fix punctuation (#1149)
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@ -256,7 +256,7 @@ Once you've mastered `read_csv()`, using readr's other functions is straightforw
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## Controlling column types {#sec-col-types}
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## Controlling column types {#sec-col-types}
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A CSV file doesn't contain any information about the type of each variable (i.e. whether it's a logical, number, string, etc), so readr will try to guess the type.
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A CSV file doesn't contain any information about the type of each variable (i.e. whether it's a logical, number, string, etc.), so readr will try to guess the type.
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This section describes how the guessing process works, how to resolve some common problems that cause it to fail, and if needed, how to supply the column types yourself.
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This section describes how the guessing process works, how to resolve some common problems that cause it to fail, and if needed, how to supply the column types yourself.
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Finally, we'll mention a couple of general strategies that are a useful if readr is failing catastrophically and you need to get more insight in to the structure of your file.
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Finally, we'll mention a couple of general strategies that are a useful if readr is failing catastrophically and you need to get more insight in to the structure of your file.
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@ -625,7 +625,7 @@ While our examples have mostly focused on dplyr, tidy evaluation also underpins
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weather |> summarize_weather(temp)
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weather |> summarize_weather(temp)
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```
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```
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5. Converts the user supplied variable that uses clock time (e.g. `dep_time`, `arr_time`, etc) into a decimal time (i.e. hours + minutes / 60).
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5. Converts the user supplied variable that uses clock time (e.g. `dep_time`, `arr_time`, etc.) into a decimal time (i.e. hours + (minutes / 60)).
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```{r}
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```{r}
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#| eval: false
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#| eval: false
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@ -930,7 +930,7 @@ This makes it very obvious that something unusual is happening.
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2. Take a function that you've written recently and spend 5 minutes brainstorming a better name for it and its arguments.
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2. Take a function that you've written recently and spend 5 minutes brainstorming a better name for it and its arguments.
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3. Make a case for why `norm_r()`, `norm_d()` etc would be better than `rnorm()`, `dnorm()`.
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3. Make a case for why `norm_r()`, `norm_d()` etc. would be better than `rnorm()`, `dnorm()`.
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Make a case for the opposite.
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Make a case for the opposite.
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## Summary
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## Summary
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