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Tibble tweaks

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tibble.Rmd
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## Introduction ## 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. 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.
If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`. If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.
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I often add a comment (the line starting with `#`), to make it really clear where the header is. I often add a comment (the line starting with `#`), to make it really clear where the header is.
## Tibbles vs. data frames ## Tibbles vs. data.frame
There are two main differences in the usage of a data frame vs a tibble: printing and subsetting. There are two main differences in the usage of a tibble vs. a classic `data.frame`: printing and subsetting.
### Printing ### Printing
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To use these in a pipe, you'll need to use the special placeholder `.`: To use these in a pipe, you'll need to use the special placeholder `.`:
```{r, include = FALSE}
library(magrittr)
```
```{r} ```{r}
df %>% .$x df %>% .$x
df %>% .[["x"]] df %>% .[["x"]]
``` ```
Compared to a `data.frame`, tibbles are more strict: they never do partial matching, and they will generate a warning if the column you are trying to access does not exist.
## Interacting with older code ## Interacting with older code
Some older functions don't work with tibbles. If you encounter one of these functions, use `as.data.frame()` to turn a tibble back to a data frame: Some older functions don't work with tibbles. If you encounter one of these functions, use `as.data.frame()` to turn a tibble back to a `data.frame`:
```{r} ```{r}
class(as.data.frame(tb)) class(as.data.frame(tb))
@ -149,6 +147,17 @@ The main reason that some older functions don't work with tibble is the `[` func
1. How can you tell if an object is a tibble? (Hint: try printing `mtcars`, 1. How can you tell if an object is a tibble? (Hint: try printing `mtcars`,
which is a regular data frame). which is a regular data frame).
1. Compare and contrast the following operations on a `data.frame` and
equivalent tibble. What is different? Why might the default data frame
behaviours cause you frustration?
```{r, eval = FALSE}
df <- data.frame(abc = 1, xyz = "a")
df$x
df[, "xyz"]
df[, c("abc", "xyz")]
```
1. Practice referring to non-syntactic names by: 1. Practice referring to non-syntactic names by:
1. Plotting a scatterplot of `1` vs `2`. 1. Plotting a scatterplot of `1` vs `2`.