#567 fix for ordered factors in tbl_summary

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: gtsummary
 Title: Presentation-Ready Data Summary and Analytic Result
     Tables
-Version: 1.3.2.9008
+Version: 1.3.2.9009
 Authors@R: 
     c(person(given = "Daniel D.",
              family = "Sjoberg",

NEWS.md

@@ -1,5 +1,9 @@
 # gtsummary (development version)
 
+* Bug fix `tbl_summary()` when a data frame contained an ordered factor column (#567)
+
+* Updated API for `set_gtsummary_theme()`. Users no longer need call `set_gtsummary_theme(theme_gtsummary_journal())`; rather, they call `theme_gtsummary_journal()`. Each built-in theme now has an argument `set_theme = TRUE`. When `FALSE`, the theme function will invisibly return the named list the theme elements, and the theme will not be set. (#522)
+
 * Bug fix when only categorical summary statistics were requested for continuous variables in `tbl_summary()` and `tbl_svysummary()` (#528)
 
 * Added `tbl_svysummary()` function to summarize complex and weighted survey designs. `tbl_svysummary` is now its own class that works with `add_n()`, `add_p()`,

R/tbl_summary.R

@@ -195,10 +195,10 @@ tbl_summary <- function(data, by = NULL, label = NULL, statistic = NULL,
   # checking input data --------------------------------------------------------
   tbl_summary_data_checks(data)
 
-  # removing orderered class from factor by variables --------------------------
-  if (!is.null(by) && inherits(data[[by]], "ordered") && inherits(data[[by]], "factor")) {
-    data[[by]] <- factor(data[[by]], ordered = FALSE)
-  }
+  # removing ordered class from factor variables -------------------------------
+  data <- dplyr::mutate_if(data,
+                           ~inherits(., "ordered") && inherits(., "factor"),
+                           ~factor(., levels = attr(., "levels"), ordered = FALSE))
 
   # deleting obs with missing by values ----------------------------------------
   # saving variable labels
@@ -211,7 +211,7 @@ tbl_summary <- function(data, by = NULL, label = NULL, statistic = NULL,
     lbls <- purrr::map(data, ~ attr(.x, "label"))
     data <- data[!is.na(data[[by]]), ]
 
-    # re-applying labels---I think this will NOT be necessary after dplyr 0.9.0
+    # re-applying labels---I think this will NOT be necessary after dplyr 1.0
     for (i in names(lbls)) {
       attr(data[[i]], "label") <- lbls[[i]]
     }

tests/testthat/test-add_overall.R

@@ -25,6 +25,18 @@ test_that("no errors/warnings with missing data in by variable", {
   expect_error(trial %>% tbl_summary(by = response) %>% add_overall(), NA)
 })
 
+test_that("add_overall-works with ordered factors", {
+  expect_error(
+    trial %>%
+      select(response, trt) %>%
+      dplyr::mutate_at(vars(response, trt),
+                       ~factor(., ordered = TRUE)) %>%
+      tbl_summary(by = trt) %>%
+      add_overall(),
+    NA
+  )
+})
+
 test_that("no errors/warnings with standard use for tbl_svysummary", {
   t <- trial %>%
     survey::svydesign(data = ., ids = ~ 1, weights = ~ 1) %>%

tests/testthat/test-tbl_summary.R

@@ -401,3 +401,14 @@ test_that("tbl_summary- works with grouped data (it ungroups it first)", {
   )
 })
 
+test_that("tbl_summary-works with ordered factors", {
+  expect_error(
+    trial %>%
+      select(response, trt) %>%
+      dplyr::mutate_at(vars(response, trt),
+                       ~factor(., ordered = TRUE)) %>%
+      tbl_summary(by = trt),
+    NA
+  )
+})
+