Remove tidyr::spread()

DESCRIPTION

@@ -32,7 +32,7 @@ Imports:
     stringr,
     snakecase (>= 0.9.2),
     tidyselect (>= 1.0.0),
-    tidyr (>= 0.7.0)
+    tidyr (>= 1.0.0)
 License: MIT + file LICENSE
 LazyData: true
 RoxygenNote: 7.2.3

R/adorn_title.R

@@ -22,7 +22,7 @@
 #' mtcars %>%
 #'   group_by(gear, am) %>%
 #'   summarise(avg_mpg = mean(mpg), .groups = "drop") %>%
-#'   spread(gear, avg_mpg) %>%
+#'   pivot_wider(names_from = am, values_from = avg_mpg) %>%
 #'   adorn_rounding() %>%
 #'   adorn_title("top", row_name = "Gears", col_name = "Cylinders")
 adorn_title <- function(dat, placement = "top", row_name, col_name) {

R/tabyl.R

@@ -207,7 +207,12 @@ tabyl_2way <- function(dat, var1, var2, show_na = TRUE, show_missing_levels = TR
   tabl[2][is.na(tabl[2])] <- "NA_"
   tabl[2][tabl[2] == ""] <- "emptystring_"
   result <- tabl %>%
-    tidyr::spread(!!var2, "tabyl_2way_n", fill = 0)
+    tidyr::pivot_wider(
+      names_from = !!var2,
+      values_from =  "tabyl_2way_n",
+      values_fn = ~ dplyr::coalesce(.x, 0L),
+      names_sort = TRUE
+    )
   if ("emptystring_" %in% names(result)) {
     result <- result[c(setdiff(names(result), "emptystring_"), "emptystring_")]
     if (getOption("tabyl.emptystring", TRUE) & interactive()) {

tests/testthat/test-adorn-title.R

@@ -38,7 +38,7 @@ test_that("name overrides work", {
 test_that("non-tabyls are treated correctly", {
   non_tab <- mtcars %>%
     dplyr::count(gear, cyl) %>%
-    tidyr::spread(gear, n)
+    tidyr::pivot_wider(names_from = gear, values_from = n)
   expect_error(adorn_title(non_tab), "When input is not a data.frame of class tabyl, a value must be specified for the col_name argument")
 
   expect_equal(
@@ -111,7 +111,7 @@ test_that("for printing purposes: tabyl class stays tabyl, data.frame stays data
     mtcars %>%
     dplyr::group_by(cyl, am) %>%
     dplyr::summarise(mean_mpg = mean(mpg)) %>%
-    tidyr::spread(am, mean_mpg)
+    tidyr::pivot_wider(names_from = am, values_from = mean_mpg)
 
   # handles tibble input
   expect_s3_class(

tests/testthat/test-adorn-totals.R

@@ -120,15 +120,15 @@ ct_2 <-
   mtcars %>%
   dplyr::group_by(cyl, gear) %>%
   dplyr::tally() %>%
-  tidyr::spread(gear, n)
+  tidyr::pivot_wider(names_from = gear, values_from = n)
 df1 <- data.frame(x = c(1, 2), y = c(NA, 4))
 
 test_that("grouped_df gets ungrouped and succeeds", {
   ct_2 <-
     mtcars %>%
     dplyr::group_by(cyl, gear) %>%
     dplyr::tally() %>%
-    tidyr::spread(gear, n)
+    tidyr::pivot_wider(names_from = gear, values_from = n)
   expect_equal(
     ct_2 %>% adorn_totals(),
     ct_2 %>% dplyr::ungroup() %>% adorn_totals()

tests/testthat/test-tabyl-classifiers.R

@@ -5,11 +5,16 @@ a <- mtcars %>%
 
 b <- mtcars %>%
   dplyr::count(cyl, carb) %>%
-  tidyr::spread(carb, n, fill = 0) %>%
+  tidyr::pivot_wider(
+    names_from = carb,
+    values_from = n,
+    values_fill = 0,
+    names_sort = TRUE
+  ) %>%
   as.data.frame() # for comparison purposes, remove the tbl_df aspect
 
 
-test_that("as_tabyl works on result of a non-janitor count/spread", {
+test_that("as_tabyl works on result of a non-janitor count/pivot_wider", {
   expect_equal(
     as_tabyl(a),
     as_tabyl(b, 2, "cyl", "carb")

vignettes/tabyls.Rmd

@@ -208,11 +208,11 @@ cases %>%
 Here's a more complex example that uses a data.frame of means, not counts.  We create a table containing the mean of a 3rd variable when grouped by two other variables, then use `adorn_` functions to round the values and append Ns.  The first part is pretty straightforward: 
 
 ```{r more_non_tabyls, warning = FALSE, message = FALSE}
-library(tidyr) # for spread()
+library(tidyr) # for pivot_wider()
 mpg_by_cyl_and_am <- mtcars %>%
   group_by(cyl, am) %>%
   summarise(mpg = mean(mpg), .groups = "drop") %>%
-  spread(am, mpg)
+  pivot_wider(names_from = am, values_from =  mpg)
 
 mpg_by_cyl_and_am
 ```

vignettes/tabyls.md

@@ -353,11 +353,11 @@ grouped by two other variables, then use `adorn_` functions to round the
 values and append Ns. The first part is pretty straightforward:
 
 ``` r
-library(tidyr) # for spread()
+library(tidyr) # for pivot_wider()
 mpg_by_cyl_and_am <- mtcars %>%
   group_by(cyl, am) %>%
   summarise(mpg = mean(mpg), .groups = "drop") %>%
-  spread(am, mpg)
+  pivot_wider(names_from = am, values_from = mpg)
 
 mpg_by_cyl_and_am
 #> # A tibble: 3 × 3