From 1bf0b5d105485d2afd18298b62d914e55ad5f182 Mon Sep 17 00:00:00 2001
From: mine-cetinkaya-rundel <cetinkaya.mine@gmail.com>
Date: Mon, 12 Dec 2022 13:37:08 -0500
Subject: [PATCH] Catch a few more UK spellings, closes #1160

---
 arrow.qmd     | 8 ++++----
 functions.qmd | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/arrow.qmd b/arrow.qmd
index e203384..ceb4c80 100644
--- a/arrow.qmd
+++ b/arrow.qmd
@@ -208,7 +208,7 @@ For example, we could count the total number of books checked out in each month
 query <- seattle_pq |> 
   filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
   group_by(CheckoutYear, CheckoutMonth) |>
-  summarise(TotalCheckouts = sum(Checkouts)) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
   arrange(CheckoutYear, CheckoutMonth)
 ```
 
@@ -239,7 +239,7 @@ First, let's time how long it takes to calculate the number of books checked out
 seattle_csv |> 
   filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
   group_by(CheckoutMonth) |>
-  summarise(TotalCheckouts = sum(Checkouts)) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
   arrange(desc(CheckoutMonth)) |>
   collect() |> 
   system.time()
@@ -253,7 +253,7 @@ Now let's use our new version of the data set in which the Seattle library check
 seattle_pq |> 
   filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
   group_by(CheckoutMonth) |>
-  summarise(TotalCheckouts = sum(Checkouts)) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
   arrange(desc(CheckoutMonth)) |>
   collect() |> 
   system.time()
@@ -275,7 +275,7 @@ seattle_pq |>
   to_duckdb() |>
   filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
   group_by(CheckoutYear) |>
-  summarise(TotalCheckouts = sum(Checkouts)) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
   arrange(desc(CheckoutYear)) |>
   collect()
 ```
diff --git a/functions.qmd b/functions.qmd
index e72089b..447e94b 100644
--- a/functions.qmd
+++ b/functions.qmd
@@ -424,7 +424,7 @@ df |> grouped_mean(group, x)
 df |> grouped_mean(group, y)
 ```
 
-Regardless of how we call `grouped_mean()` it always does `df |> group_by(group_var) |> summarise(mean(mean_var))`, instead of `df |> group_by(group) |> summarise(mean(x))` or `df |> group_by(group) |> summarise(mean(y))`.
+Regardless of how we call `grouped_mean()` it always does `df |> group_by(group_var) |> summarize(mean(mean_var))`, instead of `df |> group_by(group) |> summarize(mean(x))` or `df |> group_by(group) |> summarize(mean(y))`.
 This is a problem of indirection, and it arises because dplyr uses **tidy evaluation** to allow you to refer to the names of variables inside your data frame without any special treatment.
 
 Tidy evaluation is great 95% of the time because it makes your data analyses very concise as you never have to say which data frame a variable comes from; it's obvious from the context.