Tibbles are data frames, but they tweak some older behaviors to make life a little easier. The name comes from
dplyr: originally you created these objects with
tbl_df(), which was most easily pronounced as “tibble diff”. Tibbles are provide by the
tibbles package (which also comes automatically in the
tidyverse package). This tutorial covers the basics of tibbles but you can always learn more by reading through the vignette
vignette("tibble"). This tutorial will cover the following:
tibbles package with one of the following:
# directly library(tibbles) # indirectly - also loads readr, tidyr, dplyr, purrr library(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(iris) #> # A tibble: 150 × 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fctr> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 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 5.0 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() 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 × 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 × 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 × 3 #> x y z #> <chr> <dbl> <dbl> #> 1 a 2 3.6 #> 2 b 1 8.5
Adding a comment such as with the line starting with
# makes it really clear where the header is.
There are two main differences in the usage of a tibble vs. a classic data.frame: printing and subsetting.
Tibbles have a refined print method that shows only the first 10 rows along with the number of columns that will fit on your 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
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 × 5 #> a b c d e #> <dttm> <date> <int> <dbl> <chr> #> 1 2016-12-02 20:12:04 2016-12-09 1 0.368 h #> 2 2016-12-03 14:17:13 2016-12-14 2 0.612 n #> 3 2016-12-03 08:40:52 2016-12-24 3 0.415 l #> 4 2016-12-02 22:02:10 2016-12-23 4 0.212 x #> 5 2016-12-02 18:26:26 2016-12-20 5 0.733 a #> 6 2016-12-03 05:27:23 2016-12-16 6 0.460 v #> # ... 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 m rows, print only n rows. Use options(dplyr.print_min = Inf) to always show all rows.
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
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()
If you want to pull out a single variable, you need the same tools you’ve seen for subsetting/indexing the other data structures (
[[ 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$x #>  0.434 0.395 0.548 0.762 0.254 df[["x"]] #>  0.434 0.395 0.548 0.762 0.254 # Extract by position df[] #>  0.434 0.395 0.548 0.762 0.254
To use these with the pipe operator, you’ll need to use the special placeholder
df %>% .$x #>  0.434 0.395 0.548 0.762 0.254 df %>% .[["x"]] #>  0.434 0.395 0.548 0.762 0.254
Note that 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.
airquality. Now convert
airqualityto a tibble, print it in your console and compare the difference.
nycflights13package and print off the data frame
flightscontained within this package. Now convert
flightsto a tibble, print it in your console and compare the difference.