use_evaluate WIP

Author: gogonzo

R/tm_a_pca.R

@@ -517,8 +517,7 @@ srv_a_pca <- function(id, data, dat, plot_height, plot_width, ggplot2_args, deco
         quote({
           tbl_importance <- dplyr::as_tibble(pca$importance, rownames = "Metric")
           tbl_importance
-        }),
-        keep_output = "tbl_importance"
+        })
       )
 
       teal.reporter::teal_card(qenv) <- append(teal.reporter::teal_card(qenv), "## Eigenvectors Table")
@@ -528,8 +527,7 @@ srv_a_pca <- function(id, data, dat, plot_height, plot_width, ggplot2_args, deco
         quote({
           tbl_eigenvector <- dplyr::as_tibble(pca$rotation, rownames = "Variable")
           tbl_eigenvector
-        }),
-        keep_output = "tbl_eigenvector"
+        })
       )
     })
 
@@ -1044,8 +1042,7 @@ srv_a_pca <- function(id, data, dat, plot_height, plot_width, ggplot2_args, deco
           decorators = select_decorators(decorators, obj_name),
           expr = reactive({
             substitute(.plot, env = list(.plot = as.name(obj_name)))
-          }),
-          keep_output = obj_name
+          })
         )
       },
       names(output_q),

R/tm_a_regression.R

@@ -536,7 +536,7 @@ srv_a_regression <- function(id,
         teal.code::eval_code(quote({
           fit_summary <- summary(fit)
           fit_summary
-        }), keep_output = "fit_summary")
+        }))
       teal.reporter::teal_card(anl_fit) <- append(teal.reporter::teal_card(anl_fit), "## Plot")
       anl_fit
     })
@@ -1000,8 +1000,7 @@ srv_a_regression <- function(id,
       "decorator",
       data = output_q,
       decorators = select_decorators(decorators, "plot"),
-      expr = quote(plot),
-      keep_output = "plot"
+      expr = quote(plot)
     )
 
     fitted <- reactive({
@@ -1024,9 +1023,7 @@ srv_a_regression <- function(id,
     output$text <- renderText({
       req(iv_r()$is_valid())
       req(iv_out$is_valid())
-      paste(utils::capture.output(summary(teal.code::dev_suppress(fitted())))[-1],
-        collapse = "\n"
-      )
+      paste(utils::capture.output(summary(fitted()))[-1], collapse = "\n")
     })
 
     # Render R code.

R/tm_g_association.R

@@ -492,7 +492,7 @@ srv_tm_g_association <- function(id,
             )
           )
         }
-      obj <-  merged$anl_q_r()
+      obj <- merged$anl_q_r()
       teal.reporter::teal_card(obj) <- append(teal.reporter::teal_card(obj), "## Plot")
       teal.code::eval_code(
         obj,
@@ -522,8 +522,7 @@ srv_tm_g_association <- function(id,
       id = "decorator",
       data = output_q,
       decorators = select_decorators(decorators, "plot"),
-      expr = quote(plot),
-      keep_output = "plot"
+      expr = quote(plot)
     )
 
     plot_r <- reactive({
@@ -538,9 +537,7 @@ srv_tm_g_association <- function(id,
       width = plot_width
     )
 
-    output$title <- renderText({
-      teal.code::dev_suppress(output_q()[["title"]])
-    })
+    output$title <- renderText(output_q()[["title"]])
 
     # Render R code.
     source_code_r <- reactive(teal.code::get_code(req(decorated_output_grob_q())))

R/tm_g_bivariate.R

@@ -728,8 +728,7 @@ srv_g_bivariate <- function(id,
           )
         }
         print_call
-      }),
-      keep_output = "plot"
+      })
     )
 
     plot_r <- reactive(req(decorated_output_q_facets())[["plot"]])

R/tm_g_distribution.R

@@ -637,7 +637,7 @@ srv_distribution <- function(id,
 
       obj <- merged$anl_q_r()
       teal.reporter::teal_card(obj) <- append(teal.reporter::teal_card(obj), "# Distribution Plot", after = 0)
-      
+
       ANL <- obj[["ANL"]]
       dist_var <- merge_vars()$dist_var
       s_var <- merge_vars()$s_var
@@ -1297,32 +1297,28 @@ srv_distribution <- function(id,
       "d_density",
       data = output_dist_q,
       decorators = select_decorators(decorators, "histogram_plot"),
-      expr = quote(histogram_plot),
-      keep_output = "histogram_plot"
+      expr = quote(histogram_plot)
     )
 
     decorated_output_qq_q <- srv_decorate_teal_data(
       "d_qq",
       data = output_qq_q,
       decorators = select_decorators(decorators, "qq_plot"),
-      expr = quote(qq_plot),
-      keep_output = "qq_plot"
+      expr = quote(qq_plot)
     )
 
     decorated_output_summary_q <- srv_decorate_teal_data(
       "d_summary",
       data = output_summary_q,
       decorators = select_decorators(decorators, "summary_table"),
-      expr = quote(summary_table),
-      keep_output = "summary_table"
+      expr = quote(summary_table)
     )
 
     decorated_output_test_q <- srv_decorate_teal_data(
       "d_test",
       data = output_test_q,
       decorators = select_decorators(decorators, "test_table"),
-      expr = quote(test_table),
-      keep_output = "test_table"
+      expr = quote(test_table)
     )
 
     decorated_output_q <- reactive({

R/tm_g_response.R

@@ -576,8 +576,7 @@ srv_g_response <- function(id,
       id = "decorator",
       data = output_q,
       decorators = select_decorators(decorators, "plot"),
-      expr = quote(plot),
-      keep_output = "plot"
+      expr = quote(plot)
     )
 
     plot_r <- reactive(req(decorated_output_plot_q())[["plot"]])

R/tm_g_scatterplot.R

@@ -372,7 +372,7 @@ ui_g_scatterplot <- function(id, ...) {
         DT::dataTableOutput(ns("data_table"), width = "100%")
       ),
       encoding = tags$div(
-          tags$label("Encodings", class = "text-primary"),
+        tags$label("Encodings", class = "text-primary"),
         teal.transform::datanames_input(args[c("x", "y", "color_by", "size_by", "row_facet", "col_facet")]),
         teal.transform::data_extract_ui(
           id = ns("x"),
@@ -1020,8 +1020,7 @@ srv_g_scatterplot <- function(id,
       id = "decorator",
       data = output_q,
       decorators = select_decorators(decorators, "plot"),
-      expr = quote(plot),
-      keep_output = "plot"
+      expr = quote(plot)
     )
 
     plot_r <- reactive(req(decorated_output_plot_q())[["plot"]])
@@ -1042,7 +1041,7 @@ srv_g_scatterplot <- function(id,
         validate(need(!input$add_density, "Brushing feature is currently not supported when plot has marginal density"))
       }
 
-      merged_data <- isolate(teal.code::dev_suppress(output_q()[["ANL"]]))
+      merged_data <- isolate(output_q()[["ANL"]])
 
       brushed_df <- teal.widgets::clean_brushedPoints(merged_data, plot_brush)
       numeric_cols <- names(brushed_df)[

R/tm_g_scatterplotmatrix.R

@@ -392,7 +392,7 @@ srv_g_scatterplotmatrix <- function(id,
 
       # create plot
       teal.reporter::teal_card(qenv) <- append(teal.reporter::teal_card(qenv), "## Plot")
-      
+
       if (add_cor) {
         shinyjs::show("cor_method")
         shinyjs::show("cor_use")

R/tm_missing_data.R

@@ -754,25 +754,25 @@ srv_missing_data <- function(id,
           env = list(data_keys = data_keys())
         )
       )
-      
+
       teal.reporter::teal_card(qenv) <- append(teal.reporter::teal_card(qenv), "## Summary Plot")
-      
+
       qenv <- teal.code::eval_code(
         qenv,
         substitute(
           expr = summary_plot_obs <- data_frame_call[, analysis_vars] %>%
-              dplyr::summarise_all(list(function(x) sum(is.na(x)))) %>%
-              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),
-            env = list(data_frame_call = if (!inherits(data_r(), "tbl_df")) {
-              quote(tibble::as_tibble(ANL))
-            } else {
-              quote(ANL)
-            })
-          )
-        ) %>%
+            dplyr::summarise_all(list(function(x) sum(is.na(x)))) %>%
+            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),
+          env = list(data_frame_call = if (!inherits(data_r(), "tbl_df")) {
+            quote(tibble::as_tibble(ANL))
+          } else {
+            quote(ANL)
+          })
+        )
+      ) %>%
         # x axis ordering according to number of missing values and alphabet
         teal.code::eval_code(
           quote(
@@ -1112,7 +1112,7 @@ srv_missing_data <- function(id,
 
       qenv <- common_code_q()
       teal.reporter::teal_card(qenv) <- append(teal.reporter::teal_card(qenv), "## Summary Table")
-      
+
       qenv <- if (!is.null(group_var)) {
         common_code_libraries_q <- teal.code::eval_code(
           qenv,
@@ -1190,7 +1190,7 @@ srv_missing_data <- function(id,
 
       qenv <- common_code_q()
       teal.reporter::teal_card(qenv) <- append(teal.reporter::teal_card(qenv), "## By Subject Plot")
-      
+
       qenv <- teal.code::eval_code(
         qenv,
         substitute(
@@ -1244,40 +1244,40 @@ srv_missing_data <- function(id,
             env = list(hashing_function = hashing_function)
           )
         )
-      
+
       qenv <- teal.code::eval_code(
         qenv,
         substitute(
           expr = {
             by_subject_plot <- ggplot2::ggplot(summary_plot_patients, ggplot2::aes(
-                x = factor(id, levels = order_subjects),
-                y = factor(col, levels = ordered_columns[["column"]]),
-                fill = isna
-              )) +
-                ggplot2::geom_raster() +
-                ggplot2::annotate(
-                  "text",
-                  x = length(order_subjects),
-                  y = seq_len(nrow(ordered_columns)),
-                  hjust = 1,
-                  label = sprintf("%d [%.02f%%]", ordered_columns[["na_count"]], ordered_columns[["na_percent"]])
-                ) +
-                ggplot2::scale_fill_manual(
-                  name = "",
-                  values = c("grey90", c(getOption("ggplot2.discrete.colour")[2], "#ff2951ff")[1]),
-                  labels = c("Present", "Missing (at least one)")
-                ) +
-                labs +
-                ggthemes +
-                themes
-            },
-            env = list(
-              labs = parsed_ggplot2_args$labs,
-              themes = parsed_ggplot2_args$theme,
-              ggthemes = parsed_ggplot2_args$ggtheme
-            )
+              x = factor(id, levels = order_subjects),
+              y = factor(col, levels = ordered_columns[["column"]]),
+              fill = isna
+            )) +
+              ggplot2::geom_raster() +
+              ggplot2::annotate(
+                "text",
+                x = length(order_subjects),
+                y = seq_len(nrow(ordered_columns)),
+                hjust = 1,
+                label = sprintf("%d [%.02f%%]", ordered_columns[["na_count"]], ordered_columns[["na_percent"]])
+              ) +
+              ggplot2::scale_fill_manual(
+                name = "",
+                values = c("grey90", c(getOption("ggplot2.discrete.colour")[2], "#ff2951ff")[1]),
+                labels = c("Present", "Missing (at least one)")
+              ) +
+              labs +
+              ggthemes +
+              themes
+          },
+          env = list(
+            labs = parsed_ggplot2_args$labs,
+            themes = parsed_ggplot2_args$theme,
+            ggthemes = parsed_ggplot2_args$ggtheme
           )
         )
+      )
     })
 
     # Decorated outputs
@@ -1289,8 +1289,7 @@ srv_missing_data <- function(id,
       decorators = select_decorators(decorators, "summary_plot"),
       expr = quote({
         summary_plot
-      }),
-      keep_output = "summary_plot"
+      })
     )
 
     decorated_combination_plot_q <- srv_decorate_teal_data(
@@ -1301,24 +1300,21 @@ srv_missing_data <- function(id,
         grid::grid.newpage()
         grid::grid.draw(combination_plot)
         combination_plot
-      }),
-      keep_output = "combination_plot"
+      })
     )
 
     decorated_summary_table_q <- srv_decorate_teal_data(
       id = "dec_summary_table",
       data = summary_table_q,
       decorators = select_decorators(decorators, "table"),
-      expr = quote(table),
-      keep_output = "table"
+      expr = quote(table)
     )
 
     decorated_by_subject_plot_q <- srv_decorate_teal_data(
       id = "dec_by_subject_plot",
       data = by_subject_plot_q,
       decorators = select_decorators(decorators, "by_subject_plot"),
-      expr = quote(by_subject_plot),
-      keep_output = "by_subject_plot"
+      expr = quote(by_subject_plot)
     )
 
     # Plots & tables reactives