RStudio Cheat Sheets

RStudio Cheat Sheets

The cheat sheets below make it easy to use some of our favorite packages. From time to time, we will add new cheat sheets. If you’d like us to drop you an email when we do, click the button below.

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Python with R and Reticulate Cheat Sheet

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 March 19.

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Factors with forcats Cheat Sheet

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 February 19.

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Tidy Evaluation with rlang Cheat Sheet

Tidy Evaluation (Tidy Eval) is a framework for doing non-standard evaluation in R that makes it easier to program with tidyverse functions. Non-standard evaluation, better thought of as “delayed evaluation,” lets you capture a user’s R code to run later in a new environment or against a new data frame. The tidy evaluation framework is implemented by the rlang package and used by functions throughout the tidyverse. Updated November 18.

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Deep Learning with Keras Cheat Sheet

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 TensorFlowCNTK, and Theano. Updated December 17.

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Dates and Times Cheat Sheet

Lubridate makes it easier to work with dates and times in R. This lubridate 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 December 17.

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Work with Strings Cheat Sheet

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 expresssions, a mini-language for describing, finding, and matching patterns in strings. Updated October 17.

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Apply Functions Cheat Sheet

The purrr package makes it easy to work with lists and functions. This cheatsheet will remind you how to manipulate lists with purrr as well as how to apply functions iteratively to each element of a list or vector. The back of the cheatsheet explains how to work with list-columns. With list columns, you can use a simple data frame to organize any collection of objects in R. Updated September 17.

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Data Import Cheat Sheet

The Data Import cheat sheet reminds you how to read in flat files with http://readr.tidyverse.org/, work with the results as tibbles, and reshape messy data with tidyr. Use tidyr to reshape your tables into tidy data, the data format that works the most seamlessly with R and the tidyverse. Updated January 17.

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Data Transformation Cheat Sheet

dplyr provides a grammar for manipulating tables in R. This cheat sheet will guide you through the grammar, reminding you how to select, filter, arrange, mutate, summarise, group, and join data frames and tibbles. (Previous version) Updated January 17.

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Sparklyr Cheat Sheet

Sparklyr 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 January 17.

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R Markdown Cheat Sheet

R Markdown is an authoring format that makes it easy to write reusable 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 slideshows. Updated February 16. (Old Version.

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RStudio IDE Cheat Sheet

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 cheat sheet 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 January 16.

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Shiny Cheat Sheet

If you’re ready to build interactive web apps with R, say hello to Shiny. This cheat sheet 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 January 16.

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Data Visualization Cheat Sheet

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. See docs.ggplot2.org for detailed examples. Updated November 16.

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Package Development Cheat Sheet

The devtools package makes it easy to build your own R packages, and packages make it easy to share your R code. Supplement this cheat sheet with r-pkgs.had.co.nz, Hadley’s book on package development. Updated January 15.

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R Markdown Reference Guide

R Markdown marries together three pieces of software: markdown, knitr, and pandoc. This five page guide lists each of the options from markdown, knitr, and pandoc that you can use to customize your R Markdown documents. Updated October 14.

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Contributed Cheat Sheets

These cheatsheets have been generously contributed by R Users.

Advanced R

Environments, data Structures, Functions, Subsetting and more by Arianne Colton and Sean Chen. Updated February 16.

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Base R

Vectors, Matrices, Lists, Data Frames, Functions and more in base R by Mhairi McNeill. Updated March 15.

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Caret

Modeling and Machine Learning in R with the caret package by Max Kuhn. Updated September 17.

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cartography

Thematic maps with spatial objects by Timothée Giraud. Updated August 18.

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data.table

Data manipulation with data.table, cheatsheet by  Erik Petrovski. Updated August 18.

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DeclareDesign

Tools to test research designs that use a MIDA framework. Updated April 19.

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estimatr

Fast, robust estimators for common models. Updated November 18.

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eurostat

R tools to access the eurostat database, by rOpenGov. Updated March 17.

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golem

A framework for building robust Shiny apps. By ThinkR. Updated September 19.

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h2o

The R interface to h20’s algorithms for big data and parallel computing. By Juan Telleria. Updated April 18.

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How big is your graph?

Graph sizing with base R by Stephen Simon. Updated October 16.

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LaTeX

A reference to the LaTeX typesetting language, useful in combination with knitr and R Markdown, by Winston Chang. Updated January 18.

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leaflet

Interactive maps in R with leaflet, by Kejia Shi. Updated May 17.

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Machine Learning Modelling

A tabular guide to machine learning algorithms in R, by Arnaud Amsellem. Updated March 18.

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mlr

The mlr package offers a unified interface to R’s machine learning capabilities, by Aaron Cooley. Updated February 18.

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Mosaic

The mosaic package is for teaching mathematics, statistics, computation and modeling. Cheatsheet by Michael Laviolette. Updated February 18.

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nardl

The nardl package estimates the nonlinear cointegrating autoregressive distributed lag model. Cheatsheet by Taha Zaghdoudi. Updated October 18.

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Parallel Computation

Parallel computing in R with the parallel, foreach, and future packages. By Ardalan Mirshani. Updated March 19.

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quanteda

Quantitative Analysis of Textual Data in R with the quanteda package by Stefan Müller and Kenneth Benoit. Updated May 18.

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randomizr

Automate random assignment and sampling with randomizr. By Alex Coppock. Updated June 18.

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Regular Expressions

Basics of regular expressions and pattern matching in R by Ian Kopacka. Updated September 16.

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Simple Features (sf)

Tools for working with spatial vector data: points, lines, polygons, etc. Cheatsheet by Ryan Garnett. Updated October 18.

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sjmisc

dplyr friendly Data and Variable Transformation, by Daniel Lüdecke. Updated August 17.

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stata2r

Common translations from Stata to R, by Anthony Nguyen. Updated October 19.

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survminer

Elegant survival plots, by Przemyslaw Biecek. Updated March 17.

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Syntax Comparison

Three code styles compared: $, formula, and tidyverse. By Amelia McNamara. Updated February 18.

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Teach R

Concise advice on how to teach R or anything else. By Adi Sarid. Updated March 19.

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Time Series

A reference to time series in R. By Yunjun Xia and Shuyu Huang. Updated October 19.

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tsbox

A time series toolkit for conversions, piping, and more. By Christoph Sax. Updated May 19.

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vtree

Visualize hierarchical subsets of data with variable trees. By Nick Barrowman. Updated October 19.

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xplain

Explain statistical functions with XML files and xplain. By Joachim Zuckarelli. Updated May 18.

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Translations

Chinese Translations - 中文翻译

Dutch Translations - Nederlandse Vertaling

Gern Huijberts has provided Dutch translations of the Data Wrangling, Package Development, Data Visualization, and R Markdown cheatsheets.

French Translations - Traductions Françaises

German Translations - Deutsch Übersetzungen

Greek Translations - Ελληνικές μεταφράσεις

Kleanthis Koupidis and Charalampos Bratsas of School of Mathematics, AuTH and Open Knowledge Greece have provided Greek translations of the RStudio IDE and Base R cheatsheets. [/fusion_text][fusion_separator style_type=“none” top_margin=“30” bottom_margin=“” sep_color=“” border_size=“” icon=“” icon_circle=“” icon_circle_color=“” width=“” alignment=“” class=“” id=“” /][fusion_text]

Italian Translations - Traduzioni Italiane

Angelo Salatino of Knowledge Media Institute has provided Italian translations of the Package Development, R Markdown, and RStudio IDE cheatsheets.

Japanese Translations - 日本語翻訳

Korean Translations - 한국어 로 번역

Victor Lee of xwMOOC has provided Korean translations of the Package Development, R MarkdownCaretPurrr, and Syntax Comparison cheatsheets.

Portuguese Translations - tradução para português

Augusto Queiroz de Macedo has provided Portuguese translations of the Data Visualization, Data Wrangling, and RStudio IDE cheatsheets.

Russian Translations - Переводы

Spanish Translations - Traducciones en español

Turkish Translations - Türkçe Çeviriler

Ukrainian Translations - українські переклади

Evgeni Chasnovski of QuestionFlow has provided Ukrainian translations of the Data ImportData TransformationPurrr and lubridate cheatsheets.

Uzbek Translations - O‘zbek tilidagi tarjimalar

Alisher Suyunov has provided Uzbek translations of the Data Import and Data Transformation cheatsheets.

Vietnamese Translations - Bản dịch tiếng Việt

Anh Hoang Duc and Duc Pham of RAnalytics.vn have provided Vietnamese translations of the Data Visualization, Data Wrangling, R Markdown, Shiny, Package DevelopmentBase R, purrr, stringr, and lubridate cheatsheets.


Previous versions

To find previous versions of the cheatsheets, including the original color coded sheets, visit the Cheatsheet GitHub Repository.

Want to contribute?

We accept high quality cheatsheets and translations that are licenced under the creative commons license. Details and templates are available at How to Contribute a Cheatsheet.