new release

R/app_version.R

@@ -1 +1 @@
-app_version <- function()'25.10.3'
+app_version <- function()'25.10.4'

R/hosted_version.R

@@ -1 +1 @@
-hosted_version <- function()'v25.10.3-251021'
+hosted_version <- function()'v25.10.4-251027'

R/plot_sankey.R

@@ -126,7 +126,7 @@ plot_sankey <- function(data, pri, sec, ter = NULL, color.group = "pri", colors
 plot_sankey_single <- function(data, pri, sec, color.group = c("pri", "sec"), colors = NULL,missing.level="Missing", ...) {
   color.group <- match.arg(color.group)
 
-  browser()
+  # browser()
   data_orig <- data
   data[c(pri, sec)] <- data[c(pri, sec)] |>
     dplyr::mutate(

R/redcap_read_shiny_module.R

@@ -26,6 +26,8 @@ m_redcap_readUI <- function(id, title = TRUE, url = NULL) {
       width = "100%"
     ),
     shiny::helpText(i18n$t("Format should be either 'https://redcap.your.institution/' or 'https://your.institution/redcap/'")),
+    shiny::br(),
+    shiny::br(),
     shiny::passwordInput(
       inputId = ns("api"),
       label = i18n$t("API token"),

R/ui_elements.R

@@ -254,7 +254,7 @@ ui_elements <- function(selection) {
             width = 9,
             shiny::tags$p(
               i18n$t("Below, are several options for simple data manipulation like update variables by renaming, creating new labels (for nicer tables in the report) and changing variable classes (numeric, factor/categorical etc.)."),
-              i18n$t("There are more advanced options to modify factor/categorical variables as well as create new factor from a continous variable or new variables with R code. At the bottom you can restore the original data."),
+              i18n$t("There are more advanced options to modify factor/categorical variables as well as create new factor from an existing variable or new variables with R code. At the bottom you can restore the original data."),
               i18n$t("Please note that data modifications are applied before any filtering.")
             )
           )