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.
Subscribe to cheat sheet updatesThe 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 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 (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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
These cheatsheets have been generously contributed by R Users.
Environments, data Structures, Functions, Subsetting and more by Arianne Colton and Sean Chen. Updated February 16.
Vectors, Matrices, Lists, Data Frames, Functions and more in base R by Mhairi McNeill. Updated March 15.
Modeling and Machine Learning in R with the caret package by Max Kuhn. Updated September 17.
Data manipulation with data.table, cheatsheet by Erik Petrovski. Updated August 18.
The R interface to h20’s algorithms for big data and parallel computing. By Juan Telleria. Updated April 18.
A reference to the LaTeX typesetting language, useful in combination with knitr and R Markdown, by Winston Chang. Updated January 18.
A tabular guide to machine learning algorithms in R, by Arnaud Amsellem. Updated March 18.
The mlr package offers a unified interface to R’s machine learning capabilities, by Aaron Cooley. Updated February 18.
The mosaic package is for teaching mathematics, statistics, computation and modeling. Cheatsheet by Michael Laviolette. Updated February 18.
The nardl package estimates the nonlinear cointegrating autoregressive distributed lag model. Cheatsheet by Taha Zaghdoudi. Updated October 18.
The oSCR package provides functions for working with Spatial Capture Recapture models. Cheatsheet by
Gabriela Palomo-Munoz. Updated December 19.
Parallel computing in R with the parallel, foreach, and future packages. By Ardalan Mirshani. Updated March 19.
Quantitative Analysis of Textual Data in R with the quanteda package by Stefan Müller and Kenneth Benoit. Updated May 18.
Automate random assignment and sampling with randomizr. By Alex Coppock. Updated June 18.
Basics of regular expressions and pattern matching in R by Ian Kopacka. Updated September 16.
Tools for working with spatial vector data: points, lines, polygons, etc. Cheatsheet by Ryan Garnett. Updated October 18.
dplyr friendly Data and Variable Transformation, by Daniel Lüdecke. Updated August 17.
Three code styles compared: $, formula, and tidyverse. By Amelia McNamara. Updated February 18.
A reference to time series in R. By Yunjun Xia and Shuyu Huang. Updated October 19.
A time series toolkit for conversions, piping, and more. By Christoph Sax. Updated May 19.
Visualize hierarchical subsets of data with variable trees. By Nick Barrowman. Updated October 19.
Explain statistical functions with XML files and xplain. By Joachim Zuckarelli. Updated May 18.
To find previous versions of the cheatsheets, including the original color coded sheets, visit the Cheatsheet GitHub Repository.
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.
