Feedback from twitter
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@ -70,9 +70,11 @@ There are five main types of things that you can subset a vector with, i.e. that
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x <- c(10, 3, NA, 5, 8, 1, NA)
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x <- c(10, 3, NA, 5, 8, 1, NA)
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# All non-missing values of x
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# All non-missing values of x
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!is.na(x)
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x[!is.na(x)]
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x[!is.na(x)]
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# All even (or missing!) values of x
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# All even (or missing!) values of x
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x %% 2 == 0
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x[x %% 2 == 0]
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x[x %% 2 == 0]
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```
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```
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@ -123,7 +125,7 @@ We need to use it here because `[` doesn't use tidy evaluation, so you need to b
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There's an important difference between tibbles and data frames when it comes to `[`.
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There's an important difference between tibbles and data frames when it comes to `[`.
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In this book we've mostly used tibbles, which *are* data frames, but they tweak some older behaviors to make your life a little easier.
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In this book we've mostly used tibbles, which *are* data frames, but they tweak some older behaviors to make your life a little easier.
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In most places, you can use tibbles and data frame interchangeably, so went we want to draw particular attention to R's built-in data frame, we'll write `data.frame`s.
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In most places, you can use tibbles and data frame interchangeably, so when we want to draw particular attention to R's built-in data frame, we'll write `data.frame`s.
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So if `df` is a `data.frame`, then `df[, cols]` will return a vector if `col` selects a single column and a data frame if it selects more than one column.
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So if `df` is a `data.frame`, then `df[, cols]` will return a vector if `col` selects a single column and a data frame if it selects more than one column.
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If `df` is a tibble, then `[` will always return a tibble.
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If `df` is a tibble, then `[` will always return a tibble.
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