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fix punctuation (#1149)

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Zeki Akyol 2022-11-24 08:27:02 +03:00 committed by GitHub
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data-import.qmd
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@ -256,7 +256,7 @@ Once you've mastered `read_csv()`, using readr's other functions is straightforw
## Controlling column types {#sec-col-types}
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.
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.
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.
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.

4
functions.qmd
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@ -625,7 +625,7 @@ While our examples have mostly focused on dplyr, tidy evaluation also underpins
weather |> summarize_weather(temp)
```
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).
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)).
```{r}
#| eval: false
@ -930,7 +930,7 @@ This makes it very obvious that something unusual is happening.
2. Take a function that you've written recently and spend 5 minutes brainstorming a better name for it and its arguments.
3. Make a case for why `norm_r()`, `norm_d()` etc would be better than `rnorm()`, `dnorm()`.
3. Make a case for why `norm_r()`, `norm_d()` etc. would be better than `rnorm()`, `dnorm()`.
Make a case for the opposite.
## Summary