Use .by syntax in pivot_wider dplyr helper message (#1529)

Fixes #1516

NEWS.md

@@ -1,5 +1,7 @@
 # tidyr (development version)
 
+* `pivot_wider` now uses `.by` and `|>` syntax for the dplyr helper message to identify duplicates (@boshek, #1516) 
+
 # tidyr 1.3.0
 
 ## New features

R/pivot-wide.R

@@ -441,9 +441,8 @@ pivot_wider_spec <- function(data,
       "*" = "Use `values_fn = list` to suppress this warning.",
       "*" = "Use `values_fn = {{summary_fun}}` to summarise duplicates.",
       "*" = "Use the following dplyr code to identify duplicates.",
-      " " = "  {{data}} %>%",
-      " " = "    dplyr::group_by({group_cols}) %>%",
-      " " = "    dplyr::summarise(n = dplyr::n(), .groups = \"drop\") %>%",
+      " " = "  {{data}} |>",
+      " " = "    dplyr::summarise(n = dplyr::n(), .by = c({group_cols})) |>",
       " " = "    dplyr::filter(n > 1L)"
     ))
   }

tests/testthat/_snaps/pivot-wide.md

@@ -201,9 +201,8 @@
       * Use `values_fn = list` to suppress this warning.
       * Use `values_fn = {summary_fun}` to summarise duplicates.
       * Use the following dplyr code to identify duplicates.
-        {data} %>%
-        dplyr::group_by(a, key) %>%
-        dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
+        {data} |>
+        dplyr::summarise(n = dplyr::n(), .by = c(a, key)) |>
         dplyr::filter(n > 1L)
 
 # duplicated key warning mentions every applicable column
@@ -216,9 +215,8 @@
       * Use `values_fn = list` to suppress this warning.
       * Use `values_fn = {summary_fun}` to summarise duplicates.
       * Use the following dplyr code to identify duplicates.
-        {data} %>%
-        dplyr::group_by(key) %>%
-        dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
+        {data} |>
+        dplyr::summarise(n = dplyr::n(), .by = c(key)) |>
         dplyr::filter(n > 1L)
     Output
       # A tibble: 1 x 3
@@ -236,9 +234,8 @@
       * Use `values_fn = list` to suppress this warning.
       * Use `values_fn = {summary_fun}` to summarise duplicates.
       * Use the following dplyr code to identify duplicates.
-        {data} %>%
-        dplyr::group_by(key) %>%
-        dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
+        {data} |>
+        dplyr::summarise(n = dplyr::n(), .by = c(key)) |>
         dplyr::filter(n > 1L)
     Output
       # A tibble: 1 x 3
@@ -256,9 +253,8 @@
       * Use `values_fn = list` to suppress this warning.
       * Use `values_fn = {summary_fun}` to summarise duplicates.
       * Use the following dplyr code to identify duplicates.
-        {data} %>%
-        dplyr::group_by(`a 1`, a2, `the-key`) %>%
-        dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
+        {data} |>
+        dplyr::summarise(n = dplyr::n(), .by = c(`a 1`, a2, `the-key`)) |>
         dplyr::filter(n > 1L)
 
 # values_fn is validated