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Mention glimpse

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Hadley Wickham 2022-11-18 15:25:52 -06:00
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data-transform.qmd
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@ -47,10 +47,15 @@ flights
If you've used R before, you might notice that this data frame prints a little differently to other data frames you've seen. If you've used R before, you might notice that this data frame prints a little differently to other data frames you've seen.
That's because it's a **tibble**, a special type of data frame used by the tidyverse to avoid some common gotchas. That's because it's a **tibble**, a special type of data frame used by the tidyverse to avoid some common gotchas.
The most important difference is the way it prints: tibbles are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen. The most important difference is the way it prints: tibbles are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen.
To see everything you can use `print(flights, width = Inf)` to show everything in the console, but it's generally more convenient to instead use `View(flights)` to open the dataset in the scrollable RStudio viewer. There are a few options to see everything.
If you're using RStudio, the most convenient is probably `View(flights)`, which will open an interactive scrollable and filterable view.
Otherwise you can use `print(flights, width = Inf)` to show all columns, or use call `glimpse()`:
You might have noticed the short abbreviations that follow each column name. ```{r}
These tell you the type of each variable: `<int>` is short for integer, `<dbl>` is short for double (aka real numbers), `<chr>` for character (aka strings), and `<dttm>` for date-time. glimpse(flights)
```
In both views, the variables names are followed by abbreviations that tell you the type of each variable: `<int>` is short for integer, `<dbl>` is short for double (aka real numbers), `<chr>` for character (aka strings), and `<dttm>` for date-time.
These are important because the operations you can perform on a column depend so much on its "type", and these types are used to organize the chapters in the next section of the book. These are important because the operations you can perform on a column depend so much on its "type", and these types are used to organize the chapters in the next section of the book.
### dplyr basics ### dplyr basics