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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
options(tibble.print_min = 5, tibble.print_max = 5)
```
# dplyr <a href='https://dplyr.tidyverse.org'><img src='man/figures/logo.png' align="right" height="139" /></a>
<!-- badges: start -->
[![CRAN status](https://www.r-pkg.org/badges/version/dplyr)](https://cran.r-project.org/package=dplyr)
[![R build status](https://github.com/tidyverse/dplyr/workflows/R-CMD-check/badge.svg)](https://github.com/tidyverse/dplyr/actions?workflow=R-CMD-check)
[![Codecov test coverage](https://codecov.io/gh/tidyverse/dplyr/branch/master/graph/badge.svg)](https://codecov.io/gh/tidyverse/dplyr?branch=master)
<!-- badges: end -->
## Overview
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 import chapter](http://r4ds.had.co.nz/transform.html) in R for data science.
## Backends
dplyr is designed to abstract over how the data is stored. Out of the box, dplyr works with data frames/tibbles; other packages provide alternative computational backends:
* For large, in-memory datasets, try [dtplyr](http://dtplyr.tidyverse.org/)
to access the excellent performance of [data.table](http://r-datatable.com/).
* For data in relational databases, [dbplyr](http://dbplyr.tidyverse.org/)
will automatically translate your dplyr code in to SQL.
* For very large datasets stored in [Apache Spark](https://spark.apache.org),
use [sparklyr](https://spark.rstudio.com).
## Installation
```{r, eval = FALSE}
# The easiest way to get dplyr is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just dplyr:
install.packages("dplyr")
```
### Development version
To get a bug fix, or use a feature from the development version, you can install
dplyr from GitHub.
```{r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("tidyverse/dplyr")
```
## Cheatsheet
<a href="https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf"><img src="https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-transformation-cheatsheet-thumbs.png" width="630" height="252"/></a>
## Usage
```{r, message = FALSE}
library(dplyr)
starwars %>%
filter(species == "Droid")
starwars %>%
select(name, ends_with("color"))
starwars %>%
mutate(name, bmi = mass / ((height / 100) ^ 2)) %>%
select(name:mass, bmi)
starwars %>%
arrange(desc(mass))
starwars %>%
group_by(species) %>%
summarise(
n = n(),
mass = mean(mass, na.rm = TRUE)
) %>%
filter(n > 1,
mass > 50)
```
## Getting help
If you encounter a clear bug, please file a minimal reproducible example on [github](https://github.com/tidyverse/dplyr/issues). For questions and other discussion, please use [community.rstudio.com](https://community.rstudio.com/), or the [manipulatr mailing list](https://groups.google.com/group/manipulatr).
---
Please note that this project is released with a [Contributor Code of Conduct](https://dplyr.tidyverse.org/CODE_OF_CONDUCT).
By participating in this project you agree to abide by its terms.