Learn R Programming

⚠️There's a newer version (3.2.1) of this package.Take me there.

tibble

Overview

A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not. Tibbles are data.frames that are lazy and surly: they do less (i.e. they don't change variable names or types, and don't do partial matching) and complain more (e.g. when a variable does not exist). This forces you to confront problems earlier, typically leading to cleaner, more expressive code. Tibbles also have an enhanced print method() which makes them easier to use with large datasets containing complex objects.

If you are new to tibbles, the best place to start is the tibbles in R for data science.

Installation

# The easiest way to get tibble is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just tibble:
install.packages("tibble")

# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("tidyverse/tibble")

Usage

Create a tibble from an existing object with as_tibble():

library(tibble)
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
#> 7           4.6         3.4          1.4         0.3  setosa
#> 8           5.0         3.4          1.5         0.2  setosa
#> 9           4.4         2.9          1.4         0.2  setosa
#> 10          4.9         3.1          1.5         0.1  setosa
#> # ... with 140 more rows

This will work for reasonable inputs that are already data.frames, lists, matrices, or tables.

You can also create a new tibble from column vectors with tibble():

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

tibble() does much less than data.frame(): it never changes the type of the inputs (e.g. it never converts strings to factors!), it never changes the names of variables, it only recycles inputs of length 1, and it never creates row.names(). You can read more about these features in the vignette, vignette("tibble").

You can define a tibble row-by-row with tribble():

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

Copy Link

Version

Install

install.packages('tibble')

Monthly Downloads

1,697,043

Version

1.3.1

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Kirill Müller

Last Published

May 17th, 2017

Functions in tibble (1.3.1)

is.tibble

Test if the object is a tibble.
knit_print.trunc_mat

knit_print method for trunc mat
glimpse

Get a glimpse of your data.
has_name

Does an object have an element with this name?
add_column

Add columns to a data frame
add_row

Add rows to a data frame
tibble-package

tibble: Simple Data Frames
lst

Build a list
format.tbl

Tools for describing matrices
frame_matrix

Row-wise matrix creation
obj_sum

Provide a succinct summary of an object
rownames

Tools for working with row names
enframe

Converting atomic vectors to data frames, and vice versa
as_tibble

Coerce lists and matrices to data frames.
set_tidy_names

Repair object names.
tribble

Row-wise tibble creation