More simplification of code + add missing pipes

R/cols_label.R

@@ -226,8 +226,7 @@
 #'     name, ends_with("2001"), ends_with("2006"), matches("2001_2006")
 #'   ) |>
 #'   dplyr::filter(population_2001 > 100000) |>
-#'   dplyr::arrange(desc(pop_change_2001_2006_pct)) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(pop_change_2001_2006_pct, n = 10) |>
 #'   gt() |>
 #'   fmt_integer() |>
 #'   fmt_percent(columns = matches("change"), decimals = 1) |>
@@ -254,9 +253,7 @@
 #'   dplyr::select(
 #'     name, population_2021, density_2021, land_area_km2, latitude, longitude
 #'   ) |>
-#'   dplyr::filter(population_2021 > 100000) |>
-#'   dplyr::arrange(desc(population_2021)) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(population_2021, n = 10) |>
 #'   gt() |>
 #'   fmt_integer(columns = population_2021) |>
 #'   fmt_number(

R/fmt_number.R

@@ -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::pivot_wider(names_from = year, values_from = 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

@@ -6193,9 +6193,8 @@ format_units_by_context <- function(
 #' ```r
 #' towny |>
 #'   dplyr::filter(csd_type == "city") |>
-#'   dplyr::arrange(desc(population_2021)) |>
 #'   dplyr::select(name, website, population_2021) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(population_2021, n = 10) |>
 #'   gt() |>
 #'   tab_header(
 #'     title = md("The 10 Largest Municipalities in `towny`"),
@@ -6223,9 +6222,8 @@ format_units_by_context <- function(
 #' ```r
 #' towny |>
 #'   dplyr::filter(csd_type == "city") |>
-#'   dplyr::arrange(desc(population_2021)) |>
 #'   dplyr::select(name, website, population_2021) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(population_2021, n = 10) |>
 #'   gt() |>
 #'   tab_header(
 #'     title = md("The 10 Largest Municipalities in `towny`"),
@@ -6261,9 +6259,8 @@ format_units_by_context <- function(
 #' ```r
 #' towny |>
 #'   dplyr::filter(csd_type == "city") |>
-#'   dplyr::arrange(desc(population_2021)) |>
 #'   dplyr::select(name, website, population_2021) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(population_2021, n = 10) |>
 #'   dplyr::mutate(ranking = dplyr::row_number()) |>
 #'   gt(rowname_col = "ranking") |>
 #'   tab_header(
@@ -6302,9 +6299,8 @@ format_units_by_context <- function(
 #'
 #' ```r
 #' towny |>
-#'   dplyr::arrange(population_2021) |>
 #'   dplyr::select(name, website, population_2021) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_min(population_2021, n = 10) |>
 #'   gt() |>
 #'   tab_header(
 #'     title = md("The 10 Smallest Municipalities in `towny`"),
@@ -10017,7 +10013,7 @@ 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::pivot_wider(names_from = year, values_from = 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

@@ -582,8 +582,7 @@ currency <- function(
 #'
 #' ```r
 #' towny |>
-#'   dplyr::arrange(desc(density_2021)) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(density_2021, n = 10) |>
 #'   dplyr::select(name, population_2021, density_2021, land_area_km2) |>
 #'   gt(rowname_col = "name") |>
 #'   fmt_integer(columns = population_2021) |>

R/tab_footnote.R

@@ -256,9 +256,8 @@
 #' ```r
 #' towny |>
 #'   dplyr::filter(csd_type == "city") |>
-#'   dplyr::arrange(desc(population_2021)) |>
 #'   dplyr::select(name, density_2021, population_2021) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(population_2021, n = 10) |>
 #'   gt(rowname_col = "name") |>
 #'   tab_header(
 #'     title = md("The 10 Largest Municipalities in `towny`"),

R/text_transform.R

@@ -162,9 +162,8 @@ text_replace <- function(
 #'
 #' ```r
 #' metro |>
-#'   dplyr::arrange(desc(passengers)) |>
 #'   dplyr::select(name, lines, connect_rer) |>
-#'   dplyr::slice_head(n = 10) |>
+#'   dplyr::slice_max(passengers, n = 10) |>
 #'   gt() |>
 #'   text_case_when(
 #'     stringr::str_count(x, pattern = "[ABCDE]") == 1 ~ "One connection.",

vignettes/case-study-gtcars.Rmd

@@ -335,17 +335,15 @@ In terms of structuring the code, we're taking all the previous statements and p
 # this will be used to target the correct cell for a footnote
 best_gas_mileage_city <- 
   gtcars |> 
-  arrange(desc(mpg_c)) |>
-  slice(1) |>
+  slice_max(mpg_c, n = 1) |>
   mutate(car = paste(mfr, model)) |>
   pull(car)
 
 # Use dplyr functions to get the car with the highest horsepower
 # this will be used to target the correct cell for a footnote
 highest_horsepower <- 
   gtcars |> 
-  arrange(desc(hp)) |>
-  slice(1) |>
+  slice_max(hp, n = 1) |>
   mutate(car = paste(mfr, model)) |>
   pull(car)
 

vignettes/gt.Rmd

@@ -38,8 +38,7 @@ islands_tbl <-
     name = names(islands),
     size = islands
   ) |>
-  arrange(desc(size)) |>
-  slice(1:10)
+  slice_max(size, n = 10)
 
 # Display the table
 islands_tbl
@@ -147,8 +146,7 @@ Here is a slightly more complex example of adding footnotes that use expressions
 # largest landmass ('Asia')
 largest <- 
   islands_tbl |> 
-  arrange(desc(size)) |>
-  slice(1) |>
+  slice_max(size, n = 1) |>
   pull(name)
 
 # Create two additional footnotes, using the