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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 chapter in R for data science.

Installation

Usage

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

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() 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", package = "tibble").

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

Related work

The tibble print method draws inspiration from data.table, and frame. Like data.table::data.table(), tibble() doesn’t coerce strings to factors by default, doesn’t change column names, and doesn’t use rownames.


Code of Conduct

Please note that the tibble project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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Install

install.packages('tibble')

Monthly Downloads

1,429,365

Version

3.1.1

License

MIT + file LICENSE

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Last Published

April 18th, 2021

Functions in tibble (3.1.1)