Removes unnecessary # nolint after object_name_linter was changed

.lintr

@@ -2,5 +2,5 @@ linters: linters_with_defaults(
   line_length_linter = line_length_linter(120),
   cyclocomp_linter = NULL,
   object_usage_linter = NULL,
-  object_name_linter = object_name_linter(styles = c("snake_case", "symbols"), regexes = c(ANL = "^ANL_?[0-9]*$", ADaM = "^r?AD[A-Z]{2,3}_?[0-9]*$"))
+  object_name_linter = object_name_linter(styles = c("snake_case", "symbols"), regexes = c(ANL = "^ANL_?[0-9A-Z_]*$", ADaM = "^r?AD[A-Z]{2,3}_?[0-9]*$"))
   )

R/tm_a_pca.R

@@ -391,7 +391,7 @@ 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.
+      ANL <- merged$anl_q_r()[["ANL"]]
 
       teal::validate_has_data(ANL, 10)
       validate(need(
@@ -422,7 +422,7 @@ 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.
+      ANL <- merged$anl_q_r()[["ANL"]]
 
       qenv <- teal.code::eval_code(
         merged$anl_q_r(),
@@ -435,7 +435,7 @@ 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.
+          quote(ANL <- tidyr::drop_na(ANL, keep_columns))
         )
       }
 
@@ -657,7 +657,7 @@ 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.
+      ANL <- qenv[["ANL"]]
 
       resp_col <- as.character(merged$anl_input_r()$columns_source$response)
       dat_cols <- as.character(merged$anl_input_r()$columns_source$dat)

R/tm_a_regression.R

@@ -436,7 +436,7 @@ 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.
+      ANL <- anl_merged_q()[["ANL"]]
       teal::validate_has_data(ANL, 10)
 
       validate(need(is.numeric(ANL[regression_var()$response][[1]]), "Response variable should be numeric."))
@@ -546,7 +546,7 @@ srv_a_regression <- function(id,
 
       plot_type_0 <- function() {
         fit <- fit_r()[["fit"]]
-        ANL <- anl_merged_q()[["ANL"]] # nolint: object_name.
+        ANL <- anl_merged_q()[["ANL"]]
 
         stopifnot(ncol(fit$model) == 2)
 

R/tm_g_association.R

@@ -326,7 +326,7 @@ srv_tm_g_association <- function(id,
     output_q <- reactive({
       teal::validate_inputs(iv_r())
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
+      ANL <- merged$anl_q_r()[["ANL"]]
       teal::validate_has_data(ANL, 3)
 
       vars_names <- merged$anl_input_r()$columns_source$vars

R/tm_g_bivariate.R

@@ -535,7 +535,7 @@ srv_g_bivariate <- function(id,
     output_q <- reactive({
       teal::validate_inputs(iv_r())
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
+      ANL <- merged$anl_q_r()[["ANL"]]
       teal::validate_has_data(ANL, 3)
 
       x_col_vec <- as.vector(merged$anl_input_r()$columns_source$x)

R/tm_g_distribution.R

@@ -518,7 +518,7 @@ srv_distribution <- function(id,
             )
           }
 
-          ANL <- merged$anl_q_r()[[as.character(dist_var[[1]]$dataname)]] # nolint: object_name.
+          ANL <- merged$anl_q_r()[[as.character(dist_var[[1]]$dataname)]]
           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)
@@ -558,7 +558,7 @@ srv_distribution <- function(id,
     common_q <- reactive({
       # Create a private stack for this function only.
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
+      ANL <- merged$anl_q_r()[["ANL"]]
       dist_var <- merge_vars()$dist_var
       s_var <- merge_vars()$s_var
       g_var <- merge_vars()$g_var
@@ -585,7 +585,7 @@ 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.
+            expr = ANL[[g_var]] <- forcats::fct_na_value_to_level(as.factor(ANL[[g_var]]), "NA"),
             env = list(g_var = g_var)
           )
         )
@@ -601,7 +601,7 @@ 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.
+            expr = ANL[[s_var]] <- forcats::fct_na_value_to_level(as.factor(ANL[[s_var]]), "NA"),
             env = list(s_var = s_var)
           )
         )
@@ -1024,7 +1024,7 @@ srv_distribution <- function(id,
       },
       valueExpr = {
         # Create a private stack for this function only.
-        ANL <- common_q()[["ANL"]] # nolint: object_name.
+        ANL <- common_q()[["ANL"]]
 
         dist_var <- merge_vars()$dist_var
         s_var <- merge_vars()$s_var

R/tm_g_response.R

@@ -372,7 +372,7 @@ srv_g_response <- function(id,
       teal::validate_inputs(iv_r())
 
       qenv <- merged$anl_q_r()
-      ANL <- qenv[["ANL"]] # nolint: object_name.
+      ANL <- qenv[["ANL"]]
       resp_var <- as.vector(merged$anl_input_r()$columns_source$response)
       x <- as.vector(merged$anl_input_r()$columns_source$x)
 
@@ -409,7 +409,7 @@ 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.
+            expr = ANL[[x]] <- with(ANL, forcats::fct_rev(x_cl)),
             env = list(x = x, x_cl = x_cl)
           )
         )
@@ -418,7 +418,7 @@ srv_g_response <- function(id,
       qenv <- teal.code::eval_code(
         qenv,
         substitute(
-          expr = ANL[[resp_var]] <- factor(ANL[[resp_var]]), # nolint: object_name.
+          expr = ANL[[resp_var]] <- factor(ANL[[resp_var]]),
           env = list(resp_var = resp_var)
         )
       ) %>%

R/tm_g_scatterplot.R

@@ -568,7 +568,7 @@ srv_g_scatterplot <- function(id,
     )
 
     trend_line_is_applicable <- reactive({
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
+      ANL <- merged$anl_q_r()[["ANL"]]
       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]])
@@ -595,7 +595,7 @@ srv_g_scatterplot <- function(id,
 
     output$num_na_removed <- renderUI({
       if (add_trend_line()) {
-        ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
+        ANL <- merged$anl_q_r()[["ANL"]]
         x_var <- as.vector(merged$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) {
@@ -621,7 +621,7 @@ srv_g_scatterplot <- function(id,
     output_q <- reactive({
       teal::validate_inputs(iv_r(), iv_facet)
 
-      ANL <- merged$anl_q_r()[["ANL"]] # nolint: object_name.
+      ANL <- merged$anl_q_r()[["ANL"]]
 
       x_var <- as.vector(merged$anl_input_r()$columns_source$x)
       y_var <- as.vector(merged$anl_input_r()$columns_source$y)
@@ -724,7 +724,7 @@ 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.
+            expr = ANL[, log_x_var] <- log_x_fn(ANL[, x_var]),
             env = list(
               x_var = x_var,
               log_x_fn = as.name(log_x_fn),
@@ -739,7 +739,7 @@ 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.
+            expr = ANL[, log_y_var] <- log_y_fn(ANL[, y_var]),
             env = list(
               y_var = y_var,
               log_y_fn = as.name(log_y_fn),
@@ -872,7 +872,7 @@ 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.
+                expr = ANL <- dplyr::filter(ANL, !is.na(x_var) & !is.na(y_var)),
                 env = list(x_var = as.name(x_var), y_var = as.name(y_var))
               )
             )

R/tm_g_scatterplotmatrix.R

@@ -299,7 +299,7 @@ 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.
+      ANL <- qenv[["ANL"]]
 
       cols_names <- merged$anl_input_r()$columns_source$variables
       alpha <- input$alpha
@@ -326,7 +326,7 @@ 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.
+            expr = ANL <- ANL[, cols_names] %>%
               dplyr::mutate_if(is.character, as.factor) %>%
               droplevels(),
             env = list(cols_names = cols_names)
@@ -336,7 +336,7 @@ 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.
+            expr = ANL <- ANL[, cols_names] %>%
               droplevels(),
             env = list(cols_names = cols_names)
           )
@@ -421,7 +421,7 @@ 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.
+      ANL <- merged$anl_q_r()[["ANL"]]
       cols_names <- unique(unname(do.call(c, merged$anl_input_r()$columns_source)))
       check_char <- vapply(ANL[, cols_names], is.character, logical(1))
       if (any(check_char)) {

R/tm_missing_data.R

@@ -478,22 +478,22 @@ 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.
+            expr = ANL <- anl_name[, selected_vars, drop = FALSE],
             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.
+          substitute(expr = ANL <- anl_name, env = list(anl_name = as.name(dataname)))
         )
       }
 
       if (input$summary_type == "By Variable Levels" && !is.null(group_var) && !(group_var %in% selected_vars())) {
         qenv <- teal.code::eval_code(
           qenv,
           substitute(
-            expr = ANL[[group_var]] <- anl_name[[group_var]], # nolint: object_name.
+            expr = ANL[[group_var]] <- anl_name[[group_var]],
             env = list(group_var = group_var, anl_name = as.name(dataname))
           )
         )
@@ -645,7 +645,7 @@ 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.
+            expr = ANL[[new_col_name]] <- ifelse(rowSums(is.na(ANL)) > 0, NA, FALSE),
             env = list(new_col_name = new_col_name)
           )
         )