165 lines
5.4 KiB
Plaintext
165 lines
5.4 KiB
Plaintext
# Tibbles
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## Introduction
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Throughout this book we work with "tibbles" instead of the traditional data frame. Tibbles _are_ data frames, but tweak some older behaviours to make life a littler 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.
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If this chapter leaves you wanting to learn even more about tibbles, you can read more about them in the accompanying vignette: `vignette("tibble")`.
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### Prerequisites
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In this chapter we'll specifically explore the tibble package. Most chapters don't load tibble explicitly, because most of the functions you'll use from tibble are automatically provided by dplyr. You'll only need if you are creating tibbles "by hand".
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```{r setup}
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library(tibble)
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```
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## Creating tibbles {#tibbles}
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The majority of the functions that you'll use in this book already produce tibbles. If you're working with functions from other packages, you might need to coerce a regular data frame a tibble. You can do that with `as_tibble()`:
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```{r}
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as_tibble(iris)
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```
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`as_tibble()` knows how to convert data frames, lists (provided the elements are vectors with the same length), matrices, and tables.
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You can create a new tibble from individual vectors with `tibble()`:
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```{r}
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tibble(x = 1:5, y = 1, z = x ^ 2 + y)
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```
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`tibble()` automatically recycles inputs of length 1, and you can refer to variables that you just created. Compared to `data.frame()`, `tibble()` does much less: it never changes the type of the inputs (e.g. it never converts strings to factors!), it never changes the names of variables, and it never creates row names.
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It's possible for a tibble to have column names that are not valid R variables, or __non-syntactic__ names. For example, they might not start with a letter, or they might contain unusual values like a space. To refer to these variables, you need to surround them with backticks, `` ` ``:
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```{r}
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tb <- tibble(
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`:)` = "smile",
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` ` = "space",
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`2000` = "number"
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)
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tb
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```
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Another way to create a tibble is with `frame_data()`, which is customised for data entry in R code. Column headings are defined by formulas (`~`), and entries are separated by commas:
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```{r}
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frame_data(
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~x, ~y, ~z,
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"a", 2, 3.6,
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"b", 1, 8.5
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)
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```
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### Exercises
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1. What function tells you if an object is a tibble?
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1. What does `enframe()` do? When might you use it?
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1. Practice referring to non-syntactic names by:
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1. Plotting a scatterplot of `1` vs `2`.
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1. Creating a new column called `3` which is `2` divided by `1`.
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1. Renaming the columns to `one`, `two` and `three`.
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```{r}
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annoying <- tibble(
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`1` = 1:10,
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`2` = `1` * 2 + rnorm(length(`1`))
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)
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```
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## Tibbles vs. data frames
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There are two main differences in the usage of a data frame vs a tibble: printing, and subsetting.
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### Printing
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Tibbles have a refined print method that shows only the first 10 rows, and all the columns that fit on screen. This makes it much easier to work with large data. In addition to its name, each column reports its type, a nice feature borrowed from `str()`:
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```{r}
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tibble(
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a = lubridate::now() + runif(1e3) * 60,
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b = lubridate::today() + runif(1e3),
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c = 1:1e3,
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d = runif(1e3),
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e = sample(letters, 1e3, replace = TRUE)
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)
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```
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You can control the default appearance with options:
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* `options(tibble.print_max = n, tibble.print_min = m)`: if more than `m`
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rows, print `n` rows. Use `options(dplyr.print_max = Inf)` to always
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show all rows.
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* `options(tibble.width = Inf)` will always print all columns, regardless
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of the width of the screen.
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To show all the columns in a single tibble, explicitly call `print()` with `width = Inf`:
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```{r, eval = FALSE}
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nycflights13::flights %>%
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print(width = Inf)
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```
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You can see a complete list of options by looking at the package help: `package?tibble`.
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Remember, you can also get a nicer view of the data set using RStudio's built-in data viewer. This is often useful at the end of a long chain of manipulations.
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```{r, eval = FALSE}
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nycflights13::flights %>% View()
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```
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### Subsetting
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Tibbles are stricter about subsetting. If you try to access a variable that does not exist, you'll get a warning. Unlike data frames, tibbles do not use partial matching on column names:
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```{r}
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df <- data.frame(
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abc = 1:10,
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def = runif(10),
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xyz = sample(letters, 10)
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)
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tb <- as_tibble(df)
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df$a
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tb$a
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```
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Tibbles clearly delineate `[` and `[[`: `[` always returns another tibble, `[[` always returns a vector.
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```{r}
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# With data frames, [ sometimes returns a data frame, and sometimes returns
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# a vector
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df[, "abc"]
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# With tibbles, [ always returns another tibble
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tb[, "abc"]
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# To extract a single element, you should always use [[
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tb[["abc"]]
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```
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This is useful to know if you want to extract a single column at the end of dplyr pipeline.
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### Exercises
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1. How can you print all rows of a tibble?
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1. What option controls how many additional column names are printed
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at the footer of a tibble?
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## Interacting with legacy code
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Some older functions don't work with tibbles because they expect `df[, 1]` to return a vector, not a data frame. If you encounter one of these functions, use `as.data.frame()` to turn a tibble back to a data frame:
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```{r}
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class(as.data.frame(tb))
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```
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