Merge remote-tracking branch 'origin/120_append_fs_title@main' into 1…

…20_append_fs_title@main

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: teal.modules.general
 Title: General modules to add to a teal application
-Version: 0.2.14.9008
+Version: 0.2.14.9010
 Date: 2022-06-09
 Authors@R: c(
     person("Dawid", "Kaledkowski", , "dawid.kaledkowski@roche.com", role = c("aut", "cre")),

NEWS.md

@@ -1,4 +1,4 @@
-# teal.modules.general 0.2.14.9008
+# teal.modules.general 0.2.14.9010
 
 ### Enhancements
 * Added the `teal.reporter` functionality to all modules.

R/tm_a_regression.R

@@ -182,7 +182,7 @@ ui_a_regression <- function(id, ...) {
           class = "teal-tooltip",
           tagList(
             "Outlier definition:",
-            icon("info-circle"),
+            icon("circle-info"),
             span(
               class = "tooltiptext",
               paste(

R/tm_g_distribution.R

@@ -248,7 +248,7 @@ ui_distribution <- function(id, ...) {
             class = "teal-tooltip",
             tagList(
               "Distribution:",
-              icon("info-circle"),
+              icon("circle-info"),
               span(
                 class = "tooltiptext",
                 "Default parameters are optimized with MASS::fitdistr function."

R/tm_missing_data.R

@@ -251,7 +251,7 @@ encoding_missing_data <- function(id, summary_per_patient = FALSE, ggtheme, data
           class = "teal-tooltip",
           tagList(
             "Add **anyna** variable",
-            icon("info-circle"),
+            icon("circle-info"),
             span(
               class = "tooltiptext",
               "Describes the number of observations with at least one missing value in any variable."
@@ -267,7 +267,7 @@ encoding_missing_data <- function(id, summary_per_patient = FALSE, ggtheme, data
             class = "teal-tooltip",
             tagList(
               "Add summary per patients",
-              icon("info-circle"),
+              icon("circle-info"),
               span(
                 class = "tooltiptext",
                 paste(
@@ -343,32 +343,32 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
         teal.code::chunks_push(..., chunks = common_stack)
       }
 
-      anl_name <- paste0(dataname, "_FILTERED")
-      anl_filtered <- datasets$get_data(dataname, filtered = TRUE)
-      assign(anl_name, anl_filtered)
+      anl_name <- dataname
+      anl <- datasets$get_data(dataname, filtered = TRUE)
+      assign(anl_name, anl)
       teal.code::chunks_reset(chunks = common_stack)
       group_var <- input$group_by_var
 
-      if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl_filtered)) {
+      if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl)) {
         common_stack_push(
-          id = "ANL_FILTERED_selection_call",
+          id = "ANL_selection_call",
           expression = substitute(
-            expr = ANL_FILTERED <- anl_name[, selected_vars], # nolint
+            expr = ANL <- anl_name[, selected_vars], # nolint
             env = list(anl_name = as.name(anl_name), selected_vars = selected_vars())
           )
         )
       } else {
         common_stack_push(
-          id = "ANL_FILTERED_call",
-          expression = substitute(expr = ANL_FILTERED <- anl_name, env = list(anl_name = as.name(anl_name))) # nolint
+          id = "ANL_call",
+          expression = substitute(expr = ANL <- anl_name, env = list(anl_name = as.name(anl_name))) # nolint
         )
       }
 
       if (input$summary_type == "By Variable Levels" && !is.null(group_var) && !(group_var %in% selected_vars())) {
         common_stack_push(
-          id = "ANL_FILTERED_group_var_call",
+          id = "ANL_group_var_call",
           substitute(
-            expr = ANL_FILTERED[[group_var]] <- anl_name[[group_var]], # nolint
+            expr = ANL[[group_var]] <- anl_name[[group_var]], # nolint
             env = list(group_var = group_var, anl_name = as.name(anl_name))
           )
         )
@@ -526,7 +526,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
         summary_stack_push(
           id = "ANL_na_column_call",
           expression = substitute(
-            expr = ANL_FILTERED[[new_col_name]] <- ifelse(rowSums(is.na(ANL_FILTERED)) > 0, NA, FALSE), # nolint
+            expr = AN[[new_col_name]] <- ifelse(rowSums(is.na(ANL)) > 0, NA, FALSE), # nolint
             env = list(new_col_name = new_col_name)
           )
         )
@@ -535,7 +535,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
       summary_stack_push(
         id = "analysis_vars_call",
         expression = substitute(
-          expr = analysis_vars <- setdiff(colnames(ANL_FILTERED), data_keys),
+          expr = analysis_vars <- setdiff(colnames(ANL), data_keys),
           env = list(data_keys = data_keys())
         )
       )
@@ -546,13 +546,13 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
           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") %>%
-            dplyr::mutate(n_not_na = nrow(ANL_FILTERED) - 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_FILTERED) * 100),
+            dplyr::mutate(isna = isna == "n_na", n_pct = n / nrow(ANL) * 100),
           env = list(data_frame_call = if (!inherits(datasets$get_data(dataname, filtered = TRUE), "tbl_df")) {
-            quote(tibble::as_tibble(ANL_FILTERED))
+            quote(tibble::as_tibble(ANL))
           } else {
-            quote(ANL_FILTERED)
+            quote(ANL)
           })
         )
       )
@@ -641,13 +641,13 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
         )
         summary_stack_push(
           id = "ndistinct_subjects_call",
-          expression = quote(ndistinct_subjects <- dplyr::n_distinct(ANL_FILTERED[, parent_keys]))
+          expression = quote(ndistinct_subjects <- dplyr::n_distinct(ANL[, parent_keys]))
         )
 
         summary_stack_push(
           id = "summary_plot_patients_call",
           expression = quote(
-            summary_plot_patients <- ANL_FILTERED[, c(parent_keys, analysis_vars)] %>%
+            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") %>%
@@ -757,7 +757,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
       teal.code::chunks_push(
         id = "combination_cutoff_call",
         expression = quote({
-          combination_cutoff <- ANL_FILTERED %>%
+          combination_cutoff <- ANL %>%
             dplyr::mutate_all(is.na) %>%
             dplyr::group_by_all() %>%
             dplyr::tally() %>%
@@ -951,8 +951,8 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
       # Add common code into this stack
       teal.code::chunks_push_chunks(common_code_chunks(), chunks = table_stack)
 
-      # extract the ANL_FILTERED dataset for use in further validation
-      anl_filtered <- teal.code::chunks_get_var("ANL_FILTERED", chunks = table_stack)
+      # extract the ANL dataset for use in further validation
+      anl <- teal.code::chunks_get_var("ANL", chunks = table_stack)
 
       validate(need(input$count_type, "Please select type of counts"))
       if (!is.null(input$group_by_var)) {
@@ -964,7 +964,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
       validate(
         need(
           is.null(group_var) ||
-            nrow(unique(anl_filtered[, group_var])) < 100,
+            nrow(unique(anl[, group_var])) < 100,
           "Please select group-by variable with fewer than 100 unique values"
         )
       )
@@ -974,10 +974,10 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
       vars <- unique(variables_select, group_var)
       count_type <- input$count_type # nolint (local variable is assigned and used)
 
-      if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl_filtered)) {
+      if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl)) {
         variables <- selected_vars() # nolint (local variable is assigned and used)
       } else {
-        variables <- colnames(anl_filtered)
+        variables <- colnames(anl)
       }
 
       summ_fn <- if (input$count_type == "counts") {
@@ -992,7 +992,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
           id = "summary_data_call_1",
           expression = substitute(
             expr = {
-              summary_data <- ANL_FILTERED %>%
+              summary_data <- ANL %>%
                 dplyr::mutate(group_var_name := forcats::fct_explicit_na(as.factor(group_var_name), "NA")) %>%
                 dplyr::group_by_at(group_var) %>%
                 dplyr::filter(group_var_name %in% group_vals)
@@ -1014,11 +1014,11 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
         table_stack_push(
           id = "summary_data_call_1",
           expression = substitute(
-            expr = summary_data <- ANL_FILTERED %>%
+            expr = summary_data <- ANL %>%
               dplyr::summarise_all(summ_fn) %>%
               tidyr::pivot_longer(tidyselect::everything(),
                 names_to = "Variable",
-                values_to = paste0("Missing (N=", nrow(ANL_FILTERED), ")")
+                values_to = paste0("Missing (N=", nrow(ANL), ")")
               ) %>%
               dplyr::mutate(`Variable label` = create_cols_labels(Variable), .after = Variable),
             env = list(summ_fn = summ_fn)
@@ -1060,15 +1060,15 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
       by_subject_stack_push(
         id = "analysis_vars_call",
         expression = substitute(
-          expr = analysis_vars <- setdiff(colnames(ANL_FILTERED), data_keys),
+          expr = analysis_vars <- setdiff(colnames(ANL), data_keys),
           env = list(data_keys = data_keys())
         )
       )
 
       by_subject_stack_push(
         id = "summary_plot_patients_call",
         expression = quote({
-          summary_plot_patients <- ANL_FILTERED[, c(parent_keys, analysis_vars)] %>%
+          summary_plot_patients <- ANL[, c(parent_keys, analysis_vars)] %>%
             dplyr::group_by_at(parent_keys) %>%
             dplyr::mutate(id = dplyr::cur_group_id()) %>%
             dplyr::ungroup() %>%

R/tm_outliers.R

@@ -840,10 +840,10 @@ srv_outliers <- function(id, datasets, reporter, outlier_var,
               all_categories <- lapply(
                 unique(anl[[categorical_var]]),
                 function(x) {
-                  anl_filtered <- anl %>% dplyr::filter(get(categorical_var) == x)
+                  anl <- anl %>% dplyr::filter(get(categorical_var) == x)
                   anl_outlier2 <- ANL_OUTLIER %>% dplyr::filter(get(categorical_var) == x)
-                  ecdf_df <- anl_filtered %>%
-                    dplyr::mutate(y = stats::ecdf(anl_filtered[[outlier_var]])(anl_filtered[[outlier_var]]))
+                  ecdf_df <- anl %>%
+                    dplyr::mutate(y = stats::ecdf(anl[[outlier_var]])(anl[[outlier_var]]))
 
                   dplyr::left_join(
                     ecdf_df,

R/tm_variable_browser.R

@@ -330,7 +330,7 @@ srv_variable_browser <- function(id, datasets, reporter, datasets_selected, ggpl
           class = "teal-tooltip",
           tagList(
             "Outlier definition:",
-            icon("info-circle"),
+            icon("circle-info"),
             span(
               class = "tooltiptext",
               paste(

R/utils.R

@@ -239,16 +239,16 @@ variable_type_icons <- function(var_type) {
   checkmate::assert_character(var_type, any.missing = FALSE)
 
   class_to_icon <- list(
-    numeric = "sort-numeric-up",
-    integer = "sort-numeric-up",
+    numeric = "arrow-up-1-9",
+    integer = "arrow-up-1-9",
     logical = "pause",
     Date = "calendar",
     POSIXct = "calendar",
     POSIXlt = "calendar",
     factor = "chart-bar",
     character = "keyboard",
     primary_key = "key",
-    unknown = "question-circle"
+    unknown = "circle-question"
   )
   class_to_icon <- lapply(class_to_icon, function(icon_name) toString(icon(icon_name, lib = "font-awesome")))