643 potential removal of dependencies

.pre-commit-config.yaml

@@ -43,7 +43,6 @@ repos:
           - tern
           - tibble
           - tidyr
-          - tidyselect
           - utils
 
       - id: spell-check

DESCRIPTION

@@ -13,9 +13,10 @@ Authors@R: c(
     person("Nikolas", "Burkoff", role = "aut"),
     person("F. Hoffmann-La Roche AG", role = c("cph", "fnd"))
   )
-Description: Prebuilt 'shiny' modules containing tools for viewing data, visualizing data,
-    understanding missing and outlier values within your data and performing simple data analysis. 
-    This extends 'teal' framework that supports reproducible research and analysis.
+Description: Prebuilt 'shiny' modules containing tools for viewing data,
+    visualizing data, understanding missing and outlier values within your
+    data and performing simple data analysis.  This extends 'teal'
+    framework that supports reproducible research and analysis.
 License: Apache License 2.0
 URL: https://insightsengineering.github.io/teal.modules.general/,
     https://github.com/insightsengineering/teal.modules.general/
@@ -26,7 +27,6 @@ Depends:
     ggplot2 (>= 3.4.0),
     R (>= 3.6),
     shiny (>= 1.6.0),
-    shinyTree,
     teal (>= 0.14.0.9027),
     teal.transform (>= 0.4.0.9011)
 Imports:
@@ -36,21 +36,21 @@ Imports:
     forcats (>= 1.0.0),
     grid,
     logger (>= 0.2.0),
-    magrittr (>= 1.5),
     scales,
     shinyjs,
+    shinyTree,
     shinyvalidate,
     shinyWidgets (>= 0.5.1),
     stats,
     stringr (>= 1.4.1),
     teal.code (>= 0.4.1.9009),
+    teal.data (>= 0.3.0.9018),
     teal.logger (>= 0.1.1),
     teal.reporter (>= 0.2.0),
     teal.widgets (>= 0.4.0),
     tern (>= 0.7.10),
     tibble (>= 2.0.0),
     tidyr (>= 0.8.3),
-    tidyselect,
     utils
 Suggests:
     broom (>= 0.7.10),
@@ -65,12 +65,10 @@ Suggests:
     knitr (>= 1.42),
     lattice (>= 0.18-4),
     MASS,
-    methods,
     nestcolor (>= 0.1.0),
     rlang (>= 1.0.0),
     rtables (>= 0.5.1),
     sparkline,
-    teal.data (>= 0.3.0.9018),
     testthat (>= 3.0.4)
 VignetteBuilder:
     knitr
@@ -82,7 +80,7 @@ Config/Needs/verdepcheck: haleyjeppson/ggmosaic, tidyverse/ggplot2,
     insightsengineering/teal.code, insightsengineering/teal.logger,
     insightsengineering/teal.reporter, insightsengineering/teal.transform,
     insightsengineering/teal.widgets, insightsengineering/tern,
-    tidyverse/tibble, tidyverse/tidyr, r-lib/tidyselect, tidymodels/broom,
+    tidyverse/tibble, tidyverse/tidyr, tidymodels/broom,
     daattali/colourpicker, daattali/ggExtra, aphalo/ggpmisc, aphalo/ggpp,
     baddstats/goftest, gridExtra, ramnathv/htmlwidgets, jeroen/jsonlite,
     yihui/knitr, deepayan/lattice, MASS, insightsengineering/nestcolor,

NAMESPACE

@@ -28,7 +28,6 @@ export(tm_variable_browser)
 import(ggmosaic)
 import(ggplot2)
 import(shiny)
-import(shinyTree)
 import(teal)
 import(teal.transform)
-importFrom(magrittr,"%>%")
+importFrom(dplyr,"%>%")

R/teal.modules.general.R

@@ -6,10 +6,9 @@
 #' @import ggplot2
 #' @import ggmosaic
 #' @import shiny
-#' @import shinyTree
 #' @import teal
 #' @import teal.transform
-#' @importFrom magrittr %>%
+#' @importFrom dplyr %>%
 #'
 #'
 #' @name teal.modules.general
@@ -19,7 +18,7 @@
 # nolint start
 # Note ggmosaic (version <= 0.3.3) needs to be in DEPENDS as the following does not work if it is imported
 # df <- data.frame(x = c("A", "B", "C", "A"), y = c("Z", "Z", "W", "W"))
-# ggplot(df) +  ggmosaic::geom_mosaic(aes(x = ggmosaic::product(x), fill = y))
+# ggplot(df) + ggmosaic::geom_mosaic(aes(x = ggmosaic::product(x), fill = y))
 # nolint end
 
 # Needed to avoid R CMD note on no visible binding

R/tm_a_pca.R

@@ -24,7 +24,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   USArrests <- USArrests
 #' })
 #' datanames(data) <- "USArrests"
@@ -61,7 +61,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #' })
 #' datanames(data) <- "ADSL"

R/tm_a_regression.R

@@ -35,7 +35,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   CO2 <- CO2
 #' })
 #' datanames(data) <- c("CO2")
@@ -80,7 +80,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #' })
 #' datanames(data) <- "ADSL"

R/tm_data_table.R

@@ -31,7 +31,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   iris <- iris
 #' })
 #' datanames(data) <- c("iris")
@@ -54,7 +54,7 @@
 #' # CDISC data example
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #' })
 #' datanames(data) <- "ADSL"
@@ -301,7 +301,7 @@ srv_data_table <- function(id,
       teal::validate_has_data(df, min_nrow = 1L, msg = paste("data", dataname, "is empty"))
 
       dataframe_selected <- if (if_distinct()) {
-        dplyr::count(df, dplyr::across(tidyselect::all_of(variables)))
+        dplyr::count(df, dplyr::across(dplyr::all_of(variables)))
       } else {
         df[variables]
       }

R/tm_front_page.R

@@ -19,7 +19,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- teal.modules.general::rADSL
 #'   attr(ADSL, "metadata") <- list("Author" = "NEST team", "data_source" = "synthetic data")
 #' })

R/tm_g_association.R

@@ -24,7 +24,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   CO2 <- CO2
 #'   factors <- names(Filter(isTRUE, vapply(CO2, is.factor, logical(1L))))
 #'   CO2[factors] <- lapply(CO2[factors], as.character)
@@ -69,7 +69,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #' })
 #' datanames(data) <- "ADSL"

R/tm_g_bivariate.R

@@ -49,7 +49,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   CO2 <- data.frame(CO2)
 #' })
 #' datanames(data) <- c("CO2")
@@ -112,7 +112,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #' })
 #' datanames(data) <- c("ADSL")

R/tm_g_response.R

@@ -30,7 +30,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   mtcars <- mtcars
 #'   for (v in c("cyl", "vs", "am", "gear")) {
 #'     mtcars[[v]] <- as.factor(mtcars[[v]])
@@ -78,7 +78,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #' })
 #' datanames(data) <- c("ADSL")

R/tm_g_scatterplot.R

@@ -39,7 +39,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   CO2 <- CO2
 #' })
 #' datanames(data) <- "CO2"
@@ -128,7 +128,7 @@
 #'
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #' })
 #' datanames(data) <- c("ADSL")

R/tm_missing_data.R

@@ -28,7 +28,7 @@
 #' # general example data
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'
 #'   add_nas <- function(x) {
 #'     x[sample(seq_along(x), floor(length(x) * runif(1, .05, .17)))] <- NA
@@ -56,7 +56,7 @@
 #' # CDISC example data
 #' data <- teal_data()
 #' data <- within(data, {
-#'   library(nestcolor)
+#'   require(nestcolor)
 #'   ADSL <- rADSL
 #'   ADRS <- rADRS
 #' })
@@ -640,7 +640,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
           substitute(
             expr = summary_plot_obs <- data_frame_call[, analysis_vars] %>%
               dplyr::summarise_all(list(function(x) sum(is.na(x)))) %>%
-              tidyr::pivot_longer(tidyselect::everything(), names_to = "col", values_to = "n_na") %>%
+              tidyr::pivot_longer(dplyr::everything(), names_to = "col", values_to = "n_na") %>%
               dplyr::mutate(n_not_na = nrow(ANL) - n_na) %>%
               tidyr::pivot_longer(-col, names_to = "isna", values_to = "n") %>%
               dplyr::mutate(isna = isna == "n_na", n_pct = n / nrow(ANL) * 100),
@@ -733,7 +733,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
               summary_plot_patients <- ANL[, c(parent_keys, analysis_vars)] %>%
                 dplyr::group_by_at(parent_keys) %>%
                 dplyr::summarise_all(anyNA) %>%
-                tidyr::pivot_longer(cols = !tidyselect::all_of(parent_keys), names_to = "col", values_to = "anyna") %>%
+                tidyr::pivot_longer(cols = !dplyr::all_of(parent_keys), names_to = "col", values_to = "anyna") %>%
                 dplyr::group_by_at(c("col")) %>%
                 dplyr::summarise(count_na = sum(anyna)) %>%
                 dplyr::mutate(count_not_na = ndistinct_subjects - count_na) %>%
@@ -1041,7 +1041,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
 
               summary_data <- dplyr::summarise_all(summary_data, summ_fn) %>%
                 dplyr::mutate(group_var_name := paste0(group_var, ":", group_var_name, "(N=", count_data$n, ")")) %>%
-                tidyr::pivot_longer(!tidyselect::all_of(group_var), names_to = "Variable", values_to = "out") %>%
+                tidyr::pivot_longer(!dplyr::all_of(group_var), names_to = "Variable", values_to = "out") %>%
                 tidyr::pivot_wider(names_from = group_var, values_from = "out") %>%
                 dplyr::mutate(`Variable label` = create_cols_labels(Variable, just_label = TRUE), .after = Variable)
             },
@@ -1056,7 +1056,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
           substitute(
             expr = summary_data <- ANL %>%
               dplyr::summarise_all(summ_fn) %>%
-              tidyr::pivot_longer(tidyselect::everything(),
+              tidyr::pivot_longer(dplyr::everything(),
                 names_to = "Variable",
                 values_to = paste0("Missing (N=", nrow(ANL), ")")
               ) %>%
@@ -1119,7 +1119,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
             # order subjects by decreasing number of missing and then by
             # missingness pattern (defined using sha1)
             order_subjects <- summary_plot_patients %>%
-              dplyr::select(-"id", -tidyselect::all_of(parent_keys)) %>%
+              dplyr::select(-"id", -dplyr::all_of(parent_keys)) %>%
               dplyr::transmute(
                 id = dplyr::row_number(),
                 number_NA = apply(., 1, sum),
@@ -1130,7 +1130,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
 
             # order columns by decreasing percent of missing values
             ordered_columns <- summary_plot_patients %>%
-              dplyr::select(-"id", -tidyselect::all_of(parent_keys)) %>%
+              dplyr::select(-"id", -dplyr::all_of(parent_keys)) %>%
               dplyr::summarise(
                 column = create_cols_labels(colnames(.)),
                 na_count = apply(., MARGIN = 2, FUN = sum),
@@ -1139,7 +1139,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
               dplyr::arrange(na_percent, dplyr::desc(column))
 
             summary_plot_patients <- summary_plot_patients %>%
-              tidyr::gather("col", "isna", -"id", -tidyselect::all_of(parent_keys)) %>%
+              tidyr::gather("col", "isna", -"id", -dplyr::all_of(parent_keys)) %>%
               dplyr::mutate(col = create_cols_labels(col))
           })
         ) %>%