Tibbles are the modern reimagination of data frames and share many commonalities with their ancestors. The most visible difference is how tibble contents are printed to the console. Tibbles are part of the tidyverse and used for their more consistent behaviour compared to data frames.

• Learn the difference between data frames and tibbles
• Create tibbles from vectors
• Convert data frames into tibbles

tibble(___ = ___, 
       ___ = ___, 
       ...)
as_tibble(___)

Introduction to Tibbles

A modern reimagining of the data frame

https://tibble.tidyverse.org

Tibbles are in many ways similar to data frames. In fact, they are inherited from data frames which means that all functions and features available for data frames also work for tibbles. Therefore, when we speak of data frames we also mean tibbles.

In addition to everything a data frame has to offer, tibbles have a more consistent behaviour with better usability in many cases. Most importantly, when a tibble object is printed to the console it automatically shows only the first 10 rows and condenses additional columns. By contrast, a data frame fills up the entire console screen with values which can lead to confusion. Let’s take a look the the gapminder dataset from the gapminder package:

gapminder
# A tibble: 1,704 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# … with 1,694 more rows

We immediately see that the gapminder dataset is a tibble consisting of 1,704 rows and 6 columns on the top line. In the second line we can see the column names and their corresponding data types directly below.

For example, the column country has the type <fct> (which is short for “factor”), year is an integer <int> and life expectancy lifeExp is a <dbl>—a decimal number.

Quiz: Tibbles versus Data Frames

• The printed output to the console is the same for tibbles and data frames
• All functions defined for data frames also work on tibbles.
• Tibbles also show the data types in the console output.
• To use tibble objects the tibbles package needs to be loaded.
• The table dimensions are not shown in the console output for tibbles.

Creating Tibbles

tibble(___ = ___, 
       ___ = ___, 
       ...)
as_tibble(___)

The creation of tibbles works exactly the same as for data frames. We can use the tibble() function from the tibble package to create a new tabular object.

For example, a tibble containing data from four different people and three columns can be created like this:

library(tibble)
tibble(
  id = c(1, 2, 3, 4),
  name = c("Louisa", "Jonathan", "Luigi", "Rachel"),
  female = c(TRUE, FALSE, FALSE, TRUE)
)
# A tibble: 4 x 3
     id name     female
  <dbl> <chr>     
1     1 Louisa   TRUE  
2     2 Jonathan FALSE 
3     3 Luigi    FALSE 
4     4 Rachel   TRUE  

Converting data frames to Tibbles

If you prefer tibbles to data frames for their additional features they can also be converted from existing data frames with the as_tibble() function.

For example, the Davis data frame from the carData package can be converted to a tibble like so:

as_tibble(Davis)
# A tibble: 200 x 5
   sex   weight height repwt repht
   <fct>  <int>  <int> <int> <int>
 1 M         77    182    77   180
 2 F         58    161    51   159
 3 F         53    161    54   158
 4 M         68    177    70   175
 5 F         59    157    59   155
 6 M         76    170    76   165
 7 M         76    167    77   165
 8 M         69    186    73   180
 9 M         71    178    71   175
10 M         65    171    64   170
# … with 190 more rows

Exercise: Convert data frame to Tibble

  speed dist
1     4    2
2     4   10
3     7    4
 [ reached 'max' / getOption("max.print") -- omitted 47 rows ]

The data frame cars reports the speed of cars and distances taken to stop. To have a nicer printed output in the console use the as_tibble() function and create a tibble object out of it.

Create and convert tibbles is an excerpt from the course Introduction to R, which is available for free at quantargo.com

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