Catch a few more UK spellings, closes #1160
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@ -208,7 +208,7 @@ For example, we could count the total number of books checked out in each month
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query <- seattle_pq |>
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query <- seattle_pq |>
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filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
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filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
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group_by(CheckoutYear, CheckoutMonth) |>
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group_by(CheckoutYear, CheckoutMonth) |>
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summarise(TotalCheckouts = sum(Checkouts)) |>
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summarize(TotalCheckouts = sum(Checkouts)) |>
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arrange(CheckoutYear, CheckoutMonth)
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arrange(CheckoutYear, CheckoutMonth)
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```
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```
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@ -239,7 +239,7 @@ First, let's time how long it takes to calculate the number of books checked out
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seattle_csv |>
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seattle_csv |>
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filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
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filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
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group_by(CheckoutMonth) |>
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group_by(CheckoutMonth) |>
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summarise(TotalCheckouts = sum(Checkouts)) |>
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summarize(TotalCheckouts = sum(Checkouts)) |>
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arrange(desc(CheckoutMonth)) |>
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arrange(desc(CheckoutMonth)) |>
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collect() |>
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collect() |>
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system.time()
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system.time()
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@ -253,7 +253,7 @@ Now let's use our new version of the data set in which the Seattle library check
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seattle_pq |>
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seattle_pq |>
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filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
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filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
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group_by(CheckoutMonth) |>
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group_by(CheckoutMonth) |>
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summarise(TotalCheckouts = sum(Checkouts)) |>
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summarize(TotalCheckouts = sum(Checkouts)) |>
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arrange(desc(CheckoutMonth)) |>
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arrange(desc(CheckoutMonth)) |>
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collect() |>
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collect() |>
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system.time()
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system.time()
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@ -275,7 +275,7 @@ seattle_pq |>
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to_duckdb() |>
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to_duckdb() |>
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filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
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filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
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group_by(CheckoutYear) |>
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group_by(CheckoutYear) |>
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summarise(TotalCheckouts = sum(Checkouts)) |>
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summarize(TotalCheckouts = sum(Checkouts)) |>
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arrange(desc(CheckoutYear)) |>
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arrange(desc(CheckoutYear)) |>
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collect()
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collect()
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```
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```
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@ -424,7 +424,7 @@ df |> grouped_mean(group, x)
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df |> grouped_mean(group, y)
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df |> grouped_mean(group, y)
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```
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```
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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))`.
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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))`.
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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.
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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.
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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.
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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.
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