The cheatsheets below make it easy to use some of our favorite packages. From time to time, we will add new cheatsheets. If you’d like us to drop you an email when we do, click the button below.Subscribe to cheatsheet updates
The ggplot2 package lets you make beautiful and customizable plots of your data. It implements the grammar of graphics, an easy to use system for building plots. Updated August 2021.
The dplyr package provides a grammar for manipulating tables in R. This cheatsheet will guide you through the grammar, reminding you how to select, filter, arrange, mutate, summarise, group, and join data frames and tibbles. Updated July 2021.
The tidyr package provides a framework for creating and shaping tidy data, the data format that works the most seamlessly with R and the tidyverse. The front page of this cheatsheet provides an overview of tibbles and reshaping tidy data. The back page provides an overview of creating, reshaping, and transforming nested data and list-columns with tidyr, tibble, and dplyr. With list-columns, you can use a simple data frame to organize any collection of objects in R. Updated August 2021.
The tidyverse provides several packages for importing data into R and this cheatsheet covers three of them. On the front page: read and parse text files including csv, tsv, or fwf with readr. On the back page: read and write Excel spreadsheets with readxl and work with Google Sheets files with googlesheets4. Updated August 2021.
The purrr package makes it easy to work with lists and functions. This cheatsheet will remind you how to manipulate lists with purrr functions as well as how to apply functions iteratively to each element of a list or vector using map functions. Updated July 2021.
The stringr package provides an easy to use toolkit for working with strings, i.e. character data, in R. This cheatsheet guides you through stringr’s functions for manipulating strings. The back page provides a concise reference to regular expressions, a mini-language for describing, finding, and matching patterns in strings. Updated August 2021.
Factors are R’s data structure for categorical data. The forcats package makes it easy to work with factors. This cheatsheet reminds you how to make factors, reorder their levels, recode their values, and more. Updated July 2021.
The lubridate package makes it easier to work with dates and times in R. This cheatsheet covers how to round dates, work with time zones, extract elements of a date or time, parse dates into R and more. The back of the cheatsheet describes lubridate’s three timespan classes: periods, durations, and intervals; and explains how to do math with date-times. Updated July 2021.
R Markdown is an authoring format that makes it easy to write reproducible reports with R. You combine your R code with narration written in markdown (an easy-to-write plain text format) and then export the results as an HTML, PDF, or Word file. You can even use R Markdown to build interactive documents and slide decks. Updated August 2021.
If you’re ready to build interactive web apps with R, say hello to Shiny. This cheatsheet provides a tour of the shiny package and explains how to build and customize an interactive app. Be sure to follow the links on the sheet for even more information. Updated July 2021.
The RStudio IDE is the most popular integrated development environment for R. Do you want to write, run, and debug your own R code? Work collaboratively on R projects with version control? Build packages or create documents and apps? No matter what you do with R, the RStudio IDE can help you do it faster. This cheatsheet will guide you through the most useful features of the IDE, as well as the long list of keyboard shortcuts built into the RStudio IDE. Updated July 2021.
The plumber package enables R developers to build web APIs. Plumber uses special R comments combined with standard R functions to create API endpoints. This cheatsheet provides everything you need to get started building APIs in R with Plumber. Updated March 2021.
The reticulate package provides a comprehensive set of tools for interoperability between Python and R. With reticulate, you can call Python from R in a variety of ways including importing Python modules into R scripts, writing R Markdown Python chunks, sourcing Python scripts, and using Python interactively within the RStudio IDE. This cheatsheet will remind you how. Updated August 2021.
Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Keras supports both convolution based networks and recurrent networks (as well as combinations of the two), runs seamlessly on both CPU and GPU devices, and is capable of running on top of multiple back-ends including TensorFlow, CNTK, and Theano. Updated December 2017.
The sparklyr package provides an R interface to Apache Spark, a fast and general engine for processing Big Data. With sparklyr, you can connect to a local or remote Spark session, use dplyr to manipulate data in Spark, and run Spark’s built-in machine learning algorithms. Updated October 2019.
The devtools package makes it easy to build your own R packages, and packages make it easy to share your R code. Supplement this cheatsheet with r-pkgs.had.co.nz, Hadley’s book on package development. Updated January 2015.
These cheatsheets have been generously contributed by R users.
Vectors, matrices, lists, data frames, functions and more in base R, by Mhairi McNeill. Updated March 2015.
The mlr package offers a unified interface to R’s machine learning capabilities, by Aaron Cooley. Updated February 2018.
The mosaic package is for teaching mathematics, statistics, computation and modeling, by Michael Laviolette. Updated June 2020.
Automate random assignment and sampling with randomizr, by Alex Coppock. Updated June 2018.
A guide to familiarise SAS users with R, and vice versa, by Brendan O’Dowd. Updated August 2021.
dplyr friendly data and variable transformation, by Daniel Lüdecke. Updated February 2018.
Three code styles compared: $, formula, and tidyverse, by Amelia McNamara. Updated January 2018.
To see version histories of the cheatsheets and translations, visit the Cheatsheet GitHub Repository.