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Fixes typos in tibble.Rmd

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Garrett Grolemund
2016-11-03 13:33:05 -05:00
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tibble.Rmd
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## Introduction
Throughout this book we work with "tibbles" instead of R's traditional `data.frame`. Tibbles _are_ data frames, but they tweak some older behaviours to make life a little easier. R is an old language, and some things that were useful 10 or 20 years ago now get in your way. It's difficult to change base R without breaking existing code, so most innovation occurs in packages. Here we will describe the __tibble__ package, which provides opinionated data frames that make working in the tidyverse a little easier. In most places, I'll use the term tibble and data frame interchangeably; when I want to draw particular attention to R's build-in data frame, I'll call them `data.frame`s.
Throughout this book we work with "tibbles" instead of R's traditional `data.frame`. Tibbles _are_ data frames, but they tweak some older behaviours to make life a little easier. R is an old language, and some things that were useful 10 or 20 years ago now get in your way. It's difficult to change base R without breaking existing code, so most innovation occurs in packages. Here we will describe the __tibble__ package, which provides opinionated data frames that make working in the tidyverse a little easier. In most places, I'll use the term tibble and data frame interchangeably; when I want to draw particular attention to R's built-in data frame, I'll call them `data.frame`s.
If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.
@@ -140,7 +140,7 @@ Some older functions don't work with tibbles. If you encounter one of these func
class(as.data.frame(tb))
```
The main reason that some older functions don't work with tibble is the `[` function. We don't use `[` much in this book much because `dplyr::filter()` and `dplyr::select()` allow you to solve the same problems with clearer code (but you will learn a little about it in [vector subsetting](#vector-subsetting). With base R data frames, `[` sometimes returns a data frame, and sometimes returns a vector. With tibbles, `[` always returns another tibble.
The main reason that some older functions don't work with tibble is the `[` function. We don't use `[` much in this book because `dplyr::filter()` and `dplyr::select()` allow you to solve the same problems with clearer code (but you will learn a little about it in [vector subsetting](#vector-subsetting)). With base R data frames, `[` sometimes returns a data frame, and sometimes returns a vector. With tibbles, `[` always returns another tibble.
## Exercises