NAMESPACE

@@ -9,13 +9,10 @@ export(allign_axes)
 export(append_list)
 export(argsstring2list)
 export(baseline_table)
-export(clean_common_axis)
 export(clean_date)
 export(clean_sep)
 export(columnSelectInput)
 export(contrast_text)
-export(create_baseline)
-export(create_log_tics)
 export(create_overview_datagrid)
 export(create_plot)
 export(custom_theme)
@@ -23,10 +20,8 @@ export(cut_variable_server)
 export(cut_variable_ui)
 export(data_correlations_server)
 export(data_correlations_ui)
-export(data_description)
 export(data_summary_server)
 export(data_summary_ui)
-export(data_type)
 export(data_visuals_server)
 export(data_visuals_ui)
 export(default_format_arguments)
@@ -66,9 +61,8 @@ export(missing_fraction)
 export(modal_cut_variable)
 export(modal_update_factor)
 export(modify_qmd)
+export(outcome_type)
 export(overview_vars)
-export(plot_box)
-export(plot_box_single)
 export(plot_euler)
 export(plot_euler_single)
 export(plot_hbars)

R/app_version.R

@@ -1 +1 @@
-app_version <- function()'250324_1432'
+app_version <- function()'250320_1310'

R/baseline_table.R

@@ -20,55 +20,3 @@ baseline_table <- function(data, fun.args = NULL, fun = gtsummary::tbl_summary,
   return(out)
 }
 
-
-
-#' Create a baseline table
-#'
-#' @param data data
-#' @param ... passed as fun.arg to baseline_table()
-#' @param strat.var grouping/strat variable
-#' @param add.p add comparison/p-value
-#' @param add.overall add overall column
-#'
-#' @returns gtsummary table list object
-#' @export
-#'
-#' @examples
-#' mtcars |> create_baseline(by.var = "gear", add.p="yes"=="yes")
-create_baseline <- function(data,...,by.var,add.p=FALSE,add.overall=FALSE){
-  if (by.var == "none" | !by.var %in% names(data)) {
-    by.var <- NULL
-  }
-
-  ## These steps are to handle logicals/booleans, that messes up the order of columns
-  ## Has been reported
-
-  if (!is.null(by.var)) {
-    if (identical("logical",class(data[[by.var]]))){
-      data[by.var] <- as.character(data[[by.var]])
-    }
-  }
-
-  out <- data |>
-    baseline_table(
-      fun.args =
-        list(
-          by = by.var,
-          ...
-        )
-    )
-
-  if (!is.null(by.var)) {
-    if (isTRUE(add.overall)){
-  out <- out |> gtsummary::add_overall()
-    }
-    if (isTRUE(add.p)) {
-      out <- out |>
-        gtsummary::add_p() |>
-        gtsummary::bold_p()
-    }
-
-    }
-
-  out
-}

R/data_plots.R

@@ -302,7 +302,6 @@ data_visuals_server <- function(id,
         {
           tryCatch(
             {
-              shiny::withProgress(message = "Drawing the plot. Hold tight for a moment..", {
               rv$plot <- create_plot(
                 data = data(),
                 type = rv$plot.params()[["fun"]],
@@ -310,7 +309,6 @@ data_visuals_server <- function(id,
                 y = input$secondary,
                 z = input$tertiary
               )
-              })
             },
             # warning = function(warn) {
             #   showNotification(paste0(warn), type = "warning")

R/helpers.R

@@ -29,7 +29,7 @@ getfun <- function(x) {
 #' @return output file name
 #' @export
 #'
-write_quarto <- function(data, ...) {
+write_quarto <- function(data,...) {
   # Exports data to temporary location
   #
   # I assume this is more secure than putting it in the www folder and deleting
@@ -50,7 +50,7 @@ write_quarto <- function(data, ...) {
   )
 }
 
-write_rmd <- function(data, ...) {
+write_rmd <- function(data,...) {
   # Exports data to temporary location
   #
   # I assume this is more secure than putting it in the www folder and deleting
@@ -210,17 +210,17 @@ file_export <- function(data, output.format = c("df", "teal", "list"), filename,
 #'   default_parsing() |>
 #'   str()
 default_parsing <- function(data) {
-  name_labels <- lapply(data, \(.x) REDCapCAST::get_attr(.x, attr = "label"))
+  name_labels <- lapply(data,\(.x) REDCapCAST::get_attr(.x,attr = "label"))
 
   out <- data |>
     REDCapCAST::parse_data() |>
     REDCapCAST::as_factor() |>
-    REDCapCAST::numchar2fct(numeric.threshold = 8, character.throshold = 10) |>
+    REDCapCAST::numchar2fct(numeric.threshold = 8,character.throshold = 10) |>
     REDCapCAST::as_logical() |>
     REDCapCAST::fct_drop()
 
-  purrr::map2(out, name_labels, \(.x, .l){
+  purrr::map2(out,name_labels,\(.x,.l){
-    if (!(is.na(.l) | .l == "")) {
+    if (!(is.na(.l) | .l=="")) {
       REDCapCAST::set_attr(.x, .l, attr = "label")
     } else {
       attr(x = .x, which = "label") <- NULL
@@ -238,14 +238,12 @@ default_parsing <- function(data) {
 #' @export
 #'
 #' @examples
-#' ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label"))
+#' ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x,label=NA,attr = "label"))
-#' ds |>
+#' ds |> remove_na_attr() |> str()
-#'   remove_na_attr() |>
+remove_na_attr <- function(data,attr="label"){
-#'   str()
-remove_na_attr <- function(data, attr = "label") {
   out <- data |> lapply(\(.x){
-    ls <- REDCapCAST::get_attr(data = .x, attr = attr)
+    ls <- REDCapCAST::get_attr(data = .x,attr = attr)
-    if (is.na(ls) | ls == "") {
+    if (is.na(ls) | ls == ""){
       attr(x = .x, which = attr) <- NULL
     }
     .x
@@ -263,10 +261,10 @@ remove_na_attr <- function(data, attr = "label") {
 #' @export
 #'
 #' @examples
-#' data.frame(a = 1:10, b = NA, c = c(2, NA)) |> remove_empty_cols(cutoff = .5)
+#'data.frame(a=1:10,b=NA, c=c(2,NA)) |> remove_empty_cols(cutoff=.5)
-remove_empty_cols <- function(data, cutoff = .7) {
+remove_empty_cols <- function(data,cutoff=.7){
-  filter <- apply(X = data, MARGIN = 2, FUN = \(.x){
+  filter <- apply(X = data,MARGIN = 2,FUN = \(.x){
-    sum(as.numeric(!is.na(.x))) / length(.x)
+    sum(as.numeric(!is.na(.x)))/length(.x)
   }) >= cutoff
   data[filter]
 }
@@ -282,18 +280,18 @@ remove_empty_cols <- function(data, cutoff = .7) {
 #' @export
 #'
 #' @examples
-#' ls_d <- list(test = c(1:20))
+#' ls_d <- list(test=c(1:20))
 #' ls_d <- list()
-#' data.frame(letters[1:20], 1:20) |> append_list(ls_d, "letters")
+#' data.frame(letters[1:20],1:20) |> append_list(ls_d,"letters")
-#' letters[1:20] |> append_list(ls_d, "letters")
+#' letters[1:20]|> append_list(ls_d,"letters")
-append_list <- function(data, list, index) {
+append_list <- function(data,list,index){
   ## This will overwrite and not warn
   ## Not very safe, but convenient to append code to list
-  if (index %in% names(list)) {
+  if (index %in% names(list)){
     list[[index]] <- data
     out <- list
   } else {
-    out <- setNames(c(list, list(data)), c(names(list), index))
+    out <- setNames(c(list,list(data)),c(names(list),index))
   }
   out
 }
@@ -307,33 +305,7 @@ append_list <- function(data, list, index) {
 #' @export
 #'
 #' @examples
-#' c(NA, 1:10, rep(NA, 3)) |> missing_fraction()
+#' c(NA,1:10,rep(NA,3)) |> missing_fraction()
-missing_fraction <- function(data) {
+missing_fraction <- function(data){
-  NROW(data[is.na(data)]) / NROW(data)
+  NROW(data[is.na(data)])/NROW(data)
-}
-
-
-
-#' Ultra short data dascription
-#'
-#' @param data
-#'
-#' @returns character vector
-#' @export
-#'
-#' @examples
-#' data.frame(
-#'   sample(1:8, 20, TRUE),
-#'   sample(c(1:8, NA), 20, TRUE)
-#' ) |> data_description()
-data_description <- function(data) {
-  data <- if (shiny::is.reactive(data)) data() else data
-
-  sprintf(
-    i18n("Data has %s observations and %s variables, with %s (%s%%) complete cases"),
-    nrow(data),
-    ncol(data),
-    sum(complete.cases(data)),
-    signif(100 * (1 - missing_fraction(data)), 3)
-  )
 }

R/plot_box.R

@@ -36,7 +36,7 @@ plot_box <- function(data, x, y, z = NULL) {
 #'
 #' @name data-plots
 #'
-#' @returns ggplot object
+#' @returns
 #' @export
 #'
 #' @examples

R/regression_plot.R

@@ -43,16 +43,15 @@ plot.tbl_regression <- function(x,
 
   # Removes redundant label
   df_coefs$label[df_coefs$row_type == "label"] <- ""
-  # browser()
+
   # Add estimate value to reference level
-  if (plot_ref == TRUE) {
+  if (plot_ref == TRUE){
-    df_coefs[df_coefs$var_type %in% c("categorical", "dichotomous") & df_coefs$reference_row & !is.na(df_coefs$reference_row), "estimate"] <- if (x$inputs$exponentiate) 1 else 0
+  df_coefs[df_coefs$var_type == "categorical" & is.na(df_coefs$reference_row),"estimate"] <- if (x$inputs$exponentiate) 1 else 0}
-  }
 
   p <- df_coefs |>
     ggstats::ggcoef_plot(exponentiate = x$inputs$exponentiate, ...)
 
-  if (x$inputs$exponentiate) {
+  if (x$inputs$exponentiate){
     p <- symmetrical_scale_x_log10(p)
   }
   p
@@ -90,8 +89,7 @@ merge_long <- function(list, model.names) {
       )
       setNames(d, gsub("_[0-9]{,}$", "", names(d)))
     }) |>
-    dplyr::bind_rows() |>
+    dplyr::bind_rows() |> dplyr::mutate(model=as_factor(model))
-    dplyr::mutate(model = as_factor(model))
 
   l_merged$table_body <- df_body_long
 
@@ -111,25 +109,12 @@ merge_long <- function(list, model.names) {
 #' @export
 #'
 #' @examples
-#' limit_log(-.1, floor)
+#' limit_log(-.1,floor)
-#' limit_log(.1, ceiling)
+#' limit_log(.1,ceiling)
-#' limit_log(-2.1, ceiling)
+#' limit_log(-2.1,ceiling)
-#' limit_log(2.1, ceiling)
+#' limit_log(2.1,ceiling)
-limit_log <- function(data, fun, ...) {
+limit_log <- function(data,fun,...){
-  fun(10^-floor(data) * 10^data) / 10^-floor(data)
+  fun(10^-floor(data)*10^data)/10^-floor(data)
-}
-
-#' Create summetric log ticks
-#'
-#' @param data numeric vector
-#'
-#' @returns numeric vector
-#' @export
-#'
-#' @examples
-#' c(sample(seq(.1, 1, .1), 3), sample(1:10, 3)) |> create_log_tics()
-create_log_tics <- function(data) {
-  sort(round(unique(c(1 / data, data, 1)), 2))
 }
 
 #' Ensure symmetrical plot around 1 on a logarithmic x scale for ratio plots
@@ -141,18 +126,18 @@ create_log_tics <- function(data) {
 #' @returns ggplot2 object
 #' @export
 #'
-symmetrical_scale_x_log10 <- function(plot, breaks = c(1, 2, 3, 5, 10), ...) {
+symmetrical_scale_x_log10 <- function(plot,breaks=c(1,2,3,5,10),...){
   rx <- ggplot2::layer_scales(plot)$x$get_limits()
 
-  x_min <- floor(10 * rx[1]) / 10
+  x_min <- floor(10*rx[1])/10
-  x_max <- ceiling(10 * rx[2]) / 10
+  x_max <- ceiling(10*rx[2])/10
 
-  rx_min <- limit_log(rx[1], floor)
+  rx_min <- limit_log(rx[1],floor)
-  rx_max <- limit_log(rx[2], ceiling)
+  rx_max <- limit_log(rx[2],ceiling)
 
-  max_abs_x <- max(abs(c(x_min, x_max)))
+  max_abs_x <- max(abs(c(x_min,x_max)))
 
-  ticks <- log10(breaks) + (ceiling(max_abs_x) - 1)
+  ticks <- log10(breaks)+(ceiling(max_abs_x)-1)
 
-  plot + ggplot2::scale_x_log10(limits = c(rx_min, rx_max), breaks = create_log_tics(10^ticks[ticks <= max_abs_x]))
+  plot + ggplot2::scale_x_log10(limits=c(rx_min,rx_max),breaks=create_log_tics(10^ticks[ticks<=max_abs_x]))
 }

R/regression_table.R

@@ -119,8 +119,8 @@ regression_table_create <- function(x, ..., args.list = NULL, fun = "gtsummary::
   }
 
   out <- do.call(getfun(fun), c(list(x = x), args.list))
-  out #|>
+  out |>
-    # gtsummary::add_glance_source_note() # |>
+    gtsummary::add_glance_source_note() # |>
   # gtsummary::bold_p()
 }
 

inst/apps/FreesearchR/app.R

@@ -10,7 +10,7 @@
 #### Current file: R//app_version.R 
 ########
 
-app_version <- function()'250324_1432'
+app_version <- function()'250320_1310'
 
 
 ########
@@ -41,58 +41,6 @@ baseline_table <- function(data, fun.args = NULL, fun = gtsummary::tbl_summary,
 
 
 
-#' Create a baseline table
-#'
-#' @param data data
-#' @param ... passed as fun.arg to baseline_table()
-#' @param strat.var grouping/strat variable
-#' @param add.p add comparison/p-value
-#' @param add.overall add overall column
-#'
-#' @returns gtsummary table list object
-#' @export
-#'
-#' @examples
-#' mtcars |> create_baseline(by.var = "gear", add.p="yes"=="yes")
-create_baseline <- function(data,...,by.var,add.p=FALSE,add.overall=FALSE){
-  if (by.var == "none" | !by.var %in% names(data)) {
-    by.var <- NULL
-  }
-
-  ## These steps are to handle logicals/booleans, that messes up the order of columns
-  ## Has been reported
-
-  if (!is.null(by.var)) {
-    if (identical("logical",class(data[[by.var]]))){
-      data[by.var] <- as.character(data[[by.var]])
-    }
-  }
-
-  out <- data |>
-    baseline_table(
-      fun.args =
-        list(
-          by = by.var,
-          ...
-        )
-    )
-
-  if (!is.null(by.var)) {
-    if (isTRUE(add.overall)){
-  out <- out |> gtsummary::add_overall()
-    }
-    if (isTRUE(add.p)) {
-      out <- out |>
-        gtsummary::add_p() |>
-        gtsummary::bold_p()
-    }
-
-    }
-
-  out
-}
-
-
 ########
 #### Current file: R//contrast_text.R 
 ########
@@ -408,6 +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
 #'
@@ -1458,7 +1476,6 @@ data_visuals_server <- function(id,
         {
           tryCatch(
             {
-              shiny::withProgress(message = "Drawing the plot. Hold tight for a moment..", {
               rv$plot <- create_plot(
                 data = data(),
                 type = rv$plot.params()[["fun"]],
@@ -1466,7 +1483,6 @@ data_visuals_server <- function(id,
                 y = input$secondary,
                 z = input$tertiary
               )
-              })
             },
             # warning = function(warn) {
             #   showNotification(paste0(warn), type = "warning")
@@ -2517,7 +2533,7 @@ getfun <- function(x) {
 #' @return output file name
 #' @export
 #'
-write_quarto <- function(data, ...) {
+write_quarto <- function(data,...) {
   # Exports data to temporary location
   #
   # I assume this is more secure than putting it in the www folder and deleting
@@ -2538,7 +2554,7 @@ write_quarto <- function(data, ...) {
   )
 }
 
-write_rmd <- function(data, ...) {
+write_rmd <- function(data,...) {
   # Exports data to temporary location
   #
   # I assume this is more secure than putting it in the www folder and deleting
@@ -2698,17 +2714,17 @@ file_export <- function(data, output.format = c("df", "teal", "list"), filename,
 #'   default_parsing() |>
 #'   str()
 default_parsing <- function(data) {
-  name_labels <- lapply(data, \(.x) REDCapCAST::get_attr(.x, attr = "label"))
+  name_labels <- lapply(data,\(.x) REDCapCAST::get_attr(.x,attr = "label"))
 
   out <- data |>
     REDCapCAST::parse_data() |>
     REDCapCAST::as_factor() |>
-    REDCapCAST::numchar2fct(numeric.threshold = 8, character.throshold = 10) |>
+    REDCapCAST::numchar2fct(numeric.threshold = 8,character.throshold = 10) |>
     REDCapCAST::as_logical() |>
     REDCapCAST::fct_drop()
 
-  purrr::map2(out, name_labels, \(.x, .l){
+  purrr::map2(out,name_labels,\(.x,.l){
-    if (!(is.na(.l) | .l == "")) {
+    if (!(is.na(.l) | .l=="")) {
       REDCapCAST::set_attr(.x, .l, attr = "label")
     } else {
       attr(x = .x, which = "label") <- NULL
@@ -2726,14 +2742,12 @@ default_parsing <- function(data) {
 #' @export
 #'
 #' @examples
-#' ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label"))
+#' ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x,label=NA,attr = "label"))
-#' ds |>
+#' ds |> remove_na_attr() |> str()
-#'   remove_na_attr() |>
+remove_na_attr <- function(data,attr="label"){
-#'   str()
-remove_na_attr <- function(data, attr = "label") {
   out <- data |> lapply(\(.x){
-    ls <- REDCapCAST::get_attr(data = .x, attr = attr)
+    ls <- REDCapCAST::get_attr(data = .x,attr = attr)
-    if (is.na(ls) | ls == "") {
+    if (is.na(ls) | ls == ""){
       attr(x = .x, which = attr) <- NULL
     }
     .x
@@ -2751,10 +2765,10 @@ remove_na_attr <- function(data, attr = "label") {
 #' @export
 #'
 #' @examples
-#' data.frame(a = 1:10, b = NA, c = c(2, NA)) |> remove_empty_cols(cutoff = .5)
+#'data.frame(a=1:10,b=NA, c=c(2,NA)) |> remove_empty_cols(cutoff=.5)
-remove_empty_cols <- function(data, cutoff = .7) {
+remove_empty_cols <- function(data,cutoff=.7){
-  filter <- apply(X = data, MARGIN = 2, FUN = \(.x){
+  filter <- apply(X = data,MARGIN = 2,FUN = \(.x){
-    sum(as.numeric(!is.na(.x))) / length(.x)
+    sum(as.numeric(!is.na(.x)))/length(.x)
   }) >= cutoff
   data[filter]
 }
@@ -2770,18 +2784,18 @@ remove_empty_cols <- function(data, cutoff = .7) {
 #' @export
 #'
 #' @examples
-#' ls_d <- list(test = c(1:20))
+#' ls_d <- list(test=c(1:20))
 #' ls_d <- list()
-#' data.frame(letters[1:20], 1:20) |> append_list(ls_d, "letters")
+#' data.frame(letters[1:20],1:20) |> append_list(ls_d,"letters")
-#' letters[1:20] |> append_list(ls_d, "letters")
+#' letters[1:20]|> append_list(ls_d,"letters")
-append_list <- function(data, list, index) {
+append_list <- function(data,list,index){
   ## This will overwrite and not warn
   ## Not very safe, but convenient to append code to list
-  if (index %in% names(list)) {
+  if (index %in% names(list)){
     list[[index]] <- data
     out <- list
   } else {
-    out <- setNames(c(list, list(data)), c(names(list), index))
+    out <- setNames(c(list,list(data)),c(names(list),index))
   }
   out
 }
@@ -2795,35 +2809,9 @@ append_list <- function(data, list, index) {
 #' @export
 #'
 #' @examples
-#' c(NA, 1:10, rep(NA, 3)) |> missing_fraction()
+#' c(NA,1:10,rep(NA,3)) |> missing_fraction()
-missing_fraction <- function(data) {
+missing_fraction <- function(data){
-  NROW(data[is.na(data)]) / NROW(data)
+  NROW(data[is.na(data)])/NROW(data)
-}
-
-
-
-#' Ultra short data dascription
-#'
-#' @param data
-#'
-#' @returns character vector
-#' @export
-#'
-#' @examples
-#' data.frame(
-#'   sample(1:8, 20, TRUE),
-#'   sample(c(1:8, NA), 20, TRUE)
-#' ) |> data_description()
-data_description <- function(data) {
-  data <- if (shiny::is.reactive(data)) data() else data
-
-  sprintf(
-    i18n("Data has %s observations and %s variables, with %s (%s%%) complete cases"),
-    nrow(data),
-    ncol(data),
-    sum(complete.cases(data)),
-    signif(100 * (1 - missing_fraction(data)), 3)
-  )
 }
 
 
@@ -5364,16 +5352,15 @@ plot.tbl_regression <- function(x,
 
   # Removes redundant label
   df_coefs$label[df_coefs$row_type == "label"] <- ""
-  # browser()
+
   # Add estimate value to reference level
-  if (plot_ref == TRUE) {
+  if (plot_ref == TRUE){
-    df_coefs[df_coefs$var_type %in% c("categorical", "dichotomous") & df_coefs$reference_row & !is.na(df_coefs$reference_row), "estimate"] <- if (x$inputs$exponentiate) 1 else 0
+  df_coefs[df_coefs$var_type == "categorical" & is.na(df_coefs$reference_row),"estimate"] <- if (x$inputs$exponentiate) 1 else 0}
-  }
 
   p <- df_coefs |>
     ggstats::ggcoef_plot(exponentiate = x$inputs$exponentiate, ...)
 
-  if (x$inputs$exponentiate) {
+  if (x$inputs$exponentiate){
     p <- symmetrical_scale_x_log10(p)
   }
   p
@@ -5411,8 +5398,7 @@ merge_long <- function(list, model.names) {
       )
       setNames(d, gsub("_[0-9]{,}$", "", names(d)))
     }) |>
-    dplyr::bind_rows() |>
+    dplyr::bind_rows() |> dplyr::mutate(model=as_factor(model))
-    dplyr::mutate(model = as_factor(model))
 
   l_merged$table_body <- df_body_long
 
@@ -5432,25 +5418,12 @@ merge_long <- function(list, model.names) {
 #' @export
 #'
 #' @examples
-#' limit_log(-.1, floor)
+#' limit_log(-.1,floor)
-#' limit_log(.1, ceiling)
+#' limit_log(.1,ceiling)
-#' limit_log(-2.1, ceiling)
+#' limit_log(-2.1,ceiling)
-#' limit_log(2.1, ceiling)
+#' limit_log(2.1,ceiling)
-limit_log <- function(data, fun, ...) {
+limit_log <- function(data,fun,...){
-  fun(10^-floor(data) * 10^data) / 10^-floor(data)
+  fun(10^-floor(data)*10^data)/10^-floor(data)
-}
-
-#' Create summetric log ticks
-#'
-#' @param data numeric vector
-#'
-#' @returns
-#' @export
-#'
-#' @examples
-#' c(sample(seq(.1, 1, .1), 3), sample(1:10, 3)) |> create_log_tics()
-create_log_tics <- function(data) {
-  sort(round(unique(c(1 / data, data, 1)), 2))
 }
 
 #' Ensure symmetrical plot around 1 on a logarithmic x scale for ratio plots
@@ -5462,20 +5435,20 @@ create_log_tics <- function(data) {
 #' @returns ggplot2 object
 #' @export
 #'
-symmetrical_scale_x_log10 <- function(plot, breaks = c(1, 2, 3, 5, 10), ...) {
+symmetrical_scale_x_log10 <- function(plot,breaks=c(1,2,3,5,10),...){
   rx <- ggplot2::layer_scales(plot)$x$get_limits()
 
-  x_min <- floor(10 * rx[1]) / 10
+  x_min <- floor(10*rx[1])/10
-  x_max <- ceiling(10 * rx[2]) / 10
+  x_max <- ceiling(10*rx[2])/10
 
-  rx_min <- limit_log(rx[1], floor)
+  rx_min <- limit_log(rx[1],floor)
-  rx_max <- limit_log(rx[2], ceiling)
+  rx_max <- limit_log(rx[2],ceiling)
 
-  max_abs_x <- max(abs(c(x_min, x_max)))
+  max_abs_x <- max(abs(c(x_min,x_max)))
 
-  ticks <- log10(breaks) + (ceiling(max_abs_x) - 1)
+  ticks <- log10(breaks)+(ceiling(max_abs_x)-1)
 
-  plot + ggplot2::scale_x_log10(limits = c(rx_min, rx_max), breaks = create_log_tics(10^ticks[ticks <= max_abs_x]))
+  plot + ggplot2::scale_x_log10(limits=c(rx_min,rx_max),breaks=create_log_tics(10^ticks[ticks<=max_abs_x]))
 }
 
 
@@ -5604,8 +5577,8 @@ regression_table_create <- function(x, ..., args.list = NULL, fun = "gtsummary::
   }
 
   out <- do.call(getfun(fun), c(list(x = x), args.list))
-  out #|>
+  out |>
-    # gtsummary::add_glance_source_note() # |>
+    gtsummary::add_glance_source_note() # |>
   # gtsummary::bold_p()
 }
 
@@ -7128,7 +7101,6 @@ ui_elements <- list(
             shinyWidgets::noUiSliderInput(
               inputId = "complete_cutoff",
               label = NULL,
-              update_on = "change",
               min = 0,
               max = 100,
               step = 5,
@@ -7139,8 +7111,7 @@ ui_elements <- list(
             shiny::helpText("Filter variables with completeness above the specified percentage."),
             shiny::br(),
             shiny::br(),
-            shiny::uiOutput(outputId = "import_var"),
+            shiny::uiOutput(outputId = "import_var")
-            shiny::uiOutput(outputId = "data_info_import", inline = TRUE)
           )
         ),
         shiny::br(),
@@ -7177,9 +7148,10 @@ ui_elements <- list(
           fluidRow(
             shiny::column(
               width = 9,
-              shiny::uiOutput(outputId = "data_info", inline = TRUE),
               shiny::tags$p(
-                "Below is a short summary table, on the right you can create data filters."
+                "Below is a short summary table of the provided data.
+              On the right hand side you have the option to create filters.
+              At the bottom you'll find a raw overview of the original vs the modified data."
               )
             )
           ),
@@ -7434,7 +7406,6 @@ ui_elements <- list(
         # bslib::layout_sidebar(
         #   fillable = TRUE,
         sidebar = bslib::sidebar(
-          shiny::uiOutput(outputId = "data_info_regression", inline = TRUE),
           bslib::accordion(
             open = "acc_reg",
             multiple = FALSE,
@@ -7496,7 +7467,7 @@ ui_elements <- list(
               ),
               shiny::conditionalPanel(
                 condition = "input.all==1",
-                shiny::uiOutput("regression_vars")
+                shiny::uiOutput("include_vars")
               )
             )
           ),
@@ -7852,12 +7823,11 @@ server <- function(input, output, session) {
 
   shiny::observeEvent(
     eventExpr = list(
-      input$import_var,
+      input$import_var
-      input$complete_cutoff
     ),
     handlerExpr = {
       shiny::req(rv$data_temp)
-# browser()
+
       rv$data_original <- rv$data_temp |>
         dplyr::select(input$import_var) |>
         default_parsing()
@@ -7877,11 +7847,6 @@ server <- function(input, output, session) {
     }
   )
 
-  output$data_info_import <- shiny::renderUI({
-    shiny::req(rv$data_original)
-    data_description(rv$data_original)
-  })
-
 
   shiny::observeEvent(rv$data_original, {
     if (is.null(rv$data_original) | NROW(rv$data_original) == 0) {
@@ -7946,17 +7911,6 @@ server <- function(input, output, session) {
     modal_update_variables("modal_variables", title = "Update and select variables")
   )
 
-  output$data_info <- shiny::renderUI({
-    shiny::req(data_filter())
-    data_description(data_filter())
-  })
-
-  output$data_info_regression <- shiny::renderUI({
-    shiny::req(regression_vars())
-    shiny::req(rv$list$data)
-    data_description(rv$list$data[regression_vars()])
-  })
-
 
   #########  Create factor
 
@@ -8177,25 +8131,40 @@ server <- function(input, output, session) {
 
   ## Keep these "old" selection options as a simple alternative to the modification pane
 
-
+  output$include_vars <- shiny::renderUI({
-  output$regression_vars <- shiny::renderUI({
+    columnSelectInputStat(
-    columnSelectInput(
+      inputId = "include_vars",
-      inputId = "regression_vars",
       selected = NULL,
       label = "Covariables to include",
       data = rv$data_filtered,
-      multiple = TRUE,
+      multiple = TRUE
     )
+
+    # shiny::selectizeInput(
+    #   inputId = "include_vars",
+    #   selected = NULL,
+    #   label = "Covariables to include",
+    #   choices = colnames(rv$data_filtered),
+    #   multiple = TRUE
+    # )
   })
 
   output$outcome_var <- shiny::renderUI({
-    columnSelectInput(
+    columnSelectInputStat(
       inputId = "outcome_var",
       selected = NULL,
       label = "Select outcome variable",
       data = rv$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({
@@ -8229,16 +8198,16 @@ server <- function(input, output, session) {
 
   ## Collected regression variables
   regression_vars <- shiny::reactive({
-    if (is.null(input$regression_vars)) {
+    if (is.null(input$include_vars)) {
       out <- colnames(rv$data_filtered)
     } else {
-      out <- unique(c(input$regression_vars, input$outcome_var))
+      out <- unique(c(input$include_vars, input$outcome_var))
     }
     return(out)
   })
 
   output$strat_var <- shiny::renderUI({
-    columnSelectInput(
+    columnSelectInputStat(
       inputId = "strat_var",
       selected = "none",
       label = "Select variable to stratify baseline",
@@ -8248,6 +8217,27 @@ server <- function(input, output, session) {
         names(rv$data_filtered)[unlist(lapply(rv$data_filtered, data_type)) %in% c("dichotomous", "categorical", "ordinal")]
       )
     )
+
+    # shiny::selectInput(
+    #   inputId = "strat_var",
+    #   selected = "none",
+    #   label = "Select variable to stratify baseline",
+    #   choices = c(
+    #     "none",
+    #     names(rv$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
+    # )
   })
 
 
@@ -8277,7 +8267,7 @@ server <- function(input, output, session) {
       # shiny::reactive(rv$data_original),
       # data_filter(),
       # input$strat_var,
-      # input$regression_vars,
+      # input$include_vars,
       # input$complete_cutoff,
       # input$add_p
       input$act_eval
@@ -8286,16 +8276,48 @@ server <- function(input, output, session) {
       shiny::req(input$strat_var)
       shiny::req(rv$list$data)
 
-      # data_tbl1 <- rv$list$data
+      data_tbl1 <- rv$list$data
 
-      shiny::withProgress(message = "Creating the table. Hold on for a moment..", {
+      if (input$strat_var == "none" | !input$strat_var %in% names(data_tbl1)) {
-        rv$list$table1 <- create_baseline(
+        by.var <- NULL
-          rv$list$data,
+      } else {
-          by.var = input$strat_var,
+        by.var <- input$strat_var
-          add.p = input$add_p == "yes",
+      }
-          add.overall = TRUE
+
+      ## These steps are to handle logicals/booleans, that messes up the order of columns
+      ## Has been reported
+
+      if (!is.null(by.var) & identical("logical",class(data_tbl1[[by.var]]))) {
+        data_tbl1[by.var] <- as.character(data_tbl1[[by.var]])
+      }
+
+      rv$list$table1 <-
+        data_tbl1 |>
+        baseline_table(
+          fun.args =
+            list(
+              by = by.var
             )
-      })
+        ) |>
+        (\(.x){
+          if (!is.null(by.var)) {
+            .x |> gtsummary::add_overall()
+          } else {
+            .x
+          }
+        })() |>
+        (\(.x){
+          if (input$add_p == "yes" & !is.null(by.var)) {
+            .x |>
+              gtsummary::add_p() |>
+              gtsummary::bold_p()
+          } else {
+            .x
+          }
+        })()
+
+      # gtsummary::as_kable(rv$list$table1) |>
+      #   readr::write_lines(file="./www/_table1.md")
     }
   )
 
@@ -8394,9 +8416,9 @@ server <- function(input, output, session) {
           #   .x$model
           # })
         },
-        # warning = function(warn) {
+        warning = function(warn) {
-        #   showNotification(paste0(warn), type = "warning")
+          showNotification(paste0(warn), type = "warning")
-        # },
+        },
         error = function(err) {
           showNotification(paste0("Creating regression models failed with the following error: ", err), type = "err")
         }
@@ -8419,9 +8441,9 @@ server <- function(input, output, session) {
             purrr::pluck("Multivariable") |>
             performance::check_model()
         },
-        # warning = function(warn) {
+        warning = function(warn) {
-        #   showNotification(paste0(warn), type = "warning")
+          showNotification(paste0(warn), type = "warning")
-        # },
+        },
         error = function(err) {
           showNotification(paste0("Running model assumptions checks failed with the following error: ", err), type = "err")
         }

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: 9994253
+bundleId: 9974967
 url: https://agdamsbo.shinyapps.io/freesearcheR/
 version: 1

inst/apps/FreesearchR/server.R

@@ -160,12 +160,11 @@ server <- function(input, output, session) {
 
   shiny::observeEvent(
     eventExpr = list(
-      input$import_var,
+      input$import_var
-      input$complete_cutoff
     ),
     handlerExpr = {
       shiny::req(rv$data_temp)
-# browser()
+
       rv$data_original <- rv$data_temp |>
         dplyr::select(input$import_var) |>
         default_parsing()
@@ -185,11 +184,6 @@ server <- function(input, output, session) {
     }
   )
 
-  output$data_info_import <- shiny::renderUI({
-    shiny::req(rv$data_original)
-    data_description(rv$data_original)
-  })
-
 
   shiny::observeEvent(rv$data_original, {
     if (is.null(rv$data_original) | NROW(rv$data_original) == 0) {
@@ -254,17 +248,6 @@ server <- function(input, output, session) {
     modal_update_variables("modal_variables", title = "Update and select variables")
   )
 
-  output$data_info <- shiny::renderUI({
-    shiny::req(data_filter())
-    data_description(data_filter())
-  })
-
-  output$data_info_regression <- shiny::renderUI({
-    shiny::req(regression_vars())
-    shiny::req(rv$list$data)
-    data_description(rv$list$data[regression_vars()])
-  })
-
 
   #########  Create factor
 
@@ -485,25 +468,40 @@ server <- function(input, output, session) {
 
   ## Keep these "old" selection options as a simple alternative to the modification pane
 
-
+  output$include_vars <- shiny::renderUI({
-  output$regression_vars <- shiny::renderUI({
+    columnSelectInputStat(
-    columnSelectInput(
+      inputId = "include_vars",
-      inputId = "regression_vars",
       selected = NULL,
       label = "Covariables to include",
       data = rv$data_filtered,
-      multiple = TRUE,
+      multiple = TRUE
     )
+
+    # shiny::selectizeInput(
+    #   inputId = "include_vars",
+    #   selected = NULL,
+    #   label = "Covariables to include",
+    #   choices = colnames(rv$data_filtered),
+    #   multiple = TRUE
+    # )
   })
 
   output$outcome_var <- shiny::renderUI({
-    columnSelectInput(
+    columnSelectInputStat(
       inputId = "outcome_var",
       selected = NULL,
       label = "Select outcome variable",
       data = rv$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({
@@ -537,16 +535,16 @@ server <- function(input, output, session) {
 
   ## Collected regression variables
   regression_vars <- shiny::reactive({
-    if (is.null(input$regression_vars)) {
+    if (is.null(input$include_vars)) {
       out <- colnames(rv$data_filtered)
     } else {
-      out <- unique(c(input$regression_vars, input$outcome_var))
+      out <- unique(c(input$include_vars, input$outcome_var))
     }
     return(out)
   })
 
   output$strat_var <- shiny::renderUI({
-    columnSelectInput(
+    columnSelectInputStat(
       inputId = "strat_var",
       selected = "none",
       label = "Select variable to stratify baseline",
@@ -556,6 +554,27 @@ server <- function(input, output, session) {
         names(rv$data_filtered)[unlist(lapply(rv$data_filtered, data_type)) %in% c("dichotomous", "categorical", "ordinal")]
       )
     )
+
+    # shiny::selectInput(
+    #   inputId = "strat_var",
+    #   selected = "none",
+    #   label = "Select variable to stratify baseline",
+    #   choices = c(
+    #     "none",
+    #     names(rv$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
+    # )
   })
 
 
@@ -585,7 +604,7 @@ server <- function(input, output, session) {
       # shiny::reactive(rv$data_original),
       # data_filter(),
       # input$strat_var,
-      # input$regression_vars,
+      # input$include_vars,
       # input$complete_cutoff,
       # input$add_p
       input$act_eval
@@ -594,16 +613,48 @@ server <- function(input, output, session) {
       shiny::req(input$strat_var)
       shiny::req(rv$list$data)
 
-      # data_tbl1 <- rv$list$data
+      data_tbl1 <- rv$list$data
 
-      shiny::withProgress(message = "Creating the table. Hold on for a moment..", {
+      if (input$strat_var == "none" | !input$strat_var %in% names(data_tbl1)) {
-        rv$list$table1 <- create_baseline(
+        by.var <- NULL
-          rv$list$data,
+      } else {
-          by.var = input$strat_var,
+        by.var <- input$strat_var
-          add.p = input$add_p == "yes",
+      }
-          add.overall = TRUE
+
+      ## These steps are to handle logicals/booleans, that messes up the order of columns
+      ## Has been reported
+
+      if (!is.null(by.var) & identical("logical",class(data_tbl1[[by.var]]))) {
+        data_tbl1[by.var] <- as.character(data_tbl1[[by.var]])
+      }
+
+      rv$list$table1 <-
+        data_tbl1 |>
+        baseline_table(
+          fun.args =
+            list(
+              by = by.var
             )
-      })
+        ) |>
+        (\(.x){
+          if (!is.null(by.var)) {
+            .x |> gtsummary::add_overall()
+          } else {
+            .x
+          }
+        })() |>
+        (\(.x){
+          if (input$add_p == "yes" & !is.null(by.var)) {
+            .x |>
+              gtsummary::add_p() |>
+              gtsummary::bold_p()
+          } else {
+            .x
+          }
+        })()
+
+      # gtsummary::as_kable(rv$list$table1) |>
+      #   readr::write_lines(file="./www/_table1.md")
     }
   )
 
@@ -702,9 +753,9 @@ server <- function(input, output, session) {
           #   .x$model
           # })
         },
-        # warning = function(warn) {
+        warning = function(warn) {
-        #   showNotification(paste0(warn), type = "warning")
+          showNotification(paste0(warn), type = "warning")
-        # },
+        },
         error = function(err) {
           showNotification(paste0("Creating regression models failed with the following error: ", err), type = "err")
         }
@@ -727,9 +778,9 @@ server <- function(input, output, session) {
             purrr::pluck("Multivariable") |>
             performance::check_model()
         },
-        # warning = function(warn) {
+        warning = function(warn) {
-        #   showNotification(paste0(warn), type = "warning")
+          showNotification(paste0(warn), type = "warning")
-        # },
+        },
         error = function(err) {
           showNotification(paste0("Running model assumptions checks failed with the following error: ", err), type = "err")
         }

inst/apps/FreesearchR/ui.R

@@ -84,7 +84,6 @@ ui_elements <- list(
             shinyWidgets::noUiSliderInput(
               inputId = "complete_cutoff",
               label = NULL,
-              update_on = "change",
               min = 0,
               max = 100,
               step = 5,
@@ -95,8 +94,7 @@ ui_elements <- list(
             shiny::helpText("Filter variables with completeness above the specified percentage."),
             shiny::br(),
             shiny::br(),
-            shiny::uiOutput(outputId = "import_var"),
+            shiny::uiOutput(outputId = "import_var")
-            shiny::uiOutput(outputId = "data_info_import", inline = TRUE)
           )
         ),
         shiny::br(),
@@ -133,9 +131,10 @@ ui_elements <- list(
           fluidRow(
             shiny::column(
               width = 9,
-              shiny::uiOutput(outputId = "data_info", inline = TRUE),
               shiny::tags$p(
-                "Below is a short summary table, on the right you can create data filters."
+                "Below is a short summary table of the provided data.
+              On the right hand side you have the option to create filters.
+              At the bottom you'll find a raw overview of the original vs the modified data."
               )
             )
           ),
@@ -390,7 +389,6 @@ ui_elements <- list(
         # bslib::layout_sidebar(
         #   fillable = TRUE,
         sidebar = bslib::sidebar(
-          shiny::uiOutput(outputId = "data_info_regression", inline = TRUE),
           bslib::accordion(
             open = "acc_reg",
             multiple = FALSE,
@@ -452,7 +450,7 @@ ui_elements <- list(
               ),
               shiny::conditionalPanel(
                 condition = "input.all==1",
-                shiny::uiOutput("regression_vars")
+                shiny::uiOutput("include_vars")
               )
             )
           ),

man/append_list.Rd

@@ -20,8 +20,8 @@ list
 Append list with named index
 }
 \examples{
-ls_d <- list(test = c(1:20))
+ls_d <- list(test=c(1:20))
 ls_d <- list()
-data.frame(letters[1:20], 1:20) |> append_list(ls_d, "letters")
+data.frame(letters[1:20],1:20) |> append_list(ls_d,"letters")
-letters[1:20] |> append_list(ls_d, "letters")
+letters[1:20]|> append_list(ls_d,"letters")
 }

man/clean_common_axis.Rd

@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_plots.R
-\name{clean_common_axis}
-\alias{clean_common_axis}
-\title{Extract and clean axis ranges}
-\usage{
-clean_common_axis(p, axis)
-}
-\arguments{
-\item{p}{plot}
-
-\item{axis}{axis. x or y.}
-}
-\value{
-vector
-}
-\description{
-Extract and clean axis ranges
-}

man/create_baseline.Rd

@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/baseline_table.R
-\name{create_baseline}
-\alias{create_baseline}
-\title{Create a baseline table}
-\usage{
-create_baseline(data, ..., by.var, add.p = FALSE, add.overall = FALSE)
-}
-\arguments{
-\item{data}{data}
-
-\item{...}{passed as fun.arg to baseline_table()}
-
-\item{add.p}{add comparison/p-value}
-
-\item{add.overall}{add overall column}
-
-\item{strat.var}{grouping/strat variable}
-}
-\value{
-gtsummary table list object
-}
-\description{
-Create a baseline table
-}
-\examples{
-mtcars |> create_baseline(by.var = "gear", add.p="yes"=="yes")
-}

man/create_log_tics.Rd

@@ -1,20 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/regression_plot.R
-\name{create_log_tics}
-\alias{create_log_tics}
-\title{Create summetric log ticks}
-\usage{
-create_log_tics(data)
-}
-\arguments{
-\item{data}{numeric vector}
-}
-\value{
-numeric vector
-}
-\description{
-Create summetric log ticks
-}
-\examples{
-c(sample(seq(.1, 1, .1), 3), sample(1:10, 3)) |> create_log_tics()
-}

man/data-plots.Rd

@@ -1,13 +1,11 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_plots.R, R/plot_box.R, R/plot_hbar.R,
+% Please edit documentation in R/data_plots.R, R/plot_hbar.R, R/plot_ridge.R,
-%   R/plot_ridge.R, R/plot_sankey.R, R/plot_scatter.R, R/plot_violin.R
+%   R/plot_sankey.R, R/plot_scatter.R, R/plot_violin.R
 \name{data-plots}
 \alias{data-plots}
 \alias{data_visuals_ui}
 \alias{data_visuals_server}
 \alias{create_plot}
-\alias{plot_box}
-\alias{plot_box_single}
 \alias{plot_hbars}
 \alias{plot_ridge}
 \alias{sankey_ready}
@@ -22,10 +20,6 @@ data_visuals_server(id, data, ...)
 
 create_plot(data, type, x, y, z = NULL, ...)
 
-plot_box(data, x, y, z = NULL)
-
-plot_box_single(data, x, y = NULL, seed = 2103)
-
 plot_hbars(data, x, y, z = NULL)
 
 plot_ridge(data, x, y, z = NULL, ...)
@@ -62,10 +56,6 @@ ggplot2 object
 
 ggplot2 object
 
-ggplot object
-
-ggplot2 object
-
 ggplot2 object
 
 data.frame
@@ -81,10 +71,6 @@ Data correlations evaluation module
 
 Wrapper to create plot based on provided type
 
-Beautiful box plot(s)
-
-Create nice box-plots
-
 Nice horizontal stacked bars (Grotta bars)
 
 Plot nice ridge plot
@@ -99,11 +85,6 @@ Beatiful violin plot
 }
 \examples{
 create_plot(mtcars, "plot_violin", "mpg", "cyl")
-mtcars |> plot_box(x = "mpg", y = "cyl", z = "gear")
-mtcars |>
-  default_parsing() |>
-  plot_box(x = "mpg", y = "cyl", z = "gear")
-mtcars |> plot_box_single("mpg","cyl")
 mtcars |> plot_hbars(x = "carb", y = "cyl")
 mtcars |> plot_hbars(x = "carb", y = NULL)
 mtcars |>

man/data_description.Rd

@@ -1,23 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/helpers.R
-\name{data_description}
-\alias{data_description}
-\title{Ultra short data dascription}
-\usage{
-data_description(data)
-}
-\arguments{
-\item{data}{}
-}
-\value{
-character vector
-}
-\description{
-Ultra short data dascription
-}
-\examples{
-data.frame(
-  sample(1:8, 20, TRUE),
-  sample(c(1:8, NA), 20, TRUE)
-) |> data_description()
-}

man/data_type.Rd

@@ -1,27 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/regression_model.R
-\name{data_type}
-\alias{data_type}
-\title{Data type assessment}
-\usage{
-data_type(data)
-}
-\arguments{
-\item{data}{data}
-}
-\value{
-outcome type
-}
-\description{
-Data type assessment
-}
-\examples{
-mtcars |>
-  default_parsing() |>
-  lapply(data_type)
-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()
-}

man/limit_log.Rd

@@ -20,8 +20,8 @@ numeric vector
 Easily round log scale limits for nice plots
 }
 \examples{
-limit_log(-.1, floor)
+limit_log(-.1,floor)
-limit_log(.1, ceiling)
+limit_log(.1,ceiling)
-limit_log(-2.1, ceiling)
+limit_log(-2.1,ceiling)
-limit_log(2.1, ceiling)
+limit_log(2.1,ceiling)
 }

man/missing_fraction.Rd

@@ -16,5 +16,5 @@ numeric vector
 Get missingsness fraction
 }
 \examples{
-c(NA, 1:10, rep(NA, 3)) |> missing_fraction()
+c(NA,1:10,rep(NA,3)) |> missing_fraction()
 }

man/outcome_type.Rd

@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/regression_model.R
+\name{outcome_type}
+\alias{outcome_type}
+\title{Outcome data type assessment}
+\usage{
+outcome_type(data)
+}
+\arguments{
+\item{data}{data}
+}
+\value{
+outcome type
+}
+\description{
+Outcome data type assessment
+}
+\examples{
+mtcars |>
+  default_parsing() |>
+  lapply(outcome_type)
+}

man/regression_model.Rd

@@ -111,7 +111,7 @@ m <- mtcars |>
     args.list = NULL,
     vars = c("mpg", "cyl")
   )
-broom::tidy(m)
+  broom::tidy(m)
 \dontrun{
 gtsummary::trial |>
   regression_model_uv(outcome.str = "age")
@@ -126,7 +126,7 @@ m <- gtsummary::trial |> regression_model_uv(
   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()
 }
 \dontrun{
 gtsummary::trial |>
@@ -154,15 +154,12 @@ broom::tidy(ls$model)
 broom::tidy(m)
 }
 \dontrun{
-gtsummary::trial |>
+gtsummary::trial |> regression_model_uv(
-  regression_model_uv(
   outcome.str = "trt",
   fun = "stats::glm",
   args.list = list(family = stats::binomial(link = "logit"))
-  ) |>
+) |> lapply(broom::tidy) |> dplyr::bind_rows()
-  lapply(broom::tidy) |>
-  dplyr::bind_rows()
 ms <- regression_model_uv_list(data = default_parsing(mtcars), outcome.str = "mpg", fun.descr = "Linear regression model")
-lapply(ms$model, broom::tidy) |> dplyr::bind_rows()
+lapply(ms$model,broom::tidy) |> dplyr::bind_rows()
 }
 }

man/remove_empty_cols.Rd

@@ -18,5 +18,5 @@ data frame
 Removes columns with completenes below cutoff
 }
 \examples{
-data.frame(a = 1:10, b = NA, c = c(2, NA)) |> remove_empty_cols(cutoff = .5)
+data.frame(a=1:10,b=NA, c=c(2,NA)) |> remove_empty_cols(cutoff=.5)
 }

man/remove_na_attr.Rd

@@ -16,8 +16,6 @@ data.frame
 Remove NA labels
 }
 \examples{
-ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label"))
+ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x,label=NA,attr = "label"))
-ds |>
+ds |> remove_na_attr() |> str()
-  remove_na_attr() |>
-  str()
 }

man/subset_types.Rd

@@ -4,7 +4,7 @@
 \alias{subset_types}
 \title{Easily subset by data type function}
 \usage{
-subset_types(data, types, type.fun = data_type)
+subset_types(data, types, type.fun = outcome_type)
 }
 \arguments{
 \item{data}{data}
@@ -21,6 +21,6 @@ Easily subset by data type function
 }
 \examples{
 default_parsing(mtcars) |> subset_types("ordinal")
-default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal" ,"categorical"))
+default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal"))
 #' default_parsing(mtcars) |> subset_types("factor",class)
 }