Use tidyr new functions (tidyr::gather() -> tidyr::pivot_longer()…

… and `tidyr::spread()` -> `tidyr::pivot_wider()` )

DESCRIPTION

@@ -68,7 +68,7 @@ Suggests:
     rvest,
     shiny (>= 1.9.1),
     testthat (>= 3.1.9),
-    tidyr,
+    tidyr (>= 1.0.0),
     webshot2 (>= 0.1.0),
     withr
 Config/Needs/coverage: officer

R/data_color.R

@@ -623,7 +623,11 @@
 #'   dplyr::filter(latitude == 20 & tst <= "1200") |>
 #'   dplyr::select(-latitude) |>
 #'   dplyr::filter(!is.na(sza)) |>
-#'   tidyr::spread(key = "tst", value = sza) |>
+#'   tidyr::pivot_wider(
+#'     names_from = tst,
+#'     values_from = sza,
+#'     names_sort = TRUE
+#'   ) |>
 #'   gt(rowname_col = "month") |>
 #'   sub_missing(missing_text = "") |>
 #'   data_color(
@@ -679,7 +683,7 @@ data_color <- function(
   direction <- rlang::arg_match0(direction, values = c("column", "row"))
 
   # Get the correct `method` value
-  method <- 
+  method <-
     rlang::arg_match0(
       method,
       values = c("auto", "numeric", "bin", "quantile", "factor")

R/fmt_number.R

@@ -308,7 +308,7 @@
 #'   dplyr::select(country_code_3, year, population) |>
 #'   dplyr::filter(country_code_3 %in% c("CHN", "IND", "USA", "PAK", "IDN")) |>
 #'   dplyr::filter(year > 1975 & year %% 5 == 0) |>
-#'   tidyr::spread(year, population) |>
+#'   tidyr::pivot_wider(names_from = year, values_from = population) |>
 #'   dplyr::arrange(desc(`2015`)) |>
 #'   gt(rowname_col = "country_code_3") |>
 #'   fmt_number(suffixing = TRUE)
@@ -327,7 +327,7 @@
 #'   dplyr::select(country_code_3, year, population) |>
 #'   dplyr::filter(country_code_3 %in% c("CHN", "IND", "USA", "PAK", "IDN")) |>
 #'   dplyr::filter(year > 1975 & year %% 5 == 0) |>
-#'   tidyr::spread(year, population) |>
+#'   tidyr::pivot_wider(names_from = year, values_from = population) |>
 #'   dplyr::arrange(desc(`2015`)) |>
 #'   gt(rowname_col = "country_code_3") |>
 #'   fmt_number(suffixing = TRUE, n_sigfig = 3)
@@ -676,7 +676,7 @@ fmt_number <- function(
 #'
 #' @section Compatibility of arguments with the `from_column()` helper function:
 #'
-#' [from_column()] can be used with certain arguments of  `fmt_integer()` to
+#' [from_column()] can be used with certain arguments of `fmt_integer()` to
 #' obtain varying parameter values from a specified column within the table.
 #' This means that each row could be formatted a little bit differently. These
 #' arguments provide support for [from_column()]:
@@ -742,7 +742,7 @@ fmt_number <- function(
 #'   dplyr::select(country_code_3, year, population) |>
 #'   dplyr::filter(country_code_3 %in% c("CHN", "IND", "USA", "PAK", "IDN")) |>
 #'   dplyr::filter(year > 1975 & year %% 5 == 0) |>
-#'   tidyr::spread(year, population) |>
+#'   tidyr::pivot_wider(names_from = year, values_from = population)
 #'   dplyr::arrange(desc(`2015`)) |>
 #'   gt(rowname_col = "country_code_3") |>
 #'   fmt_integer(scale_by = 1 / 1E6) |>

R/format_data.R

@@ -1763,7 +1763,7 @@ fmt_partsper <- function(
   #
 
   # Ensure that arguments are matched
-  to_units <- 
+  to_units <-
     rlang::arg_match0(
       to_units,
       values = c("per-mille", "per-myriad", "pcm", "ppm", "ppb", "ppt", "ppq")
@@ -4240,7 +4240,7 @@ fmt_duration <- function(
   stop_if_not_gt_tbl(data = data)
 
   # Ensure that arguments are matched
-  duration_style <- 
+  duration_style <-
     rlang::arg_match0(
       duration_style,
       values = c("narrow", "wide", "colon-sep", "iso")
@@ -9665,7 +9665,7 @@ fmt_markdown <- function(
   #
 
   # Ensure that arguments are matched
-  md_engine <- 
+  md_engine <-
     rlang::arg_match0(
       md_engine,
       values = c("markdown", "commonmark")
@@ -10017,7 +10017,7 @@ fmt_passthrough <- function(
 #'   dplyr::select(country_code_3, year, population) |>
 #'   dplyr::filter(country_code_3 %in% c("CHN", "IND", "USA", "PAK", "IDN")) |>
 #'   dplyr::filter(year > 1975 & year %% 5 == 0) |>
-#'   tidyr::spread(year, population) |>
+#'   tidyr::pivot_wider(names_from = year, values_from = population)
 #'   dplyr::arrange(desc(`2020`)) |>
 #'   gt(rowname_col = "country_code_3") |>
 #'   fmt_auto(lg_num_pref = "suf")

R/helpers.R

@@ -1542,7 +1542,11 @@ cells_group <- function(groups = everything()) {
 #'   dplyr::filter(latitude == 20 & tst <= "1000") |>
 #'   dplyr::select(-latitude) |>
 #'   dplyr::filter(!is.na(sza)) |>
-#'   tidyr::spread(key = "tst", value = sza) |>
+#'   tidyr::pivot_wider(
+#'     names_from = "tst",
+#'     values_from = sza,
+#'     names_sort = TRUE
+#'   ) |>
 #'   gt(rowname_col = "month") |>
 #'   sub_missing(missing_text = "") |>
 #'   tab_style(

data-raw/01-countrypops.R

@@ -71,8 +71,14 @@ countrypops <-
         `2021` = col_integer(),
         `2022` = col_integer(),
         `2023` = col_integer()
-    ), na = character()
+    ),
+    na = ""
   ) %>%
-  tidyr::gather("year", "population", `1960`:`2023`) %>%
-  dplyr::arrange(country_code_3, year) %>%
-  dplyr::mutate(year = as.integer(year))
+  tidyr::pivot_longer(
+    cols = `1960`:`2023`,
+    names_to = "year",
+    # transform year to integer
+    names_transform = as.integer,
+    values_to = "population"
+  ) %>%
+  dplyr::arrange(country_code_3, year)

data-raw/05-pizzaplace.R

@@ -1,5 +1,4 @@
 library(tidyverse)
-library(lubridate)
 library(progress)
 
 # Define the pizza names, types, available sizes, and prices for each;
@@ -503,7 +502,11 @@ orders_year <-
     daily_random_orders =
       sample(floor(rnorm(100, mean = 15, sd = 4)), n(), replace = TRUE)
   ) %>%
-  tidyr::gather("order_type", "n", grp_ord_n:daily_random_orders) %>%
+  tidyr::pivot_longer(
+    cols = grp_ord_n:daily_random_orders,
+    names_to = "order_type",
+    values_to = "n"
+  ) %>%
   dplyr::arrange(date, order_type) %>%
   dplyr::filter(!(date %in% full_closures)) %>%
   apply_exceptional_days(exceptional_df = exceptional_tbl)

tests/testthat/test-data_color.R

@@ -907,7 +907,7 @@ test_that("The direction of coloring can be column-wise or row-wise", {
     dplyr::filter(latitude == 20, tst <= "1200") %>%
     dplyr::select(-latitude) %>%
     dplyr::filter(!is.na(sza)) %>%
-    tidyr::spread(key = "tst", value = sza) %>%
+    tidyr::pivot_wider(names_from = "tst", values_from = sza, names_sort = TRUE) %>%
     gt(rowname_col = "month") %>%
     sub_missing(missing_text = "")