Update docs

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

@@ -41,7 +41,8 @@ jobs:
 
       - name: Query dependencies
         run: |
-          install.packages('remotes')
+          install.packages('remotes', repos='http://cran.us.r-project.org')
+          install.packages('rcmdcheck', repos='http://cran.us.r-project.org')
           saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
           writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
         shell: Rscript {0}
@@ -68,26 +69,13 @@ jobs:
           remotes::install_cran("rcmdcheck")
         shell: Rscript {0}
 
-      - name: Check
-        run: |
-          options(crayon.enabled = TRUE)
-          rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), check_dir = "check")
-        shell: Rscript {0}
-
-      - name: Check testthat
-        if: runner.os != 'Windows'
-        id: check_check
-        run: |
-            error_num=$(Rscript --vanilla '.github/workflows/check_testthat.R')
-            echo "error_num=$error_num" >> $GITHUB_OUTPUT
-
-      - name: Stop if there are errors!
-        if: ${{ steps.check_check.outputs.error_num != '0' }}
-        run: exit 1
+      - name: Check package
+        uses: r-lib/actions/check-r-package@v2
+        with:
+          args: 'c("--no-manual")'
 
       - name: Upload check results
         if: failure()
         uses: actions/upload-artifact@main
         with:
-          name: ${{ runner.os }}-r${{ matrix.config.r }}-results
           path: check

DESCRIPTION

@@ -17,7 +17,7 @@ Authors@R: c(
     )
     )
 Description: This package allows a pipeline to be built for analyzing genetic interactions of paired guide CRISPR screens. It is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center.
-License: GPL-3
+License: GPL-3 + file LICENSE
 URL: https://github.com/FredHutch/gimap
 BugReports: https://github.com/FredHutch/gimap/issues
 Imports:

R/06-calculate_gi.R

@@ -26,7 +26,7 @@
 #' @param .data Data can be piped in with tidyverse pipes from function to function. But the data must still be a gimap_dataset
 #' @param gimap_dataset A special dataset structure that is setup using the `setup_data()` function.
 #' @export
-#' @examples {
+#' @examples \dontrun{
 #'   gimap_dataset <- get_example_data("gimap")
 #'
 #'   # Highly recommended but not required

R/utils.R

@@ -1,5 +1,5 @@
 utils::globalVariables(c(
-  "timepoints", "value", "timepoint_avg", "target_type",
+  "mean_observed_cs", "timepoints", "value", "timepoint_avg", "target_type",
   "unexpressed_ctrl_flag", "median", "lfc_adj", "median", "gRNA1_seq", "gRNA2_seq",
   "control_gRNA_seq", "crispr_score", "pgRNA_target", "mean_double_control_crispr",
   "pgRNA_target", "targeting_gRNA_seq", "mean_single_crispr", "double_crispr_score",
@@ -92,33 +92,6 @@ get_example_data <- function(which_data) {
   }
 }
 
-#' @import ggplot2
-
-## ggplot themes
-## see: https://www.rdocumentation.org/packages/ggplot2/versions/2.1.0/topics/theme_update
-## and https://stackoverflow.com/questions/23173915/can-ggplot-theme-formatting-be-saved-as-an-object
-plot_theme <- function() {
-  theme(
-    axis.text = element_text(colour = "black"),
-    axis.ticks = element_line(color = "black")
-  )
-}
-
-#' @import ggplot2
-plot_options <- function() {
-  list(theme_bw(base_size = 12))
-}
-
-#' @import kableExtra
-print_kbl <- function(tbl) {
-  kbl(tbl) %>%
-    kable_styling(
-      full_width = FALSE,
-      position = "left",
-      bootstrap_options = c("striped", "hover", "responsive")
-    )
-}
-
 
 #' Get file path to an default credentials RDS
 #' @export

README.md

@@ -135,7 +135,7 @@ Now you can [go to our quick start tutorial to get started!](https://fredhutch.g
 We also have tutorial examples that show how to run timepoint or treatment experimental set ups with gimap:
 
 - [Timepoint example](https://fredhutch.github.io/gimap/articles/timepoint-example.html)
-- [Treatment example](https://fredhutch.github.io/gimap/articles/treatment-example.html)
+- [Treatment example](https://fredhutch.github.io/gimap/articles/treatment_example.html)
 
 Follow the steps there that will walk you through the example data. Then you can tailor that tutorial to use your own data.
 

tests/testthat/test-gi-calc.R

@@ -20,7 +20,7 @@ test_that("Test Genetic Interaction score calculations", {
 
      testthat::expect_true(all.equal(gimap_dataset$overall_results$rep, results$rep))
      testthat::expect_identical(round(gimap_dataset$overall_results$intercept[1], 3), round(results$intercept[1], 3))
-     testthat::expect_identical(round(gimap_dataset$overall_results$rep[1], 3), round(results$rep[1], 3))
+     testthat::expect_identical(gimap_dataset$overall_results$rep[1], results$rep[1])
 
      testthat::expect_identical(round(gimap_dataset$gi_scores$mean_expected_cs[1], 3), round(3.691114, 3))
      testthat::expect_identical(round(gimap_dataset$gi_scores$mean_observed_cs[1], 3), round(-2.321601, 3))