From 7d6d48fed9ea98a898a0742272a13ba3711bcacc Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Mon, 9 Mar 2026 10:58:14 -0600
Subject: [PATCH 1/4] Switch to native pipe

---
 R/pairwise_cor.R             | 14 +++++++-------
 R/pairwise_count.R           |  8 ++++----
 R/pairwise_delta.R           | 36 ++++++++++++++++++------------------
 R/pairwise_dist.R            | 18 +++++++++---------
 R/pairwise_pmi.R             | 10 +++++-----
 R/pairwise_similarity.R      | 18 +++++++++---------
 R/squarely.R                 | 14 +++++++-------
 R/widely.R                   | 20 ++++++++++----------
 R/widely_hclust.R            | 22 +++++++++++-----------
 R/widely_kmeans.R            | 12 ++++++------
 R/widely_svd.R               | 10 +++++-----
 README.Rmd                   | 12 ++++++------
 vignettes/intro.Rmd          | 12 ++++++------
 vignettes/united_nations.Rmd | 28 ++++++++++++++--------------
 14 files changed, 117 insertions(+), 117 deletions(-)

diff --git a/R/pairwise_cor.R b/R/pairwise_cor.R
index 198f4c2..239e952 100644
--- a/R/pairwise_cor.R
+++ b/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)
 }
diff --git a/R/pairwise_count.R b/R/pairwise_count.R
index 9c8de60..15e825e 100644
--- a/R/pairwise_count.R
+++ b/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)
 }
diff --git a/R/pairwise_delta.R b/R/pairwise_delta.R
index eaff548..0e01d3b 100644
--- a/R/pairwise_delta.R
+++ b/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)
 }
diff --git a/R/pairwise_dist.R b/R/pairwise_dist.R
index 4c50b46..cb6994d 100644
--- a/R/pairwise_dist.R
+++ b/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)
 }
diff --git a/R/pairwise_pmi.R b/R/pairwise_pmi.R
index 6b37e71..98f0ab3 100644
--- a/R/pairwise_pmi.R
+++ b/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)
 }
diff --git a/R/pairwise_similarity.R b/R/pairwise_similarity.R
index 71d95a8..664f592 100644
--- a/R/pairwise_similarity.R
+++ b/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)
 }
diff --git a/R/squarely.R b/R/squarely.R
index d57127e..60ccfb3 100644
--- a/R/squarely.R
+++ b/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)
diff --git a/R/widely.R b/R/widely.R
index 50a8b15..7a3f2b4 100644
--- a/R/widely.R
+++ b/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) {
diff --git a/R/widely_hclust.R b/R/widely_hclust.R
index dfcca45..47c7eaf 100644
--- a/R/widely_hclust.R
+++ b/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)
 }
diff --git a/R/widely_kmeans.R b/R/widely_kmeans.R
index c473eff..6337ecd 100644
--- a/R/widely_kmeans.R
+++ b/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, ...) {
@@ -41,7 +41,7 @@ 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, ...)
@@ -49,6 +49,6 @@ widely_kmeans <- function(tbl, item, feature, value, k, fill = 0, ...) {
   # 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)
 }
diff --git a/R/widely_svd.R b/R/widely_svd.R
index a6aacb1..d8ffab6 100644
--- a/R/widely_svd.R
+++ b/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)
diff --git a/README.Rmd b/README.Rmd
index 46df85d..a088c19 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -79,7 +79,7 @@ The widyr package offers `pairwise_` functions that operate on pairs of items wi
 ```{r}
 library(widyr)
 
-gapminder %>%
+gapminder |>
   pairwise_dist(country, year, lifeExp)
 ```
 
@@ -88,23 +88,23 @@ This finds the Euclidean distance between the `lifeExp` value in each pair of co
 We could find the closest pairs of countries overall with `arrange()`:
 
 ```{r}
-gapminder %>%
-  pairwise_dist(country, year, lifeExp) %>%
+gapminder |>
+  pairwise_dist(country, year, lifeExp) |>
   arrange(distance)
 ```
 
 Notice that this includes duplicates (Germany/Belgium and Belgium/Germany). To avoid those (the upper triangle of the distance matrix), use `upper = FALSE`:
 
 ```{r}
-gapminder %>%
-  pairwise_dist(country, year, lifeExp, upper = FALSE) %>%
+gapminder |>
+  pairwise_dist(country, year, lifeExp, upper = FALSE) |>
   arrange(distance)
 ```
 
 In some analyses, we may be interested in correlation rather than distance of pairs. For this we would use `pairwise_cor`:
 
 ```{r}
-gapminder %>%
+gapminder |>
   pairwise_cor(country, year, lifeExp, upper = FALSE)
 ```
 
diff --git a/vignettes/intro.Rmd b/vignettes/intro.Rmd
index 94acbaa..3a08946 100644
--- a/vignettes/intro.Rmd
+++ b/vignettes/intro.Rmd
@@ -47,29 +47,29 @@ The widyr package offers `pairwise_` functions that operate on pairs of items wi
 ```{r}
 library(widyr)
 
-gapminder %>%
+gapminder |>
   pairwise_dist(country, year, lifeExp)
 ```
 
 In a single step, this finds the Euclidean distance between the `lifeExp` value in each pair of countries, matching pairs based on year. We could find the closest pairs of countries overall with `arrange()`:
 
 ```{r}
-gapminder %>%
-  pairwise_dist(country, year, lifeExp) %>%
+gapminder |>
+  pairwise_dist(country, year, lifeExp) |>
   arrange(distance)
 ```
 
 Notice that this includes duplicates (Germany/Belgium and Belgium/Germany). To avoid those (the upper triangle of the distance matrix), use `upper = FALSE`:
 
 ```{r}
-gapminder %>%
-  pairwise_dist(country, year, lifeExp, upper = FALSE) %>%
+gapminder |>
+  pairwise_dist(country, year, lifeExp, upper = FALSE) |>
   arrange(distance)
 ```
 
 In some analyses, we may be interested in correlation rather than distance of pairs. For this we would use `pairwise_cor`:
 
 ```{r}
-gapminder %>%
+gapminder |>
   pairwise_cor(country, year, lifeExp, upper = FALSE, sort = TRUE)
 ```
diff --git a/vignettes/united_nations.Rmd b/vignettes/united_nations.Rmd
index 95d7d14..b13b74b 100644
--- a/vignettes/united_nations.Rmd
+++ b/vignettes/united_nations.Rmd
@@ -50,8 +50,8 @@ We may then be interested in obtaining a measure of country-to-country agreement
 ```{r cors}
 library(widyr)
 
-cors <- un_votes %>%
-  mutate(vote = as.numeric(vote)) %>%
+cors <- un_votes |>
+  mutate(vote = as.numeric(vote)) |>
   pairwise_cor(country, rcid, vote, use = "pairwise.complete.obs", sort = TRUE)
 
 cors
@@ -60,14 +60,14 @@ cors
 We could, for example, find the countries that the US is most and least in agreement with:
 
 ```{r US_cors}
-US_cors <- cors %>%
+US_cors <- cors |>
   filter(item1 == "United States")
 
 # Most in agreement
 US_cors
 
 # Least in agreement
-US_cors %>%
+US_cors |>
   arrange(correlation)
 ```
 
@@ -78,13 +78,13 @@ if (require("maps", quietly = TRUE) &&
     require("fuzzyjoin", quietly = TRUE) &&
     require("countrycode", quietly = TRUE) &&
     require("ggplot2", quietly = TRUE)) {
-  world_data <- map_data("world") %>%
-    regex_full_join(iso3166, by = c("region" = "mapname")) %>%
+  world_data <- map_data("world") |>
+    regex_full_join(iso3166, by = c("region" = "mapname")) |>
     filter(region != "Antarctica")
   
-  US_cors %>%
-    mutate(a2 = countrycode(item2, "country.name", "iso2c")) %>%
-    full_join(world_data, by = "a2") %>%
+  US_cors |>
+    mutate(a2 = countrycode(item2, "country.name", "iso2c")) |>
+    full_join(world_data, by = "a2") |>
     ggplot(aes(long, lat, group = group, fill = correlation)) +
     geom_polygon(color = "gray", size = .1) +
     scale_fill_gradient2() +
@@ -104,18 +104,18 @@ Another useful kind of visualization is a network plot, which can be created wit
 if (require("ggraph", quietly = TRUE) &&
     require("igraph", quietly = TRUE) &&
     require("countrycode", quietly = TRUE)) {
-  cors_filtered <- cors %>%
+  cors_filtered <- cors |>
     filter(correlation > .6)
   
-  continents <- tibble(country = unique(un_votes$country)) %>%
+  continents <- tibble(country = unique(un_votes$country)) |>
     filter(country %in% cors_filtered$item1 |
-             country %in% cors_filtered$item2) %>%
+             country %in% cors_filtered$item2) |>
     mutate(continent = countrycode(country, "country.name", "continent"))
   
   set.seed(2017)
   
-  cors_filtered %>%
-    graph_from_data_frame(vertices = continents) %>%
+  cors_filtered |>
+    graph_from_data_frame(vertices = continents) |>
     ggraph() +
     geom_edge_link(aes(edge_alpha = correlation)) +
     geom_node_point(aes(color = continent), size = 3) +

From 1a1132cf4bd35b8c8a5a6d4261b669b7cfde4cef Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Mon, 9 Mar 2026 10:58:20 -0600
Subject: [PATCH 2/4] Redocument

---
 man/pairwise_cor.Rd        |  8 ++++----
 man/pairwise_delta.Rd      | 32 ++++++++++++++++----------------
 man/pairwise_dist.Rd       | 14 +++++++-------
 man/pairwise_similarity.Rd | 14 +++++++-------
 man/squarely.Rd            |  4 ++--
 man/widely.Rd              |  8 ++++----
 man/widely_hclust.Rd       |  8 ++++----
 man/widely_kmeans.Rd       |  8 ++++----
 man/widely_svd.Rd          |  8 ++++----
 9 files changed, 52 insertions(+), 52 deletions(-)

diff --git a/man/pairwise_cor.Rd b/man/pairwise_cor.Rd
index f5fecd2..e515db9 100644
--- a/man/pairwise_cor.Rd
+++ b/man/pairwise_cor.Rd
@@ -53,16 +53,16 @@ that links them together. This is an example of the spread-operate-retidy patter
 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)
 }
 
diff --git a/man/pairwise_delta.Rd b/man/pairwise_delta.Rd
index 053b74e..f09c398 100644
--- a/man/pairwise_delta.Rd
+++ b/man/pairwise_delta.Rd
@@ -34,32 +34,32 @@ library(dplyr)
 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)
 
 }
diff --git a/man/pairwise_dist.Rd b/man/pairwise_dist.Rd
index 0c28775..702fd34 100644
--- a/man/pairwise_dist.Rd
+++ b/man/pairwise_dist.Rd
@@ -33,23 +33,23 @@ library(gapminder)
 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)
 
 }
diff --git a/man/pairwise_similarity.Rd b/man/pairwise_similarity.Rd
index 9c93b73..5ef18f4 100644
--- a/man/pairwise_similarity.Rd
+++ b/man/pairwise_similarity.Rd
@@ -38,20 +38,20 @@ library(dplyr)
 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")
 
 }
diff --git a/man/squarely.Rd b/man/squarely.Rd
index 19249ff..ed8ff8b 100644
--- a/man/squarely.Rd
+++ b/man/squarely.Rd
@@ -39,8 +39,8 @@ distance or correlation matrix.
 library(dplyr)
 library(gapminder)
 
-closest_continent <- gapminder \%>\%
-  group_by(continent) \%>\%
+closest_continent <- gapminder |>
+  group_by(continent) |>
   squarely(dist)(country, year, lifeExp)
 
 }
diff --git a/man/widely.Rd b/man/widely.Rd
index ceea2a9..0384356 100644
--- a/man/widely.Rd
+++ b/man/widely.Rd
@@ -44,17 +44,17 @@ library(gapminder)
 
 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)
 
 }
diff --git a/man/widely_hclust.Rd b/man/widely_hclust.Rd
index 3be99c0..1491070 100644
--- a/man/widely_hclust.Rd
+++ b/man/widely_hclust.Rd
@@ -30,18 +30,18 @@ library(dplyr)
 
 # 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{
diff --git a/man/widely_kmeans.Rd b/man/widely_kmeans.Rd
index 7793b0f..4d2737b 100644
--- a/man/widely_kmeans.Rd
+++ b/man/widely_kmeans.Rd
@@ -31,17 +31,17 @@ one-row-per-cluster.
 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)
 
 }
 \seealso{
diff --git a/man/widely_svd.Rd b/man/widely_svd.Rd
index 6defa51..a8e6354 100644
--- a/man/widely_svd.Rd
+++ b/man/widely_svd.Rd
@@ -41,7 +41,7 @@ library(dplyr)
 library(gapminder)
 
 # principal components driving change
-gapminder_svd <- gapminder \%>\%
+gapminder_svd <- gapminder |>
   widely_svd(country, year, lifeExp)
 
 gapminder_svd
@@ -50,9 +50,9 @@ gapminder_svd
 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)

From 9acee4a978a3ec3b8b894836bf541ce746467ff8 Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Mon, 9 Mar 2026 10:58:27 -0600
Subject: [PATCH 3/4] Update tests

---
 tests/testthat/test-pairwise-cor.R        |  8 ++--
 tests/testthat/test-pairwise-count.R      | 46 +++++++++++------------
 tests/testthat/test-pairwise-dist.R       |  2 +-
 tests/testthat/test-pairwise-similarity.R |  4 +-
 tests/testthat/test-squarely.R            |  6 +--
 tests/testthat/test-widely.R              |  8 ++--
 6 files changed, 37 insertions(+), 37 deletions(-)

diff --git a/tests/testthat/test-pairwise-cor.R b/tests/testthat/test-pairwise-cor.R
index 0174151..f116db3 100644
--- a/tests/testthat/test-pairwise-cor.R
+++ b/tests/testthat/test-pairwise-cor.R
@@ -7,7 +7,7 @@ d <- tibble(col = rep(c("a", "b", "c"), each = 3),
             value = c(1, 2, 3, 6, 5, 4, 7, 9, 8))
 
 test_that("pairwise_cor computes pairwise correlations", {
-  ret <- d %>%
+  ret <- d |>
     pairwise_cor(col, row, value)
 
   ret1 <- ret$correlation[ret$item1 == "a" & ret$item2 == "b"]
@@ -20,7 +20,7 @@ test_that("pairwise_cor computes pairwise correlations", {
 })
 
 test_that("pairwise_cor can compute Spearman correlations", {
-  ret <- d %>%
+  ret <- d |>
     pairwise_cor(col, row, value, method = "spearman")
 
   ret1 <- ret$correlation[ret$item1 == "a" & ret$item2 == "b"]
@@ -29,7 +29,7 @@ test_that("pairwise_cor can compute Spearman correlations", {
 
 test_that("pairwise_cor works on binary matrices", {
   cors <- data.frame(x = c("a", "a", "a", "b", "b", "b", "c", "c", "c"),
-                     y = c(1, 2, 3, 1, 2, 3, 1, 3, 4)) %>%
+                     y = c(1, 2, 3, 1, 2, 3, 1, 3, 4)) |>
     pairwise_cor(x, y, sort = TRUE)
 
   expect_equal(colnames(cors), c("item1", "item2", "correlation"))
@@ -39,7 +39,7 @@ test_that("pairwise_cor works on binary matrices", {
 test_that("pairwise_cor retains factor levels", {
   d$col <- factor(d$col, levels = c("b", "c", "a"))
 
-  ret <- d %>%
+  ret <- d |>
     pairwise_cor(col, row, value, method = "spearman")
 
   expect_is(ret$item1, "factor")
diff --git a/tests/testthat/test-pairwise-count.R b/tests/testthat/test-pairwise-count.R
index 35702c8..a2f7406 100644
--- a/tests/testthat/test-pairwise-count.R
+++ b/tests/testthat/test-pairwise-count.R
@@ -8,12 +8,12 @@ suppressPackageStartupMessages(library(tidytext))
 original <- tibble(txt = c("I felt a funeral in my brain,",
                            "And mourners, to and fro,",
                            "Kept treading, treading, till it seemed",
-                           "That sense was breaking through.")) %>%
-  mutate(line = row_number()) %>%
+                           "That sense was breaking through.")) |>
+  mutate(line = row_number()) |>
   unnest_tokens(char, txt, token = "characters")
 
 test_that("pairing and counting works", {
-  d <- original %>%
+  d <- original |>
     pairwise_count(char, line, sort = TRUE, upper = FALSE, diag = FALSE)
 
   expect_equal(nrow(d), 164)
@@ -29,25 +29,25 @@ test_that("pairing and counting works", {
 
   # for self-pairs, the number of occurrences should be the number of distinct
   # lines
-  d2 <- original %>%
+  d2 <- original |>
     pairwise_count(char, line, sort = TRUE, upper = FALSE, diag = TRUE)
 
   expect_equal(nrow(d2), nrow(d) + 20)
 
-  self_pairs <- d2 %>%
-    filter(item1 == item2) %>%
+  self_pairs <- d2 |>
+    filter(item1 == item2) |>
     arrange(item1)
 
-  char_counts <- original %>%
-    distinct(line, char) %>%
-    count(char) %>%
+  char_counts <- original |>
+    distinct(line, char) |>
+    count(char) |>
     arrange(char)
 
   expect_true(all(self_pairs$item1 == char_counts$char))
   expect_true(all(self_pairs$n == char_counts$n))
 
   # when upper is TRUE, should include twice as many items as original
-  d3 <- original %>%
+  d3 <- original |>
     pairwise_count(char, line, sort = TRUE, upper = TRUE)
 
   expect_equal(nrow(d) * 2, nrow(d3))
@@ -72,27 +72,27 @@ test_that("We can count with a weight column", {
 
 test_that("Counts co-occurrences of words in Pride & Prejudice", {
   if (require("janeaustenr", quietly = TRUE)) {
-    words <- tibble(text = prideprejudice) %>%
-      mutate(line = row_number()) %>%
+    words <- tibble(text = prideprejudice) |>
+      mutate(line = row_number()) |>
       unnest_tokens(word, text)
 
-    pairs <- words %>%
+    pairs <- words |>
       pairwise_count(word, line, upper = TRUE, diag = TRUE, sort = TRUE)
 
     # check it is sorted in descending order
     expect_false(is.unsorted(rev(pairs$n)))
 
     # check occurrences of words that appear with "elizabeth"
-    words_with_elizabeth <- words %>%
-      filter(word == "elizabeth") %>%
-      select(line) %>%
-      inner_join(words, by = "line") %>%
-      distinct(word, line) %>%
-      count(word) %>%
+    words_with_elizabeth <- words |>
+      filter(word == "elizabeth") |>
+      select(line) |>
+      inner_join(words, by = "line", relationship = "many-to-many") |>
+      distinct(word, line) |>
+      count(word) |>
       arrange(n, word)
 
-    pairs_with_elizabeth <- pairs %>%
-      filter(item1 == "elizabeth") %>%
+    pairs_with_elizabeth <- pairs |>
+      filter(item1 == "elizabeth") |>
       arrange(n, item2)
 
     expect_true(all(words_with_elizabeth$word == pairs_with_elizabeth$item2))
@@ -101,8 +101,8 @@ test_that("Counts co-occurrences of words in Pride & Prejudice", {
 })
 
 test_that("Can count within groups", {
-  grouped_result <- mtcars %>%
-    group_by(cyl) %>%
+  grouped_result <- mtcars |>
+    group_by(cyl) |>
     pairwise_count(vs, am)
 
   expect_equal(as.character(groups(grouped_result)), c("cyl"))
diff --git a/tests/testthat/test-pairwise-dist.R b/tests/testthat/test-pairwise-dist.R
index bf19c45..6cc4d70 100644
--- a/tests/testthat/test-pairwise-dist.R
+++ b/tests/testthat/test-pairwise-dist.R
@@ -7,7 +7,7 @@ test_that("pairwise_dist computes a distance matrix", {
                   row = rep(c("d", "e", "f"), 3),
                   value = c(1, 2, 3, 6, 5, 4, 7, 9, 8))
 
-  ret <- d %>%
+  ret <- d |>
     pairwise_dist(col, row, value)
 
   ret1 <- ret$distance[ret$item1 == "a" & ret$item2 == "b"]
diff --git a/tests/testthat/test-pairwise-similarity.R b/tests/testthat/test-pairwise-similarity.R
index 833ff55..9d8656f 100644
--- a/tests/testthat/test-pairwise-similarity.R
+++ b/tests/testthat/test-pairwise-similarity.R
@@ -11,7 +11,7 @@ cosine_similarity <- function(x, y) {
 }
 
 test_that("pairwise_similarity computes pairwise cosine similarity", {
-  ret <- d %>%
+  ret <- d |>
     pairwise_similarity(col, row, value)
 
   ret1 <- ret$similarity[ret$item1 == "a" & ret$item2 == "b"]
@@ -26,7 +26,7 @@ test_that("pairwise_similarity computes pairwise cosine similarity", {
 test_that("pairwise_similarity retains factor levels", {
   d$col <- factor(d$col, levels = c("b", "c", "a"))
 
-  ret <- d %>%
+  ret <- d |>
     pairwise_similarity(col, row, value)
 
   expect_is(ret$item1, "factor")
diff --git a/tests/testthat/test-squarely.R b/tests/testthat/test-squarely.R
index f3ef14b..f6d155f 100644
--- a/tests/testthat/test-squarely.R
+++ b/tests/testthat/test-squarely.R
@@ -6,7 +6,7 @@ test_that("Can perform 'squarely' operations on pairs of items", {
   if (require("gapminder", quietly = TRUE)) {
     ncountries <- length(unique(gapminder$country))
 
-    closest <- gapminder %>%
+    closest <- gapminder |>
       squarely(dist)(country, year, lifeExp)
 
     expect_equal(colnames(closest), c("item1", "item2", "value"))
@@ -17,8 +17,8 @@ test_that("Can perform 'squarely' operations on pairs of items", {
 
 test_that("Can perform 'squarely' within groups", {
   if (require("gapminder", quietly = TRUE)) {
-    closest_continent <- gapminder %>%
-      group_by(continent) %>%
+    closest_continent <- gapminder |>
+      group_by(continent) |>
       squarely(dist)(country, year, lifeExp)
 
     expect_equal(colnames(closest_continent), c("continent", "item1", "item2", "value"))
diff --git a/tests/testthat/test-widely.R b/tests/testthat/test-widely.R
index 2243b00..ec5d75c 100644
--- a/tests/testthat/test-widely.R
+++ b/tests/testthat/test-widely.R
@@ -4,7 +4,7 @@ test_that("widely can widen, operate, and re-tidy", {
   skip_if_not_installed("gapminder")
   library(gapminder)
 
-  ret <- gapminder %>%
+  ret <- gapminder |>
     widely(cor)(year, country, lifeExp)
 
   expect_is(ret$item1, "character")
@@ -17,7 +17,7 @@ test_that("widely can widen, operate, and re-tidy", {
 
   expect_equal(nrow(ret), length(unique(gapminder$country)) ^ 2)
 
-  ret2 <- gapminder %>%
+  ret2 <- gapminder |>
     widely(cor, sort = TRUE)(year, country, lifeExp)
 
   expect_equal(sort(ret$value, decreasing = TRUE), ret2$value)
@@ -27,8 +27,8 @@ test_that("widely works within groups", {
   skip_if_not_installed("gapminder")
   library(gapminder)
 
-  ret <- gapminder %>%
-    group_by(continent) %>%
+  ret <- gapminder |>
+    group_by(continent) |>
     widely(cor)(year, country, lifeExp)
 
   expect_equal(colnames(ret), c("continent", "item1", "item2", "value"))

From 06d8e0013230379db49e178e9bacaa46e9ea192c Mon Sep 17 00:00:00 2001
From: Julia Silge <julia.silge@gmail.com>
Date: Mon, 9 Mar 2026 10:58:41 -0600
Subject: [PATCH 4/4] Render README

---
 README.md | 35 +++++++++++++++--------------------
 1 file changed, 15 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index 1121273..14f1403 100644
--- a/README.md
+++ b/README.md
@@ -45,10 +45,10 @@ useful.
 
 A **wide** dataset is one or more matrices where:
 
--   Each row is one **item**
--   Each column is one **feature**
--   Each value is one **observation**
--   Each matrix is one **variable**
+- Each row is one **item**
+- Each column is one **feature**
+- Each value is one **observation**
+- Each matrix is one **variable**
 
 When would you want data to be wide rather than tidy? Notable examples
 include classification, clustering, correlation, factorization, or other
@@ -85,8 +85,7 @@ gapminder
 #>  8 Afghanistan Asia       1987    40.8 13867957      852.
 #>  9 Afghanistan Asia       1992    41.7 16317921      649.
 #> 10 Afghanistan Asia       1997    41.8 22227415      635.
-#> # … with 1,694 more rows
-#> # ℹ Use `print(n = ...)` to see more rows
+#> # ℹ 1,694 more rows
 ```
 
 This tidy format (one-row-per-country-per-year) is very useful for
@@ -103,7 +102,7 @@ items within data. An example is `pairwise_dist`:
 ``` r
 library(widyr)
 
-gapminder %>%
+gapminder |>
   pairwise_dist(country, year, lifeExp)
 #> # A tibble: 20,022 × 3
 #>    item1      item2       distance
@@ -118,8 +117,7 @@ gapminder %>%
 #>  8 Bangladesh Afghanistan    45.3 
 #>  9 Belgium    Afghanistan   125.  
 #> 10 Benin      Afghanistan    39.3 
-#> # … with 20,012 more rows
-#> # ℹ Use `print(n = ...)` to see more rows
+#> # ℹ 20,012 more rows
 ```
 
 This finds the Euclidean distance between the `lifeExp` value in each
@@ -129,8 +127,8 @@ with `year`, which is the feature column.
 We could find the closest pairs of countries overall with `arrange()`:
 
 ``` r
-gapminder %>%
-  pairwise_dist(country, year, lifeExp) %>%
+gapminder |>
+  pairwise_dist(country, year, lifeExp) |>
   arrange(distance)
 #> # A tibble: 20,022 × 3
 #>    item1          item2          distance
@@ -145,8 +143,7 @@ gapminder %>%
 #>  8 Israel         Italy              1.66
 #>  9 Finland        Austria            1.94
 #> 10 Austria        Finland            1.94
-#> # … with 20,012 more rows
-#> # ℹ Use `print(n = ...)` to see more rows
+#> # ℹ 20,012 more rows
 ```
 
 Notice that this includes duplicates (Germany/Belgium and
@@ -154,8 +151,8 @@ Belgium/Germany). To avoid those (the upper triangle of the distance
 matrix), use `upper = FALSE`:
 
 ``` r
-gapminder %>%
-  pairwise_dist(country, year, lifeExp, upper = FALSE) %>%
+gapminder |>
+  pairwise_dist(country, year, lifeExp, upper = FALSE) |>
   arrange(distance)
 #> # A tibble: 10,011 × 3
 #>    item1       item2          distance
@@ -170,15 +167,14 @@ gapminder %>%
 #>  8 Comoros     Mauritania         2.01
 #>  9 Belgium     United States      2.09
 #> 10 Germany     Ireland            2.10
-#> # … with 10,001 more rows
-#> # ℹ Use `print(n = ...)` to see more rows
+#> # ℹ 10,001 more rows
 ```
 
 In some analyses, we may be interested in correlation rather than
 distance of pairs. For this we would use `pairwise_cor`:
 
 ``` r
-gapminder %>%
+gapminder |>
   pairwise_cor(country, year, lifeExp, upper = FALSE)
 #> # A tibble: 10,011 × 3
 #>    item1       item2     correlation
@@ -193,8 +189,7 @@ gapminder %>%
 #>  8 Albania     Argentina       0.949
 #>  9 Algeria     Argentina       0.991
 #> 10 Angola      Argentina       0.936
-#> # … with 10,001 more rows
-#> # ℹ Use `print(n = ...)` to see more rows
+#> # ℹ 10,001 more rows
 ```
 
 ### Code of Conduct