From 9f8ea57e7f47f16db8bf38e7750168b06a457da4 Mon Sep 17 00:00:00 2001
From: Aleksander Chlebowski <114988527+chlebowa@users.noreply.github.com>
Date: Mon, 19 Feb 2024 13:03:07 +0100
Subject: [PATCH] fix nolint syntax (#638)

https://github.com/insightsengineering/teal.modules.general/pull/636#issuecomment-1952150583
---
 R/tm_a_pca.R               | 10 +++----
 R/tm_a_regression.R        |  4 +--
 R/tm_g_association.R       |  6 ++--
 R/tm_g_bivariate.R         |  4 +--
 R/tm_g_distribution.R      | 10 +++----
 R/tm_g_response.R          |  6 ++--
 R/tm_g_scatterplot.R       | 12 ++++----
 R/tm_g_scatterplotmatrix.R |  8 +++---
 R/tm_missing_data.R        |  8 +++---
 R/tm_outliers.R            | 59 +++++++++++++++++++-------------------
 R/tm_t_crosstable.R        |  4 +--
 R/tm_variable_browser.R    |  4 +--
 data-raw/data.R            | 10 +++----
 13 files changed, 73 insertions(+), 72 deletions(-)

diff --git a/R/tm_a_pca.R b/R/tm_a_pca.R
index c1fda7608..74eb8ebb5 100644
--- a/R/tm_a_pca.R
+++ b/R/tm_a_pca.R
@@ -345,7 +345,7 @@ srv_a_pca <- function(id, data, reporter, filter_panel_api, dat, plot_height, pl
       standardization <- input$standardization
       center <- standardization %in% c("center", "center_scale")
       scale <- standardization == "center_scale"
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
 
       teal::validate_has_data(ANL, 10)
       validate(need(
@@ -376,7 +376,7 @@ srv_a_pca <- function(id, data, reporter, filter_panel_api, dat, plot_height, pl
       standardization <- input$standardization
       center <- standardization %in% c("center", "center_scale")
       scale <- standardization == "center_scale"
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
 
       qenv <- teal.code::eval_code(
         merged$anl_q_r(),
@@ -389,7 +389,7 @@ srv_a_pca <- function(id, data, reporter, filter_panel_api, dat, plot_height, pl
       if (na_action == "drop") {
         qenv <- teal.code::eval_code(
           qenv,
-          quote(ANL <- tidyr::drop_na(ANL, keep_columns)) # nolint object_name_linter
+          quote(ANL <- tidyr::drop_na(ANL, keep_columns)) # nolint: object_name.
         )
       }
 
@@ -611,7 +611,7 @@ srv_a_pca <- function(id, data, reporter, filter_panel_api, dat, plot_height, pl
     plot_biplot <- function(base_q) {
       qenv <- base_q
 
-      ANL <- qenv[["ANL"]] # nolint object_name_linter
+      ANL <- qenv[["ANL"]] # nolint: object_name.
 
       resp_col <- as.character(merged$anl_input_r()$columns_source$response)
       dat_cols <- as.character(merged$anl_input_r()$columns_source$dat)
@@ -707,7 +707,7 @@ srv_a_pca <- function(id, data, reporter, filter_panel_api, dat, plot_height, pl
         dev_labs <- list(color = varname_w_label(resp_col, ANL))
 
         scales_biplot <-
-          if (is.character(response) || is.factor(response) || (is.numeric(response) && length(unique(response)) <= 6)) { # nolint line_length_linter
+          if (is.character(response) || is.factor(response) || (is.numeric(response) && length(unique(response)) <= 6)) { # nolint: line_length.
             qenv <- teal.code::eval_code(
               qenv,
               quote(pca_rot$response <- as.factor(response))
diff --git a/R/tm_a_regression.R b/R/tm_a_regression.R
index af146d997..d82a17639 100644
--- a/R/tm_a_regression.R
+++ b/R/tm_a_regression.R
@@ -312,7 +312,7 @@ srv_a_regression <- function(id,
 
     # sets qenv object and populates it with data merge call and fit expression
     fit_r <- reactive({
-      ANL <- anl_merged_q()[["ANL"]] # nolint object_name_linter
+      ANL <- anl_merged_q()[["ANL"]] # nolint: object_name.
       teal::validate_has_data(ANL, 10)
 
       validate(need(is.numeric(ANL[regression_var()$response][[1]]), "Response variable should be numeric."))
@@ -409,7 +409,7 @@ srv_a_regression <- function(id,
 
       plot_type_0 <- function() {
         fit <- fit_r()[["fit"]]
-        ANL <- anl_merged_q()[["ANL"]] # nolint object_name_linter
+        ANL <- anl_merged_q()[["ANL"]] # nolint: object_name.
 
         stopifnot(ncol(fit$model) == 2)
 
diff --git a/R/tm_g_association.R b/R/tm_g_association.R
index da605728b..43b37062b 100644
--- a/R/tm_g_association.R
+++ b/R/tm_g_association.R
@@ -74,8 +74,8 @@ tm_g_association <- function(label = "Association",
                              show_association = TRUE,
                              plot_height = c(600, 400, 5000),
                              plot_width = NULL,
-                             distribution_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"), # nolint line_length_linter
-                             association_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"), # nolint line_length_linter
+                             distribution_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"), # nolint: line_length.
+                             association_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"), # nolint: line_length.
                              pre_output = NULL,
                              post_output = NULL,
                              ggplot2_args = teal.widgets::ggplot2_args()) {
@@ -258,7 +258,7 @@ srv_tm_g_association <- function(id,
     output_q <- reactive({
       teal::validate_inputs(iv_r())
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
       teal::validate_has_data(ANL, 3)
 
       vars_names <- merged$anl_input_r()$columns_source$vars
diff --git a/R/tm_g_bivariate.R b/R/tm_g_bivariate.R
index 39f95e05e..460ba1b9c 100644
--- a/R/tm_g_bivariate.R
+++ b/R/tm_g_bivariate.R
@@ -458,7 +458,7 @@ srv_g_bivariate <- function(id,
     output_q <- reactive({
       teal::validate_inputs(iv_r())
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
       teal::validate_has_data(ANL, 3)
 
       x_col_vec <- as.vector(merged$anl_input_r()$columns_source$x)
@@ -585,7 +585,7 @@ srv_g_bivariate <- function(id,
         substitute(
           expr = {
             # Add facetting labels
-            # optional: grid.newpage() # nolint commented_code_linter
+            # optional: grid.newpage() # nolint: commented_code.
             p <- add_facet_labels(p, xfacet_label = nulled_col_facet_name, yfacet_label = nulled_row_facet_name)
             grid::grid.newpage()
             grid::grid.draw(p)
diff --git a/R/tm_g_distribution.R b/R/tm_g_distribution.R
index d42cbf535..48a0adef4 100644
--- a/R/tm_g_distribution.R
+++ b/R/tm_g_distribution.R
@@ -493,7 +493,7 @@ srv_distribution <- function(id,
             )
           }
 
-          ANL <- merged$anl_q_r()[[as.character(dist_var[[1]]$dataname)]] # nolint object_name_linter
+          ANL <- merged$anl_q_r()[[as.character(dist_var[[1]]$dataname)]] # nolint: object_name.
           params <- get_dist_params(as.numeric(stats::na.omit(ANL[[dist_var2]])), input$t_dist)
           params_vec <- round(unname(unlist(params)), 2)
           params_names <- names(params)
@@ -533,7 +533,7 @@ srv_distribution <- function(id,
     common_q <- reactive({
       # Create a private stack for this function only.
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
       dist_var <- merge_vars()$dist_var
       s_var <- merge_vars()$s_var
       g_var <- merge_vars()$g_var
@@ -560,7 +560,7 @@ srv_distribution <- function(id,
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL[[g_var]] <- forcats::fct_na_value_to_level(as.factor(ANL[[g_var]]), "NA"), # nolint object_name_linter
+            expr = ANL[[g_var]] <- forcats::fct_na_value_to_level(as.factor(ANL[[g_var]]), "NA"), # nolint: object_name.
             env = list(g_var = g_var)
           )
         )
@@ -576,7 +576,7 @@ srv_distribution <- function(id,
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL[[s_var]] <- forcats::fct_na_value_to_level(as.factor(ANL[[s_var]]), "NA"), # nolint object_name_linter
+            expr = ANL[[s_var]] <- forcats::fct_na_value_to_level(as.factor(ANL[[s_var]]), "NA"), # nolint: object_name.
             env = list(s_var = s_var)
           )
         )
@@ -994,7 +994,7 @@ srv_distribution <- function(id,
       },
       valueExpr = {
         # Create a private stack for this function only.
-        ANL <- common_q()[["ANL"]] # nolint object_name_linter
+        ANL <- common_q()[["ANL"]] # nolint: object_name.
 
         dist_var <- merge_vars()$dist_var
         s_var <- merge_vars()$s_var
diff --git a/R/tm_g_response.R b/R/tm_g_response.R
index 754433d3a..3ca9acdf0 100644
--- a/R/tm_g_response.R
+++ b/R/tm_g_response.R
@@ -295,7 +295,7 @@ srv_g_response <- function(id,
       teal::validate_inputs(iv_r())
 
       qenv <- merged$anl_q_r()
-      ANL <- qenv[["ANL"]] # nolint object_name_linter
+      ANL <- qenv[["ANL"]] # nolint: object_name.
       resp_var <- as.vector(merged$anl_input_r()$columns_source$response)
       x <- as.vector(merged$anl_input_r()$columns_source$x)
 
@@ -332,7 +332,7 @@ srv_g_response <- function(id,
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL[[x]] <- with(ANL, forcats::fct_rev(x_cl)), # nolint object_name_linter
+            expr = ANL[[x]] <- with(ANL, forcats::fct_rev(x_cl)), # nolint: object_name.
             env = list(x = x, x_cl = x_cl)
           )
         )
@@ -341,7 +341,7 @@ srv_g_response <- function(id,
       qenv <- teal.code::eval_code(
         qenv,
         substitute(
-          expr = ANL[[resp_var]] <- factor(ANL[[resp_var]]), # nolint object_name_linter
+          expr = ANL[[resp_var]] <- factor(ANL[[resp_var]]), # nolint: object_name.
           env = list(resp_var = resp_var)
         )
       ) %>%
diff --git a/R/tm_g_scatterplot.R b/R/tm_g_scatterplot.R
index 1cf20b3d8..9caab3f2c 100644
--- a/R/tm_g_scatterplot.R
+++ b/R/tm_g_scatterplot.R
@@ -485,7 +485,7 @@ srv_g_scatterplot <- function(id,
     )
 
     trend_line_is_applicable <- reactive({
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
       x_var <- as.vector(merged$anl_input_r()$columns_source$x)
       y_var <- as.vector(merged$anl_input_r()$columns_source$y)
       length(x_var) > 0 && length(y_var) > 0 && is.numeric(ANL[[x_var]]) && is.numeric(ANL[[y_var]])
@@ -512,7 +512,7 @@ srv_g_scatterplot <- function(id,
 
     output$num_na_removed <- renderUI({
       if (add_trend_line()) {
-        ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+        ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
         x_var <- as.vector(merged$anl_input_r()$columns_source$x)
         y_var <- as.vector(merged$anl_input_r()$columns_source$y)
         if ((num_total_na <- nrow(ANL) - nrow(stats::na.omit(ANL[, c(x_var, y_var)]))) > 0) {
@@ -536,7 +536,7 @@ srv_g_scatterplot <- function(id,
     output_q <- reactive({
       teal::validate_inputs(iv_r(), iv_facet)
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
 
       x_var <- as.vector(merged$anl_input_r()$columns_source$x)
       y_var <- as.vector(merged$anl_input_r()$columns_source$y)
@@ -637,7 +637,7 @@ srv_g_scatterplot <- function(id,
         plot_q <- teal.code::eval_code(
           object = plot_q,
           code = substitute(
-            expr = ANL[, log_x_var] <- log_x_fn(ANL[, x_var]), # nolint object_name_linter
+            expr = ANL[, log_x_var] <- log_x_fn(ANL[, x_var]), # nolint: object_name.
             env = list(
               x_var = x_var,
               log_x_fn = as.name(log_x_fn),
@@ -652,7 +652,7 @@ srv_g_scatterplot <- function(id,
         plot_q <- teal.code::eval_code(
           object = plot_q,
           code = substitute(
-            expr = ANL[, log_y_var] <- log_y_fn(ANL[, y_var]), # nolint object_name_linter
+            expr = ANL[, log_y_var] <- log_y_fn(ANL[, y_var]), # nolint: object_name.
             env = list(
               y_var = y_var,
               log_y_fn = as.name(log_y_fn),
@@ -785,7 +785,7 @@ srv_g_scatterplot <- function(id,
             plot_q <- teal.code::eval_code(
               plot_q,
               substitute(
-                expr = ANL <- dplyr::filter(ANL, !is.na(x_var) & !is.na(y_var)), # nolint object_name_linter
+                expr = ANL <- dplyr::filter(ANL, !is.na(x_var) & !is.na(y_var)), # nolint: object_name.
                 env = list(x_var = as.name(x_var), y_var = as.name(y_var))
               )
             )
diff --git a/R/tm_g_scatterplotmatrix.R b/R/tm_g_scatterplotmatrix.R
index f4437161c..b82e1d41d 100644
--- a/R/tm_g_scatterplotmatrix.R
+++ b/R/tm_g_scatterplotmatrix.R
@@ -200,7 +200,7 @@ srv_g_scatterplotmatrix <- function(id, data, reporter, filter_panel_api, variab
       teal::validate_inputs(iv_r())
 
       qenv <- merged$anl_q_r()
-      ANL <- qenv[["ANL"]] # nolint object_name_linter
+      ANL <- qenv[["ANL"]] # nolint: object_name.
 
       cols_names <- merged$anl_input_r()$columns_source$variables
       alpha <- input$alpha
@@ -227,7 +227,7 @@ srv_g_scatterplotmatrix <- function(id, data, reporter, filter_panel_api, variab
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL <- ANL[, cols_names] %>% # nolint object_name_linter
+            expr = ANL <- ANL[, cols_names] %>% # nolint: object_name.
               dplyr::mutate_if(is.character, as.factor) %>%
               droplevels(),
             env = list(cols_names = cols_names)
@@ -237,7 +237,7 @@ srv_g_scatterplotmatrix <- function(id, data, reporter, filter_panel_api, variab
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL <- ANL[, cols_names] %>% # nolint object_name_linter
+            expr = ANL <- ANL[, cols_names] %>% # nolint: object_name.
               droplevels(),
             env = list(cols_names = cols_names)
           )
@@ -322,7 +322,7 @@ srv_g_scatterplotmatrix <- function(id, data, reporter, filter_panel_api, variab
     output$message <- renderText({
       shiny::req(iv_r()$is_valid())
       req(selector_list()$variables())
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
       cols_names <- unique(unname(do.call(c, merged$anl_input_r()$columns_source)))
       check_char <- vapply(ANL[, cols_names], is.character, logical(1))
       if (any(check_char)) {
diff --git a/R/tm_missing_data.R b/R/tm_missing_data.R
index 60a8f33f4..64a81c968 100644
--- a/R/tm_missing_data.R
+++ b/R/tm_missing_data.R
@@ -425,14 +425,14 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
         teal.code::eval_code(
           data(),
           substitute(
-            expr = ANL <- anl_name[, selected_vars, drop = FALSE], # nolint object_name_linter
+            expr = ANL <- anl_name[, selected_vars, drop = FALSE], # nolint: object_name.
             env = list(anl_name = as.name(dataname), selected_vars = selected_vars())
           )
         )
       } else {
         teal.code::eval_code(
           data(),
-          substitute(expr = ANL <- anl_name, env = list(anl_name = as.name(dataname))) # nolint object_name_linter
+          substitute(expr = ANL <- anl_name, env = list(anl_name = as.name(dataname))) # nolint: object_name.
         )
       }
 
@@ -440,7 +440,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL[[group_var]] <- anl_name[[group_var]], # nolint object_name_linter
+            expr = ANL[[group_var]] <- anl_name[[group_var]], # nolint: object_name.
             env = list(group_var = group_var, anl_name = as.name(dataname))
           )
         )
@@ -590,7 +590,7 @@ srv_missing_data <- function(id, data, reporter, filter_panel_api, dataname, par
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL[[new_col_name]] <- ifelse(rowSums(is.na(ANL)) > 0, NA, FALSE), # nolint object_name_linter
+            expr = ANL[[new_col_name]] <- ifelse(rowSums(is.na(ANL)) > 0, NA, FALSE), # nolint: object_name.
             env = list(new_col_name = new_col_name)
           )
         )
diff --git a/R/tm_outliers.R b/R/tm_outliers.R
index 40b3c728d..e58d2c53f 100644
--- a/R/tm_outliers.R
+++ b/R/tm_outliers.R
@@ -311,7 +311,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
     n_outlier_missing <- reactive({
       shiny::req(iv_r()$is_valid())
       outlier_var <- as.vector(merged$anl_input_r()$columns_source$outlier_var)
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
       sum(is.na(ANL[[outlier_var]]))
     })
 
@@ -321,7 +321,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
     common_code_q <- reactive({
       shiny::req(iv_r()$is_valid())
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
       qenv <- merged$anl_q_r()
 
       outlier_var <- as.vector(merged$anl_input_r()$columns_source$outlier_var)
@@ -350,7 +350,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
           qenv <- teal.code::eval_code(
             qenv,
             substitute(
-              expr = ANL <- ANL %>% dplyr::filter(!is.na(outlier_var_name)), # nolint object_name_linter
+              expr = ANL <- ANL %>% dplyr::filter(!is.na(outlier_var_name)), # nolint: object_name.
               env = list(outlier_var_name = as.name(outlier_var))
             )
           )
@@ -367,7 +367,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
           qenv <- teal.code::eval_code(
             qenv,
             substitute(
-              expr = ANL <- ANL %>% dplyr::filter(!is.na(outlier_var_name)), # nolint object_name_linter
+              expr = ANL <- ANL %>% dplyr::filter(!is.na(outlier_var_name)), # nolint: object_name.
               env = list(outlier_var_name = as.name(outlier_var))
             )
           )
@@ -399,7 +399,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
         qenv,
         substitute(
           expr = {
-            ANL_OUTLIER <- ANL %>% # nolint object_name_linter
+            ANL_OUTLIER <- ANL %>% # nolint: object_name.
               group_expr %>% # styler: off
               dplyr::mutate(is_outlier = {
                 q1_q3 <- stats::quantile(outlier_var_name, probs = c(0.25, 0.75))
@@ -465,7 +465,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
         qenv,
         substitute(
           expr = {
-            ANL_OUTLIER_EXTENDED <- dplyr::left_join( # nolint object_name_linter
+            ANL_OUTLIER_EXTENDED <- dplyr::left_join( # nolint: object_name.
               ANL_OUTLIER,
               dplyr::select(
                 dataname,
@@ -544,13 +544,13 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
               # In order for geom_rug to work properly when reordering takes place inside facet_grid,
               # all tables must have the column used for reording.
               # In this case, the column used for reordering is `order`.
-              ANL_OUTLIER <- dplyr::left_join( # nolint object_name_linter
+              ANL_OUTLIER <- dplyr::left_join( # nolint: object_name.
                 ANL_OUTLIER,
                 summary_table_pre[, c("order", categorical_var)],
                 by = categorical_var
               )
               # so that x axis of plot aligns with columns of summary table, from most outliers to least by percentage
-              ANL <- ANL %>% # nolint object_name_linter
+              ANL <- ANL %>% # nolint: object_name.
                 dplyr::left_join(
                   dplyr::select(summary_table_pre, categorical_var_name, order),
                   by = categorical_var
@@ -602,11 +602,11 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
       }
     )
 
-    # boxplot/violinplot # nolint commented_code_linter
+    # boxplot/violinplot # nolint commented_code
     boxplot_q <- reactive({
       req(common_code_q())
-      ANL <- common_code_q()[["ANL"]] # nolint object_name_linter
-      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint object_name_linter
+      ANL <- common_code_q()[["ANL"]] # nolint: object_name.
+      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint: object_name.
 
       outlier_var <- as.vector(merged$anl_input_r()$columns_source$outlier_var)
       categorical_var <- as.vector(merged$anl_input_r()$columns_source$categorical_var)
@@ -697,8 +697,8 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
 
     # density plot
     density_plot_q <- reactive({
-      ANL <- common_code_q()[["ANL"]] # nolint object_name_linter
-      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint object_name_linter
+      ANL <- common_code_q()[["ANL"]] # nolint: object_name.
+      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint: object_name.
 
       outlier_var <- as.vector(merged$anl_input_r()$columns_source$outlier_var)
       categorical_var <- as.vector(merged$anl_input_r()$columns_source$categorical_var)
@@ -757,8 +757,8 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
 
     # Cumulative distribution plot
     cumulative_plot_q <- reactive({
-      ANL <- common_code_q()[["ANL"]] # nolint object_name_linter
-      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint object_name_linter
+      ANL <- common_code_q()[["ANL"]] # nolint: object_name.
+      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint: object_name.
 
       qenv <- common_code_q()
 
@@ -802,7 +802,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
               all_categories <- lapply(
                 unique(ANL[[categorical_var]]),
                 function(x) {
-                  ANL <- ANL %>% dplyr::filter(get(categorical_var) == x) # nolint object_name_linter
+                  ANL <- ANL %>% dplyr::filter(get(categorical_var) == x) # nolint: object_name.
                   anl_outlier2 <- ANL_OUTLIER %>% dplyr::filter(get(categorical_var) == x)
                   ecdf_df <- ANL %>%
                     dplyr::mutate(y = stats::ecdf(ANL[[outlier_var]])(ANL[[outlier_var]]))
@@ -979,7 +979,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
     choices <- teal.transform::variable_choices(data()[[dataname_first]])
 
     observeEvent(common_code_q(), {
-      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint object_name_linter
+      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint: object_name.
       teal.widgets::updateOptionalSelectInput(
         session,
         inputId = "table_ui_columns",
@@ -995,9 +995,9 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
         outlier_var <- as.vector(merged$anl_input_r()$columns_source$outlier_var)
         categorical_var <- as.vector(merged$anl_input_r()$columns_source$categorical_var)
 
-        ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint object_name_linter
-        ANL_OUTLIER_EXTENDED <- common_code_q()[["ANL_OUTLIER_EXTENDED"]] # nolint object_name_linter
-        ANL <- common_code_q()[["ANL"]] # nolint object_name_linter
+        ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint: object_name.
+        ANL_OUTLIER_EXTENDED <- common_code_q()[["ANL_OUTLIER_EXTENDED"]] # nolint: object_name.
+        ANL <- common_code_q()[["ANL"]] # nolint: object_name.
         plot_brush <- if (tab == "Boxplot") {
           boxplot_r()
           box_pws$brush()
@@ -1010,7 +1010,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
         }
 
         # removing unused column ASAP
-        ANL_OUTLIER$order <- ANL$order <- NULL # nolint object_name_linter
+        ANL_OUTLIER$order <- ANL$order <- NULL # nolint: object_name.
 
         display_table <- if (!is.null(plot_brush)) {
           if (length(categorical_var) > 0) {
@@ -1026,7 +1026,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
             if (tab == "Boxplot") {
               # in boxplot with no categorical variable, there is no column in ANL that would correspond to x-axis
               # so a column needs to be inserted with the value "Entire dataset" because that's the label used in plot
-              ANL[[plot_brush$mapping$x]] <- "Entire dataset" # nolint object_name_linter
+              ANL[[plot_brush$mapping$x]] <- "Entire dataset" # nolint: object_name.
             }
           }
 
@@ -1034,15 +1034,16 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
           # so they need to be computed and attached to ANL
           if (tab == "Density Plot") {
             plot_brush$mapping$y <- "density"
-            ANL$density <- plot_brush$ymin # nolint #either ymin or ymax will work
+            ANL$density <- plot_brush$ymin # nolint: object_name.
+            # either ymin or ymax will work
           } else if (tab == "Cumulative Distribution Plot") {
             plot_brush$mapping$y <- "cdf"
             if (length(categorical_var) > 0) {
-              ANL <- ANL %>% # nolint object_name_linter
+              ANL <- ANL %>% # nolint: object_name.
                 dplyr::group_by(!!as.name(plot_brush$mapping$panelvar1)) %>%
                 dplyr::mutate(cdf = stats::ecdf(!!as.name(outlier_var))(!!as.name(outlier_var)))
             } else {
-              ANL$cdf <- stats::ecdf(ANL[[outlier_var]])(ANL[[outlier_var]]) # nolint object_name_linter
+              ANL$cdf <- stats::ecdf(ANL[[outlier_var]])(ANL[[outlier_var]]) # nolint: object_name.
             }
           }
 
@@ -1087,10 +1088,10 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
 
     output$total_outliers <- renderUI({
       shiny::req(iv_r()$is_valid())
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
-      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
+      ANL_OUTLIER <- common_code_q()[["ANL_OUTLIER"]] # nolint: object_name.
       teal::validate_has_data(ANL, 1)
-      ANL_OUTLIER_SELECTED <- ANL_OUTLIER[ANL_OUTLIER$is_outlier_selected, ] # nolint object_name_linter
+      ANL_OUTLIER_SELECTED <- ANL_OUTLIER[ANL_OUTLIER$is_outlier_selected, ] # nolint: object_name.
       h5(
         sprintf(
           "%s %d / %d [%.02f%%]",
@@ -1104,7 +1105,7 @@ srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
 
     output$total_missing <- renderUI({
       if (n_outlier_missing() > 0) {
-        ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+        ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
         helpText(
           sprintf(
             "%s %d / %d [%.02f%%]",
diff --git a/R/tm_t_crosstable.R b/R/tm_t_crosstable.R
index 4aecdb030..809956335 100644
--- a/R/tm_t_crosstable.R
+++ b/R/tm_t_crosstable.R
@@ -233,7 +233,7 @@ srv_t_crosstable <- function(id, data, reporter, filter_panel_api, label, x, y,
 
     output_q <- reactive({
       teal::validate_inputs(iv_r())
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint object_name_linter
+      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
 
       # As this is a summary
       x_name <- as.vector(merged$anl_input_r()$columns_source$x)
@@ -317,7 +317,7 @@ srv_t_crosstable <- function(id, data, reporter, filter_panel_api, label, x, y,
         teal.code::eval_code(
           substitute(
             expr = {
-              ANL <- tern::df_explicit_na(ANL) # nolint object_name_linter
+              ANL <- tern::df_explicit_na(ANL) # nolint: object_name.
               tbl <- rtables::build_table(lyt = lyt, df = ANL[order(ANL[[y_name]]), ])
               tbl
             },
diff --git a/R/tm_variable_browser.R b/R/tm_variable_browser.R
index a68ff2a0e..71730bff8 100644
--- a/R/tm_variable_browser.R
+++ b/R/tm_variable_browser.R
@@ -181,7 +181,7 @@ srv_variable_browser <- function(id,
     .unique_records_for_factor <- 30
     # if there are < this number of unique records then a numeric
     # variable is by default treated as a factor
-    .unique_records_default_as_factor <- 6 # nolint object_length_linter
+    .unique_records_default_as_factor <- 6 # nolint: object_length.
 
     datanames <- isolate(teal.data::datanames(data()))
     datanames <- Filter(function(name) {
@@ -879,7 +879,7 @@ plot_var_summary <- function(var,
                              wrap_character = NULL,
                              numeric_as_factor,
                              display_density = is.numeric(var),
-                             remove_NA_hist = FALSE, # nolint object_name_linter
+                             remove_NA_hist = FALSE, # nolint: object_name.
                              outlier_definition,
                              records_for_factor,
                              ggplot2_args) {
diff --git a/data-raw/data.R b/data-raw/data.R
index ab21d2721..10d16120e 100644
--- a/data-raw/data.R
+++ b/data-raw/data.R
@@ -1,16 +1,16 @@
 ## code to prepare `data` for testing examples
 library(scda)
-rADAE <- synthetic_cdisc_data("latest")$adae # nolint object_name_linter
+rADAE <- synthetic_cdisc_data("latest")$adae # nolint: object_name.
 usethis::use_data(rADAE)
 
-rADLB <- synthetic_cdisc_data("latest")$adlb # nolint object_name_linter
+rADLB <- synthetic_cdisc_data("latest")$adlb # nolint: object_name.
 usethis::use_data(rADLB)
 
-rADRS <- synthetic_cdisc_data("latest")$adrs # nolint object_name_linter
+rADRS <- synthetic_cdisc_data("latest")$adrs # nolint: object_name.
 usethis::use_data(rADRS)
 
-rADSL <- synthetic_cdisc_data("latest")$adsl # nolint object_name_linter
+rADSL <- synthetic_cdisc_data("latest")$adsl # nolint: object_name.
 usethis::use_data(rADSL)
 
-rADTTE <- synthetic_cdisc_data("latest")$adtte # nolint object_name_linter
+rADTTE <- synthetic_cdisc_data("latest")$adtte # nolint: object_name.
 usethis::use_data(rADTTE)