Merge pull request #406 from ldecicco-USGS/main

Getting ready for minor CRAN update

.github/workflows/R-CMD-check.yaml

@@ -32,7 +32,7 @@ jobs:
 
  - uses: r-lib/actions/setup-pandoc@52330cc136b963487918a8867f948ddf954e9e63
 
- - uses: r-lib/actions/setup-r@52330cc136b963487918a8867f948ddf954e9e63
+ - uses: r-lib/actions/setup-r@756399d909bf9c180bbdafe8025f794f51f2da02
  with:
  r-version: ${{ matrix.config.r }}
  http-user-agent: ${{ matrix.config.http-user-agent }}

.github/workflows/test-coverage.yaml

@@ -14,30 +14,14 @@ jobs:
  steps:
  - uses: actions/checkout@c0a81a463886bb75afe234e07a9fd5bb79219196
 
- - uses: r-lib/actions/setup-r@52330cc136b963487918a8867f948ddf954e9e63
+ - uses: r-lib/actions/setup-r@756399d909bf9c180bbdafe8025f794f51f2da02
 
  - uses: r-lib/actions/setup-pandoc@52330cc136b963487918a8867f948ddf954e9e63
 
- - name: Query dependencies
- run: |
- install.packages('remotes')
- saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
- shell: Rscript {0}
-
- - name: Cache R packages
- uses: actions/cache@v1
+ - uses: r-lib/actions/setup-r-dependencies@756399d909bf9c180bbdafe8025f794f51f2da02
  with:
- path: ${{ env.R_LIBS_USER }}
- key: macOS-r-4.0-2-${{ hashFiles('.github/depends.Rds') }}
- restore-keys: macOS-r-4.0-2-
-
- - name: Install dependencies
- run: |
- options(pkgType = "binary")
- install.packages(c("remotes"))
- remotes::install_deps(dependencies = TRUE)
- remotes::install_cran("covr")
- shell: Rscript {0}
+ extra-packages: any::rcmdcheck
+ needs: check
 
  - name: Test coverage
  run: covr::codecov()

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: toxEval
 Type: Package
 Title: Exploring Biological Relevance of Environmental Chemistry Observations
-Version: 1.3.0
+Version: 1.3.1
 Authors@R: c(person("Laura", "DeCicco", 
  role = c("aut","cre"),
  email = "ldecicco@usgs.gov",
@@ -60,4 +60,4 @@ BugReports: https://github.com/DOI-USGS/toxEval/issues
 VignetteBuilder: knitr
 BuildVignettes: true
 LazyLoad: yes
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1

NAMESPACE

@@ -33,7 +33,6 @@ export(remove_flags)
 export(side_by_side_data)
 export(tox_boxplot_data)
 export(tox_chemicals)
-import(dplyr)
 import(ggplot2)
 import(shinydashboard)
 importFrom(grDevices,colorRampPalette)

NEWS

@@ -1,3 +1,8 @@
+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.
+
 toxEval 1.3.0
 ===========
 * Switched to version 3.5 of the ToxCast database

R/clean_endPoint_info.R

@@ -1,8 +1,8 @@
 #' clean_endPoint_info
 #'
 #' Define a subset of the ToxCast database for relevance to toxEval analyses.
-#' Subsetting is done based upon methods defined by
-#' \doi{10.1021/acs.est.7b01613}{Blackwell et al., 2017}.
+#' Subsetting is done based upon methods defined by Blackwell et al., 2017 (
+#' \doi{10.1021/acs.est.7b01613}).
 #' Specifically, this function removes endPoints that are ATG sources with
 #' signal loss, and NVS with signal gain (basically: some assay/signal combinations
 #' are removed because they target non-specific endpoints). Also, this function adds additional

R/create_toxEval.R

@@ -82,10 +82,10 @@ create_toxEval <- function(excel_file_path, ...) {
  names(chem_info) <- names(chem_info) %>%
  allowed_names(c("casrn", "casn", "CASRN", "CASN"), "CAS")
 
- if ("CAS" %in% names(chem_info)) {
+ if (all(c("CAS", "Chemical") %in% names(chem_info))){
  chem_info$CAS <- as.character(chem_info$CAS)
  } else {
- message("Chemical tab missing CAS column")
+ message("Chemical tab missing CAS or Chemical column")
  }
  }
 
@@ -230,21 +230,21 @@ rm_em_dash <- function(df) {
 #' tox_list <- create_toxEval(excel_file_path)
 #' summary(tox_list)
 summary.toxEval <- function(object, ...) {
- CAS <- endPoint <- chnm <- flags <- ".dplyr"
+
 
  if (is.null(object[["benchmarks"]])) {
  ACC <- ToxCast_ACC %>%
- filter(CAS %in% unique(object$chem_info$CAS))
+ dplyr::filter(CAS %in% unique(object$chem_info$CAS))
  bench_word <- "ToxCast"
  } else {
  ACC <- object[["benchmarks"]]
  bench_word <- "benchmark"
  }
 
  CAS_w_data <- ACC %>%
- select(CAS) %>%
- distinct() %>%
- pull(CAS)
+ dplyr::select(CAS) %>%
+ dplyr::distinct() %>%
+ dplyr::pull(CAS)
 
  message(length(CAS_w_data), " chemicals have ", bench_word, " information")
  message("Chemicals returned from this function do NOT have ", bench_word, " information:")

R/endpoint_hits.R

@@ -57,7 +57,6 @@ endpoint_hits_DT <- function(chemical_summary,
  sum_logic = TRUE,
  hit_threshold = 0.1,
  include_links = TRUE) {
- chnm <- CAS <- ".dplyr"
 
  fullData <- endpoint_hits(
  chemical_summary = chemical_summary,
@@ -70,8 +69,8 @@ endpoint_hits_DT <- function(chemical_summary,
  if (category == "Chemical") {
  orig_names <- names(fullData)
 
- casKey <- select(chemical_summary, chnm, CAS) %>%
- distinct()
+ casKey <- dplyr::select(chemical_summary, chnm, CAS) %>%
+ dplyr::distinct()
 
  numeric_hits <- fullData
  hits <- sapply(fullData, function(x) as.character(x))
@@ -149,54 +148,52 @@ endpoint_hits <- function(chemical_summary,
  mean_logic = FALSE,
  sum_logic = TRUE,
  hit_threshold = 0.1) {
- Bio_category <- Class <- EAR <- sumEAR <- value <- calc <- chnm <- choice_calc <- n <- nHits <- site <- ".dplyr"
- endPoint <- meanEAR <- nSites <- CAS <- ".dplyr"
 
  match.arg(category, c("Biological", "Chemical Class", "Chemical"))
 
  fullData_init <- data.frame(endPoint = "", stringsAsFactors = FALSE)
  fullData <- fullData_init
 
  if (category == "Chemical") {
- chemical_summary <- mutate(chemical_summary, category = chnm)
+ chemical_summary <- dplyr::mutate(chemical_summary, category = chnm)
  } else if (category == "Chemical Class") {
- chemical_summary <- mutate(chemical_summary, category = Class)
+ chemical_summary <- dplyr::mutate(chemical_summary, category = Class)
  } else {
- chemical_summary <- mutate(chemical_summary, category = Bio_category)
+ chemical_summary <- dplyr::mutate(chemical_summary, category = Bio_category)
  }
 
  if (length(unique(chemical_summary$site)) > 1) {
  if (!sum_logic) {
  fullData <- chemical_summary %>%
- group_by(site, category, endPoint, date) %>%
- summarize(sumEAR = max(EAR)) %>%
- group_by(site, category, endPoint) %>%
- summarize(meanEAR = ifelse(mean_logic, mean(sumEAR), max(sumEAR))) %>%
- group_by(category, endPoint) %>%
- summarize(nSites = sum(meanEAR > hit_threshold)) %>%
+ dplyr::group_by(site, category, endPoint, date) %>%
+ dplyr::summarize(sumEAR = max(EAR)) %>%
+ dplyr::group_by(site, category, endPoint) %>%
+ dplyr::summarize(meanEAR = ifelse(mean_logic, mean(sumEAR), max(sumEAR))) %>%
+ dplyr::group_by(category, endPoint) %>%
+ dplyr::summarize(nSites = sum(meanEAR > hit_threshold)) %>%
  tidyr::spread(category, nSites)
  } else {
  fullData <- chemical_summary %>%
- group_by(site, category, endPoint, date) %>%
- summarize(sumEAR = sum(EAR)) %>%
- group_by(site, category, endPoint) %>%
- summarize(meanEAR = ifelse(mean_logic, mean(sumEAR), max(sumEAR))) %>%
- group_by(category, endPoint) %>%
- summarize(nSites = sum(meanEAR > hit_threshold)) %>%
+ dplyr::group_by(site, category, endPoint, date) %>%
+ dplyr::summarize(sumEAR = sum(EAR)) %>%
+ dplyr::group_by(site, category, endPoint) %>%
+ dplyr::summarize(meanEAR = ifelse(mean_logic, mean(sumEAR), max(sumEAR))) %>%
+ dplyr::group_by(category, endPoint) %>%
+ dplyr::summarize(nSites = sum(meanEAR > hit_threshold)) %>%
  tidyr::spread(category, nSites)
  }
  } else {
  if (!sum_logic) {
  fullData <- chemical_summary %>%
- group_by(category, endPoint) %>%
- summarise(nSites = sum(EAR > hit_threshold)) %>%
+ dplyr::group_by(category, endPoint) %>%
+ dplyr::summarise(nSites = sum(EAR > hit_threshold)) %>%
  tidyr::spread(category, nSites)
  } else {
  fullData <- chemical_summary %>%
- group_by(category, endPoint, date) %>%
- summarize(sumEAR = sum(EAR)) %>%
- group_by(category, endPoint) %>%
- summarise(nSites = sum(sumEAR > hit_threshold)) %>%
+ dplyr::group_by(category, endPoint, date) %>%
+ dplyr::summarize(sumEAR = sum(EAR)) %>%
+ dplyr::group_by(category, endPoint) %>%
+ dplyr::summarise(nSites = sum(sumEAR > hit_threshold)) %>%
  tidyr::spread(category, nSites)
  }
  }
@@ -224,7 +221,6 @@ endpoint_hits <- function(chemical_summary,
 #'
 #' Create links
 #' @param cas character
-#' @param hits character
 #' @export
 #' @keywords internal
 createLink <- function(cas) {

R/filter_endPoint_info.R

@@ -49,7 +49,7 @@ filter_groups <- function(ep,
  assay_source_name <- assay_component_endpoint_name <- ".dplyr"
 
  ep <- ep[, c("assay_component_endpoint_name", groupCol, "assay_source_name")] %>%
- rename(
+ dplyr::rename(
  endPoint = assay_component_endpoint_name,
  assaysFull = assay_source_name
  )

R/get_ACC.R

@@ -10,7 +10,6 @@
 #' to units of \eqn{\mu}g/L, and reformat the data as input to toxEval.
 #'
 #' @param CAS Vector of CAS.
-#' @import dplyr
 #'
 #' @return data frame with columns CAS, chnm, flags, endPoint, ACC, MlWt, and ACC_value
 #' @export
@@ -20,10 +19,6 @@
 #' head(ACC)
 get_ACC <- function(CAS) {
 
- # Getting rid of NSE warnings:
- Structure_MolWt <- Substance_CASRN <- casn <- Substance_Name <- ".dplyr"
- chnm <- flags <- MlWt <- ACC_value <- casrn <- endPoint <- ".dplyr"
-
  chem_list <- dplyr::select(tox_chemicals,
  casrn = Substance_CASRN,
  MlWt = Structure_MolWt
@@ -36,12 +31,12 @@ get_ACC <- function(CAS) {
  by = c("CAS" = "casrn")
  )
 
- ACC <- mutate(ACC,
+ ACC <- dplyr::mutate(ACC,
  ACC_value = 10^ACC,
  ACC_value = ACC_value * MlWt
  )
- ACC <- filter(ACC, !is.na(ACC_value))
- ACC <- left_join(ACC, select(tox_chemicals,
+ ACC <- dplyr::filter(ACC, !is.na(ACC_value))
+ ACC <- dplyr::left_join(ACC, dplyr::select(tox_chemicals,
  CAS = Substance_CASRN,
  chnm = Substance_Name
  ), by = "CAS")