dplyr data wrangling, data analysis The essential data-munging R package when working with data frames. Especially useful for operating on data by categories. CRAN. See the intro vignette Hadley Wickham

purrr data wrangling purrr makes it easy to apply a function to each item in a list and return results in the format of your choice. It's more complex to learn than the older plyr package, but also more robust. And, its functions are more standardized than base R's apply family -- plus it's got functions for tasks like error-checking. CRAN. map_df(mylist, myfunction)

More: Charlotte Wickham's purr tutorial video, the purrr cheat sheet PDF download. Hadley Wickham

readxl data import Fast way to read Excel files in R, without dependencies such as Java. CRAN. read_excel("my-spreadsheet.xls", sheet = 1) Hadley Wickham

readr and vroom data import Base R handles most of these functions; but if you have huge files, these packages offer faster and standardized way to read CSVs and similar files into R. readr has been around for awhile; vroom is a speedier alternative, useful for larger data sets. Eventually the packages will likely merge. CRAN. read_csv(myfile.csv) or vroom(myfile.csv) Hadley Wickham (readr), Jim Hester (vroom)

rio data import, data export rio has a good idea: Pull a lot of separate data-reading packages into one, so you just need to remember 2 functions: import and export. CRAN. import("myfile") Thomas J. Leeper & others

tidyxl data import, data wrangling If you've ever wanted to tear your hair out over an Excel file with merged cells, data in column headers, headers mixed in data, and key information in color coding, this is the package for you. Each cell is imported in its own row, with information about data type, position, and color, not just value, allowing you to reshape the data from there. Super time saver for messy data. CRAN. xlsx_cells("my_nightmare_file.xlsx") Duncan Garmonsway

Hmisc data analysis There are a number of useful functions in here. Two of my favorites: describe, a more robust summary function, and Cs, which creates a vector of quoted character strings from unquoted comma-separated text. Cs(so, it, goes) creates c("so", "it", "goes"). CRAN. describe(mydf)

Cs(so, it, goes) Frank E Harrell Jr & others

datapasta data import Data copy and paste: Meet reproducible research. If you've copied data from the Web, a spreadsheet, or other source into your clipboard, datapasta lets you paste it into R as an R object, with the code to reproduce it. It includes RStudio add-ins as well as command-line functions for transposing data, turning it into markdown format, and more. CRAN. df_paste() to create a data frame, vector_paste() to create a vector. Miles McBain

sqldf data wrangling, data analysis Do you know a great SQL query you'd use if your R data frame were in a SQL database? Run SQL queries on your data frame with sqldf. CRAN. sqldf("select * from mydf where mycol > 4") G. Grothendieck

jsonlite data import, data wrangling Parse json within R or turn R data frames into json. CRAN. myjson <- toJSON(mydf, pretty=TRUE)

mydf2 <- fromJSON(myjson) Jeroen Ooms & others

XML data import, data wrangling Many functions for elegantly dealing with XML and HTML, such as readHTMLTable. CRAN. mytables <- readHTMLTable(myurl) Duncan Temple Lang

httr data import, data wrangling An R interface to http protocols; useful for pulling data from APIs. See the httr quickstart guide. CRAN. r <- GET("http://httpbin.org/get")

content(r, "text") Hadley Wickham

quantmod data import, data visualization, data analysis Even if you're not interested in analyzing and graphing financial investment data, quantmod has easy-to-use functions for importing economic as well as financial data from sources like the Federal Reserve. CRAN. getSymbols("AITINO", src="FRED") Jeffrey A. Ryan

tidyquant data import, data visualization, data analysis Another financial package that's useful for importing, analyzing and visualizing data, integrating aspects of other popular finance packages as well as tidyverse tools. With thorough documentation. CRAN. aapl_key_ratios <- tq_get("AAPL", get = "key.ratios") Matt Dancho

rvest data import, web scraping Web scraping: Extract data from HTML pages. Inspired by Python's Beautiful Soup. Works well with Selectorgadget. CRAN. See the SelectorGadget vignette Hadley Wickham

tidyr data wrangling tidyr initially won me over with specialized functions like fill (fill in missing columns from data above) and replace_na. But now I also use it for its main purpose too: helping you change data row and column formats from "wide" to "long". CRAN. See my YouTube video How to reshape data with tidyr’s new pivot functions. Hadley Wickham

splitstackshape data wrangling The package's cSplit() function solves a rather complex shaping problem in an astonishingly easy way. If you have a data frame column with one or more comma-separated values (think a survey question with "select all that apply"), this is worth an install if you want to separate each item into its own new data frame row.. CRAN. cSplit(mydata, "multi_val_column", sep = ",", direction = "long"). Ananda Mahto

magrittr data wrangling This package gave us the %>% symbol for chaining R operations, but it's got other useful operators such as %<>% for mutating a data frame in place and and . as a placeholder for the original object being operated upon. CRAN. mydf %<>% mutate(newcol = myfun(colname)) Stefan Milton Bache & Hadley Wickham

validate data wrangling Intuitive data validation based on rules you can define, save and re-use. CRAN. See the introductory vignette. Mark van der Loo & Edwin de Jonge

testthat programming Package that makes it easy to write unit tests for your R code. CRAN. See the testing chapter of Hadley Wickham's book on R packages. Hadley Wickham

data.table data wrangling, data analysis Popular package for heavy-duty data wrangling. While I often prefer dplyr, data.table has many fans for its speed with large data sets and concise syntax. CRAN. Intro vignette Matt Dowle & others

stringr data wrangling Numerous functions for text manipulation. Some are similar to existing base R functions but in a more standard format, including working with regular expressions. Some of my favorites: str_pad and str_trim. CRAN. str_pad(myzipcodevector, 5, "left", "0") Hadley Wickham

lubridate data wrangling Everything you ever wanted to do with date arithmetic, although understanding & using available functionality can be somewhat complex. CRAN. mdy("05/06/2015") + months(1)

More examples in the package vignette Garrett Grolemund, Hadley Wickham & others

DataExplorer data analysis Not sure where to get started looking at a data set? Want to get a basic handle on that data without running multiple commands like str() and plot()? DataExplorer attempts to offer one-click report generation to show and visualize basics about a data set, such as distributions and missing data. CRAN. create_report(mydataframe) Boxuan Cui

zoo data wrangling, data analysis Robust package with a slew of functions for dealing with time series data; I like the handy rollmean function with its align=right and fill=NA options for calculating moving averages. CRAN. rollmean(mydf, 7) Achim Zeileis & others

tsbox data wrangling, data analysis Super easy way to convert data between different R time-series data formats: xts, data frame, zoo, tsibble, and more. Plus some basic analysis functions. CRAN. ts_zoo(mydf) Christoph Sax

knitr and rmarkdown data display Add R to a markdown document and easily generate reports in HTML, Word and other formats. A must-have if you're interested in reproducible research and automating the journey from data analysis to report creation. CRAN. See the Minimal Examples knitr page and RStudio's R Markdown page. Yihui Xie & others (knitr), RStudio (rmarkdown)

remedy data display RStudio add-in offers a menu for R Markdown formatting commands, so you no longer need to remember and/or type code for things like making an HTML list or embedding a YouTube video. And, since add-in commands can be assigned custom keyboard shortcuts, you can create your own shortcuts for tasks like bolding text. GitHub. See the package website. Colin Fay & others

officeR data display Import and edit Microsoft Word and PowerPoint documents, making it easy to add R-generated analysis and visualizations to existing as well as new reports and presentations. CRAN. my_doc <- read_docx() %>%

body_add_img(src = myplot)

The package website has many more examples. David Gohel

listviewer data display, data wrangling While RStudio has since added a list-viewing option, this HTML widget still offers an elegant way to view complex nested lists within R. GitHub timelyportfolio/listviewer. jsonedit(mylist) Kent Russell

DT data display Create a sortable, searchable table in one line of code with this R interface to the jQuery DataTables plug-in. GitHub rstudio/DT. datatable(mydf) RStudio

ggplot2 data visualization Powerful, flexible and well-thought-out dataviz package following 'grammar of graphics' syntax to create static graphics, but be prepared for a steep learning curve. CRAN. qplot(factor(myfactor), data=mydf, geom="bar", fill=factor(myfactor))

See my searchable ggplot2 cheat sheet and

time-saving code snippets. Hadley Wickham

patchwork data visualization Easily combine ggplot2 plots and keep the new, merged plot a ggplot2 object. plot_layout() adds ability to set columns, rows, and relative sizes of each component graphic. GitHub. plot1 + plot2 + plot_layout(ncol=1) Thomas Lin Pedersen

ggforce data visualization Adds some design functionality to base ggplot2 including easy labeling of plot groups. CRAN. See this blog post by RStudio's Edgar Ruiz for several useful examples. Thomas Lin Pedersen

esquisse data visualization This RStudio add-in offers a drag-and-drop interface for ggplot2. And, it generates codes for the graph you create with the GUI. It's a useful tool for exploring different color palettes and themes, even if you're comfortable creating your visualizations directly in R. CRAN. See examples on the project's website . Victor Perrier and Fanny Meyer, dreamRs

dygraphs data visualization Create HTML/JavaScript graphs of time series - one-line command if your data is an xts object. CRAN. dygraph(myxtsobject) JJ Allaire & RStudio

googleVis data visualization Tap into the Google Charts API using R. CRAN. mychart <- gvisColumnChart(mydata)

plot(Column)

Numerous examples here Markus Gesmann & others

metricsgraphics data visualization R interface to the metricsgraphics JavaScript library for bare-bones line, scatterplot and bar charts. GitHub hrbrmstr/metricsgraphics. See package intro Bob Rudis

taucharts data visualization This html widget library is especially useful for scatterplots where you want to view multiple regression options. However, it does much more than that, including line and bar charts with legends and tooltips. GitHub hrbrmstr/taucharts. See the author's post on RPubs Bob Rudis

RColorBrewer data visualization Not a designer? RColorBrewer helps you select color palettes for your visualizations. CRAN. See Jennifer Bryan's tutorial Erich Neuwirth

paletteer data visualization This package is a collection of dozens of R color palettes, all with a common interface. Extremely handy if you want to move beyond built-in and RColorBrewer options. See the package site for examples on accessing palettes and using them with ggplot2. Emil Hvitfeldt

sf mapping, data wrangling This package makes it much easier to do GIS work in R. Simple features protocols make geospatial data look a lot like regular data frames, while various functions allow for analysis such as determining whether points are in a polygons. A GIS game-changer for R. CRAN. See the package vignettes, starting with the introduction, Simple Features for R. Edzer Pebesma & others

leaflet mapping Map data using the Leaflet JavaScript library within R. GitHub rstudio/leaflet. See my tutorial RStudio

ggmap mapping I don't use this package often for its main purpose of pulling down background map tiles, it's also useful for geocoding addresses with the Google Maps API with its geocode and mutate_geocode functions. However, an API key is required and credit card needed to register, although there are some free lookups available each day. CRAN. geocode("492 Old Connecticut Path, Framingham, MA") David Kahle &Hadley Wickham

rgeocodio mapping This is my new geocoding go-to. It uses the geocod.io service. An API key is needed, but you can get one free that includes 2,500 lookups a day. GitHub hrbrmstr/rgeocodio. gio_geocode("492 Old Connecticut Path, Framingham, MA") Bob Rudis

tmap & tmaptools mapping This package offer an easy way to read in shape files and join data files with geographic info, as well as do some exploratory mapping. Recent functionality adds support for simple features, interactive maps and creating leaflet objects. Plus, tmaptools::palette_explorer() is a great tool for picking ColorBrewer palettes. CRAN. See the package vignette or my mapping in R tutorial Martijn Tennekes

colourpicker data visualization The package's RStudio add-in makes it easy to browse through and select R's built-in colors, or get hex codes for custom colors not available by name. The plotHelper() function lets you select colors and see how they'd look on a scatter plot. CRAN. See the GitHub repo. Dean Attali

mapsapi mapping, data wrangling This interface to the Google Maps Direction and Distance Matrix APIs let you analyze and map distances and driving routes. CRAN. google_directions( origin = c(my_longitude, my_latitude),

destination = c(my_address),

alternatives = TRUE

Also see the vignette Michael Dorman

tidycensus mapping, data wrangling Want to analyze and map U.S. Census Bureau data from 5-year American Community Surveys or 10-year censuses? This makes it easy to download numerical and geospatial info in R-ready format. CRAN. See Basic usage of tidycensus. Kyle E. Walker

glue data wrangling Main function, also glue, evaluates variables and R expressions within a quoted string, as long as they're enclosed by {} braces. This makes for an elegant paste() replacement. CRAN. glue("Today is {Sys.Date()}") Jim Hester

googleanalyticsR Web analytics Pull data from Google Analytics, including GA's version 4 API. Also has anti-sampling options. CRAN. See package website. Mark Edmonson

RSiteCatalyst Web analytics Use Adobe Analytics with R. GitHub randyzwitch/RSiteCatalyst. See Examples section on the package website. Randy Zwitch

shiny data visualization Turn R data into interactive Web applications. I've seen some nice (if sometimes sluggish) apps and it's got many enthusiasts. CRAN. See the tutorial RStudio

flexdashboard data visualization If Shiny is too complex and involved for your needs, this package offers a simpler (if somewhat less robust) solution based on R Markdown. CRAN. More info in Using flexdashboard JJ Allaire, RStudio & others

openxlsx misc If you need to write to an Excel file as well as read, this package is easy to use and offers a lot of options for formatting your spreadsheet. CRAN. write.xlsx(mydf, "myfile.xlsx") Alexander Walker

gmodels data wrangling, data analysis There are several functions for modeling data here, but the one I use, CrossTable, simply creates cross-tabs with loads of options -- totals, proprotions and several statistical tests. CRAN. CrossTable(myxvector, myyvector, prop.t=FALSE, prop.chisq = FALSE) Gregory R. Warnes

janitor data wrangling, data analysis Basic data cleaning made easy, such as finding duplicates by multiple columns, making R-friendly column names and removing empty columns. It also has some nice tabulating tools, like adding a total row, as well as generating tables with percentages and easy crosstabs. And, its get_dupes() function is an elegant way of finding duplicate rows in data frames, either based on one column, several columns, or entire rows. CRAN. tabyl(mydf, sort = TRUE) %>% adorn_totals("row") Samuel Firke

car data wrangling car's recode function makes it easy to bin continuous numerical data into categories or factors. While base R's cut accomplishes the same task, I find recode's syntax to be more intuitive - just remember to put the entire recoding formula within double quotation marks. dplyr's case_when() function is another option worth considering. CRAN. recode(x, "1:3='Low'; 4:7='Mid'; 8:hi='High'") John Fox & others

rcdimple data visualization R interface to the dimple JavaScript library with numerous customization options. Good choice for JavaScript bar charts, among others. GitHub timelyportfolio/rcdimple. dimple(mtcars, mpg ~ cyl, type = "bar") Kent Russell

scales data wrangling While this package has many more sophisticated ways to help you format data for graphing, it's worth a download just for the comma(), percent() and dollar() functions. CRAN. comma(mynumvec) Hadley Wickham

plotly data visualization R interface to the Plotly JavaScript library that was open-sourced in late 2015. Basic graphs have a distinctive look which may not be for everyone, but it's full-featured, relatively easy to learn (especially if you know ggplot2) and includes a ggplotly() function to turn graphs created with ggplot2 interactive. CRAN. d <- diamonds[sample(nrow(diamonds), 1000), ]

plot_ly(d, x = carat, y = price, text = paste("Clarity: ", clarity), mode = "markers", color = carat, size = carat) Carson Sievert & others

highcharter data visualization R wrapper for the robust and well documented Highcharts JavaScript library, one of my favorite choices for presentation-quality interactive graphics. The package uses ggplot2-like syntax, including options for handling both long and wide data, and comes with plenty of examples. Note that a paid Highcharts license is needed to use this for commercial or government work (it's free for personal and non-profit projects). CRAN. hchart(mydf, "charttype", hcaes(x = xcol, y = ycol, group = groupbycol)) Joshua Kunst & others

profvis programming Is your R code sluggish? This package gives you a visual representative of your code line by line so you can find the speed bottlenecks. CRAN. profvis({ your code here }) Winston Chang & others

tidytext text mining Elegant implementation of text mining functions using Hadley Wickham's "tidy data" principles. CRAN. See tidytextmining.com for numerous examples. Julia Silge & David Robinson

diffobj data analysis Base R's identical() function tells you whether or not two objects are the same; but if they're not, it won't tell you why. diffobj gives you a visual representation of how two R objects differ. CRAN. diffObj(x,y) Brodie Gaslam & Michael B. Allen

prophet forecasting I don't do much forecasting analysis; but if I did, I'd start with this package. CRAN. See the Quick start guide. Sean Taylor & Ben Letham at Facebook

feather data import, data export This binary data-file format can be read by both Python and R, making data interchange easier between the two languages. It's also built for I/O speed. The arrow package also reads and writes feather files. CRAN. write_feather(mydf, "myfile") Wes McKinney & Hadley Wickham

fst data import, data export Another alternative for binary file storage (R-only), fst was built for fast storage and retrieval, with access speeds above 1 GB/sec. It also offers compression that doesn't slow data access too much, as well as the ability to import a specific range of rows (by row number). CRAN. write.fst(mydf, "myfile.fst", 100) Mark Klik

googleAuthR data import If you want to use data from a Google API in an R project and there's not yet a specific package for that API, this is the place to turn for authenticating CRAN. See examples on the package website and this gist for use with Google Calendars. CRAN. Mark Edmondson

devtools package development, package installation devtools has a slew of functions aimed at helping you create your own R packages, such as automatically running all example code in your help files to make sure everything works. Requires Rtools on Windows and XCode on a Mac. CRAN. run_examples() Hadley Wickham & others

remotes package installation remotes is a lighter-weight alternative to devtools if all you want is to install packages from GitHub, Bitbucket and some other sources. CRAN. install_github("mangothecat/franc") Gabor Csardi & others

githubinstall package installation Do you want to install a package from GitHub but can't remember the creator's name -- or just don't feel like typing it out? With githubinstall, simply run githubinstall("packagename") and the function will suggest an account; you just respond Y to install or n if it's the wrong one. It even includes fuzzy matching if you misspell a package name! githubinstall("AnomalyDetection") Koji Makiyama

installr misc Windows only: Update your installed version of R from within R. On CRAN. updateR() Tal Galili & others

reinstallr misc Seeks to find packages that had previously been installed on your system and need to be re-installed after upgrading R. CRAN. reinstallr() Calli Gross

usethis package development, programming Initially aimed at package development, usethis now includes useful functions for any coding project. Among its handy features are an edit family that lets you easily update your .Renvironment and .Rprofile files. On CRAN, but install GitHub version from "r-lib/usethis" for latest updates. edit_r_environ() Hadley Wickham, Jennifer Bryan & RStudio

here misc This package has one function with a single, useful purpose: find your project's working directory. Surprisingly helpful if you want your code to run on more than one system. CRAN. my_project_directory <- here() Kirill Müller

pacman misc, package installation This package is another that aims to solve one problem, and solve it well: package installation. The main functions will loadi a package that's already installed or installing it first if it's not available. While this is certainly possible to do with base R's require() and an if statement, p_load() is so much more elegant for CRAN packages, or p_load_gh() for GitHub. Other useful options include p_temp(), which allows for a temporary, this-session-only package installation. CRAN. p_load(dplyr, here, tidycensus) Tyler Rinker

plumber data export, programming Turn any R function into a host-able API with a line or two of code. This well-thought-out package makes it easy to use R for data handling in other, non-R coding projects. CRAN. See the documentation or my article Create your own Slack bots -- and Web APIs -- with R Jeff Allen, Trestle Technology & others

echarts4r data visualization R wrapper for the powerful and flexible ECharts JavaScript library. It features dozens of chart and graph types, from bar and line charts to sunbursts, heat maps, and geographical maps. Hundreds of customizations not explicitly mentioned in the package docs are nevertheless available; you just need to peruse the original ECharts documentation. (ECharts is an Apache Software Foundation incubator project.) CRAN. mtcars %>% e_charts(wt) %>% e_line(mpg) John Coene

dataCompareR data wrangling A quick and elegant way to compare two data frames, either row by row or by a specified key. CRAN. rCompare(mydf1, mydf2) Rob Noble-Eddy at CapitalOne & others

cloudyR project data import, data export This is a collection of packages aimed at making it easier for R to work with cloud platforms such as Amazon Web Services, Google and Travis-CI. Some are already on CRAN, some can be found on GitHub. See the list of packages. Various

flyio data import, data export This is a bit like rio, but for the cloud: It offers a common set of functions whether you're using Amazon's S3 or Google Cloud. Set your data source, authenticate with your credentials (which can be stored in an R environmental variable), set a bucket name, and off you go. GitHub. See the GitHub repo or YouTube video of a demo at the Delhi useR meetup. SocialCops

geofacet data visualization, mapping While I rarely need to create "geofacets" -- maps with same-sized blocks in geospatially appropriate locations -- this package is so cool that I had to include it. The package lets you create your own geofacet visualizations using ggplot2 and built-in grids such as US states and EU countries. And, it comes with design-your-own geofacet grid capabilities. CRAN. grid_design() Ryan Hafen

reticulate programming If you know Python as well as R, this package offers a suite of tools for calling Python from within R, as well as "translating" between R and Python objects such as Pandas data frames and R data frames. CRAN. See the reticulate package website. JJ Allaire

slackr collaboration Do you use Slack? If so, you can send messages and files into a Slack channel, as long as you've got a token from that Slack. Useful to run analysis and then quickly share results with a team. GitHub hrbrmstr/slackr See the GitHub repo. Bob Rudis