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Catch a few more UK spellings, closes #1160

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mine-cetinkaya-rundel 2022-12-12 13:37:08 -05:00
parent ff893361e8
commit 1bf0b5d105
2 changed files with 5 additions and 5 deletions
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8
arrow.qmd
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@ -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()
```

2
functions.qmd
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@ -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.