RStudio Cheatsheets
RStudio Cheatsheets
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 updatesPython with R and Reticulate Cheatsheet
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.
Factors with forcats Cheatsheet
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.
Tidy Evaluation with rlang Cheatsheet
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.
Deep Learning with Keras Cheatsheet
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 17.
Dates and Times Cheatsheet
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.
Work with Strings Cheatsheet
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.
Apply Functions Cheatsheet
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.
Data Import Cheatsheet
The Data Import cheatsheet 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.
Data Transformation Cheatsheet
dplyr 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. (Previous version) Updated January 17.
Sparklyr Cheatsheet
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.
R Markdown Cheatsheet
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.
RStudio IDE Cheatsheet
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 January 16.
Shiny Cheatsheet
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 January 16.
Data Visualization Cheatsheet
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 ggplot2.tidyverse.org for more. Updated November 16.
Package Development Cheatsheet
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 15.
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.
Contributed Cheatsheets
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.
Base R
Vectors, Matrices, Lists, Data Frames, Functions and more in base R by Mhairi McNeill. Updated March 15.
Caret
Modeling and Machine Learning in R with the caret package by Max Kuhn. Updated September 17.
collapse
Advanced and fast data transformation with R by Sebastian Krantz. Updated November 20.
data.table
Data manipulation with data.table, cheatsheet by Erik Petrovski. Updated August 18.
h2o
The R interface to h20’s algorithms for big data and parallel computing. By Juan Telleria. Updated April 18.
LaTeX
A reference to the LaTeX typesetting language, useful in combination with knitr and R Markdown, by Winston Chang. Updated January 18.
Machine Learning Modelling
A tabular guide to machine learning algorithms in R, by Arnaud Amsellem. Updated March 18.
mlr
The mlr package offers a unified interface to R’s machine learning capabilities, by Aaron Cooley. Updated February 18.
Mosaic
The mosaic package is for teaching mathematics, statistics, computation and modeling. Cheatsheet by Michael Laviolette. Updated February 18.
nardl
The nardl package estimates the nonlinear cointegrating autoregressive distributed lag model. Cheatsheet by Taha Zaghdoudi. Updated October 18.
nimble
Hierarchical statistical models that extend BUGS and JAGS by
Nimble development team. Updated May 20.
oSCR
The nardl package estimates the nonlinear cointegrating autoregressive distributed lag model. Cheatsheet by Taha Zaghdoudi. Updated October 18.
Parallel Computation
Parallel computing in R with the parallel, foreach, and future packages. By Ardalan Mirshani. Updated March 19.
quanteda
Quantitative Analysis of Textual Data in R with the quanteda package by Stefan Müller and Kenneth Benoit. Updated May 20.
randomizr
Automate random assignment and sampling with randomizr. By Alex Coppock. Updated June 18.
Regular Expressions
Basics of regular expressions and pattern matching in R by Ian Kopacka. Updated September 16.
SamplingStrata
Optimal stratification for survey sampling. Cheatsheet by Giulio Barcaroli. Updated April 20.
Simple Features (sf)
Tools for working with spatial vector data: points, lines, polygons, etc. Cheatsheet by Ryan Garnett. Updated October 18.
sjmisc
dplyr friendly Data and Variable Transformation, by Daniel Lüdecke. Updated August 17.
Syntax Comparison
Three code styles compared: $, formula, and tidyverse. By Amelia McNamara. Updated February 18.
Time Series
A reference to time series in R. By Yunjun Xia and Shuyu Huang. Updated October 19.
tsbox
A time series toolkit for conversions, piping, and more. By Christoph Sax. Updated May 19.
vegan
Tools for descriptive community ecology. Cheatsheey by Bruna L Silva. Updated April 20.
vtree
Visualize hierarchical subsets of data with variable trees. By Nick Barrowman. Updated October 19.
xplain
Explain statistical functions with XML files and xplain. By Joachim Zuckarelli. Updated May 18.
Translations
Chinese Translations - 中文翻译
- Data Visualization translated by taoruicn
- sparklyr translated by Ke Zhang in Simplified Chinese and Traditional Chinese
Dutch Translations - Nederlandse Vertaling
- Data Visualization translated by Gern Huijberts
- Data Wrangling translated by Gern Huijberts
- Package Development translated by Gern Huijberts
- R Markdown translated by Gern Huijberts
French Translations - Traductions Françaises
- caret translated by Ahmadou Dicko
- Data Visualization translated by Vincent Guyader and Diane Beldame of ThinkR
- Data Wrangling translated by Vincent Guyader and Diane Beldame of ThinkR
- Quanteda translated by Ahmadou Dicko
- RStudio IDE translated by Vincent Guyader and Diane Beldame of ThinkR
- Shiny translated by Vincent Guyader and Diane Beldame of ThinkR
German Translations - Deutsch Übersetzungen
- Base R translated by Annika Kies and Martin Kies from LeverageData
- Data Transformation translated by Lucia Gjeltema of Research Triangle Analysts
- Data Visualization translated by Lucia Gjeltema of Research Triangle Analysts
- Data Wrangling translated by Lucia Gjeltema of Research Triangle Analysts
- Package Development translated by Lucia Gjeltema of Research Triangle Analysts
- R Markdown translated by Lucia Gjeltema of Research Triangle Analysts
- Shiny translated by Lucia Gjeltema of Research Triangle Analysts
- sparklyr translated by Ke Zhang
Greek Translations - Ελληνικές μεταφράσεις
- Base R translated by Kleanthis Koupidis, Charalampos Bratsas, and Open Knowledge Greece
- RStudio IDE translated by Kleanthis Koupidis, Charalampos Bratsas, and Open Knowledge Greece
Italian Translations - Traduzioni Italiane
- Package Development translated by Angelo Salatino of Knowledge Media Institute
- R Markdown translated by Angelo Salatino of Knowledge Media Institute
- RStudio IDE translated by Angelo Salatino of Knowledge Media Institute
Japanese Translations - 日本語翻訳
- Base R translated by Takeshi Sasayama
- Deep Learning with Keras translated by Masato Takahashi
- Data Visualization translated by Takeshi Sasayama
- Data Wrangling translated by Tomoaki Masuda of the MIT Sloan Data Analytics Club
- R Markdown translated by Masato Takahashi
- RStudio IDE translated by Masato Takahashi
- sparklyr translated by Masato Takahashi
Korean Translations - 한국어 로 번역
- Package Development translated by Victor Lee of xwMOOC
- R Markdown translated by Victor Lee of xwMOOC
- caret translated by Victor Lee of xwMOOC
- purrr translated by Victor Lee of xwMOOC
- Syntax Comparison translated by Victor Lee of xwMOOC
Portuguese Translations - tradução para português
- Caret translated by Karen da Silva Lopes
- Data Visualization translated by Augusto Queiroz de Macedo
- Data Wrangling translated by Augusto Queiroz de Macedo
- RStudio IDE translated by Augusto Queiroz de Macedo
Russian Translations - Переводы
- Data Import translated by Evgeni Chasnovski of QuestionFlow
- Data Transformation translated by Evgeni Chasnovski of QuestionFlow
- Data Visualization translated by Kirill Voronov of Data Science(R)
- lubridate translated by Evgeni Chasnovski of QuestionFlow
- purrr translated by Evgeni Chasnovski of QuestionFlow
Spanish Translations - Traducciones en español
- caret translated by Carlos Ortega of the Grupo de Usuarios de R de Madrid
- Data Import translated by Yanina Bellini Saibene, previous version by Carlos Ortega and Santiago Mota of the Grupo de Usuarios de R de Madrid
- Data Visualization translated by Edgar Ruiz, previous versions by Carlos Ortega and Santiago Mota of the Grupo de Usuarios de R de Madrid and Carolina Mengoni Goñalons.
- Data Wrangling translated by Frans van Dunné of ixpantia
- Data Transformation translated by Frans van Dunné of ixpantia
- Entorno de Desarrollo RStudio translated by Rosana Ferraro of Máxima Formación
- Estadística Descriptiva con R translated by Rosana Ferraro of Máxima Formación
- Factors with forcats translated by Laura Acion
- Introducción a R translated by Rosana Ferraro of Máxima Formación
- keras translated by Carlos Ortega of the Grupo de Usuarios de R de Madrid
- lubridate translated by Yanina Bellini Saibene, previous version by Carlos Ortega and Santiago Mota of the Grupo de Usuarios de R de Madrid
- Package Development translated by Paola Corrales, previous version by Frans van Dunné of ixpantia
- purrr translated by Carlos Ortega of the Grupo de Usuarios de R de Madrid
- Python with R and reticulate translated by Vanesa Maribel
- R Markdown translated by Jessica Formoso
- RStudio IDE translated by Monica Alonso
- Shiny translated by Frans van Dunné of ixpantia
- sparklyr translated by DaniPrina, previous version by by Carlos Ortega of the Grupo de Usuarios de R de Madrid
- stringr translated by Carlos Ortega of the Grupo de Usuarios de R de Madrid (updated by L. Paloma Rojas Saunero)
- Syntax Comparison translated by Riva Quiroga
- tidyeval translated by Violeta Roizman
Turkish Translations - Türkçe Çeviriler
- Base R translated by Elif Kartal
- caret translated by İlkim Ecem Emre
- Data Import translated by Metin Yazici
- Data Transformation translated by Hazel kavılı and Berk Orbay
- Data Visualization translated by Hazel kavılı and Berk Orbay, previous version by Metin Yazici
- R Markdown translated by Metin Yazici
- Regular Expressions translated by Zeki Özen
- Shiny translated by Metin Yazici
Ukrainian Translations - українські переклади
- Data Import translated by Evgeni Chasnovski of QuestionFlow
- Data Transformation translated by Evgeni Chasnovski of QuestionFlow
- purrr translated by Evgeni Chasnovski of QuestionFlow
- lubridate translated by Evgeni Chasnovski of QuestionFlow
Uzbek Translations - O‘zbek tilidagi tarjimalar
- Data Import translated by Alisher Suyunov
- Data Transformation translated by Alisher Suyunov
Vietnamese Translations - Bản dịch tiếng Việt
- Base R translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- Data Visualization translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- Data Wrangling translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- lubridate translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- Package Development translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- purrr translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- R Markdown translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- Shiny translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
- stringr translated by Anh Hoang Duc and Duc Pham of RAnalytics.vn
Previous versions
To find previous versions of the cheatsheets, including the original color coded sheets, visit the Cheatsheet GitHub Repository.
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