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 built-in data frame, I'll call them data.frames.

If this chapter leaves you wanting to learn more about tibbles, you might enjoy vignette("tibble").


Creating tibbles

Almost all of the functions that you'll use in this book produce tibbles, as tibbles are one of the unifying features of the tidyverse. Most other R packages use regular data frames, so you might want to coerce a data frame to a tibble. You can do that with as_tibble():

as_tibble(iris)
#> # A tibble: 150 x 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> 5          5           3.6          1.4         0.2 setosa 
#> 6          5.4         3.9          1.7         0.4 setosa 
#> # … with 144 more rows

You can create a new tibble from individual vectors with tibble(). tibble() will automatically recycle inputs of length 1, and allows you to refer to variables that you just created, as shown below.

tibble(
  x = 1:5, 
  y = 1, 
  z = x ^ 2 + y
)
#> # A tibble: 5 x 3
#>       x     y     z
#>   <int> <dbl> <dbl>
#> 1     1     1     2
#> 2     2     1     5
#> 3     3     1    10
#> 4     4     1    17
#> 5     5     1    26

If you're already familiar with data.frame(), note that 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.

It's possible for a tibble to have column names that are not valid R variable names, aka non-syntactic names. For example, they might not start with a letter, or they might contain unusual characters like a space. To refer to these variables, you need to surround them with backticks, `:

tb <- tibble(
  `:)` = "smile", 
  ` ` = "space",
  `2000` = "number"
)
tb
#> # A tibble: 1 x 3
#>   `:)`  ` `   `2000`
#>   <chr> <chr> <chr> 
#> 1 smile space number

You'll also need the backticks when working with these variables in other packages, like ggplot2, dplyr, and tidyr.

Another way to create a tibble is with tribble(), short for transposed tibble. tribble() is customised for data entry in code: column headings are defined by formulas (i.e. they start with ~), and entries are separated by commas. This makes it possible to lay out small amounts of data in easy to read form.

tribble(
  ~x, ~y, ~z,
  #--|--|----
  "a", 2, 3.6,
  "b", 1, 8.5
)
#> # A tibble: 2 x 3
#>   x         y     z
#>   <chr> <dbl> <dbl>
#> 1 a         2   3.6
#> 2 b         1   8.5

I often add a comment (the line starting with #), to make it really clear where the header is.


Tibbles vs. data.frame

There are two main differences in the usage of a tibble vs. a classic data.frame: printing and subsetting.

Printing

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():

tibble(
  a = lubridate::now() + runif(1e3) * 86400,
  b = lubridate::today() + runif(1e3) * 30,
  c = 1:1e3,
  d = runif(1e3),
  e = sample(letters, 1e3, replace = TRUE)
)
#> # A tibble: 1,000 x 5
#>   a                   b              c     d e    
#>   <dttm>              <date>     <int> <dbl> <chr>
#> 1 2020-10-09 13:55:17 2020-10-16     1 0.368 n    
#> 2 2020-10-10 08:00:26 2020-10-21     2 0.612 l    
#> 3 2020-10-10 02:24:06 2020-10-31     3 0.415 p    
#> 4 2020-10-09 15:45:23 2020-10-30     4 0.212 m    
#> 5 2020-10-09 12:09:39 2020-10-27     5 0.733 i    
#> 6 2020-10-09 23:10:37 2020-10-23     6 0.460 n    
#> # … with 994 more rows

Tibbles are designed so that you don't accidentally overwhelm your console when you print large data frames. But sometimes you need more output than the default display. There are a few options that can help.

First, you can explicitly print() the data frame and control the number of rows (n) and the width of the display. width = Inf will display all columns:

nycflights13::flights %>% 
  print(n = 10, width = Inf)

You can also control the default print behaviour by setting options:

  • options(tibble.print_max = n, tibble.print_min = m): if more than n rows, print only m rows. Use options(tibble.print_min = Inf) to always show all rows.

  • Use options(tibble.width = Inf) to always print all columns, regardless of the width of the screen.

You can see a complete list of options by looking at the package help with package?tibble.

A final option is to use RStudio's built-in data viewer to get a scrollable view of the complete dataset. This is also often useful at the end of a long chain of manipulations.

nycflights13::flights %>% 
  View()


Subsetting

So far all the tools you've learned have worked with complete data frames. If you want to pull out a single variable, you need some new tools, $ and [[. [[ can extract by name or position; $ only extracts by name but is a little less typing.

df <- tibble(
  x = runif(5),
  y = rnorm(5)
)

# Extract by name
df$x
#> [1] 0.73296674 0.23436542 0.66035540 0.03285612 0.46049161
df[["x"]]
#> [1] 0.73296674 0.23436542 0.66035540 0.03285612 0.46049161

# Extract by position
df[[1]]
#> [1] 0.73296674 0.23436542 0.66035540 0.03285612 0.46049161

To use these in a pipe, you'll need to use the special placeholder .:

df %>% .$x
#> [1] 0.73296674 0.23436542 0.66035540 0.03285612 0.46049161
df %>% .[["x"]]
#> [1] 0.73296674 0.23436542 0.66035540 0.03285612 0.46049161

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

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:

class(as.data.frame(tb))
#> [1] "data.frame"

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). With base R data frames, [ sometimes returns a data frame, and sometimes returns a vector. With tibbles, [ always returns another tibble.

Source: H. Wickham and G. Grolemund, https://r4ds.had.co.nz/tibbles.html
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Last modified: Saturday, 12 November 2022, 3:40 PM