diff --git a/DESCRIPTION b/DESCRIPTION
index 8c9250c6..310b03c7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: FreesearchR
 Title: Browser Based Data Analysis
-Version: 25.4.3
+Version: 25.4.2
 Authors@R: 
     person("Andreas Gammelgaard", "Damsbo", , "agdamsbo@clin.au.dk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-7559-1154"))
diff --git a/NEWS.md b/NEWS.md
index 3099454b..8c6b6bcd 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,8 +1,3 @@
-# FreesearchR 25.4.3
-
-- *NEW*: Added a variables type filter to easily exclude unwanted types. This also includes having data type rather than data class in the summary table. Will evaluate. Types are a simpler, more practical version of the *R* data class to easy interpretation.
-
-
 # FreesearchR 25.4.2
 
 Polished and simplified data import module including a much improved REDCap import module.
diff --git a/R/app_version.R b/R/app_version.R
index 6587a1ca..ba443ff1 100644
--- a/R/app_version.R
+++ b/R/app_version.R
@@ -1 +1 @@
-app_version <- function()'Version: 25.4.3.250414_1045'
+app_version <- function()'Version: 25.4.1.250411_1313'
diff --git a/R/data-summary.R b/R/data-summary.R
index b3aff31d..b7216b96 100644
--- a/R/data-summary.R
+++ b/R/data-summary.R
@@ -156,7 +156,7 @@ overview_vars <- function(data) {
 
   dplyr::tibble(
     class = get_classes(data),
-    type = data_type(data),
+    type = get_classes(data),
     name = names(data),
     n_missing = unname(colSums(is.na(data))),
     p_complete = 1 - n_missing / nrow(data),
diff --git a/R/data_plots.R b/R/data_plots.R
index a0ab2f00..662e5a79 100644
--- a/R/data_plots.R
+++ b/R/data_plots.R
@@ -23,7 +23,6 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
           icon = bsicons::bs_icon("graph-up"),
           shiny::uiOutput(outputId = ns("primary")),
           shiny::helpText('Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.'),
-          shiny::tags$br(),
           shiny::uiOutput(outputId = ns("type")),
           shiny::uiOutput(outputId = ns("secondary")),
           shiny::uiOutput(outputId = ns("tertiary")),
@@ -460,7 +459,7 @@ supported_plots <- function() {
       fun = "plot_violin",
       descr = "Violin plot",
       note = "A modern alternative to the classic boxplot to visualise data distribution",
-      primary.type = c("datatime","continuous", "dichotomous", "ordinal" ,"categorical"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
       secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       secondary.extra = "none",
@@ -488,8 +487,8 @@ supported_plots <- function() {
       fun = "plot_scatter",
       descr = "Scatter plot",
       note = "A classic way of showing the association between to variables",
-      primary.type = c("datatime","continuous"),
-      secondary.type = c("datatime","continuous", "ordinal" ,"categorical"),
+      primary.type = "continuous",
+      secondary.type = c("continuous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = NULL
@@ -498,7 +497,7 @@ supported_plots <- function() {
       fun = "plot_box",
       descr = "Box plot",
       note = "A classic way to plot data distribution by groups",
-      primary.type = c("datatime","continuous", "dichotomous", "ordinal" ,"categorical"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
       secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
diff --git a/R/helpers.R b/R/helpers.R
index 3a5cf372..de93d52c 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -340,7 +340,7 @@ missing_fraction <- function(data) {
 #'   sample(1:8, 20, TRUE),
 #'   sample(c(1:8, NA), 20, TRUE)
 #' ) |> data_description()
-data_description <- function(data, data_text = "Data") {
+data_description <- function(data) {
   data <- if (shiny::is.reactive(data)) data() else data
 
   n <- nrow(data)
@@ -349,8 +349,7 @@ data_description <- function(data, data_text = "Data") {
   p_complete <- n_complete / n
 
   sprintf(
-    i18n("%s has %s observations and %s variables, with %s (%s%%) complete cases."),
-    data_text,
+    i18n("Data has %s observations and %s variables, with %s (%s%%) complete cases."),
     n,
     n_var,
     n_complete,
@@ -358,30 +357,6 @@ data_description <- function(data, data_text = "Data") {
   )
 }
 
-
-#' Filter function to filter data set by variable type
-#'
-#' @param data data frame
-#' @param type vector of data types (recognised: data_types)
-#'
-#' @returns data.frame
-#' @export
-#'
-#' @examples
-#' default_parsing(mtcars) |> data_type_filter(type=c("categorical","continuous")) |> attributes()
-#' \dontrun{
-#' default_parsing(mtcars) |> data_type_filter(type=c("test","categorical","continuous"))
-#' }
-data_type_filter <- function(data,type){
-  ## Please ensure to only provide recognised data types
-  assertthat::assert_that(all(type %in% data_types()))
-
-  out <- data[data_type(data) %in% type]
-  code <- rlang::call2("data_type_filter",!!!list(type=type),.ns = "FreesearchR")
-  attr(out, "code") <- code
-  out
-}
-
 #' Drop-in replacement for the base::sort_by with option to remove NAs
 #'
 #' @param x x
diff --git a/R/regression_model.R b/R/regression_model.R
index 4ad0d599..44fe5869 100644
--- a/R/regression_model.R
+++ b/R/regression_model.R
@@ -242,13 +242,9 @@ regression_model_uv <- function(data,
 
 ### HELPERS
 
-#' Data type assessment.
+#' 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.
+#' @param data data
 #'
 #' @returns outcome type
 #' @export
@@ -257,60 +253,39 @@ regression_model_uv <- function(data,
 #' 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)
-    if (all(is.na(data))) {
-      out <- "empty"
-    } else if (length(unique(data)) < 2) {
-      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 (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 (!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"
+  cl_d <- class(data)
+  if (all(is.na(data))) {
+    out <- "empty"
+  } else if (length(unique(data)) < 2) {
+    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 {
-      out <- "unknown"
+      if (is.ordered(data)) {
+        out <- "ordinal"
+      } else {
+        out <- "categorical"
+      }
     }
-
-    out
+  } 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"
   }
-}
 
-#' Recognised data types from data_type
-#'
-#' @returns vector
-#' @export
-#'
-#' @examples
-#' data_types()
-data_types <- function() {
-  c("dichotomous", "ordinal", "categorical", "datatime", "continuous", "text", "empty", "monotone", "unknown")
+  out
 }
 
 
@@ -550,17 +525,17 @@ regression_model_list <- function(data,
   parameters_code <- Filter(
     length,
     modifyList(parameters, list(
-      data = as.symbol("df"),
+      data=as.symbol("df"),
       formula.str = as.character(glue::glue(formula.str.c)),
       outcome.str = NULL
       # args.list = NULL,
-    ))
-  )
+    )
+  ))
 
   ## The easiest solution was to simple paste as a string
   ## The rlang::call2 or rlang::expr functions would probably work as well
   # code <- glue::glue("FreesearchR::regression_model({parameters_print}, args.list=list({list2str(args.list.c)}))", .null = "NULL")
-  code <- rlang::call2("regression_model", !!!parameters_code, .ns = "FreesearchR")
+  code <- rlang::call2("regression_model",!!!parameters_code,.ns = "FreesearchR")
 
   list(
     options = options,
@@ -671,6 +646,7 @@ regression_model_uv_list <- function(data,
 
   model <- vars |>
     lapply(\(.var){
+
       parameters <-
         list(
           fun = fun.c,
@@ -687,7 +663,7 @@ regression_model_uv_list <- function(data,
       ## This is the very long version
       ## Handles deeply nested glue string
       # code <- glue::glue("FreesearchR::regression_model(data=df,{list2str(modifyList(parameters,list(data=NULL,args.list=list2str(args.list.c))))})")
-      code <- rlang::call2("regression_model", !!!modifyList(parameters, list(data = as.symbol("df"), args.list = args.list.c)), .ns = "FreesearchR")
+      code <- rlang::call2("regression_model",!!!modifyList(parameters,list(data=as.symbol("df"),args.list=args.list.c)),.ns = "FreesearchR")
       REDCapCAST::set_attr(out, code, "code")
     })
 
diff --git a/inst/apps/FreesearchR/app.R b/inst/apps/FreesearchR/app.R
index 8f9beb55..c882833a 100644
--- a/inst/apps/FreesearchR/app.R
+++ b/inst/apps/FreesearchR/app.R
@@ -10,7 +10,7 @@
 #### Current file: /Users/au301842/FreesearchR/R//app_version.R 
 ########
 
-app_version <- function()'Version: 25.4.3.250414_1045'
+app_version <- function()'Version: 25.4.1.250411_1313'
 
 
 ########
@@ -1141,7 +1141,6 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
           icon = bsicons::bs_icon("graph-up"),
           shiny::uiOutput(outputId = ns("primary")),
           shiny::helpText('Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.'),
-          shiny::tags$br(),
           shiny::uiOutput(outputId = ns("type")),
           shiny::uiOutput(outputId = ns("secondary")),
           shiny::uiOutput(outputId = ns("tertiary")),
@@ -1578,7 +1577,7 @@ supported_plots <- function() {
       fun = "plot_violin",
       descr = "Violin plot",
       note = "A modern alternative to the classic boxplot to visualise data distribution",
-      primary.type = c("datatime","continuous", "dichotomous", "ordinal" ,"categorical"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
       secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       secondary.extra = "none",
@@ -1606,8 +1605,8 @@ supported_plots <- function() {
       fun = "plot_scatter",
       descr = "Scatter plot",
       note = "A classic way of showing the association between to variables",
-      primary.type = c("datatime","continuous"),
-      secondary.type = c("datatime","continuous", "ordinal" ,"categorical"),
+      primary.type = "continuous",
+      secondary.type = c("continuous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.extra = NULL
@@ -1616,7 +1615,7 @@ supported_plots <- function() {
       fun = "plot_box",
       descr = "Box plot",
       note = "A classic way to plot data distribution by groups",
-      primary.type = c("datatime","continuous", "dichotomous", "ordinal" ,"categorical"),
+      primary.type = c("continuous", "dichotomous", "ordinal" ,"categorical"),
       secondary.type = c("dichotomous", "ordinal" ,"categorical"),
       secondary.multi = FALSE,
       tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
@@ -2199,7 +2198,7 @@ overview_vars <- function(data) {
 
   dplyr::tibble(
     class = get_classes(data),
-    type = data_type(data),
+    type = get_classes(data),
     name = names(data),
     n_missing = unname(colSums(is.na(data))),
     p_complete = 1 - n_missing / nrow(data),
@@ -2699,7 +2698,7 @@ missing_fraction <- function(data) {
 #'   sample(1:8, 20, TRUE),
 #'   sample(c(1:8, NA), 20, TRUE)
 #' ) |> data_description()
-data_description <- function(data, data_text = "Data") {
+data_description <- function(data) {
   data <- if (shiny::is.reactive(data)) data() else data
 
   n <- nrow(data)
@@ -2708,8 +2707,7 @@ data_description <- function(data, data_text = "Data") {
   p_complete <- n_complete / n
 
   sprintf(
-    i18n("%s has %s observations and %s variables, with %s (%s%%) complete cases."),
-    data_text,
+    i18n("Data has %s observations and %s variables, with %s (%s%%) complete cases."),
     n,
     n_var,
     n_complete,
@@ -2717,30 +2715,6 @@ data_description <- function(data, data_text = "Data") {
   )
 }
 
-
-#' Filter function to filter data set by variable type
-#'
-#' @param data data frame
-#' @param type vector of data types (recognised: data_types)
-#'
-#' @returns data.frame
-#' @export
-#'
-#' @examples
-#' default_parsing(mtcars) |> data_type_filter(type=c("categorical","continuous")) |> attributes()
-#' \dontrun{
-#' default_parsing(mtcars) |> data_type_filter(type=c("test","categorical","continuous"))
-#' }
-data_type_filter <- function(data,type){
-  ## Please ensure to only provide recognised data types
-  assertthat::assert_that(all(type %in% data_types()))
-
-  out <- data[data_type(data) %in% type]
-  code <- rlang::call2("data_type_filter",!!!list(type=type),.ns = "FreesearchR")
-  attr(out, "code") <- code
-  out
-}
-
 #' Drop-in replacement for the base::sort_by with option to remove NAs
 #'
 #' @param x x
@@ -5222,13 +5196,9 @@ regression_model_uv <- function(data,
 
 ### HELPERS
 
-#' Data type assessment.
+#' 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.
+#' @param data data
 #'
 #' @returns outcome type
 #' @export
@@ -5237,60 +5207,39 @@ regression_model_uv <- function(data,
 #' 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)
-    if (all(is.na(data))) {
-      out <- "empty"
-    } else if (length(unique(data)) < 2) {
-      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 (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 (!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"
+  cl_d <- class(data)
+  if (all(is.na(data))) {
+    out <- "empty"
+  } else if (length(unique(data)) < 2) {
+    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 {
-      out <- "unknown"
+      if (is.ordered(data)) {
+        out <- "ordinal"
+      } else {
+        out <- "categorical"
+      }
     }
-
-    out
+  } 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"
   }
-}
 
-#' Recognised data types from data_type
-#'
-#' @returns vector
-#' @export
-#'
-#' @examples
-#' data_types()
-data_types <- function() {
-  c("dichotomous", "ordinal", "categorical", "datatime", "continuous", "text", "empty", "monotone", "unknown")
+  out
 }
 
 
@@ -5530,17 +5479,17 @@ regression_model_list <- function(data,
   parameters_code <- Filter(
     length,
     modifyList(parameters, list(
-      data = as.symbol("df"),
+      data=as.symbol("df"),
       formula.str = as.character(glue::glue(formula.str.c)),
       outcome.str = NULL
       # args.list = NULL,
-    ))
-  )
+    )
+  ))
 
   ## The easiest solution was to simple paste as a string
   ## The rlang::call2 or rlang::expr functions would probably work as well
   # code <- glue::glue("FreesearchR::regression_model({parameters_print}, args.list=list({list2str(args.list.c)}))", .null = "NULL")
-  code <- rlang::call2("regression_model", !!!parameters_code, .ns = "FreesearchR")
+  code <- rlang::call2("regression_model",!!!parameters_code,.ns = "FreesearchR")
 
   list(
     options = options,
@@ -5651,6 +5600,7 @@ regression_model_uv_list <- function(data,
 
   model <- vars |>
     lapply(\(.var){
+
       parameters <-
         list(
           fun = fun.c,
@@ -5667,7 +5617,7 @@ regression_model_uv_list <- function(data,
       ## This is the very long version
       ## Handles deeply nested glue string
       # code <- glue::glue("FreesearchR::regression_model(data=df,{list2str(modifyList(parameters,list(data=NULL,args.list=list2str(args.list.c))))})")
-      code <- rlang::call2("regression_model", !!!modifyList(parameters, list(data = as.symbol("df"), args.list = args.list.c)), .ns = "FreesearchR")
+      code <- rlang::call2("regression_model",!!!modifyList(parameters,list(data=as.symbol("df"),args.list=args.list.c)),.ns = "FreesearchR")
       REDCapCAST::set_attr(out, code, "code")
     })
 
@@ -8290,13 +8240,7 @@ ui_elements <- list(
               ),
               shiny::tags$br(),
               shiny::tags$br(),
-              shiny::uiOutput(outputId = "column_filter"),
-              shiny::helpText("Variable data type filtering."),
-              shiny::tags$br(),
-              shiny::tags$br(),
               IDEAFilter::IDEAFilter_ui("data_filter"),
-              shiny::helpText("Observations level filtering."),
-              shiny::tags$br(),
               shiny::tags$br()
             )
           ),
@@ -8314,8 +8258,7 @@ ui_elements <- list(
               width = 9,
               shiny::tags$p(
                 shiny::markdown("Below, are several options for simple data manipulation like update variables by renaming, creating new labels (for nicer tables in the report) and changing variable classes (numeric, factor/categorical etc.)."),
-                shiny::markdown("There are also more advanced options to modify factor/categorical variables as well as create new factor from a continous variable or new variables with *R* code. At the bottom you can restore the original data."),
-                shiny::markdown("Please note that data modifications are applied before any data or variable filtering is applied.")
+                shiny::tags$p("There are also 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.")
               )
             )
           ),
@@ -8336,7 +8279,6 @@ ui_elements <- list(
               ),
               shiny::tags$br(),
               shiny::helpText("Reorder the levels of factor/categorical variables."),
-              shiny::tags$br()
             ),
             shiny::column(
               width = 4,
@@ -8346,8 +8288,7 @@ ui_elements <- list(
                 width = "100%"
               ),
               shiny::tags$br(),
-              shiny::helpText("Create factor/categorical variable from a continous variable (number/date/time)."),
-              shiny::tags$br()
+              shiny::helpText("Create factor/categorical variable from a continous variable (number/date/time).")
             ),
             shiny::column(
               width = 4,
@@ -8357,11 +8298,11 @@ ui_elements <- list(
                 width = "100%"
               ),
               shiny::tags$br(),
-              shiny::helpText(shiny::markdown("Create a new variable/column based on an *R*-expression.")),
-              shiny::tags$br()
+              shiny::helpText(shiny::markdown("Create a new variable/column based on an *R*-expression."))
             )
           ),
           shiny::tags$br(),
+          shiny::tags$br(),
           tags$h4("Compare modified data to original"),
           shiny::tags$br(),
           shiny::tags$p(
@@ -8406,7 +8347,6 @@ ui_elements <- list(
       bslib::navset_bar(
         title = "",
         sidebar = bslib::sidebar(
-            shiny::uiOutput(outputId = "data_info_nochar", inline = TRUE),
           bslib::accordion(
             open = "acc_chars",
             multiple = FALSE,
@@ -8590,7 +8530,7 @@ ui_elements <- list(
           shiny::tagList(
             lapply(
               paste0("code_", c(
-                "import", "data", "variables", "filter", "table1", "univariable", "multivariable"
+                "import", "data", "filter", "table1", "univariable", "multivariable"
               )),
               \(.x)shiny::htmlOutput(outputId = .x)
             )
@@ -8712,7 +8652,6 @@ library(gtsummary)
 
 data(starwars)
 data(mtcars)
-mtcars <- mtcars |> append_column(as.Date(sample(1:365, nrow(mtcars))), "rand_dates")
 data(trial)
 
 
@@ -8766,7 +8705,6 @@ server <- function(input, output, session) {
     data_original = NULL,
     data_temp = NULL,
     data = NULL,
-    data_variables = NULL,
     data_filtered = NULL,
     models = NULL,
     code = list()
@@ -8796,6 +8734,7 @@ server <- function(input, output, session) {
   )
 
   shiny::observeEvent(from_redcap$data(), {
+    # rv$data_original <- purrr::pluck(data_redcap(), "data")()
     rv$data_temp <- from_redcap$data()
     rv$code <- modifyList(x = rv$code, list(import = from_redcap$code()))
   })
@@ -8804,6 +8743,7 @@ server <- function(input, output, session) {
   output$redcap_prev <- DT::renderDT(
     {
       DT::datatable(head(from_redcap$data(), 5),
+        # DT::datatable(head(purrr::pluck(data_redcap(), "data")(), 5),
         caption = "First 5 observations"
       )
     },
@@ -8878,6 +8818,17 @@ server <- function(input, output, session) {
         pipe_string() |>
         expression_string(assign.str = "df <-")
 
+
+      # rv$code$import <- rv$code$import |>
+      #   deparse() |>
+      #   paste(collapse = "") |>
+      #   paste("|>
+      #   dplyr::select(", paste(input$import_var, collapse = ","), ") |>
+      #   FreesearchR::default_parsing()") |>
+      #   (\(.x){
+      #     paste0("data <- ", .x)
+      #   })()
+
       rv$code$filter <- NULL
       rv$code$modify <- NULL
     }, ignoreNULL = FALSE
@@ -8894,6 +8845,7 @@ server <- function(input, output, session) {
       shiny::updateActionButton(inputId = "act_start", disabled = TRUE)
       shiny::updateActionButton(inputId = "modal_browse", disabled = TRUE)
       shiny::updateActionButton(inputId = "act_eval", disabled = TRUE)
+
     } else {
       shiny::updateActionButton(inputId = "act_start", disabled = FALSE)
       shiny::updateActionButton(inputId = "modal_browse", disabled = FALSE)
@@ -8925,7 +8877,6 @@ server <- function(input, output, session) {
         shiny::req(rv$data_original)
         rv$data <- rv$data_original
         rv$code$filter <- NULL
-        rv$code$variables <- NULL
         rv$code$modify <- NULL
       }
     },
@@ -8951,11 +8902,23 @@ server <- function(input, output, session) {
   ## Further modifications are needed to have cut/bin options based on class of variable
   ## Could be defined server-side
 
+  shiny::observeEvent(
+    input$modal_variables,
+    modal_update_variables(
+      id = "modal_variables",
+      title = "Update and select variables",
+      footer = tagList(
+        actionButton("ok", "OK")
+      )
+    )
+  )
+
   output$data_info <- shiny::renderUI({
     shiny::req(data_filter())
-    data_description(data_filter(), "The filtered data")
+    data_description(data_filter())
   })
 
+
   #########  Create factor
 
   shiny::observeEvent(
@@ -9026,48 +8989,16 @@ server <- function(input, output, session) {
     rv$code$modify[[length(rv$code$modify) + 1]] <- attr(rv$data, "code")
   })
 
-  ### Column filter
-  ### Completely implemented, but it takes a little considering where in the
-  ### data flow to implement, as it will act destructively on previous
-  ### manipulations
-
-  output$column_filter <- shiny::renderUI({
-    shiny::req(rv$data)
-    # c("dichotomous", "ordinal", "categorical", "datatime", "continuous")
-    shinyWidgets::virtualSelectInput(
-      inputId = "column_filter",
-      label = "Select variable types to include",
-      selected = unique(data_type(rv$data)),
-      choices = unique(data_type(rv$data)),
-      updateOn = "change",
-      multiple = TRUE,
-      search = FALSE,
-      showValueAsTags = TRUE
-    )
-  })
-
-  shiny::observeEvent(list(
-    input$column_filter # ,
-    # rv$data
-  ), {
-    shiny::req(input$column_filter)
-    out <- data_type_filter(rv$data, input$column_filter)
-    rv$data_variables <- out
-    rv$code$variables <- attr(out, "code")
-    # rv$code$modify[[length(rv$code$modify) + 1]] <- attr(rv$data, "code")
-  })
-
-
   #########  Data filter
   # IDEAFilter has the least cluttered UI, but might have a License issue
   data_filter <- IDEAFilter::IDEAFilter("data_filter",
-    data = shiny::reactive(rv$data_variables),
+    data = shiny::reactive(rv$data),
     verbose = TRUE
   )
 
   shiny::observeEvent(
     list(
-      shiny::reactive(rv$data_variables),
+      shiny::reactive(rv$data),
       shiny::reactive(rv$data_original),
       data_filter(),
       # regression_vars(),
@@ -9080,10 +9011,7 @@ server <- function(input, output, session) {
       ###  Save filtered data
       ###  without empty factor levels
       rv$list$data <- data_filter() |>
-        REDCapCAST::fct_drop() |>
-        (\(.x){
-          .x[!sapply(.x, is.character)]
-        })()
+        REDCapCAST::fct_drop()
 
       ## This looks messy!! But it works as intended for now
 
@@ -9171,14 +9099,7 @@ server <- function(input, output, session) {
     prismCodeBlock(paste0("#Data modifications\n", out))
   })
 
-  output$code_variables <- shiny::renderUI({
-    shiny::req(rv$code$variables)
-    out <- expression_string(rv$code$variables, assign.str = "df <- df |>\n")
-    prismCodeBlock(paste0("#Variables filter\n", out))
-  })
-
   output$code_filter <- shiny::renderUI({
-    shiny::req(rv$code$filter)
     prismCodeBlock(paste0("#Data filter\n", rv$code$filter))
   })
 
@@ -9193,7 +9114,7 @@ server <- function(input, output, session) {
   shiny::observe({
     rv$regression()$regression$models |> purrr::imap(\(.x, .i){
       output[[paste0("code_", tolower(.i))]] <- shiny::renderUI({
-        prismCodeBlock(paste0(paste("#", .i, "regression model\n"), .x$code_table))
+        prismCodeBlock(paste0(paste("#",.i,"regression model\n"),.x$code_table))
       })
     })
   })
@@ -9205,6 +9126,68 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
+  ## Keep these "old" selection options as a simple alternative to the modification pane
+
+
+  # output$regression_vars <- shiny::renderUI({
+  #   columnSelectInput(
+  #     inputId = "regression_vars",
+  #     selected = NULL,
+  #     label = "Covariables to include",
+  #     data = rv$data_filtered,
+  #     multiple = TRUE,
+  #   )
+  # })
+  #
+  # output$outcome_var <- shiny::renderUI({
+  #   columnSelectInput(
+  #     inputId = "outcome_var",
+  #     selected = NULL,
+  #     label = "Select outcome variable",
+  #     data = rv$data_filtered,
+  #     multiple = FALSE
+  #   )
+  # })
+  #
+  # output$regression_type <- shiny::renderUI({
+  #   shiny::req(input$outcome_var)
+  #   shiny::selectizeInput(
+  #     inputId = "regression_type",
+  #     label = "Choose regression analysis",
+  #     ## The below ifelse statement handles the case of loading a new dataset
+  #     choices = possible_functions(
+  #       data = dplyr::select(
+  #         rv$data_filtered,
+  #         ifelse(input$outcome_var %in% names(rv$data_filtered),
+  #           input$outcome_var,
+  #           names(rv$data_filtered)[1]
+  #         )
+  #       ), design = "cross-sectional"
+  #     ),
+  #     multiple = FALSE
+  #   )
+  # })
+  #
+  # output$factor_vars <- shiny::renderUI({
+  #   shiny::selectizeInput(
+  #     inputId = "factor_vars",
+  #     selected = colnames(rv$data_filtered)[sapply(rv$data_filtered, is.factor)],
+  #     label = "Covariables to format as categorical",
+  #     choices = colnames(rv$data_filtered),
+  #     multiple = TRUE
+  #   )
+  # })
+  #
+  # ## Collected regression variables
+  # regression_vars <- shiny::reactive({
+  #   if (is.null(input$regression_vars)) {
+  #     out <- colnames(rv$data_filtered)
+  #   } else {
+  #     out <- unique(c(input$regression_vars, input$outcome_var))
+  #   }
+  #   return(out)
+  # })
+  #
   output$strat_var <- shiny::renderUI({
     columnSelectInput(
       inputId = "strat_var",
@@ -9217,6 +9200,19 @@ server <- function(input, output, session) {
       )
     )
   })
+  #
+  #
+  # output$plot_model <- shiny::renderUI({
+  #   shiny::req(rv$list$regression$tables)
+  #   shiny::selectInput(
+  #     inputId = "plot_model",
+  #     selected = "none",
+  #     label = "Select models to plot",
+  #     choices = names(rv$list$regression$tables),
+  #     multiple = TRUE
+  #   )
+  # })
+
 
   ##############################################################################
   #########
@@ -9224,14 +9220,17 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
-
-  output$data_info_nochar <- shiny::renderUI({
-    shiny::req(rv$list$data)
-    data_description(rv$list$data, data_text = "The dataset without text variables")
-  })
-
   shiny::observeEvent(
+    # ignoreInit = TRUE,
     list(
+      # shiny::reactive(rv$list$data),
+      # shiny::reactive(rv$data),
+      # shiny::reactive(rv$data_original),
+      # data_filter(),
+      # input$strat_var,
+      # input$regression_vars,
+      # input$complete_cutoff,
+      # input$add_p
       input$act_eval
     ),
     {
@@ -9246,9 +9245,24 @@ server <- function(input, output, session) {
 
       shiny::withProgress(message = "Creating the table. Hold on for a moment..", {
         rv$list$table1 <- rlang::exec(create_baseline, !!!append_list(rv$list$data, parameters, "data"))
+
+        # rv$list$table1 <- create_baseline(
+        #   data = rv$list$data,
+        #   by.var = input$strat_var,
+        #   add.p = input$add_p == "yes",
+        #   add.overall = TRUE
+        # )
       })
 
       rv$code$table1 <- glue::glue("FreesearchR::create_baseline(data,{list2str(parameters)})")
+
+      #   list(
+      #   rv$code$import,
+      #   rlang::call2(.fn = "select",!!!list(input$import_var),.ns = "dplyr"),
+      #   rlang::call2(.fn = "default_parsing",.ns = "FreesearchR")
+      # ) |>
+      #   merge_expression() |>
+      #   expression_string()
     }
   )
 
@@ -9293,7 +9307,7 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
-  pl <- data_visuals_server("visuals", data = shiny::reactive(rv$list$data))
+  pl <- data_visuals_server("visuals", data = shiny::reactive(rv$data))
 
   ##############################################################################
   #########
@@ -9301,7 +9315,201 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
-  rv$regression <- regression_server("regression", data = shiny::reactive(rv$list$data))
+  rv$regression <- regression_server("regression", data = shiny::reactive(rv$data_filtered))
+
+  # rv$list$regression <- regression_server("regression", data = shiny::reactive(rv$data_filtered))
+
+  # shiny::observeEvent(
+  #   input$load,
+  #   {
+  #     shiny::req(input$outcome_var)
+  #     # browser()
+  #     # Assumes all character variables can be formatted as factors
+  #     # data <- data_filter$filtered() |>
+  #     tryCatch(
+  #       {
+  #         ## Which models to create should be decided by input
+  #         ## Could also include
+  #         ##   imputed or
+  #         ##   minimally adjusted
+  #         model_lists <- list(
+  #           "Univariable" = regression_model_uv_list,
+  #           "Multivariable" = regression_model_list
+  #         ) |>
+  #           lapply(\(.fun){
+  #             ls <- do.call(
+  #               .fun,
+  #               c(
+  #                 list(data = rv$list$data |>
+  #                   (\(.x){
+  #                     .x[regression_vars()]
+  #                   })()),
+  #                 list(outcome.str = input$outcome_var),
+  #                 list(fun.descr = input$regression_type)
+  #               )
+  #             )
+  #           })
+  #
+  #         # browser()
+  #
+  #         rv$list$regression$params <- get_fun_options(input$regression_type) |>
+  #           (\(.x){
+  #             .x[[1]]
+  #           })()
+  #
+  #         rv$list$regression$models <- model_lists
+  #
+  #         # names(rv$list$regression)
+  #
+  #         # rv$models <- lapply(model_lists, \(.x){
+  #         #   .x$model
+  #         # })
+  #       },
+  #       # warning = function(warn) {
+  #       #   showNotification(paste0(warn), type = "warning")
+  #       # },
+  #       error = function(err) {
+  #         showNotification(paste0("Creating regression models failed with the following error: ", err), type = "err")
+  #       }
+  #     )
+  #   }
+  # )
+  #
+  # shiny::observeEvent(
+  #   ignoreInit = TRUE,
+  #   list(
+  #     rv$list$regression$models
+  #   ),
+  #   {
+  #     shiny::req(rv$list$regression$models)
+  #     tryCatch(
+  #       {
+  #         rv$check <- lapply(rv$list$regression$models, \(.x){
+  #           .x$model
+  #         }) |>
+  #           purrr::pluck("Multivariable") |>
+  #           performance::check_model()
+  #       },
+  #       # warning = function(warn) {
+  #       #   showNotification(paste0(warn), type = "warning")
+  #       # },
+  #       error = function(err) {
+  #         showNotification(paste0("Running model assumptions checks failed with the following error: ", err), type = "err")
+  #       }
+  #     )
+  #   }
+  # )
+  #
+  # output$check <- shiny::renderPlot(
+  #   {
+  #     shiny::req(rv$check)
+  #     # browser()
+  #     # p <- plot(rv$check) +
+  #     #   patchwork::plot_annotation(title = "Multivariable regression model checks")
+  #
+  #     p <- plot(rv$check) +
+  #       patchwork::plot_annotation(title = "Multivariable regression model checks")
+  #
+  #     for (i in seq_len(length(p))) {
+  #       p[[i]] <- p[[i]] + gg_theme_shiny()
+  #     }
+  #
+  #     p
+  #
+  #     # p + patchwork::plot_layout(ncol = 1, design = ggplot2::waiver())
+  #
+  #     # Generate checks in one column
+  #     # layout <- sapply(seq_len(length(p)), \(.x){
+  #     #   patchwork::area(.x, 1)
+  #     # })
+  #     #
+  #     # p + patchwork::plot_layout(design = Reduce(c, layout))
+  #
+  #     # patchwork::wrap_plots(ncol=1) +
+  #     # patchwork::plot_annotation(title = 'Multivariable regression model checks')
+  #   },
+  #   height = 600,
+  #   alt = "Assumptions testing of the multivariable regression model"
+  # )
+  #
+  #
+  # shiny::observeEvent(
+  #   input$load,
+  #   {
+  #     shiny::req(rv$list$regression$models)
+  #     tryCatch(
+  #       {
+  #         out <- lapply(rv$list$regression$models, \(.x){
+  #           .x$model
+  #         }) |>
+  #           purrr::map(regression_table)
+  #
+  #         if (input$add_regression_p == "no") {
+  #           out <- out |>
+  #             lapply(\(.x){
+  #               .x |>
+  #                 gtsummary::modify_column_hide(
+  #                   column = "p.value"
+  #                 )
+  #             })
+  #         }
+  #
+  #         rv$list$regression$tables <- out
+  #
+  #         # rv$list$regression$table <- out |>
+  #         #   tbl_merge()
+  #
+  #         # gtsummary::as_kable(rv$list$regression$table) |>
+  #         #   readr::write_lines(file="./www/_regression_table.md")
+  #
+  #         rv$list$input <- input
+  #       },
+  #       warning = function(warn) {
+  #         showNotification(paste0(warn), type = "warning")
+  #       },
+  #       error = function(err) {
+  #         showNotification(paste0("Creating a regression table failed with the following error: ", err), type = "err")
+  #       }
+  #     )
+  #     rv$ready <- "ready"
+  #   }
+  # )
+  #
+  # output$table2 <- gt::render_gt({
+  #   shiny::req(rv$list$regression$tables)
+  #   rv$list$regression$tables |>
+  #     tbl_merge() |>
+  #     gtsummary::as_gt() |>
+  #     gt::tab_header(gt::md(glue::glue("**Table 2: {rv$list$regression$params$descr}**")))
+  # })
+  #
+  # output$regression_plot <- shiny::renderPlot(
+  #   {
+  #     # shiny::req(rv$list$regression$plot)
+  #     shiny::req(input$plot_model)
+  #
+  #     out <- merge_long(rv$list$regression, input$plot_model) |>
+  #       plot.tbl_regression(
+  #         colour = "variable",
+  #         facet_col = "model"
+  #       )
+  #
+  #     out +
+  #       ggplot2::scale_y_discrete(labels = scales::label_wrap(15)) +
+  #       gg_theme_shiny()
+  #
+  #     # rv$list$regression$tables$Multivariable |>
+  #     #   plot(colour = "variable") +
+  #     #   ggplot2::scale_y_discrete(labels = scales::label_wrap(15)) +
+  #     #   gg_theme_shiny()
+  #   },
+  #   height = 500,
+  #   alt = "Regression coefficient plot"
+  # )
+
+  # shiny::conditionalPanel(
+  #   condition = "output.uploaded == 'yes'",
+  # )
 
   ##############################################################################
   #########
@@ -9339,6 +9547,17 @@ server <- function(input, output, session) {
 
   shiny::outputOptions(output, "ready", suspendWhenHidden = FALSE)
 
+  # Reimplement from environment at later time
+  # output$has_input <- shiny::reactive({
+  #   if (rv$input) {
+  #     "yes"
+  #   } else {
+  #     "no"
+  #   }
+  # })
+
+  # shiny::outputOptions(output, "has_input", suspendWhenHidden = FALSE)
+
   ##############################################################################
   #########
   #########  Downloads
diff --git a/inst/apps/FreesearchR/rsconnect/shinyapps.io/agdamsbo/freesearcheR.dcf b/inst/apps/FreesearchR/rsconnect/shinyapps.io/agdamsbo/freesearcheR.dcf
index 9cf3c2a2..9263ffb6 100644
--- a/inst/apps/FreesearchR/rsconnect/shinyapps.io/agdamsbo/freesearcheR.dcf
+++ b/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: 10111316
+bundleId: 10098670
 url: https://agdamsbo.shinyapps.io/freesearcheR/
 version: 1
diff --git a/inst/apps/FreesearchR/server.R b/inst/apps/FreesearchR/server.R
index eefe63d9..491e3be5 100644
--- a/inst/apps/FreesearchR/server.R
+++ b/inst/apps/FreesearchR/server.R
@@ -32,7 +32,6 @@ library(gtsummary)
 
 data(starwars)
 data(mtcars)
-mtcars <- mtcars |> append_column(as.Date(sample(1:365, nrow(mtcars))), "rand_dates")
 data(trial)
 
 
@@ -86,7 +85,6 @@ server <- function(input, output, session) {
     data_original = NULL,
     data_temp = NULL,
     data = NULL,
-    data_variables = NULL,
     data_filtered = NULL,
     models = NULL,
     code = list()
@@ -116,6 +114,7 @@ server <- function(input, output, session) {
   )
 
   shiny::observeEvent(from_redcap$data(), {
+    # rv$data_original <- purrr::pluck(data_redcap(), "data")()
     rv$data_temp <- from_redcap$data()
     rv$code <- modifyList(x = rv$code, list(import = from_redcap$code()))
   })
@@ -124,6 +123,7 @@ server <- function(input, output, session) {
   output$redcap_prev <- DT::renderDT(
     {
       DT::datatable(head(from_redcap$data(), 5),
+        # DT::datatable(head(purrr::pluck(data_redcap(), "data")(), 5),
         caption = "First 5 observations"
       )
     },
@@ -198,6 +198,17 @@ server <- function(input, output, session) {
         pipe_string() |>
         expression_string(assign.str = "df <-")
 
+
+      # rv$code$import <- rv$code$import |>
+      #   deparse() |>
+      #   paste(collapse = "") |>
+      #   paste("|>
+      #   dplyr::select(", paste(input$import_var, collapse = ","), ") |>
+      #   FreesearchR::default_parsing()") |>
+      #   (\(.x){
+      #     paste0("data <- ", .x)
+      #   })()
+
       rv$code$filter <- NULL
       rv$code$modify <- NULL
     }, ignoreNULL = FALSE
@@ -214,6 +225,7 @@ server <- function(input, output, session) {
       shiny::updateActionButton(inputId = "act_start", disabled = TRUE)
       shiny::updateActionButton(inputId = "modal_browse", disabled = TRUE)
       shiny::updateActionButton(inputId = "act_eval", disabled = TRUE)
+
     } else {
       shiny::updateActionButton(inputId = "act_start", disabled = FALSE)
       shiny::updateActionButton(inputId = "modal_browse", disabled = FALSE)
@@ -245,7 +257,6 @@ server <- function(input, output, session) {
         shiny::req(rv$data_original)
         rv$data <- rv$data_original
         rv$code$filter <- NULL
-        rv$code$variables <- NULL
         rv$code$modify <- NULL
       }
     },
@@ -271,11 +282,23 @@ server <- function(input, output, session) {
   ## Further modifications are needed to have cut/bin options based on class of variable
   ## Could be defined server-side
 
+  shiny::observeEvent(
+    input$modal_variables,
+    modal_update_variables(
+      id = "modal_variables",
+      title = "Update and select variables",
+      footer = tagList(
+        actionButton("ok", "OK")
+      )
+    )
+  )
+
   output$data_info <- shiny::renderUI({
     shiny::req(data_filter())
-    data_description(data_filter(), "The filtered data")
+    data_description(data_filter())
   })
 
+
   #########  Create factor
 
   shiny::observeEvent(
@@ -346,48 +369,16 @@ server <- function(input, output, session) {
     rv$code$modify[[length(rv$code$modify) + 1]] <- attr(rv$data, "code")
   })
 
-  ### Column filter
-  ### Completely implemented, but it takes a little considering where in the
-  ### data flow to implement, as it will act destructively on previous
-  ### manipulations
-
-  output$column_filter <- shiny::renderUI({
-    shiny::req(rv$data)
-    # c("dichotomous", "ordinal", "categorical", "datatime", "continuous")
-    shinyWidgets::virtualSelectInput(
-      inputId = "column_filter",
-      label = "Select variable types to include",
-      selected = unique(data_type(rv$data)),
-      choices = unique(data_type(rv$data)),
-      updateOn = "change",
-      multiple = TRUE,
-      search = FALSE,
-      showValueAsTags = TRUE
-    )
-  })
-
-  shiny::observeEvent(list(
-    input$column_filter # ,
-    # rv$data
-  ), {
-    shiny::req(input$column_filter)
-    out <- data_type_filter(rv$data, input$column_filter)
-    rv$data_variables <- out
-    rv$code$variables <- attr(out, "code")
-    # rv$code$modify[[length(rv$code$modify) + 1]] <- attr(rv$data, "code")
-  })
-
-
   #########  Data filter
   # IDEAFilter has the least cluttered UI, but might have a License issue
   data_filter <- IDEAFilter::IDEAFilter("data_filter",
-    data = shiny::reactive(rv$data_variables),
+    data = shiny::reactive(rv$data),
     verbose = TRUE
   )
 
   shiny::observeEvent(
     list(
-      shiny::reactive(rv$data_variables),
+      shiny::reactive(rv$data),
       shiny::reactive(rv$data_original),
       data_filter(),
       # regression_vars(),
@@ -400,10 +391,7 @@ server <- function(input, output, session) {
       ###  Save filtered data
       ###  without empty factor levels
       rv$list$data <- data_filter() |>
-        REDCapCAST::fct_drop() |>
-        (\(.x){
-          .x[!sapply(.x, is.character)]
-        })()
+        REDCapCAST::fct_drop()
 
       ## This looks messy!! But it works as intended for now
 
@@ -491,14 +479,7 @@ server <- function(input, output, session) {
     prismCodeBlock(paste0("#Data modifications\n", out))
   })
 
-  output$code_variables <- shiny::renderUI({
-    shiny::req(rv$code$variables)
-    out <- expression_string(rv$code$variables, assign.str = "df <- df |>\n")
-    prismCodeBlock(paste0("#Variables filter\n", out))
-  })
-
   output$code_filter <- shiny::renderUI({
-    shiny::req(rv$code$filter)
     prismCodeBlock(paste0("#Data filter\n", rv$code$filter))
   })
 
@@ -513,7 +494,7 @@ server <- function(input, output, session) {
   shiny::observe({
     rv$regression()$regression$models |> purrr::imap(\(.x, .i){
       output[[paste0("code_", tolower(.i))]] <- shiny::renderUI({
-        prismCodeBlock(paste0(paste("#", .i, "regression model\n"), .x$code_table))
+        prismCodeBlock(paste0(paste("#",.i,"regression model\n"),.x$code_table))
       })
     })
   })
@@ -525,6 +506,68 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
+  ## Keep these "old" selection options as a simple alternative to the modification pane
+
+
+  # output$regression_vars <- shiny::renderUI({
+  #   columnSelectInput(
+  #     inputId = "regression_vars",
+  #     selected = NULL,
+  #     label = "Covariables to include",
+  #     data = rv$data_filtered,
+  #     multiple = TRUE,
+  #   )
+  # })
+  #
+  # output$outcome_var <- shiny::renderUI({
+  #   columnSelectInput(
+  #     inputId = "outcome_var",
+  #     selected = NULL,
+  #     label = "Select outcome variable",
+  #     data = rv$data_filtered,
+  #     multiple = FALSE
+  #   )
+  # })
+  #
+  # output$regression_type <- shiny::renderUI({
+  #   shiny::req(input$outcome_var)
+  #   shiny::selectizeInput(
+  #     inputId = "regression_type",
+  #     label = "Choose regression analysis",
+  #     ## The below ifelse statement handles the case of loading a new dataset
+  #     choices = possible_functions(
+  #       data = dplyr::select(
+  #         rv$data_filtered,
+  #         ifelse(input$outcome_var %in% names(rv$data_filtered),
+  #           input$outcome_var,
+  #           names(rv$data_filtered)[1]
+  #         )
+  #       ), design = "cross-sectional"
+  #     ),
+  #     multiple = FALSE
+  #   )
+  # })
+  #
+  # output$factor_vars <- shiny::renderUI({
+  #   shiny::selectizeInput(
+  #     inputId = "factor_vars",
+  #     selected = colnames(rv$data_filtered)[sapply(rv$data_filtered, is.factor)],
+  #     label = "Covariables to format as categorical",
+  #     choices = colnames(rv$data_filtered),
+  #     multiple = TRUE
+  #   )
+  # })
+  #
+  # ## Collected regression variables
+  # regression_vars <- shiny::reactive({
+  #   if (is.null(input$regression_vars)) {
+  #     out <- colnames(rv$data_filtered)
+  #   } else {
+  #     out <- unique(c(input$regression_vars, input$outcome_var))
+  #   }
+  #   return(out)
+  # })
+  #
   output$strat_var <- shiny::renderUI({
     columnSelectInput(
       inputId = "strat_var",
@@ -537,6 +580,19 @@ server <- function(input, output, session) {
       )
     )
   })
+  #
+  #
+  # output$plot_model <- shiny::renderUI({
+  #   shiny::req(rv$list$regression$tables)
+  #   shiny::selectInput(
+  #     inputId = "plot_model",
+  #     selected = "none",
+  #     label = "Select models to plot",
+  #     choices = names(rv$list$regression$tables),
+  #     multiple = TRUE
+  #   )
+  # })
+
 
   ##############################################################################
   #########
@@ -544,14 +600,17 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
-
-  output$data_info_nochar <- shiny::renderUI({
-    shiny::req(rv$list$data)
-    data_description(rv$list$data, data_text = "The dataset without text variables")
-  })
-
   shiny::observeEvent(
+    # ignoreInit = TRUE,
     list(
+      # shiny::reactive(rv$list$data),
+      # shiny::reactive(rv$data),
+      # shiny::reactive(rv$data_original),
+      # data_filter(),
+      # input$strat_var,
+      # input$regression_vars,
+      # input$complete_cutoff,
+      # input$add_p
       input$act_eval
     ),
     {
@@ -566,9 +625,24 @@ server <- function(input, output, session) {
 
       shiny::withProgress(message = "Creating the table. Hold on for a moment..", {
         rv$list$table1 <- rlang::exec(create_baseline, !!!append_list(rv$list$data, parameters, "data"))
+
+        # rv$list$table1 <- create_baseline(
+        #   data = rv$list$data,
+        #   by.var = input$strat_var,
+        #   add.p = input$add_p == "yes",
+        #   add.overall = TRUE
+        # )
       })
 
       rv$code$table1 <- glue::glue("FreesearchR::create_baseline(data,{list2str(parameters)})")
+
+      #   list(
+      #   rv$code$import,
+      #   rlang::call2(.fn = "select",!!!list(input$import_var),.ns = "dplyr"),
+      #   rlang::call2(.fn = "default_parsing",.ns = "FreesearchR")
+      # ) |>
+      #   merge_expression() |>
+      #   expression_string()
     }
   )
 
@@ -613,7 +687,7 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
-  pl <- data_visuals_server("visuals", data = shiny::reactive(rv$list$data))
+  pl <- data_visuals_server("visuals", data = shiny::reactive(rv$data))
 
   ##############################################################################
   #########
@@ -621,7 +695,201 @@ server <- function(input, output, session) {
   #########
   ##############################################################################
 
-  rv$regression <- regression_server("regression", data = shiny::reactive(rv$list$data))
+  rv$regression <- regression_server("regression", data = shiny::reactive(rv$data_filtered))
+
+  # rv$list$regression <- regression_server("regression", data = shiny::reactive(rv$data_filtered))
+
+  # shiny::observeEvent(
+  #   input$load,
+  #   {
+  #     shiny::req(input$outcome_var)
+  #     # browser()
+  #     # Assumes all character variables can be formatted as factors
+  #     # data <- data_filter$filtered() |>
+  #     tryCatch(
+  #       {
+  #         ## Which models to create should be decided by input
+  #         ## Could also include
+  #         ##   imputed or
+  #         ##   minimally adjusted
+  #         model_lists <- list(
+  #           "Univariable" = regression_model_uv_list,
+  #           "Multivariable" = regression_model_list
+  #         ) |>
+  #           lapply(\(.fun){
+  #             ls <- do.call(
+  #               .fun,
+  #               c(
+  #                 list(data = rv$list$data |>
+  #                   (\(.x){
+  #                     .x[regression_vars()]
+  #                   })()),
+  #                 list(outcome.str = input$outcome_var),
+  #                 list(fun.descr = input$regression_type)
+  #               )
+  #             )
+  #           })
+  #
+  #         # browser()
+  #
+  #         rv$list$regression$params <- get_fun_options(input$regression_type) |>
+  #           (\(.x){
+  #             .x[[1]]
+  #           })()
+  #
+  #         rv$list$regression$models <- model_lists
+  #
+  #         # names(rv$list$regression)
+  #
+  #         # rv$models <- lapply(model_lists, \(.x){
+  #         #   .x$model
+  #         # })
+  #       },
+  #       # warning = function(warn) {
+  #       #   showNotification(paste0(warn), type = "warning")
+  #       # },
+  #       error = function(err) {
+  #         showNotification(paste0("Creating regression models failed with the following error: ", err), type = "err")
+  #       }
+  #     )
+  #   }
+  # )
+  #
+  # shiny::observeEvent(
+  #   ignoreInit = TRUE,
+  #   list(
+  #     rv$list$regression$models
+  #   ),
+  #   {
+  #     shiny::req(rv$list$regression$models)
+  #     tryCatch(
+  #       {
+  #         rv$check <- lapply(rv$list$regression$models, \(.x){
+  #           .x$model
+  #         }) |>
+  #           purrr::pluck("Multivariable") |>
+  #           performance::check_model()
+  #       },
+  #       # warning = function(warn) {
+  #       #   showNotification(paste0(warn), type = "warning")
+  #       # },
+  #       error = function(err) {
+  #         showNotification(paste0("Running model assumptions checks failed with the following error: ", err), type = "err")
+  #       }
+  #     )
+  #   }
+  # )
+  #
+  # output$check <- shiny::renderPlot(
+  #   {
+  #     shiny::req(rv$check)
+  #     # browser()
+  #     # p <- plot(rv$check) +
+  #     #   patchwork::plot_annotation(title = "Multivariable regression model checks")
+  #
+  #     p <- plot(rv$check) +
+  #       patchwork::plot_annotation(title = "Multivariable regression model checks")
+  #
+  #     for (i in seq_len(length(p))) {
+  #       p[[i]] <- p[[i]] + gg_theme_shiny()
+  #     }
+  #
+  #     p
+  #
+  #     # p + patchwork::plot_layout(ncol = 1, design = ggplot2::waiver())
+  #
+  #     # Generate checks in one column
+  #     # layout <- sapply(seq_len(length(p)), \(.x){
+  #     #   patchwork::area(.x, 1)
+  #     # })
+  #     #
+  #     # p + patchwork::plot_layout(design = Reduce(c, layout))
+  #
+  #     # patchwork::wrap_plots(ncol=1) +
+  #     # patchwork::plot_annotation(title = 'Multivariable regression model checks')
+  #   },
+  #   height = 600,
+  #   alt = "Assumptions testing of the multivariable regression model"
+  # )
+  #
+  #
+  # shiny::observeEvent(
+  #   input$load,
+  #   {
+  #     shiny::req(rv$list$regression$models)
+  #     tryCatch(
+  #       {
+  #         out <- lapply(rv$list$regression$models, \(.x){
+  #           .x$model
+  #         }) |>
+  #           purrr::map(regression_table)
+  #
+  #         if (input$add_regression_p == "no") {
+  #           out <- out |>
+  #             lapply(\(.x){
+  #               .x |>
+  #                 gtsummary::modify_column_hide(
+  #                   column = "p.value"
+  #                 )
+  #             })
+  #         }
+  #
+  #         rv$list$regression$tables <- out
+  #
+  #         # rv$list$regression$table <- out |>
+  #         #   tbl_merge()
+  #
+  #         # gtsummary::as_kable(rv$list$regression$table) |>
+  #         #   readr::write_lines(file="./www/_regression_table.md")
+  #
+  #         rv$list$input <- input
+  #       },
+  #       warning = function(warn) {
+  #         showNotification(paste0(warn), type = "warning")
+  #       },
+  #       error = function(err) {
+  #         showNotification(paste0("Creating a regression table failed with the following error: ", err), type = "err")
+  #       }
+  #     )
+  #     rv$ready <- "ready"
+  #   }
+  # )
+  #
+  # output$table2 <- gt::render_gt({
+  #   shiny::req(rv$list$regression$tables)
+  #   rv$list$regression$tables |>
+  #     tbl_merge() |>
+  #     gtsummary::as_gt() |>
+  #     gt::tab_header(gt::md(glue::glue("**Table 2: {rv$list$regression$params$descr}**")))
+  # })
+  #
+  # output$regression_plot <- shiny::renderPlot(
+  #   {
+  #     # shiny::req(rv$list$regression$plot)
+  #     shiny::req(input$plot_model)
+  #
+  #     out <- merge_long(rv$list$regression, input$plot_model) |>
+  #       plot.tbl_regression(
+  #         colour = "variable",
+  #         facet_col = "model"
+  #       )
+  #
+  #     out +
+  #       ggplot2::scale_y_discrete(labels = scales::label_wrap(15)) +
+  #       gg_theme_shiny()
+  #
+  #     # rv$list$regression$tables$Multivariable |>
+  #     #   plot(colour = "variable") +
+  #     #   ggplot2::scale_y_discrete(labels = scales::label_wrap(15)) +
+  #     #   gg_theme_shiny()
+  #   },
+  #   height = 500,
+  #   alt = "Regression coefficient plot"
+  # )
+
+  # shiny::conditionalPanel(
+  #   condition = "output.uploaded == 'yes'",
+  # )
 
   ##############################################################################
   #########
@@ -659,6 +927,17 @@ server <- function(input, output, session) {
 
   shiny::outputOptions(output, "ready", suspendWhenHidden = FALSE)
 
+  # Reimplement from environment at later time
+  # output$has_input <- shiny::reactive({
+  #   if (rv$input) {
+  #     "yes"
+  #   } else {
+  #     "no"
+  #   }
+  # })
+
+  # shiny::outputOptions(output, "has_input", suspendWhenHidden = FALSE)
+
   ##############################################################################
   #########
   #########  Downloads
diff --git a/inst/apps/FreesearchR/ui.R b/inst/apps/FreesearchR/ui.R
index 617688fc..386337c9 100644
--- a/inst/apps/FreesearchR/ui.R
+++ b/inst/apps/FreesearchR/ui.R
@@ -157,13 +157,7 @@ ui_elements <- list(
               ),
               shiny::tags$br(),
               shiny::tags$br(),
-              shiny::uiOutput(outputId = "column_filter"),
-              shiny::helpText("Variable data type filtering."),
-              shiny::tags$br(),
-              shiny::tags$br(),
               IDEAFilter::IDEAFilter_ui("data_filter"),
-              shiny::helpText("Observations level filtering."),
-              shiny::tags$br(),
               shiny::tags$br()
             )
           ),
@@ -181,8 +175,7 @@ ui_elements <- list(
               width = 9,
               shiny::tags$p(
                 shiny::markdown("Below, are several options for simple data manipulation like update variables by renaming, creating new labels (for nicer tables in the report) and changing variable classes (numeric, factor/categorical etc.)."),
-                shiny::markdown("There are also more advanced options to modify factor/categorical variables as well as create new factor from a continous variable or new variables with *R* code. At the bottom you can restore the original data."),
-                shiny::markdown("Please note that data modifications are applied before any data or variable filtering is applied.")
+                shiny::tags$p("There are also 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.")
               )
             )
           ),
@@ -203,7 +196,6 @@ ui_elements <- list(
               ),
               shiny::tags$br(),
               shiny::helpText("Reorder the levels of factor/categorical variables."),
-              shiny::tags$br()
             ),
             shiny::column(
               width = 4,
@@ -213,8 +205,7 @@ ui_elements <- list(
                 width = "100%"
               ),
               shiny::tags$br(),
-              shiny::helpText("Create factor/categorical variable from a continous variable (number/date/time)."),
-              shiny::tags$br()
+              shiny::helpText("Create factor/categorical variable from a continous variable (number/date/time).")
             ),
             shiny::column(
               width = 4,
@@ -224,11 +215,11 @@ ui_elements <- list(
                 width = "100%"
               ),
               shiny::tags$br(),
-              shiny::helpText(shiny::markdown("Create a new variable/column based on an *R*-expression.")),
-              shiny::tags$br()
+              shiny::helpText(shiny::markdown("Create a new variable/column based on an *R*-expression."))
             )
           ),
           shiny::tags$br(),
+          shiny::tags$br(),
           tags$h4("Compare modified data to original"),
           shiny::tags$br(),
           shiny::tags$p(
@@ -273,7 +264,6 @@ ui_elements <- list(
       bslib::navset_bar(
         title = "",
         sidebar = bslib::sidebar(
-            shiny::uiOutput(outputId = "data_info_nochar", inline = TRUE),
           bslib::accordion(
             open = "acc_chars",
             multiple = FALSE,
@@ -457,7 +447,7 @@ ui_elements <- list(
           shiny::tagList(
             lapply(
               paste0("code_", c(
-                "import", "data", "variables", "filter", "table1", "univariable", "multivariable"
+                "import", "data", "filter", "table1", "univariable", "multivariable"
               )),
               \(.x)shiny::htmlOutput(outputId = .x)
             )
diff --git a/man/data_description.Rd b/man/data_description.Rd
index f54a8f7d..97a0b0db 100644
--- a/man/data_description.Rd
+++ b/man/data_description.Rd
@@ -4,7 +4,7 @@
 \alias{data_description}
 \title{Ultra short data dascription}
 \usage{
-data_description(data, data_text = "Data")
+data_description(data)
 }
 \arguments{
 \item{data}{}