyr::summarise(median = median(logEAR[!is.na(logEAR)], na.rm = TRUE)) %>%
+      dplyr::summarise(median = stats::median(logEAR[!is.na(logEAR)], na.rm = TRUE)) %>%
       dplyr::arrange(median) %>%
       dplyr::ungroup()
 
@@ -101,7 +101,7 @@ plot_chemical_boxplots <- function(chemical_summary, ...,
     orderedLevels <- chemical_summary %>%
       dplyr::mutate(logEAR = log(EAR)) %>%
       dplyr::group_by(chnm, Class, ...) %>%
-      dplyr::summarise(median = median(logEAR[!is.na(logEAR)])) %>%
+      dplyr::summarise(median = stats::median(logEAR[!is.na(logEAR)])) %>%
       dplyr::ungroup() %>%
       dplyr::mutate(
         Class = factor(Class, levels = rev(class_order)),

R/plot_group_boxplots.R

@@ -47,7 +47,6 @@
 #' @export
 #' @rdname plot_tox_boxplots
 #' @import ggplot2
-#' @importFrom stats median
 #' @examples
 #' # This is the example workflow:
 #' path_to_tox <- system.file("extdata", package = "toxEval")
@@ -170,7 +169,7 @@ plot_tox_boxplots <- function(chemical_summary,
       orderColsBy <- chemical_summary %>%
         dplyr::mutate(logEAR = log(EAR)) %>%
         dplyr::group_by(category) %>%
-        dplyr::summarise(median = median(logEAR[logEAR != 0], na.rm = TRUE)) %>%
+        dplyr::summarise(median = stats::median(logEAR[logEAR != 0], na.rm = TRUE)) %>%
         dplyr::ungroup() %>%
         dplyr::arrange(median)
 
@@ -402,7 +401,7 @@ tox_boxplot_data <- function(chemical_summary,
 
   orderColsBy <- tox_boxplot_data %>%
     dplyr::group_by(category) %>%
-    dplyr::summarise(median = median(meanEAR[meanEAR != 0], na.rm = TRUE)) %>%
+    dplyr::summarise(median = stats::median(meanEAR[meanEAR != 0], na.rm = TRUE)) %>%
     dplyr::ungroup() %>%
     dplyr::arrange(median)
 

R/plot_heat_chemical.R

@@ -37,7 +37,6 @@
 #' @export
 #' @rdname plot_tox_heatmap
 #' @import ggplot2
-#' @importFrom stats median
 #' @examples
 #' path_to_tox <- system.file("extdata", package = "toxEval")
 #' file_name <- "OWC_data_fromSup.xlsx"

R/plot_tox_endpoints.R

@@ -35,7 +35,6 @@
 #' endpoints will be included.
 #' @export
 #' @import ggplot2
-#' @importFrom stats median
 #' @examples
 #' # This is the example workflow:
 #' path_to_tox <- system.file("extdata", package = "toxEval")

R/plot_tox_endpoints2.R

@@ -32,7 +32,6 @@
 #' @param ... Additional group_by arguments. This can be handy for creating facet graphs.
 #' @export
 #' @import ggplot2
-#' @importFrom stats median
 #' @examples
 #' 
 #' \donttest{

R/plot_tox_stacks.R

@@ -38,7 +38,6 @@
 #' data will be included.
 #' @export
 #' @import ggplot2
-#' @importFrom stats median
 #' @importFrom grDevices colorRampPalette
 #' @examples
 #' # This is the example workflow:

R/rank_sites.R

@@ -23,7 +23,6 @@
 #' hits based on the category.
 #'
 #' @rdname rank_sites_DT
-#' @importFrom stats median
 #' @examples
 #' # This is the example workflow:
 #' path_to_tox <- system.file("extdata", package = "toxEval")

R/side_by_side.R

@@ -83,7 +83,7 @@ side_by_side_data <- function(gd_left,
 
   orderChem_1_2 <- chem_data_no_factors %>%
     dplyr::group_by(chnm, Class) %>%
-    dplyr::summarise(median = quantile(meanEAR[meanEAR != 0], 0.5)) %>%
+    dplyr::summarise(median = stats::quantile(meanEAR[meanEAR != 0], 0.5)) %>%
     dplyr::ungroup()
 
   class_order <- orderClass(chem_data_no_factors)

R/toxEval.R

@@ -10,7 +10,7 @@ ToxCast database: version", dbVersion()), width = 40),
 }
 
 dbVersion <- function() {
-  "3.5"
+  "4.0"
 }
 
 #' Analyze ToxCast data in relation to measured concentrations.
@@ -50,7 +50,7 @@ dbVersion <- function() {
 #' Downloaded on October 2022 from ToxCast. The data were
 #' combined from files in the "INVITRODB_V3_5_LEVEL5" folder.
 #' At the time of toxEval package release, this information was found:
-#' \url{https://www.epa.gov/chemical-research/exploring-toxcast-data}
+#' \url{https://www.epa.gov/comptox-tools/exploring-toxcast-data}
 #' in the "ToxCast & Tox21 Data Spreadsheet" data set.
 #' ACC values are the in the "ACC" column (winning model) and units are
 #' log micro-Molarity (log \eqn{\mu}M).
@@ -87,11 +87,10 @@ NULL
 #' @docType data
 #' @keywords datasets
 #' @references U.S. EPA. 2014. ToxCast Assay Annotation Data User Guide.
-#' \url{https://www.epa.gov/chemical-research/toxcast-assay-annotation-data-user-guide}.
 #'
 #' @source \doi{10.23645/epacomptox.6062479.v3}
 #' @export end_point_info
-#' @return data frame with 86 columns. The columns and definitions
+#' @return data frame with 72 columns. The columns and definitions
 #' are discussed in the "ToxCast Assay Annotation Version 1.0 Data User Guide (PDF)" (see source).
 #' The column "Relevance Category" was included for consideration of 
 #' grouping/filtering endpoints based on user goals.
@@ -105,7 +104,12 @@ NULL
 # end_point_info <- end_point_info |>
 #   dplyr::select(-reagent_reagent_name_value_type,
 #          -reagent_reagent_name_value,
-#          -citations_citation)
+#          -citations_citation,
+#          -citations_title,
+#          -citations_author,
+#          -assay_source_desc,
+#          -assay_component_endpoint_desc)
+# save(end_point_info, tox_chemicals, ToxCast_ACC, file = "sysdata.rda", compress = "xz")
 
 #' ToxCast Chemical Information
 #'

inst/CITATION

@@ -9,11 +9,11 @@ bibentry(bibtype = "Manual",
               as.person("Gerald T. Ankley")),
   title = "toxEval: Evaluation of measured concentration data using the ToxCast high-throughput screening database or a user-defined set of concentration benchmarks.",
   publisher = "U.S. Geological Survey",
-  version = "1.3.0",
+  version = "1.3.2",
   address="Reston, VA",
   institution = "U.S. Geological Survey",
-  year = 2023,
+  year = 2024,
   doi = "10.5066/P906UQ5I",
   url = "https://code.usgs.gov/water/toxEval",
-  textVersion = "De Cicco, L.A., Corsi, S.R., Villeneuve D.L, Blackwell, and B.R, Ankley, G.T., 2023, toxEval: Evaluation of measured concentration data using the ToxCast high-throughput screening database or a user-defined set of concentration benchmarks. R package version 1.3.0., https://code.usgs.gov/water/toxEval, doi:10.5066/P906UQ5I"
+  textVersion = "De Cicco, L.A., Corsi, S.R., Villeneuve D.L, Blackwell, and B.R, Ankley, G.T., 2024, toxEval: Evaluation of measured concentration data using the ToxCast high-throughput screening database or a user-defined set of concentration benchmarks. R package version 1.3.1., https://code.usgs.gov/water/toxEval, doi:10.5066/P906UQ5I"
 )