Switch package to use native pipe

R/pairwise_cor.R

@@ -20,16 +20,16 @@
 #' library(dplyr)
 #' library(gapminder)
 #'
-#' gapminder %>%
+#' gapminder |>
 #'   pairwise_cor(country, year, lifeExp)
 #'
-#' gapminder %>%
+#' gapminder |>
 #'   pairwise_cor(country, year, lifeExp, sort = TRUE)
 #'
 #' # United Nations voting data
 #' if (require("unvotes", quietly = TRUE)) {
-#'   country_cors <- un_votes %>%
-#'     mutate(vote = as.numeric(vote)) %>%
+#'   country_cors <- un_votes |>
+#'     mutate(vote = as.numeric(vote)) |>
 #'     pairwise_cor(country, rcid, vote, sort = TRUE)
 #' }
 #'
@@ -68,8 +68,8 @@ pairwise_cor_ <- function(tbl, item, feature, value,
   }
   cor_func <- squarely_(f, sparse = sparse, ...)
 
-  tbl %>%
-    ungroup() %>%
-    cor_func(item, feature, value) %>%
+  tbl |>
+    ungroup() |>
+    cor_func(item, feature, value) |>
     rename(correlation = value)
 }

R/pairwise_count.R

@@ -51,9 +51,9 @@ pairwise_count_ <- function(tbl, item, feature, wt = NULL, ...) {
     func <- squarely_(function(m) m %*% t(m > 0), sparse = TRUE, ...)
   }
 
-  tbl %>%
-    distinct(.data[[item]], .data[[feature]], .keep_all = TRUE) %>%
-    mutate(..value = 1) %>%
-    func(item, feature, wt) %>%
+  tbl |>
+    distinct(.data[[item]], .data[[feature]], .keep_all = TRUE) |>
+    mutate(..value = 1) |>
+    func(item, feature, wt) |>
     rename(n = value)
 }

R/pairwise_delta.R

@@ -20,32 +20,32 @@
 #' library(tidytext)
 #'
 #' # closest documents in terms of 1000 most frequent words
-#' closest <- austen_books() %>%
-#'   unnest_tokens(word, text) %>%
-#'   count(book, word) %>%
-#'   top_n(1000, n) %>%
-#'   pairwise_delta(book, word, n, method = "burrows") %>%
+#' closest <- austen_books() |>
+#'   unnest_tokens(word, text) |>
+#'   count(book, word) |>
+#'   top_n(1000, n) |>
+#'   pairwise_delta(book, word, n, method = "burrows") |>
 #'   arrange(delta)
 #'
 #' closest
 #'
-#' closest %>%
+#' closest |>
 #'   filter(item1 == "Pride & Prejudice")
 #'
 #' # to remove duplicates, use upper = FALSE
-#' closest <- austen_books() %>%
-#'   unnest_tokens(word, text) %>%
-#'   count(book, word) %>%
-#'   top_n(1000, n) %>%
-#'   pairwise_delta(book, word, n, method = "burrows", upper = FALSE) %>%
+#' closest <- austen_books() |>
+#'   unnest_tokens(word, text) |>
+#'   count(book, word) |>
+#'   top_n(1000, n) |>
+#'   pairwise_delta(book, word, n, method = "burrows", upper = FALSE) |>
 #'   arrange(delta)
 #'
 #' # Can also use Argamon's Linear Delta
-#' closest <- austen_books() %>%
-#'   unnest_tokens(word, text) %>%
-#'   count(book, word) %>%
-#'   top_n(1000, n) %>%
-#'   pairwise_delta(book, word, n, method = "argamon", upper = FALSE) %>%
+#' closest <- austen_books() |>
+#'   unnest_tokens(word, text) |>
+#'   count(book, word) |>
+#'   top_n(1000, n) |>
+#'   pairwise_delta(book, word, n, method = "argamon", upper = FALSE) |>
 #'   arrange(delta)
 #'
 #' @export
@@ -79,7 +79,7 @@ pairwise_delta_ <- function(tbl, item, feature, value, method = "burrows", ...)
 
   d_func <- squarely_(delta_func, ...)
 
-  tbl %>%
-    d_func(item, feature, value) %>%
+  tbl |>
+    d_func(item, feature, value) |>
     rename(delta = value)
 }

R/pairwise_dist.R

@@ -19,23 +19,23 @@
 #' library(dplyr)
 #'
 #' # closest countries in terms of life expectancy over time
-#' closest <- gapminder %>%
-#'   pairwise_dist(country, year, lifeExp) %>%
+#' closest <- gapminder |>
+#'   pairwise_dist(country, year, lifeExp) |>
 #'   arrange(distance)
 #'
 #' closest
 #'
-#' closest %>%
+#' closest |>
 #'   filter(item1 == "United States")
 #'
 #' # to remove duplicates, use upper = FALSE
-#' gapminder %>%
-#'   pairwise_dist(country, year, lifeExp, upper = FALSE) %>%
+#' gapminder |>
+#'   pairwise_dist(country, year, lifeExp, upper = FALSE) |>
 #'   arrange(distance)
 #'
 #' # Can also use Manhattan distance
-#' gapminder %>%
-#'   pairwise_dist(country, year, lifeExp, method = "manhattan", upper = FALSE) %>%
+#' gapminder |>
+#'   pairwise_dist(country, year, lifeExp, method = "manhattan", upper = FALSE) |>
 #'   arrange(distance)
 #'
 #' @export
@@ -54,7 +54,7 @@ pairwise_dist <- function(tbl, item, feature, value,
 pairwise_dist_ <- function(tbl, item, feature, value, method = "euclidean", ...) {
   d_func <- squarely_(function(m) as.matrix(stats::dist(m, method = method)), ...)
 
-  tbl %>%
-    d_func(item, feature, value) %>%
+  tbl |>
+    d_func(item, feature, value) |>
     rename(distance = value)
 }

R/pairwise_pmi.R

@@ -56,10 +56,10 @@ pairwise_pmi_ <- function(tbl, item, feature, sort = FALSE, ...) {
   }
   pmi_func <- squarely_(f, sparse = TRUE, sort = sort, ...)
 
-  tbl %>%
-    ungroup() %>%
-    mutate(..value = 1) %>%
-    pmi_func(item, feature, "..value") %>%
-    mutate(value = log(value)) %>%
+  tbl |>
+    ungroup() |>
+    mutate(..value = 1) |>
+    pmi_func(item, feature, "..value") |>
+    mutate(value = log(value)) |>
     rename(pmi = value)
 }

R/pairwise_similarity.R

@@ -25,20 +25,20 @@
 #' library(tidytext)
 #'
 #' # Comparing Jane Austen novels
-#' austen_words <- austen_books() %>%
-#'   unnest_tokens(word, text) %>%
-#'   anti_join(stop_words, by = "word") %>%
-#'   count(book, word) %>%
+#' austen_words <- austen_books() |>
+#'   unnest_tokens(word, text) |>
+#'   anti_join(stop_words, by = "word") |>
+#'   count(book, word) |>
 #'   ungroup()
 #'
 #' # closest books to each other
-#' closest <- austen_words %>%
-#'   pairwise_similarity(book, word, n) %>%
+#' closest <- austen_words |>
+#'   pairwise_similarity(book, word, n) |>
 #'   arrange(desc(similarity))
 #'
 #' closest
 #'
-#' closest %>%
+#' closest |>
 #'   filter(item1 == "Emma")
 #'
 #' @export
@@ -59,7 +59,7 @@ pairwise_similarity_ <- function(tbl, item, feature, value, ...) {
     normed %*% t(normed)
   }, sparse = TRUE, ...)
 
-  tbl %>%
-    d_func(item, feature, value) %>%
+  tbl |>
+    d_func(item, feature, value) |>
     rename(similarity = value)
 }

R/squarely.R

@@ -27,8 +27,8 @@
 #' library(dplyr)
 #' library(gapminder)
 #'
-#' closest_continent <- gapminder %>%
-#'   group_by(continent) %>%
+#' closest_continent <- gapminder |>
+#'   group_by(continent) |>
 #'   squarely(dist)(country, year, lifeExp)
 #'
 #' @export
@@ -54,11 +54,11 @@ squarely_ <- function(.f, diag = FALSE,
   f <- function(tbl, item, feature, value, ...) {
     if (inherits(tbl, "grouped_df")) {
       # perform within each group, then restore groups
-      ret <- tbl %>%
-        tidyr::nest() %>%
-        mutate(data = purrr::map(data, f, item, feature, value)) %>%
-        filter(purrr::map_lgl(data, ~ nrow(.) > 0)) %>%
-        tidyr::unnest(data) %>%
+      ret <- tbl |>
+        tidyr::nest() |>
+        mutate(data = purrr::map(data, f, item, feature, value)) |>
+        filter(purrr::map_lgl(data, ~ nrow(.) > 0)) |>
+        tidyr::unnest(data) |>
         dplyr::group_by_at(dplyr::group_vars(tbl))
 
       return(ret)

R/widely.R

@@ -33,17 +33,17 @@
 #'
 #' gapminder
 #'
-#' gapminder %>%
+#' gapminder |>
 #'   widely(dist)(country, year, lifeExp)
 #'
 #' # can perform within groups
-#' closest_continent <- gapminder %>%
-#'   group_by(continent) %>%
+#' closest_continent <- gapminder |>
+#'   group_by(continent) |>
 #'   widely(dist)(country, year, lifeExp)
 #' closest_continent
 #'
 #' # for example, find the closest pair in each
-#' closest_continent %>%
+#' closest_continent |>
 #'   top_n(1, -value)
 #'
 #' @export
@@ -80,10 +80,10 @@ widely_ <- function(.f,
     if (inherits(tbl, "grouped_df")) {
       # perform within each group
       # (group_by_at isn't necessary since 1.0.0, but is in earlier versions)
-      ret <- tbl %>%
-        tidyr::nest() %>%
-        mutate(data = purrr::map(data, f, row, column, value)) %>%
-        tidyr::unnest(data) %>%
+      ret <- tbl |>
+        tidyr::nest() |>
+        mutate(data = purrr::map(data, f, row, column, value)) |>
+        tidyr::unnest(data) |>
         dplyr::group_by_at(dplyr::group_vars(tbl))
 
       return(ret)
@@ -111,8 +111,8 @@ widely_ <- function(.f,
     }
     output <- purrr::as_mapper(.f)(input, ...)
 
-    ret <- output %>%
-      custom_melt() %>%
+    ret <- output |>
+      custom_melt() |>
       as_tibble()
 
     if (sort) {

R/widely_hclust.R

@@ -17,18 +17,18 @@
 #'
 #' # Construct Euclidean distances between countries based on life
 #' # expectancy over time
-#' country_distances <- gapminder %>%
+#' country_distances <- gapminder |>
 #'   pairwise_dist(country, year, lifeExp)
 #'
 #' country_distances
 #'
 #' # Turn this into 5 hierarchical clusters
-#' clusters <- country_distances %>%
+#' clusters <- country_distances |>
 #'   widely_hclust(item1, item2, distance, k = 8)
 #'
 #' # Examine a few such clusters
-#' clusters %>% filter(cluster == 1)
-#' clusters %>% filter(cluster == 2)
+#' clusters |> filter(cluster == 1)
+#' clusters |> filter(cluster == 2)
 #'
 #' @seealso [cutree]
 #'
@@ -46,13 +46,13 @@ widely_hclust <- function(tbl, item1, item2, distance, k = NULL, h = NULL) {
 
   tibble(item1 = match(tbl[[col1_str]], unique_items),
          item2 = match(tbl[[col2_str]], unique_items),
-         distance = tbl[[dist_str]]) %>%
-    reshape2::acast(item1 ~ item2, value.var = "distance", fill = max_distance) %>%
-    stats::as.dist() %>%
-    stats::hclust() %>%
-    stats::cutree(k = k, h = h) %>%
-    tibble::enframe("item", "cluster") %>%
+         distance = tbl[[dist_str]]) |>
+    reshape2::acast(item1 ~ item2, value.var = "distance", fill = max_distance) |>
+    stats::as.dist() |>
+    stats::hclust() |>
+    stats::cutree(k = k, h = h) |>
+    tibble::enframe("item", "cluster") |>
     dplyr::mutate(item = unique_items[as.integer(item)],
-                  cluster = factor(cluster)) %>%
+                  cluster = factor(cluster)) |>
     dplyr::arrange(cluster)
 }

R/widely_kmeans.R

@@ -21,17 +21,17 @@
 #' library(gapminder)
 #' library(dplyr)
 #'
-#' clusters <- gapminder %>%
+#' clusters <- gapminder |>
 #'   widely_kmeans(country, year, lifeExp, k = 5)
 #'
 #' clusters
 #'
-#' clusters %>%
+#' clusters |>
 #'   count(cluster)
 #'
 #' # Examine a few clusters
-#' clusters %>% filter(cluster == 1)
-#' clusters %>% filter(cluster == 2)
+#' clusters |> filter(cluster == 1)
+#' clusters |> filter(cluster == 2)
 #'
 #' @export
 widely_kmeans <- function(tbl, item, feature, value, k, fill = 0, ...) {
@@ -41,14 +41,14 @@ widely_kmeans <- function(tbl, item, feature, value, k, fill = 0, ...) {
 
   form <- stats::as.formula(paste(item_str, "~", feature_str))
 
-  m <- tbl %>%
+  m <- tbl |>
     reshape2::acast(form, value.var = value_str, fill = fill)
 
   clustered <- stats::kmeans(m, k, ...)
 
   # Add the clusters to the original table
   i <- match(rownames(m), as.character(tbl[[item_str]]))
   tibble::tibble(!!sym(item_str) := tbl[[item_str]][i],
-                 cluster = factor(clustered$cluster)) %>%
+                 cluster = factor(clustered$cluster)) |>
     dplyr::arrange(cluster)
 }

R/widely_svd.R

@@ -25,7 +25,7 @@
 #' library(gapminder)
 #'
 #' # principal components driving change
-#' gapminder_svd <- gapminder %>%
+#' gapminder_svd <- gapminder |>
 #'   widely_svd(country, year, lifeExp)
 #'
 #' gapminder_svd
@@ -34,9 +34,9 @@
 #' library(ggplot2)
 #' library(tidyr)
 #'
-#' gapminder_svd %>%
-#'   spread(dimension, value) %>%
-#'   inner_join(distinct(gapminder, country, continent), by = "country") %>%
+#' gapminder_svd |>
+#'   spread(dimension, value) |>
+#'   inner_join(distinct(gapminder, country, continent), by = "country") |>
 #'   ggplot(aes(`1`, `2`, label = country)) +
 #'   geom_point(aes(color = continent)) +
 #'   geom_text(vjust = 1, hjust = 1)
@@ -94,7 +94,7 @@ widely_svd_ <- function(tbl, item, feature, value, nv = NULL, weight_d = FALSE,
 
   ret <- widely_(perform_svd, sparse = sparse)(tbl, item, feature, value)
 
-  ret <- ret %>%
+  ret <- ret |>
     transmute(item = item_u[as.integer(item1)],
               dimension = item2,
               value)