diff --git a/R/helpers.R b/R/helpers.R
index def92fa9..c038c5c1 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -154,7 +154,8 @@ dummy_Imports <- function() {
     parameters::ci(),
     DT::addRow(),
     bslib::accordion(),
-    NHANES::NHANES()
+    NHANES::NHANES(),
+    stRoke::add_padding()
   )
   # https://github.com/hadley/r-pkgs/issues/828
 }
@@ -668,3 +669,84 @@ is_identical_to_previous <- function(data, no.name = TRUE) {
 simple_snake <- function(data){
   gsub("[\\s+]","_",gsub("[^\\w\\s:-]", "", tolower(data), perl=TRUE), perl=TRUE)
 }
+
+#' Data type assessment.
+#'
+#' @description
+#' These are more overall than the native typeof. This is used to assess a more
+#' meaningful "clinical" data type.
+#'
+#' @param data vector or data.frame. if data frame, each column is evaluated.
+#'
+#' @returns outcome type
+#' @export
+#'
+#' @examples
+#' mtcars |>
+#'   default_parsing() |>
+#'   lapply(data_type)
+#' mtcars |>
+#'   default_parsing() |>
+#'   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()
+#' as.Date(1:20) |> data_type()
+data_type <- function(data) {
+  if (is.data.frame(data)) {
+    sapply(data, data_type)
+  } else {
+    cl_d <- class(data)
+    l_unique <- length(unique(na.omit(data)))
+    if (all(is.na(data))) {
+      out <- "empty"
+    } else if (l_unique < 2) {
+      out <- "monotone"
+    } else if (any(c("factor", "logical") %in% cl_d) | l_unique == 2) {
+      if (identical("logical", cl_d) | l_unique == 2) {
+        out <- "dichotomous"
+      } else {
+        # if (is.ordered(data)) {
+        #   out <- "ordinal"
+        # } else {
+        out <- "categorical"
+        # }
+      }
+    } else if (identical(cl_d, "character")) {
+      out <- "text"
+    } else if (any(c("hms", "Date", "POSIXct", "POSIXt") %in% cl_d)) {
+      out <- "datetime"
+    } else if (l_unique > 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"
+    }
+
+    out
+  }
+}
+
+#' Recognised data types from data_type
+#'
+#' @returns vector
+#' @export
+#'
+#' @examples
+#' data_types()
+data_types <- function() {
+  list(
+    "empty" = list(descr="Variable of all NAs",classes="Any class"),
+    "monotone" = list(descr="Variable with only one unique value",classes="Any class"),
+    "dichotomous" = list(descr="Variable with only two unique values",classes="Any class"),
+    "categorical"= list(descr="Factor variable",classes="factor (ordered or unordered)"),
+    "text"= list(descr="Character variable",classes="character"),
+    "datetime"= list(descr="Variable of time, date or datetime values",classes="hms, Date, POSIXct and POSIXt"),
+    "continuous"= list(descr="Numeric variable",classes="numeric, integer or double"),
+    "unknown"= list(descr="Anything not falling within the previous",classes="Any other class")
+  )
+}
diff --git a/R/plot_euler.R b/R/plot_euler.R
index e1e48aa8..17345020 100644
--- a/R/plot_euler.R
+++ b/R/plot_euler.R
@@ -1,7 +1,7 @@
 #' Area proportional venn diagrams
 #'
 #' @description
-#' THis is slightly modified from https://gist.github.com/danlooo/d23d8bcf8856c7dd8e86266097404ded
+#' This is slightly modified from https://gist.github.com/danlooo/d23d8bcf8856c7dd8e86266097404ded
 #'
 #' This functions uses eulerr::euler to plot area proportional venn diagramms
 #' but plots it using ggplot2
@@ -11,18 +11,27 @@
 #' @param show_quantities whether to show number of intersecting elements
 #' @param show_labels whether to show set names
 #' @param ... further arguments passed to eulerr::euler
+#'
+#' @include data_plots.R
 ggeulerr <- function(
     combinations,
     show_quantities = TRUE,
     show_labels = TRUE,
     ...) {
-  # browser()
+
+  ## Extracting labels
+  labs <- sapply(names(combinations),\(.x){
+    # browser()
+    get_label(combinations,.x)
+  })
+
   data <-
-    eulerr::euler(combinations = combinations, ...) |>
+    ## Set labels as variable names for nicer plotting
+    setNames(as.data.frame(combinations),labs) |>
+    eulerr::euler(...) |>
     plot(quantities = show_quantities) |>
     purrr::pluck("data")
 
-
   tibble::as_tibble(data$ellipses, rownames = "Variables") |>
     ggplot2::ggplot() +
     ggforce::geom_ellipse(
@@ -38,7 +47,8 @@ ggeulerr <- function(
           dplyr::mutate(
             label = labels |> purrr::map2(quantities, ~ {
               if (!is.na(.x) && !is.na(.y) && show_labels) {
-                paste0(.x, "\n", sprintf(.y, fmt = "%.2g"))
+                paste0(.x, "\n", sprintf(.y, fmt = "%.4g"))
+                # glue::glue("{.x}\n{round(.y,0)}")
               } else if (!is.na(.x) && show_labels) {
                 .x
               } else if (!is.na(.y)) {
@@ -77,6 +87,21 @@ ggeulerr <- function(
 #' ) |> plot_euler("A", c("B", "C"), "D", seed = 4)
 #' mtcars |> plot_euler("vs", "am", seed = 1)
 #' mtcars |> plot_euler("vs", "am", "cyl", seed = 1)
+#' stRoke::trial |>
+#'   dplyr::mutate(
+#'     mfi_cut = cut(mfi_6, c(0, 12, max(mfi_6, na.rm = TRUE))),
+#'     mdi_cut = cut(mdi_6, c(0, 20, max(mdi_6, na.rm = TRUE)))
+#'   ) |>
+#'   purrr::map2(
+#'     c(sapply(stRoke::trial, \(.x)REDCapCAST::get_attr(.x, attr = "label")), "Fatigue", "Depression"),
+#'     \(.x, .y){
+#'       REDCapCAST::set_attr(.x, .y, "label")
+#'     }
+#'   ) |>
+#'   dplyr::bind_cols() |>
+#'   plot_euler("mfi_cut", "mdi_cut")
+#' stRoke::trial |>
+#'   plot_euler(pri="male", sec=c("hypertension"))
 plot_euler <- function(data, pri, sec, ter = NULL, seed = 2103) {
   set.seed(seed = seed)
   if (!is.null(ter)) {
@@ -84,16 +109,13 @@ plot_euler <- function(data, pri, sec, ter = NULL, seed = 2103) {
   } else {
     ds <- list(data)
   }
-
   out <- lapply(ds, \(.x){
     .x[c(pri, sec)] |>
-      as.data.frame() |>
       na.omit() |>
       plot_euler_single()
   })
-# browser()
-  wrap_plot_list(out,title=glue::glue("Grouped by {get_label(data,ter)}"))
-  # patchwork::wrap_plots(out)
+
+  wrap_plot_list(out, title = glue::glue(i18n$t("Grouped by {get_label(data,ter)}")))
 }
 
 #' Easily plot single euler diagrams
diff --git a/R/regression-module.R b/R/regression-module.R
index d8ccc996..52898ce5 100644
--- a/R/regression-module.R
+++ b/R/regression-module.R
@@ -72,13 +72,15 @@ regression_ui <- function(id, ...) {
               shiny::radioButtons(
                 inputId = ns("all"),
                 label = i18n$t("Specify covariables"),
-                inline = TRUE, selected = 2,
+                inline = TRUE,
+                selected = 2,
                 choiceNames = c(
                   "Yes",
                   "No"
                 ),
                 choiceValues = c(1, 2)
               ),
+              # shiny::uiOutput(outputId = ns("all")),
               shiny::conditionalPanel(
                 condition = "input.all==1",
                 shiny::uiOutput(outputId = ns("regression_vars")),
@@ -131,7 +133,7 @@ regression_ui <- function(id, ...) {
       )
     ),
     bslib::nav_panel(
-      title = "Coefficient plot",
+      title = i18n$t("Coefficient plot"),
       bslib::layout_sidebar(
         sidebar = bslib::sidebar(
           bslib::accordion(
@@ -243,11 +245,6 @@ regression_server <- function(id,
         }
       })
 
-      shiny::observe({
-        bslib::accordion_panel_update(id = "acc_reg", target = "acc_pan_reg", title = i18n$t("Regression"))
-        bslib::accordion_panel_update(id = "acc_coef_plot", target = "acc_pan_coef_plot", title = i18n$t("Coefficients plot"))
-        bslib::accordion_panel_update(id = "acc_checks", target = "acc_pan_checks", title = i18n$t("Checks"))
-      })
 
       output$data_info <- shiny::renderUI({
         shiny::req(regression_vars())
@@ -255,6 +252,31 @@ regression_server <- function(id,
         data_description(data_r()[regression_vars()])
       })
 
+      ## Update on laguage change
+
+      shiny::observe({
+        bslib::accordion_panel_update(id = "acc_reg", target = "acc_pan_reg", title = i18n$t("Regression"))
+        bslib::accordion_panel_update(id = "acc_coef_plot", target = "acc_pan_coef_plot", title = i18n$t("Coefficients plot"))
+        bslib::accordion_panel_update(id = "acc_checks", target = "acc_pan_checks", title = i18n$t("Checks"))
+      })
+
+      # shiny::observe({
+      #   shiny::updateRadioButtons(
+      #     session = session,
+      #     inputId = "all",
+      #     label = i18n$t("Specify covariables"),
+      #     # inline = TRUE,
+      #     # selected = 2,
+      #     choiceNames = c(
+      #       i18n$t("Yes"),
+      #       i18n$t("No")
+      #     ),
+      #     choiceValues = c(1, 2)
+      #   )
+      # })
+
+
+
       ##############################################################################
       #########
       #########  Input fields
@@ -278,7 +300,7 @@ regression_server <- function(id,
         columnSelectInput(
           inputId = ns("outcome_var"),
           selected = NULL,
-          label = "Select outcome variable",
+          label = i18n$t("Select outcome variable"),
           data = data_r(),
           multiple = FALSE
         )
@@ -288,7 +310,7 @@ regression_server <- function(id,
         shiny::req(input$outcome_var)
         shiny::selectizeInput(
           inputId = ns("regression_type"),
-          label = "Choose regression analysis",
+          label = i18n$t("Choose regression analysis"),
           ## The below ifelse statement handles the case of loading a new dataset
           choices = possible_functions(
             data = dplyr::select(
@@ -307,7 +329,7 @@ regression_server <- function(id,
         shiny::selectizeInput(
           inputId = ns("factor_vars"),
           selected = colnames(data_r())[sapply(data_r(), is.factor)],
-          label = "Covariables to format as categorical",
+          label = i18n$t("Covariables to format as categorical"),
           choices = colnames(data_r()),
           multiple = TRUE
         )
@@ -327,7 +349,7 @@ regression_server <- function(id,
         columnSelectInput(
           inputId = ns("strat_var"),
           selected = "none",
-          label = "Select variable to stratify baseline",
+          label = i18n$t("Select variable to stratify baseline"),
           data = data_r(),
           col_subset = c(
             "none",
@@ -342,7 +364,7 @@ regression_server <- function(id,
         shiny::selectInput(
           inputId = ns("plot_model"),
           selected = 1,
-          label = "Select models to plot",
+          label = i18n$t("Select models to plot"),
           choices = names(rv$list$regression$tables),
           multiple = TRUE
         )
@@ -392,7 +414,7 @@ regression_server <- function(id,
               rv$list$regression$models <- model_lists
             },
             error = function(err) {
-              showNotification(paste0("Creating regression models failed with the following error: ", err), type = "err")
+              showNotification(paste(i18n$t("Creating regression models failed with the following error:"), err), type = "err")
             }
           )
         }
@@ -457,7 +479,7 @@ regression_server <- function(id,
               showNotification(paste0(warn), type = "warning")
             },
             error = function(err) {
-              showNotification(paste0("Creating a regression table failed with the following error: ", err), type = "err")
+              showNotification(paste(i18n$t("Creating a regression table failed with the following error:"), err), type = "err")
             }
           )
         }
@@ -558,7 +580,7 @@ regression_server <- function(id,
       output$download_plot <- shiny::downloadHandler(
         filename = paste0("regression_plot.", input$plot_type),
         content = function(file) {
-          shiny::withProgress(message = "Saving the plot. Hold on for a moment..", {
+          shiny::withProgress(message = i18n$t("Saving the plot. Hold on for a moment.."), {
             ggplot2::ggsave(
               filename = file,
               plot = rv$plot,
@@ -595,7 +617,7 @@ regression_server <- function(id,
             #   showNotification(paste0(warn), type = "warning")
             # },
             error = function(err) {
-              showNotification(paste0("Running model assumptions checks failed with the following error: ", err), type = "err")
+              showNotification(paste(i18n$t("Running model assumptions checks failed with the following error:"), err), type = "err")
             }
           )
         }
@@ -616,7 +638,7 @@ regression_server <- function(id,
         vectorSelectInput(
           inputId = ns("plot_checks"),
           selected = 1,
-          label = "Select checks to plot",
+          label = i18n$t("Select checks to plot"),
           choices = names,
           multiple = TRUE
         )
@@ -631,7 +653,7 @@ regression_server <- function(id,
           if (!is.null(rv$list$regression$tables)) {
             p <- rv$check_plot() +
               # patchwork::wrap_plots() +
-              patchwork::plot_annotation(title = "Multivariable regression model checks")
+              patchwork::plot_annotation(title = i18n$t("Multivariable regression model checks"))
 
 
             layout <- sapply(seq_len(length(p)), \(.x){
diff --git a/R/regression_model.R b/R/regression_model.R
index df79cc16..f509f7d7 100644
--- a/R/regression_model.R
+++ b/R/regression_model.R
@@ -242,87 +242,6 @@ regression_model_uv <- function(data,
 
 ### HELPERS
 
-#' Data type assessment.
-#'
-#' @description
-#' These are more overall than the native typeof. This is used to assess a more
-#' meaningful "clinical" data type.
-#'
-#' @param data vector or data.frame. if data frame, each column is evaluated.
-#'
-#' @returns outcome type
-#' @export
-#'
-#' @examples
-#' mtcars |>
-#'   default_parsing() |>
-#'   lapply(data_type)
-#' mtcars |>
-#'   default_parsing() |>
-#'   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()
-#' as.Date(1:20) |> data_type()
-data_type <- function(data) {
-  if (is.data.frame(data)) {
-    sapply(data, data_type)
-  } else {
-    cl_d <- class(data)
-    l_unique <- length(unique(na.omit(data)))
-    if (all(is.na(data))) {
-      out <- "empty"
-    } else if (l_unique < 2) {
-      out <- "monotone"
-    } else if (any(c("factor", "logical") %in% cl_d) | l_unique == 2) {
-      if (identical("logical", cl_d) | l_unique == 2) {
-        out <- "dichotomous"
-      } else {
-        # if (is.ordered(data)) {
-        #   out <- "ordinal"
-        # } else {
-          out <- "categorical"
-        # }
-      }
-    } else if (identical(cl_d, "character")) {
-      out <- "text"
-    } else if (any(c("hms", "Date", "POSIXct", "POSIXt") %in% cl_d)) {
-      out <- "datetime"
-    } else if (l_unique > 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"
-    }
-
-    out
-  }
-}
-
-#' Recognised data types from data_type
-#'
-#' @returns vector
-#' @export
-#'
-#' @examples
-#' data_types()
-data_types <- function() {
-  list(
-    "empty" = list(descr="Variable of all NAs",classes="Any class"),
-    "monotone" = list(descr="Variable with only one unique value",classes="Any class"),
-    "dichotomous" = list(descr="Variable with only two unique values",classes="Any class"),
-    "categorical"= list(descr="Factor variable",classes="factor (ordered or unordered)"),
-    "text"= list(descr="Character variable",classes="character"),
-    "datetime"= list(descr="Variable of time, date or datetime values",classes="hms, Date, POSIXct and POSIXt"),
-    "continuous"= list(descr="Numeric variable",classes="numeric, integer or double"),
-    "unknown"= list(descr="Anything not falling within the previous",classes="Any other class")
-    )
-}
-
 
 #' Implemented functions
 #'