strugling with reactivity

R/app_version.R

@@ -1 +1 @@
-app_version <- function()'250319_1327'
+app_version <- function()'250320_1310'

R/custom_SelectInput.R

@@ -31,16 +31,18 @@ columnSelectInput <- function(inputId, label, data, selected = "", ...,
     {
       "name": "%s",
       "label": "%s",
+      "dataclass": "%s",
       "datatype": "%s"
     }'),
       col,
       attr(datar()[[col]], "label") %||% "",
-      IDEAFilter:::get_dataFilter_class(datar()[[col]])
+      IDEAFilter:::get_dataFilter_class(datar()[[col]]),
+      data_type(datar()[[col]])
     )
   }, col = names(datar()))
 
   if (!"none" %in% names(datar())){
-    labels <- c("none"=list(sprintf('\n    {\n      \"name\": \"none\",\n      \"label\": \"%s\",\n      \"datatype\": \"\"\n    }',none_label)),labels)
+    labels <- c("none"=list(sprintf('\n    {\n      \"name\": \"none\",\n      \"label\": \"%s\",\n      \"dataclass\": \"\",\n      \"datatype\": \"\"\n    }',none_label)),labels)
     choices <- setNames(names(labels), labels)
     choices <- choices[match(if (length(col_subsetr()) == 0 || isTRUE(col_subsetr() == "")) names(datar()) else col_subsetr(), choices)]
   } else {
@@ -62,10 +64,16 @@ columnSelectInput <- function(inputId, label, data, selected = "", ...,
           return '<div style=\"padding: 3px 12px\">' +
                    '<div><strong>' +
                       escape(item.data.name) + ' ' +
-                      '<span style=\"opacity: 0.3;\"><code style=\"color: black;\"> ' +
+                      '</strong>' +
+                      (item.data.dataclass != '' ?
+                      '<span style=\"opacity: 0.9;\"><code style=\"color: black;\"> ' +
+                        item.data.dataclass +
+                      '</code></span>' : '' ) + ' ' +
+                      (item.data.datatype != '' ?
+                      '<span style=\"opacity: 0.9;\"><code style=\"color: black;\"> ' +
                         item.data.datatype +
-                      '</code></span>' +
+                      '</code></span>' : '' ) +
-                   '</strong></div>' +
+                   '</div>' +
                    (item.data.label != '' ? '<div style=\"line-height: 1em;\"><small>' + escape(item.data.label) + '</small></div>' : '') +
                  '</div>';
         },
@@ -84,7 +92,6 @@ columnSelectInput <- function(inputId, label, data, selected = "", ...,
 }
 
 
-
 #' A selectizeInput customized for named vectors
 #'
 #' @param inputId passed to \code{\link[shiny]{selectizeInput}}

R/data-summary.R

@@ -282,6 +282,8 @@ add_class_icon <- function(grid, column = "class") {
             shiny::icon("arrow-down-1-9")
           } else if (identical(x, "character")) {
             shiny::icon("arrow-down-a-z")
+          } else if (identical(x, "logical")) {
+            shiny::icon("toggle-off")
           } else if (any(c("Date", "POSIXct", "POSIXt") %in% x)) {
             shiny::icon("calendar-days")
           } else if ("hms" %in% x) {

R/data_plots.R

@@ -310,9 +310,9 @@ data_visuals_server <- function(id,
                 z = input$tertiary
               )
             },
-            warning = function(warn) {
+            # warning = function(warn) {
-              showNotification(paste0(warn), type = "warning")
+            #   showNotification(paste0(warn), type = "warning")
-            },
+            # },
             error = function(err) {
               showNotification(paste0(err), type = "err")
             }
@@ -378,9 +378,9 @@ all_but <- function(data, ...) {
 #'
 #' @examples
 #' default_parsing(mtcars) |> subset_types("ordinal")
-#' default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal"))
+#' default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal" ,"categorical"))
 #' #' default_parsing(mtcars) |> subset_types("factor",class)
-subset_types <- function(data, types, type.fun = outcome_type) {
+subset_types <- function(data, types, type.fun = data_type) {
   data[sapply(data, type.fun) %in% types]
 }
 
@@ -413,58 +413,58 @@ supported_plots <- function() {
       fun = "plot_hbars",
       descr = "Stacked horizontal bars",
       note = "A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars",
-      primary.type = c("dichotomous", "ordinal"),
+      primary.type = c("dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = "none"
     ),
     plot_violin = list(
       fun = "plot_violin",
       descr = "Violin plot",
       note = "A modern alternative to the classic boxplot to visualise data distribution",
-      primary.type = c("continuous", "dichotomous", "ordinal"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       secondary.extra = "none",
-      tertiary.type = c("dichotomous", "ordinal")
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical")
     ),
     # plot_ridge = list(
     #   descr = "Ridge plot",
     #   note = "An alternative option to visualise data distribution",
     #   primary.type = "continuous",
-    #   secondary.type = c("dichotomous", "ordinal"),
+    #   secondary.type = c("dichotomous", "ordinal" ,"categorical"),
-    #   tertiary.type = c("dichotomous", "ordinal"),
+    #   tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
     #   secondary.extra = NULL
     # ),
     plot_sankey = list(
       fun = "plot_sankey",
       descr = "Sankey plot",
       note = "A way of visualising change between groups",
-      primary.type = c("dichotomous", "ordinal"),
+      primary.type = c("dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       secondary.extra = NULL,
-      tertiary.type = c("dichotomous", "ordinal")
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical")
     ),
     plot_scatter = list(
       fun = "plot_scatter",
       descr = "Scatter plot",
       note = "A classic way of showing the association between to variables",
       primary.type = "continuous",
-      secondary.type = c("continuous", "ordinal"),
+      secondary.type = c("continuous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = NULL
     ),
     plot_box = list(
       fun = "plot_box",
       descr = "Box plot",
       note = "A classic way to plot data distribution by groups",
-      primary.type = c("continuous", "dichotomous", "ordinal"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = "none"
     ),
     plot_euler = list(
@@ -475,7 +475,7 @@ supported_plots <- function() {
       secondary.type = "dichotomous",
       secondary.multi = TRUE,
       secondary.max = 4,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = NULL
     )
   )
@@ -500,11 +500,12 @@ supported_plots <- function() {
 #'   possible_plots()
 possible_plots <- function(data) {
   # browser()
+  # data <- if (is.reactive(data)) data() else data
   if (is.data.frame(data)) {
     data <- data[[1]]
   }
 
-  type <- outcome_type(data)
+  type <- data_type(data)
 
   if (type == "unknown") {
     out <- type
@@ -596,6 +597,7 @@ create_plot <- function(data, type, x, y, z = NULL, ...) {
 #' gtsummary::trial |> get_label(var = "trt")
 #' 1:10 |> get_label()
 get_label <- function(data, var = NULL) {
+  # data <- if (is.reactive(data)) data() else data
   if (!is.null(var) & is.data.frame(data)) {
     data <- data[[var]]
   }
@@ -696,7 +698,9 @@ allign_axes <- function(...) {
 
   xr <- clean_common_axis(p, "x")
 
-  p |> purrr::map(~ .x + ggplot2::xlim(xr) + ggplot2::ylim(yr))
+  suppressWarnings({
+    p |> purrr::map(~ .x + ggplot2::xlim(xr) + ggplot2::ylim(yr))
+    })
 }
 
 #' Extract and clean axis ranges
@@ -714,7 +718,7 @@ clean_common_axis <- function(p, axis) {
       if (is.numeric(.x)) {
         range(.x)
       } else {
-        .x
+        as.character(.x)
       }
     })() |>
     unique()

R/plot_box.R

@@ -49,13 +49,13 @@ plot_box_single <- function(data, x, y=NULL, seed = 2103) {
     data[[y]] <- y
   }
 
-  discrete <- !outcome_type(data[[y]]) %in% "continuous"
+  discrete <- !data_type(data[[y]]) %in% "continuous"
 
   data |>
     ggplot2::ggplot(ggplot2::aes(x = !!dplyr::sym(x), y = !!dplyr::sym(y), fill = !!dplyr::sym(y), group = !!dplyr::sym(y))) +
     ggplot2::geom_boxplot(linewidth = 1.8, outliers = FALSE) +
     ## THis could be optional in future
-    ggplot2::geom_jitter(color = "black", size = 2, alpha = 0.9) +
+    ggplot2::geom_jitter(color = "black", size = 2, alpha = 0.9, width = 0.1, height = .5) +
     ggplot2::coord_flip() +
     viridis::scale_fill_viridis(discrete = discrete, option = "D") +
     # ggplot2::theme_void() +

R/plot_euler.R

@@ -95,7 +95,7 @@ plot_euler <- function(data, x, y, z = NULL, seed = 2103) {
   # patchwork::wrap_plots(out, guides = "collect")
 }
 
-
+?withCallingHandlers()
 #' Easily plot single euler diagrams
 #'
 #' @returns ggplot2 object

R/regression_model.R

@@ -44,7 +44,7 @@
 #'     args.list = NULL,
 #'     vars = c("mpg", "cyl")
 #'   )
-#'   broom::tidy(m)
+#' broom::tidy(m)
 regression_model <- function(data,
                              outcome.str,
                              auto.mode = FALSE,
@@ -60,7 +60,7 @@ regression_model <- function(data,
   }
 
   ## This will handle if outcome is not in data for nicer shiny behavior
-  if (!outcome.str %in% names(data)){
+  if (!outcome.str %in% names(data)) {
     outcome.str <- names(data)[1]
     print("outcome is not in data, first column is used")
   }
@@ -170,7 +170,7 @@ regression_model <- function(data,
 #'   fun = "stats::glm",
 #'   args.list = list(family = stats::binomial(link = "logit"))
 #' )
-#' lapply(m,broom::tidy) |> dplyr::bind_rows()
+#' lapply(m, broom::tidy) |> dplyr::bind_rows()
 #' }
 regression_model_uv <- function(data,
                                 outcome.str,
@@ -178,9 +178,8 @@ regression_model_uv <- function(data,
                                 fun = NULL,
                                 vars = NULL,
                                 ...) {
-
   ## This will handle if outcome is not in data for nicer shiny behavior
-  if (!outcome.str %in% names(data)){
+  if (!outcome.str %in% names(data)) {
     outcome.str <- names(data)[1]
     print("outcome is not in data, first column is used")
   }
@@ -241,7 +240,7 @@ regression_model_uv <- function(data,
 
 ### HELPERS
 
-#' Outcome data type assessment
+#' Data type assessment
 #'
 #' @param data data
 #'
@@ -251,17 +250,35 @@ regression_model_uv <- function(data,
 #' @examples
 #' mtcars |>
 #'   default_parsing() |>
-#'   lapply(outcome_type)
+#'   lapply(data_type)
-outcome_type <- function(data) {
+#' c(1, 2) |> data_type()
+#' 1 |> data_type()
+#' c(rep(NA, 10)) |> data_type()
+#' sample(1:100, 50) |> data_type()
+#' factor(letters[1:20]) |> data_type()
+data_type <- function(data) {
   cl_d <- class(data)
-  if (any(c("numeric", "integer") %in% cl_d)) {
+  if (all(is.na(data))) {
-    out <- "continuous"
+    out <- "empty"
-  } else if (any(c("factor", "logical") %in% cl_d)) {
+  } else if (length(unique(data)) < 2) {
-    if (length(levels(data)) == 2 | identical("logical",cl_d)) {
+    out <- "monotone"
+  } else if (any(c("factor", "logical") %in% cl_d) | length(unique(data)) == 2) {
+    if (identical("logical", cl_d) | length(unique(data)) == 2) {
       out <- "dichotomous"
-    } else if (length(levels(data)) > 2) {
+    } else {
-      out <- "ordinal"
+      if (is.ordered(data)) {
+        out <- "ordinal"
+      } else {
+        out <- "categorical"
+      }
     }
+  } else if (identical(cl_d, "character")) {
+    out <- "text"
+  } else if (!length(unique(data)) == 2) {
+    ## Previously had all thinkable classes
+    ## Now just assumes the class has not been defined above
+    ## any(c("numeric", "integer", "hms", "Date", "timediff") %in% cl_d) &
+    out <- "continuous"
   } else {
     out <- "unknown"
   }
@@ -307,7 +324,7 @@ supported_functions <- function() {
     polr = list(
       descr = "Ordinal logistic regression model",
       design = "cross-sectional",
-      out.type = "ordinal",
+      out.type = c("ordinal","categorical"),
       fun = "MASS::polr",
       args.list = list(
         Hess = TRUE,
@@ -340,12 +357,13 @@ supported_functions <- function() {
 #'   possible_functions(design = "cross-sectional")
 possible_functions <- function(data, design = c("cross-sectional")) {
   # browser()
+  # data <- if (is.reactive(data)) data() else data
   if (is.data.frame(data)) {
     data <- data[[1]]
   }
 
   design <- match.arg(design)
-  type <- outcome_type(data)
+  type <- data_type(data)
 
   design_ls <- supported_functions() |>
     lapply(\(.x){
@@ -537,13 +555,16 @@ list2str <- function(data) {
 #'
 #' @examples
 #' \dontrun{
-#' gtsummary::trial |> regression_model_uv(
+#' gtsummary::trial |>
-#'   outcome.str = "trt",
+#'   regression_model_uv(
-#'   fun = "stats::glm",
+#'     outcome.str = "trt",
-#'   args.list = list(family = stats::binomial(link = "logit"))
+#'     fun = "stats::glm",
-#' ) |> lapply(broom::tidy) |> dplyr::bind_rows()
+#'     args.list = list(family = stats::binomial(link = "logit"))
+#'   ) |>
+#'   lapply(broom::tidy) |>
+#'   dplyr::bind_rows()
 #' ms <- regression_model_uv_list(data = default_parsing(mtcars), outcome.str = "mpg", fun.descr = "Linear regression model")
-#' lapply(ms$model,broom::tidy) |> dplyr::bind_rows()
+#' lapply(ms$model, broom::tidy) |> dplyr::bind_rows()
 #' }
 regression_model_uv_list <- function(data,
                                      outcome.str,

inst/apps/FreesearchR/app.R

@@ -10,7 +10,7 @@
 #### Current file: R//app_version.R 
 ########
 
-app_version <- function()'250319_1327'
+app_version <- function()'250320_1310'
 
 
 ########
@@ -296,16 +296,18 @@ columnSelectInput <- function(inputId, label, data, selected = "", ...,
     {
       "name": "%s",
       "label": "%s",
+      "dataclass": "%s",
       "datatype": "%s"
     }'),
       col,
       attr(datar()[[col]], "label") %||% "",
-      IDEAFilter:::get_dataFilter_class(datar()[[col]])
+      IDEAFilter:::get_dataFilter_class(datar()[[col]]),
+      data_type(datar()[[col]])
     )
   }, col = names(datar()))
 
   if (!"none" %in% names(datar())){
-    labels <- c("none"=list(sprintf('\n    {\n      \"name\": \"none\",\n      \"label\": \"%s\",\n      \"datatype\": \"\"\n    }',none_label)),labels)
+    labels <- c("none"=list(sprintf('\n    {\n      \"name\": \"none\",\n      \"label\": \"%s\",\n      \"dataclass\": \"\",\n      \"datatype\": \"\"\n    }',none_label)),labels)
     choices <- setNames(names(labels), labels)
     choices <- choices[match(if (length(col_subsetr()) == 0 || isTRUE(col_subsetr() == "")) names(datar()) else col_subsetr(), choices)]
   } else {
@@ -327,10 +329,16 @@ columnSelectInput <- function(inputId, label, data, selected = "", ...,
           return '<div style=\"padding: 3px 12px\">' +
                    '<div><strong>' +
                       escape(item.data.name) + ' ' +
-                      '<span style=\"opacity: 0.3;\"><code style=\"color: black;\"> ' +
+                      '</strong>' +
+                      (item.data.dataclass != '' ?
+                      '<span style=\"opacity: 0.9;\"><code style=\"color: black;\"> ' +
+                        item.data.dataclass +
+                      '</code></span>' : '' ) + ' ' +
+                      (item.data.datatype != '' ?
+                      '<span style=\"opacity: 0.9;\"><code style=\"color: black;\"> ' +
                         item.data.datatype +
-                      '</code></span>' +
+                      '</code></span>' : '' ) +
-                   '</strong></div>' +
+                   '</div>' +
                    (item.data.label != '' ? '<div style=\"line-height: 1em;\"><small>' + escape(item.data.label) + '</small></div>' : '') +
                  '</div>';
         },
@@ -348,7 +356,76 @@ columnSelectInput <- function(inputId, label, data, selected = "", ...,
   )
 }
 
+columnSelectInputStat <- function(inputId, label, data, selected = "", ...,
+                              col_subset = NULL, placeholder = "", onInitialize, none_label="No variable selected",maxItems=NULL) {
+  data <- if (is.reactive(data)) data() else data
+  col_subsetr <- if (is.reactive(col_subset)) col_subset else reactive(col_subset)
 
+  labels <- Map(function(col) {
+    json <- sprintf(
+      IDEAFilter:::strip_leading_ws('
+    {
+      "name": "%s",
+      "label": "%s",
+      "dataclass": "%s",
+      "datatype": "%s"
+    }'),
+      col,
+      attr(data[[col]], "label") %||% "",
+      IDEAFilter:::get_dataFilter_class(data[[col]]),
+      data_type(data[[col]])
+    )
+  }, col = names(data))
+
+  if (!"none" %in% names(data)){
+    labels <- c("none"=list(sprintf('\n    {\n      \"name\": \"none\",\n      \"label\": \"%s\",\n      \"dataclass\": \"\",\n      \"datatype\": \"\"\n    }',none_label)),labels)
+    choices <- setNames(names(labels), labels)
+    choices <- choices[match(if (length(col_subsetr()) == 0 || isTRUE(col_subsetr() == "")) names(data) else col_subsetr(), choices)]
+  } else {
+    choices <- setNames(names(data), labels)
+    choices <- choices[match(if (length(col_subsetr()) == 0 || isTRUE(col_subsetr() == "")) choices else col_subsetr(), choices)]
+  }
+
+  shiny::selectizeInput(
+    inputId = inputId,
+    label = label,
+    choices = choices,
+    selected = selected,
+    ...,
+    options = c(
+      list(render = I("{
+        // format the way that options are rendered
+        option: function(item, escape) {
+          item.data = JSON.parse(item.label);
+          return '<div style=\"padding: 3px 12px\">' +
+                   '<div><strong>' +
+                      escape(item.data.name) + ' ' +
+                      '</strong>' +
+                      (item.data.dataclass != '' ?
+                      '<span style=\"opacity: 0.9;\"><code style=\"color: black;\"> ' +
+                        item.data.dataclass +
+                      '</code></span>' : '' ) + ' ' +
+                      (item.data.datatype != '' ?
+                      '<span style=\"opacity: 0.9;\"><code style=\"color: black;\"> ' +
+                        item.data.datatype +
+                      '</code></span>' : '' ) +
+                   '</div>' +
+                   (item.data.label != '' ? '<div style=\"line-height: 1em;\"><small>' + escape(item.data.label) + '</small></div>' : '') +
+                 '</div>';
+        },
+
+        // avoid data vomit splashing on screen when an option is selected
+        item: function(item, escape) {
+        item.data = JSON.parse(item.label);
+        return '<div>' +
+                 escape(item.data.name) +
+               '</div>';
+        }
+      }")),
+      if (!is.null(maxItems)) list(maxItems=maxItems)
+    )
+  )
+}
 
 #' A selectizeInput customized for named vectors
 #'
@@ -1407,9 +1484,9 @@ data_visuals_server <- function(id,
                 z = input$tertiary
               )
             },
-            warning = function(warn) {
+            # warning = function(warn) {
-              showNotification(paste0(warn), type = "warning")
+            #   showNotification(paste0(warn), type = "warning")
-            },
+            # },
             error = function(err) {
               showNotification(paste0(err), type = "err")
             }
@@ -1475,9 +1552,9 @@ all_but <- function(data, ...) {
 #'
 #' @examples
 #' default_parsing(mtcars) |> subset_types("ordinal")
-#' default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal"))
+#' default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal" ,"categorical"))
 #' #' default_parsing(mtcars) |> subset_types("factor",class)
-subset_types <- function(data, types, type.fun = outcome_type) {
+subset_types <- function(data, types, type.fun = data_type) {
   data[sapply(data, type.fun) %in% types]
 }
 
@@ -1510,58 +1587,58 @@ supported_plots <- function() {
       fun = "plot_hbars",
       descr = "Stacked horizontal bars",
       note = "A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars",
-      primary.type = c("dichotomous", "ordinal"),
+      primary.type = c("dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = "none"
     ),
     plot_violin = list(
       fun = "plot_violin",
       descr = "Violin plot",
       note = "A modern alternative to the classic boxplot to visualise data distribution",
-      primary.type = c("continuous", "dichotomous", "ordinal"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       secondary.extra = "none",
-      tertiary.type = c("dichotomous", "ordinal")
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical")
     ),
     # plot_ridge = list(
     #   descr = "Ridge plot",
     #   note = "An alternative option to visualise data distribution",
     #   primary.type = "continuous",
-    #   secondary.type = c("dichotomous", "ordinal"),
+    #   secondary.type = c("dichotomous", "ordinal" ,"categorical"),
-    #   tertiary.type = c("dichotomous", "ordinal"),
+    #   tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
     #   secondary.extra = NULL
     # ),
     plot_sankey = list(
       fun = "plot_sankey",
       descr = "Sankey plot",
       note = "A way of visualising change between groups",
-      primary.type = c("dichotomous", "ordinal"),
+      primary.type = c("dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       secondary.extra = NULL,
-      tertiary.type = c("dichotomous", "ordinal")
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical")
     ),
     plot_scatter = list(
       fun = "plot_scatter",
       descr = "Scatter plot",
       note = "A classic way of showing the association between to variables",
       primary.type = "continuous",
-      secondary.type = c("continuous", "ordinal"),
+      secondary.type = c("continuous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = NULL
     ),
     plot_box = list(
       fun = "plot_box",
       descr = "Box plot",
       note = "A classic way to plot data distribution by groups",
-      primary.type = c("continuous", "dichotomous", "ordinal"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
-      secondary.type = c("dichotomous", "ordinal"),
+      secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = "none"
     ),
     plot_euler = list(
@@ -1572,7 +1649,7 @@ supported_plots <- function() {
       secondary.type = "dichotomous",
       secondary.multi = TRUE,
       secondary.max = 4,
-      tertiary.type = c("dichotomous", "ordinal"),
+      tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = NULL
     )
   )
@@ -1597,11 +1674,12 @@ supported_plots <- function() {
 #'   possible_plots()
 possible_plots <- function(data) {
   # browser()
+  # data <- if (is.reactive(data)) data() else data
   if (is.data.frame(data)) {
     data <- data[[1]]
   }
 
-  type <- outcome_type(data)
+  type <- data_type(data)
 
   if (type == "unknown") {
     out <- type
@@ -1693,6 +1771,7 @@ create_plot <- function(data, type, x, y, z = NULL, ...) {
 #' gtsummary::trial |> get_label(var = "trt")
 #' 1:10 |> get_label()
 get_label <- function(data, var = NULL) {
+  # data <- if (is.reactive(data)) data() else data
   if (!is.null(var) & is.data.frame(data)) {
     data <- data[[var]]
   }
@@ -1793,7 +1872,9 @@ allign_axes <- function(...) {
 
   xr <- clean_common_axis(p, "x")
 
-  p |> purrr::map(~ .x + ggplot2::xlim(xr) + ggplot2::ylim(yr))
+  suppressWarnings({
+    p |> purrr::map(~ .x + ggplot2::xlim(xr) + ggplot2::ylim(yr))
+    })
 }
 
 #' Extract and clean axis ranges
@@ -1811,7 +1892,7 @@ clean_common_axis <- function(p, axis) {
       if (is.numeric(.x)) {
         range(.x)
       } else {
-        .x
+        as.character(.x)
       }
     })() |>
     unique()
@@ -2263,6 +2344,8 @@ add_class_icon <- function(grid, column = "class") {
             shiny::icon("arrow-down-1-9")
           } else if (identical(x, "character")) {
             shiny::icon("arrow-down-a-z")
+          } else if (identical(x, "logical")) {
+            shiny::icon("toggle-off")
           } else if (any(c("Date", "POSIXct", "POSIXt") %in% x)) {
             shiny::icon("calendar-days")
           } else if ("hms" %in% x) {
@@ -3392,13 +3475,13 @@ plot_box_single <- function(data, x, y=NULL, seed = 2103) {
     data[[y]] <- y
   }
 
-  discrete <- !outcome_type(data[[y]]) %in% "continuous"
+  discrete <- !data_type(data[[y]]) %in% "continuous"
 
   data |>
     ggplot2::ggplot(ggplot2::aes(x = !!dplyr::sym(x), y = !!dplyr::sym(y), fill = !!dplyr::sym(y), group = !!dplyr::sym(y))) +
     ggplot2::geom_boxplot(linewidth = 1.8, outliers = FALSE) +
     ## THis could be optional in future
-    ggplot2::geom_jitter(color = "black", size = 2, alpha = 0.9) +
+    ggplot2::geom_jitter(color = "black", size = 2, alpha = 0.9, width = 0.1, height = .5) +
     ggplot2::coord_flip() +
     viridis::scale_fill_viridis(discrete = discrete, option = "D") +
     # ggplot2::theme_void() +
@@ -3524,7 +3607,7 @@ plot_euler <- function(data, x, y, z = NULL, seed = 2103) {
   # patchwork::wrap_plots(out, guides = "collect")
 }
 
-
+?withCallingHandlers()
 #' Easily plot single euler diagrams
 #'
 #' @returns ggplot2 object
@@ -4607,7 +4690,7 @@ redcap_demo_app <- function() {
 #'     args.list = NULL,
 #'     vars = c("mpg", "cyl")
 #'   )
-#'   broom::tidy(m)
+#' broom::tidy(m)
 regression_model <- function(data,
                              outcome.str,
                              auto.mode = FALSE,
@@ -4623,7 +4706,7 @@ regression_model <- function(data,
   }
 
   ## This will handle if outcome is not in data for nicer shiny behavior
-  if (!outcome.str %in% names(data)){
+  if (!outcome.str %in% names(data)) {
     outcome.str <- names(data)[1]
     print("outcome is not in data, first column is used")
   }
@@ -4733,7 +4816,7 @@ regression_model <- function(data,
 #'   fun = "stats::glm",
 #'   args.list = list(family = stats::binomial(link = "logit"))
 #' )
-#' lapply(m,broom::tidy) |> dplyr::bind_rows()
+#' lapply(m, broom::tidy) |> dplyr::bind_rows()
 #' }
 regression_model_uv <- function(data,
                                 outcome.str,
@@ -4741,9 +4824,8 @@ regression_model_uv <- function(data,
                                 fun = NULL,
                                 vars = NULL,
                                 ...) {
-
   ## This will handle if outcome is not in data for nicer shiny behavior
-  if (!outcome.str %in% names(data)){
+  if (!outcome.str %in% names(data)) {
     outcome.str <- names(data)[1]
     print("outcome is not in data, first column is used")
   }
@@ -4804,7 +4886,7 @@ regression_model_uv <- function(data,
 
 ### HELPERS
 
-#' Outcome data type assessment
+#' Data type assessment
 #'
 #' @param data data
 #'
@@ -4814,17 +4896,35 @@ regression_model_uv <- function(data,
 #' @examples
 #' mtcars |>
 #'   default_parsing() |>
-#'   lapply(outcome_type)
+#'   lapply(data_type)
-outcome_type <- function(data) {
+#' c(1, 2) |> data_type()
+#' 1 |> data_type()
+#' c(rep(NA, 10)) |> data_type()
+#' sample(1:100, 50) |> data_type()
+#' factor(letters[1:20]) |> data_type()
+data_type <- function(data) {
   cl_d <- class(data)
-  if (any(c("numeric", "integer") %in% cl_d)) {
+  if (all(is.na(data))) {
-    out <- "continuous"
+    out <- "empty"
-  } else if (any(c("factor", "logical") %in% cl_d)) {
+  } else if (length(unique(data)) < 2) {
-    if (length(levels(data)) == 2 | identical("logical",cl_d)) {
+    out <- "monotone"
+  } else if (any(c("factor", "logical") %in% cl_d) | length(unique(data)) == 2) {
+    if (identical("logical", cl_d) | length(unique(data)) == 2) {
       out <- "dichotomous"
-    } else if (length(levels(data)) > 2) {
+    } else {
-      out <- "ordinal"
+      if (is.ordered(data)) {
+        out <- "ordinal"
+      } else {
+        out <- "categorical"
+      }
     }
+  } else if (identical(cl_d, "character")) {
+    out <- "text"
+  } else if (!length(unique(data)) == 2) {
+    ## Previously had all thinkable classes
+    ## Now just assumes the class has not been defined above
+    ## any(c("numeric", "integer", "hms", "Date", "timediff") %in% cl_d) &
+    out <- "continuous"
   } else {
     out <- "unknown"
   }
@@ -4870,7 +4970,7 @@ supported_functions <- function() {
     polr = list(
       descr = "Ordinal logistic regression model",
       design = "cross-sectional",
-      out.type = "ordinal",
+      out.type = c("ordinal","categorical"),
       fun = "MASS::polr",
       args.list = list(
         Hess = TRUE,
@@ -4903,12 +5003,13 @@ supported_functions <- function() {
 #'   possible_functions(design = "cross-sectional")
 possible_functions <- function(data, design = c("cross-sectional")) {
   # browser()
+  # data <- if (is.reactive(data)) data() else data
   if (is.data.frame(data)) {
     data <- data[[1]]
   }
 
   design <- match.arg(design)
-  type <- outcome_type(data)
+  type <- data_type(data)
 
   design_ls <- supported_functions() |>
     lapply(\(.x){
@@ -5100,13 +5201,16 @@ list2str <- function(data) {
 #'
 #' @examples
 #' \dontrun{
-#' gtsummary::trial |> regression_model_uv(
+#' gtsummary::trial |>
-#'   outcome.str = "trt",
+#'   regression_model_uv(
-#'   fun = "stats::glm",
+#'     outcome.str = "trt",
-#'   args.list = list(family = stats::binomial(link = "logit"))
+#'     fun = "stats::glm",
-#' ) |> lapply(broom::tidy) |> dplyr::bind_rows()
+#'     args.list = list(family = stats::binomial(link = "logit"))
+#'   ) |>
+#'   lapply(broom::tidy) |>
+#'   dplyr::bind_rows()
 #' ms <- regression_model_uv_list(data = default_parsing(mtcars), outcome.str = "mpg", fun.descr = "Linear regression model")
-#' lapply(ms$model,broom::tidy) |> dplyr::bind_rows()
+#' lapply(ms$model, broom::tidy) |> dplyr::bind_rows()
 #' }
 regression_model_uv_list <- function(data,
                                      outcome.str,
@@ -7217,6 +7321,15 @@ ui_elements <- list(
                   )
                 ),
                 shiny::helpText("Option to perform statistical comparisons between strata in baseline table.")
+              ),
+              shiny::br(),
+              shiny::br(),
+              shiny::actionButton(
+                inputId = "act_eval",
+                label = "Evaluate",
+                width = "100%",
+                icon = shiny::icon("calculator"),
+                disabled = FALSE
               )
             ),
             bslib::accordion_panel(
@@ -7889,6 +8002,7 @@ server <- function(input, output, session) {
       rv$data_filtered <- data_filter()
 
       ###  Save filtered data
+      ###  without empty factor levels
       rv$list$data <- data_filter() |>
         REDCapCAST::fct_drop()
 
@@ -8018,23 +8132,39 @@ server <- function(input, output, session) {
   ## Keep these "old" selection options as a simple alternative to the modification pane
 
   output$include_vars <- shiny::renderUI({
-    shiny::selectizeInput(
+    columnSelectInputStat(
       inputId = "include_vars",
       selected = NULL,
       label = "Covariables to include",
-      choices = colnames(rv$data_filtered),
+      data = rv$data_filtered,
       multiple = TRUE
     )
+
+    # shiny::selectizeInput(
+    #   inputId = "include_vars",
+    #   selected = NULL,
+    #   label = "Covariables to include",
+    #   choices = colnames(rv$data_filtered),
+    #   multiple = TRUE
+    # )
   })
 
   output$outcome_var <- shiny::renderUI({
-    shiny::selectInput(
+    columnSelectInputStat(
       inputId = "outcome_var",
       selected = NULL,
       label = "Select outcome variable",
-      choices = colnames(rv$data_filtered),
+      data = rv$data_filtered,
       multiple = FALSE
     )
+
+    # shiny::selectInput(
+    #   inputId = "outcome_var",
+    #   selected = NULL,
+    #   label = "Select outcome variable",
+    #   choices = colnames(rv$data_filtered),
+    #   multiple = FALSE
+    # )
   })
 
   output$regression_type <- shiny::renderUI({
@@ -8077,25 +8207,37 @@ server <- function(input, output, session) {
   })
 
   output$strat_var <- shiny::renderUI({
-    shiny::selectInput(
+    columnSelectInputStat(
       inputId = "strat_var",
       selected = "none",
       label = "Select variable to stratify baseline",
-      choices = c(
+      data = rv$data_filtered,
+      col_subset = c(
         "none",
-        rv$data_filtered |>
+        names(rv$data_filtered)[unlist(lapply(rv$data_filtered, data_type)) %in% c("dichotomous", "categorical", "ordinal")]
-          (\(.x){
+      )
-            lapply(.x, \(.c){
-              if (identical("factor", class(.c))) {
-                .c
-              }
-            }) |>
-              dplyr::bind_cols()
-          })() |>
-          colnames()
-      ),
-      multiple = FALSE
     )
+
+    # shiny::selectInput(
+    #   inputId = "strat_var",
+    #   selected = "none",
+    #   label = "Select variable to stratify baseline",
+    #   choices = c(
+    #     "none",
+    #     names(rv$list$data)[unlist(lapply(rv$list$data,data_type)) %in% c("dichotomous","categorical","ordinal")]
+    #     # rv$data_filtered |>
+    #     #   (\(.x){
+    #     #     lapply(.x, \(.c){
+    #     #       if (identical("factor", class(.c))) {
+    #     #         .c
+    #     #       }
+    #     #     }) |>
+    #     #       dplyr::bind_cols()
+    #     #   })() |>
+    #     #   colnames()
+    #   ),
+    #   multiple = FALSE
+    # )
   })
 
 
@@ -8120,27 +8262,37 @@ server <- function(input, output, session) {
   shiny::observeEvent(
     # ignoreInit = TRUE,
     list(
-      shiny::reactive(rv$list$data),
+      # shiny::reactive(rv$list$data),
-      shiny::reactive(rv$data),
+      # shiny::reactive(rv$data),
-      shiny::reactive(rv$data_original),
+      # shiny::reactive(rv$data_original),
-      data_filter(),
+      # data_filter(),
-      input$strat_var,
+      # input$strat_var,
-      input$include_vars,
+      # input$include_vars,
-      input$complete_cutoff,
+      # input$complete_cutoff,
-      input$add_p
+      # input$add_p
+      input$act_eval
     ),
     {
       shiny::req(input$strat_var)
       shiny::req(rv$list$data)
 
-      if (input$strat_var == "none" | !input$strat_var %in% names(rv$list$data)) {
+      data_tbl1 <- rv$list$data
+
+      if (input$strat_var == "none" | !input$strat_var %in% names(data_tbl1)) {
         by.var <- NULL
       } else {
         by.var <- input$strat_var
       }
 
+      ## These steps are to handle logicals/booleans, that messes up the order of columns
+      ## Has been reported
+
+      if (!is.null(by.var) & identical("logical",class(data_tbl1[[by.var]]))) {
+        data_tbl1[by.var] <- as.character(data_tbl1[[by.var]])
+      }
+
       rv$list$table1 <-
-        rv$list$data |>
+        data_tbl1 |>
         baseline_table(
           fun.args =
             list(

inst/apps/FreesearchR/rsconnect/shinyapps.io/agdamsbo/freesearcheR.dcf

@@ -5,6 +5,6 @@ account: agdamsbo
 server: shinyapps.io
 hostUrl: https://api.shinyapps.io/v1
 appId: 13611288
-bundleId: 9969300
+bundleId: 9974967
 url: https://agdamsbo.shinyapps.io/freesearcheR/
 version: 1

inst/apps/FreesearchR/server.R

@@ -339,6 +339,7 @@ server <- function(input, output, session) {
       rv$data_filtered <- data_filter()
 
       ###  Save filtered data
+      ###  without empty factor levels
       rv$list$data <- data_filter() |>
         REDCapCAST::fct_drop()
 
@@ -468,23 +469,39 @@ server <- function(input, output, session) {
   ## Keep these "old" selection options as a simple alternative to the modification pane
 
   output$include_vars <- shiny::renderUI({
-    shiny::selectizeInput(
+    columnSelectInputStat(
       inputId = "include_vars",
       selected = NULL,
       label = "Covariables to include",
-      choices = colnames(rv$data_filtered),
+      data = rv$data_filtered,
       multiple = TRUE
     )
+
+    # shiny::selectizeInput(
+    #   inputId = "include_vars",
+    #   selected = NULL,
+    #   label = "Covariables to include",
+    #   choices = colnames(rv$data_filtered),
+    #   multiple = TRUE
+    # )
   })
 
   output$outcome_var <- shiny::renderUI({
-    shiny::selectInput(
+    columnSelectInputStat(
       inputId = "outcome_var",
       selected = NULL,
       label = "Select outcome variable",
-      choices = colnames(rv$data_filtered),
+      data = rv$data_filtered,
       multiple = FALSE
     )
+
+    # shiny::selectInput(
+    #   inputId = "outcome_var",
+    #   selected = NULL,
+    #   label = "Select outcome variable",
+    #   choices = colnames(rv$data_filtered),
+    #   multiple = FALSE
+    # )
   })
 
   output$regression_type <- shiny::renderUI({
@@ -527,25 +544,37 @@ server <- function(input, output, session) {
   })
 
   output$strat_var <- shiny::renderUI({
-    shiny::selectInput(
+    columnSelectInputStat(
       inputId = "strat_var",
       selected = "none",
       label = "Select variable to stratify baseline",
-      choices = c(
+      data = rv$data_filtered,
+      col_subset = c(
         "none",
-        rv$data_filtered |>
+        names(rv$data_filtered)[unlist(lapply(rv$data_filtered, data_type)) %in% c("dichotomous", "categorical", "ordinal")]
-          (\(.x){
+      )
-            lapply(.x, \(.c){
-              if (identical("factor", class(.c))) {
-                .c
-              }
-            }) |>
-              dplyr::bind_cols()
-          })() |>
-          colnames()
-      ),
-      multiple = FALSE
     )
+
+    # shiny::selectInput(
+    #   inputId = "strat_var",
+    #   selected = "none",
+    #   label = "Select variable to stratify baseline",
+    #   choices = c(
+    #     "none",
+    #     names(rv$list$data)[unlist(lapply(rv$list$data,data_type)) %in% c("dichotomous","categorical","ordinal")]
+    #     # rv$data_filtered |>
+    #     #   (\(.x){
+    #     #     lapply(.x, \(.c){
+    #     #       if (identical("factor", class(.c))) {
+    #     #         .c
+    #     #       }
+    #     #     }) |>
+    #     #       dplyr::bind_cols()
+    #     #   })() |>
+    #     #   colnames()
+    #   ),
+    #   multiple = FALSE
+    # )
   })
 
 
@@ -570,27 +599,37 @@ server <- function(input, output, session) {
   shiny::observeEvent(
     # ignoreInit = TRUE,
     list(
-      shiny::reactive(rv$list$data),
+      # shiny::reactive(rv$list$data),
-      shiny::reactive(rv$data),
+      # shiny::reactive(rv$data),
-      shiny::reactive(rv$data_original),
+      # shiny::reactive(rv$data_original),
-      data_filter(),
+      # data_filter(),
-      input$strat_var,
+      # input$strat_var,
-      input$include_vars,
+      # input$include_vars,
-      input$complete_cutoff,
+      # input$complete_cutoff,
-      input$add_p
+      # input$add_p
+      input$act_eval
     ),
     {
       shiny::req(input$strat_var)
       shiny::req(rv$list$data)
 
-      if (input$strat_var == "none" | !input$strat_var %in% names(rv$list$data)) {
+      data_tbl1 <- rv$list$data
+
+      if (input$strat_var == "none" | !input$strat_var %in% names(data_tbl1)) {
         by.var <- NULL
       } else {
         by.var <- input$strat_var
       }
 
+      ## These steps are to handle logicals/booleans, that messes up the order of columns
+      ## Has been reported
+
+      if (!is.null(by.var) & identical("logical",class(data_tbl1[[by.var]]))) {
+        data_tbl1[by.var] <- as.character(data_tbl1[[by.var]])
+      }
+
       rv$list$table1 <-
-        rv$list$data |>
+        data_tbl1 |>
         baseline_table(
           fun.args =
             list(

inst/apps/FreesearchR/ui.R

@@ -304,6 +304,15 @@ ui_elements <- list(
                   )
                 ),
                 shiny::helpText("Option to perform statistical comparisons between strata in baseline table.")
+              ),
+              shiny::br(),
+              shiny::br(),
+              shiny::actionButton(
+                inputId = "act_eval",
+                label = "Evaluate",
+                width = "100%",
+                icon = shiny::icon("calculator"),
+                disabled = FALSE
               )
             ),
             bslib::accordion_panel(