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https://github.com/tidyverse/dplyr

dplyr: A grammar of data manipulation
https://github.com/tidyverse/dplyr

data-manipulation grammar r

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dplyr: A grammar of data manipulation

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README 

        
---

output: github_document

---



```{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 



[![CRAN status](https://www.r-pkg.org/badges/version/dplyr)](https://cran.r-project.org/package=dplyr)

[![R-CMD-check](https://github.com/tidyverse/dplyr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidyverse/dplyr/actions/workflows/R-CMD-check.yaml)

[![Codecov test coverage](https://codecov.io/gh/tidyverse/dplyr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/dplyr?branch=main)



## 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.hadley.nz/data-transform) 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:



- [arrow](https://arrow.apache.org/docs/r/) for larger-than-memory datasets, including on remote cloud storage like AWS S3, using the Apache Arrow C++ engine, [Acero](https://arrow.apache.org/docs/cpp/streaming_execution.html).



- [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.



- [duckplyr](https://tidyverse.github.io/duckplyr/) for using [duckdb](https://duckdb.org) on large, in-memory datasets with zero extra copies. Translates your dplyr code to high performance duckdb queries with an automatic R fallback when translation isn't possible.



- [duckdb](https://duckdb.org/docs/api/r) for large datasets that are 

  still small enough to fit on your computer.



- [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("pak")

pak::pak("tidyverse/dplyr")

```



## Cheat Sheet



  



## 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 [forum.posit.co](https://forum.posit.co/).



## Code of conduct



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.