dplyr [](https://dplyr.tidyverse.org)

Overview Copy Chapter

dplyr is a grammar of data manipulation, providing a consistent set of
verbs that help you solve the most common data manipulation challenges:

• mutate() adds new variables that are functions of existing variables
• select() picks variables based on their names.
• filter() picks cases based on their values.
• summarise() reduces multiple values down to a single summary.
• arrange() changes the ordering of the rows.

These all combine naturally with group_by() which allows you to
perform any operation “by group”. You can learn more about them in
vignette("dplyr"). As well as these single-table verbs, dplyr also
provides a variety of two-table verbs, which you can learn about in
vignette("two-table").

If you are new to dplyr, the best place to start is the data
transformation chapter in R for
Data Science.

Backends Copy Chapter

In addition to data frames/tibbles, dplyr makes working with other
computational backends accessible and efficient. Below is a list of
alternative backends:

• arrow for larger-than-memory
  datasets, including on remote cloud storage like AWS S3, using the
  Apache Arrow C++ engine,
  Acero.

• dbplyr for data stored in a
  relational database. Translates your dplyr code to SQL.

• dtplyr for large, in-memory datasets.
  Translates your dplyr code to high performance
  data.table code.

• duckplyr for large, in-memory
  datasets. Translates your dplyr code to high performance
  duckdb queries with zero extra copies and an
  automatic R fallback when translation isn’t possible.

• sparklyr for very large datasets stored in
  Apache Spark.

Installation Copy Chapter

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

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

Development version Copy Section

To get a bug fix or to use a feature from the development version, you
can install the development version of dplyr from GitHub.

# install.packages("pak")
pak::pak("tidyverse/dplyr")

Cheat Sheet Copy Chapter

Usage Copy Chapter

/* Detailed source-code truncated for AI context efficiency. */

Getting help Copy Chapter

If you encounter a clear bug, please file an issue with a minimal
reproducible example on
GitHub. For questions and
other discussion, please use forum.posit.co.

Code of conduct Copy Chapter

Please note that this project is released with a Contributor Code of
Conduct. By participating
in this project you agree to abide by its terms.