---
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 transformation chapter](https://r4ds.had.co.nz/transform.html) in R for data science.

## Backends

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

- [dtplyr](https://dtplyr.tidyverse.org/): for large, in-memory datasets. 
  Translates your dplyr code to high performance 
  [data.table](https://rdatatable.gitlab.io/data.table/) code.

- [dbplyr](https://dbplyr.tidyverse.org/): for data stored in a relational 
  database. Translates your dplyr code to SQL.

- [sparklyr](https://spark.rstudio.com): for very large datasets stored in 
  [Apache Spark](https://spark.apache.org).

## 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 to use a feature from the development version, you can install 
the development version of 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 an issue with 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/d/forum/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.