dplyr
information wrangling, information evaluation
The important data-munging R package deal when working with information frames. Especially helpful for working on information by classes. CRAN.
See the intro vignette
Hadley Wickham
purrr
information wrangling
purrr makes it straightforward to use a perform to every merchandise in a listing and return ends in the format of your alternative. It’s extra complicated to be taught than the older plyr package deal, but in addition extra strong. And, its features are extra standardized than base R’s apply household — plus it is bought features for duties like error-checking. CRAN.
map_df(mylist, myfunction)More: Charlotte Wickham’s purr tutorial video, the purrr cheat sheet PDF obtain, straightforward error checking with purrr’s presumably.
Hadley Wickham
readxl
information import
Fast strategy to learn Excel recordsdata in R, with out dependencies similar to Java. CRAN.
read_excel(“my-spreadsheet.xls”, sheet = 1)
Hadley Wickham
readr and vroom
information import
Base R handles most of those features; however you probably have big recordsdata, these packages provide sooner and standardized strategy to learn CSVs and comparable recordsdata into R. readr has been round for awhile; vroom is a speedier different, helpful for bigger information units. Eventually the packages could merge. information.desk’s fread() is one other helpful different. CRAN.
read_csv(myfile.csv) or vroom(myfile.csv)
Hadley Wickham (readr), Jim Hester (vroom)
rio
information import, information export
rio has a good suggestion: Pull plenty of separate data-reading packages into one, so that you simply want to recollect 2 features: import and export. CRAN.
import(“myfile”)
Thomas J. Leeper & others
tidyxl
information import, information wrangling
If you’ve got ever wished to tear your hair out over an Excel file with merged cells, information in column headers, headers combined in information, and key data in shade coding, that is the package deal for you. Each cell is imported in its personal row, with details about information sort, place, and shade, not simply worth, permitting you to reshape the info from there. Super time saver for messy information. CRAN.
xlsx_cells(“my_nightmare_file.xlsx”)
Duncan Garmonsway
Hmisc
information evaluation
There are various helpful features in right here. Two of my favorites: describe, a extra strong abstract perform, and Cs, which creates a vector of quoted character strings from unquoted comma-separated textual content. Cs(so, it, goes) creates c(“so”, “it”, “goes”). CRAN.
describe(mydf)Cs(so, it, goes)
Frank E Harrell Jr & others
datapasta
information import
Data copy and paste: Meet reproducible analysis. If you’ve got copied information from the Web, a spreadsheet, or different supply into your clipboard, datapasta enables you to paste it into R as an R object, with the code to breed it. It consists of RStudio add-ins in addition to command-line features for transposing information, turning it into markdown format, and extra. CRAN.
df_paste() to create an information body, vector_paste() to create a vector.
Miles McBain
sqldf
information wrangling, information evaluation
Do you already know an important SQL question you’d use in case your R information body had been in a SQL database? Run SQL queries in your information body with sqldf. CRAN.
sqldf(“select * from mydf where mycol > 4”)
G. Grothendieck
jsonlite
information import, information wrangling
Parse json inside R or flip R information frames into json. CRAN.
myjson <- toJSON(mydf, fairly=TRUE)mydf2 <- fromJSON(myjson)
Jeroen Ooms & others
XML
information import, information wrangling
Many features for elegantly coping with XML and HTML, similar to readHTMLTable. CRAN.
mytables <- readHTMLTable(myurl)
Duncan Temple Lang
httr
information import, information wrangling
An R interface to http protocols; helpful for pulling information from APIs. See the httr quickstart information. CRAN.
r <- GET(“http://httpbin.org/get”)content material(r, “text”)
Hadley Wickham
quantmod
information import, information visualization, information evaluation
Even in the event you’re not fascinated about analyzing and graphing monetary funding information, quantmod has easy-to-use features for importing financial in addition to monetary information from sources just like the Federal Reserve. CRAN.
getSymbols(“AITINO”, src=”https://www.computerworld.com/article/2921176/FRED”)
Jeffrey A. Ryan
tidyquant
information import, information visualization, information evaluation
Another monetary package deal that is helpful for importing, analyzing and visualizing information, integrating elements of different fashionable finance packages in addition to tidyverse instruments. With thorough documentation. CRAN.
aapl_key_ratios <- tq_get(“AAPL”, get = “key.ratios”)
Matt Dancho
rvest
information import, internet scraping
Web scraping: Extract information from HTML pages. Inspired by Python’s Beautiful Soup. Works properly with Selectorgadget. CRAN.
How to import information into R or see the SelectorGadget vignette
Hadley Wickham
tidyr
information wrangling
tidyr initially gained me over with specialised features like fill (fill in lacking columns from information above) and replace_na. But now I additionally use it for its primary function too: serving to you modify information row and column codecs from “wide” to “long”. CRAN.
See my YouTube video How to reshape information with tidyr’s new pivot features.
Hadley Wickham
splitstackshape
information wrangling
The package deal’s cSplit() perform solves a fairly complicated shaping downside in an astonishingly straightforward means. If you could have an information body column with a number of comma-separated values (suppose a survey query with “select all that apply”), that is value an set up if you wish to separate every merchandise into its personal new information body row.. CRAN.
cSplit(mydata, “multi_val_column”, sep = “,”, course = “long”).
Ananda Mahto
magrittr
information wrangling
This package deal gave us the %>% image for chaining R operations, nevertheless it’s bought different helpful operators similar to %<>% for mutating an information body in place and and . as a placeholder for the unique object being operated upon. CRAN.
mydf %<>% mutate(newcol = myfun(colname))
Stefan Milton Bache & Hadley Wickham
validate
information wrangling
Intuitive information validation primarily based on guidelines you may outline, save and re-use. CRAN.
See the introductory vignette.
Mark van der Loo & Edwin de Jonge
testthat
programming
Package that makes it straightforward to write down unit checks to your R code. CRAN.
See the testing chapter of Hadley Wickham’s ebook on R packages.
Hadley Wickham
information.desk
information wrangling, information evaluation
Popular package deal for heavy-duty information wrangling and computation. While I typically desire dplyr for fundamental evaluation, information.desk has grow to be my go-to for giant information units or when velocity is important (similar to in Shiny apps). CRAN.
information.desk in 5 minutes video, The final information.desk cheat sheet, Intro vignette
Matt Dowle & others
stringr
information wrangling
Numerous features for textual content manipulation. Some are much like present base R features however in a extra commonplace format, together with working with common expressions. Some of my favorites: str_pad and str_trim. CRAN.
str_pad(myzipcodevector, 5, “left”, “0”)
Hadley Wickham
lubridate
information wrangling
Everything you ever wished to do with date arithmetic, though understanding & utilizing accessible performance will be considerably complicated. CRAN.
mdy(“05/06/2015″) + months(1)More examples within the package deal vignette
Garrett Grolemund, Hadley Wickham & others
DataExplorer
information evaluation
Not certain the place to get began taking a look at an information set? Want to get a fundamental deal with on that information with out working a number of instructions like str() and plot()? DataExplorer makes an attempt to supply one-click report technology to point out and visualize fundamentals a few information set, similar to distributions and lacking information. CRAN.
create_report(mydataframe)
Boxuan Cui
zoo
information wrangling, information evaluation
Robust package deal with a slew of features for coping with time sequence information; I just like the useful rollmean perform with its align=proper and fill=NA choices for calculating shifting averages. CRAN.
rollmean(mydf, 7)
Achim Zeileis & others
tsbox
information wrangling, information evaluation
Super straightforward strategy to convert information between totally different R time-series information codecs: xts, information body, zoo, tsibble, and extra. Plus some fundamental evaluation features. CRAN.
ts_zoo(mydf)
Christoph Sax
knitr and rmarkdown
information show
Add R to a markdown doc and simply generate reviews in HTML, Word and different codecs. Vital in the event you’re fascinated about reproducible analysis and automating the journey from information evaluation to report creation. CRAN.
See the Minimal Examples knitr web page and RStudio’s R Markdown web page.
Yihui Xie & others (knitr), RStudio (rmarkdown)
treatment
information show
RStudio add-in provides a menu for R Markdown formatting instructions, so that you now not want to recollect and/or sort code for issues like making an HTML record or embedding a YouTube video. While WYSIWYG modifying is now accessible for R Markdown, this add-in nonetheless has advantages: Its instructions will be assigned customized keyboard shortcuts, so you may create your personal shortcuts for duties like bolding textual content. GitHub.
See the package deal web site.
Colin Fay & others
ymlthis
information show
Another helpful RStudio add-in for R Markdown, this helps you generate YML headers with correct format. GitHub.
See the package deal web site.
Malcolm Barrett & Richard Iannone
officeR
information show
Import and edit Microsoft Word and PowerPoint paperwork, making it straightforward so as to add R-generated evaluation and visualizations to present in addition to new reviews and shows. CRAN.
my_doc <- read_docx() %>% body_add_img(src = myplot)The package deal web site has many extra examples.
David Gohel
listviewer
information show, information wrangling
While RStudio has since added a list-viewing choice, this HTML widget nonetheless provides a chic strategy to view complicated nested lists inside R. GitHub timelyportfolio/listviewer.
jsonedit(mylist)
Kent Russell
DT and reactable
information show
Create a sortable, searchable desk in a single line of code with both of those R packages CRAN.
DT::datatable(mydf)reactable::reactable(mydf): Quick interactive HTML tablesreactable: reactable: Create tables with expandable rows
DT: RStudioreactable: Gregg Lin
ggplot2
information visualization
Powerful, versatile and well-thought-out dataviz package deal following ‘grammar of graphics’ syntax to create static graphics, however be ready for a steep studying curve. CRAN.
qplot(issue(myfactor), information=mydf, geom=”bar”, fill=issue(myfactor))See my searchable ggplot2 cheat sheet andtime-saving code snippets.
Hadley Wickham
patchwork
information visualization
Easily mix ggplot2 plots and preserve the brand new, merged plot a ggplot2 object. plot_layout() provides capability to set columns, rows, and relative sizes of every element graphic. GitHub.
plot1 + plot2 + plot_layout(ncol=1)
Thomas Lin Pedersen
ggforce
information visualization
Adds some design performance to base ggplot2 together with straightforward labeling of plot teams. CRAN.
See this weblog put up by RStudio’s Edgar Ruiz for a number of helpful examples.
Thomas Lin Pedersen
plotly
information visualization
R interface to the Plotly JavaScript library that was open-sourced in late 2015. Basic graphs have a particular look which will not be for everybody, nevertheless it’s full-featured, comparatively straightforward to be taught (particularly if you already know ggplot2) and features a ggplotly() perform to show graphs created with ggplot2 interactive. CRAN.
d <- diamonds[sample(nrow(diamonds), 1000), ] plot_ly(d, x = carat, y = worth, textual content = paste(“Clarity: “, readability), mode = “markers”, shade = carat, dimension = carat)
Carson Sievert & others
ggiraph
information visualization
Another strategy to make ggplot2 plots interactive utilizing geom features such geom_bar_interactive() that embody arguments for tooltips and JavaScript onclick occasions. CRAN.
g <- ggplot(mpg, aes( x = displ, y = cty, shade = drv) )my_gg <- g + geom_point_interactive(aes(tooltip = mannequin), dimension = 2) %>%ggiraph(code = print(my_gg), width = .7).Easy interactive ggplot graphs in R with ggiraph
David Gohel
esquisse
information visualization
This RStudio add-in provides a drag-and-drop interface for ggplot2. And, it generates codes for the graph you create with the GUI. It’s a useful gizmo for exploring totally different shade palettes and themes, even in the event you’re snug creating your visualizations instantly in R. CRAN.
See examples on the challenge’s web site .
Victor Perrier and Fanny Meyer, dreamRs
dygraphs
information visualization
Create HTML/JavaScript graphs of time sequence – one-line command in case your information is an xts object. CRAN.
dygraph(myxtsobject)
JJ Allaire & RStudio
echarts4r
information visualization
Robust R wrapper for the echarts JavaScript library. CRAN.
mydata %>%e_charts(xcol)%>% e_line(ycol)Plot in R with echarts4r or the package deal website
John Coene
taucharts
information visualization
This html widget library is particularly helpful for scatterplots the place you need to view a number of regression choices. However, it does rather more than that, together with line and bar charts with legends and tooltips. GitHub hrbrmstr/taucharts.
See the writer’s put up on RPubs
Bob Rudis
RColorBrewer
information visualization
Not a designer? RColorBrewer helps you choose shade palettes to your visualizations. CRAN.
See Jennifer Bryan’s tutorial
Erich Neuwirth
paletteer
information visualization
This package deal is a set of dozens of R shade palettes, all with a standard interface. Helpful if you wish to transfer past built-in and RColorBrewer choices.
Make essentially the most of R colours and palettes or see the paletteer package deal website for examples on accessing palettes and utilizing them with ggplot2.
Emil Hvitfeldt
sf
mapping, information wrangling
This package deal makes it a lot simpler to do GIS work in R. Simple options protocols make geospatial information look lots like common information frames, whereas numerous features enable for evaluation similar to figuring out whether or not factors are in a polygons. A GIS game-changer for R. CRAN.
See the package deal vignettes, beginning with the introduction, Simple Features for R.
Edzer Pebesma & others
leaflet
mapping
Map information utilizing the Leaflet JavaScript library inside R. GitHub rstudio/leaflet.
See my tutorial or the package deal web site
RStudio
tidygeocoder
mapping
This is my new geocoding go-to. It helps greater than a dozen totally different geocoding companies and returns ends in an instantly usable tibble format. Plus it provides reverse and batch geocoding in addition to getting lat/lengthy for an tackle.
mydata %>%; geocode(address_col, technique = ‘osm’, lat = latitude , lengthy = longitude)See the getting began vignette
Jesse Cambon & others
tmap & tmaptools
mapping
This package deal provide a simple strategy to learn in form recordsdata and be a part of information recordsdata with geographic information, in addition to do some exploratory mapping. Recent performance provides assist for easy options, interactive maps and creating leaflet objects. Plus, tmaptools::palette_explorer() is a superb device for choosing ColorBrewer palettes. CRAN.
See the package deal vignette or my mapping in R tutorial
Martijn Tennekes
colourpicker
information visualization
The package deal’s RStudio add-in makes it straightforward to flick thru and choose R’s built-in colours, or get hex codes for customized colours not accessible by identify. The plotHelper() perform lets you choose colours and see how they’d look on a scatter plot. CRAN.
See the GitHub repo.
Dean Attali
mapsapi
mapping, information wrangling
This interface to the Google Maps Direction and Distance Matrix APIs allow you to analyze and map distances and driving routes. CRAN.
google_directions( origin = c(my_longitude, my_latitude),vacation spot = c(my_address),alternate options = TRUEAlso see the vignette
Michael Dorman
tidycensus
mapping, information wrangling
Want to research and map U.S. Census Bureau information from 5-year American Community Surveys or 10-year censuses? This makes it straightforward to obtain numerical and geospatial information in R-ready format. You may also use it to obtain US, state, or native shapefiles for different use. CRAN.
See Basic utilization of tidycensus and the writer’s on-line ebook Analyzing US Census Data.
Kyle E. Walker
albersusa
mapping
Do it is advisable make a map of the US with Alaska and Hawaii insets? This package deal provides one of many easiest methods to get a well-designed shapefile. GitHub hrbrmstr/albersusa.
us_sf <- usa_sf(“lcc”) %>%mutate(State = as.character(identify))See the package deal GitHub repo.
Bob Rudis
glue
information wrangling
Main perform, additionally glue, evaluates variables and R expressions inside a quoted string, so long as they’re enclosed by {} braces. This makes for a chic paste() substitute. CRAN.
glue(“Today is {Sys.Date()}”)
Jim Hester
googleanalyticsR
Web analytics
Pull information from Google Analytics, together with GA’s model 4 API. Also has anti-sampling choices. CRAN.
See package deal web site.
Mark Edmonson
roxygen2
package deal improvement
Useful instruments for documenting features inside R packages. CRAN.
How to write down an R package deal or the roxygen2 introductory vignette.
Hadley Wickham & others
shiny
information visualization
Turn R information into interactive Web functions with this framework for R. CRAN.
See the tutorial or my Create a Shiny app to look Twitter
RStudio
shinyjs
information visualization
Includes a number of features so as to add sophistication to your UI similar to hiding and exhibiting inputs and reset enter values. Was initially revealed with industrial use restrictions however now could be beneath an MIT license.
See the get began information
Dean Attali
flexdashboard
information visualization
If Shiny is simply too complicated and concerned to your wants, this package deal provides a less complicated (if considerably much less strong) resolution primarily based on R Markdown. CRAN.
More information in Using flexdashboard
JJ Allaire, RStudio & others
openxlsx
misc
If it is advisable write to an Excel file in addition to learn, this package deal is simple to make use of and provides plenty of choices for formatting your spreadsheet. CRAN.
write.xlsx(mydf, “myfile.xlsx”)
Alexander Walker
gmodels
information wrangling, information evaluation
There are a number of features for modeling information right here, however the one I exploit, CrossTable, merely creates cross-tabs with a great deal of choices — totals, proprotions and a number of other statistical checks. CRAN.
CrossTable(myxvector, myyvector, prop.t=FALSE, prop.chisq = FALSE)
Gregory R. Warnes
janitor
information wrangling, information evaluation
Basic information cleansing made straightforward, similar to discovering duplicates by a number of columns, making R-friendly column names and eradicating empty columns. It additionally has some good tabulating instruments, like including a complete row, in addition to producing tables with percentages and straightforward crosstabs. And, its get_dupes() perform is a chic means of discovering duplicate rows in information frames, both primarily based on one column, a number of columns, or complete rows. CRAN.
tabyl(mydf, type = TRUE) %>% adorn_totals(“row”)
Samuel Firke
scales
information wrangling
While this package deal has many extra subtle methods that will help you format information for graphing, it is value a obtain only for the comma(), %() and greenback() features. CRAN.
comma(mynumvec)
Hadley Wickham
profvis
programming
Is your R code sluggish? This package deal offers you a visible consultant of your code line by line so you will discover the velocity bottlenecks. CRAN.
profvis({ your code right here })
Winston Chang & others
tidytext
textual content mining
Elegant implementation of textual content mining features utilizing Hadley Wickham’s “tidy data” rules. CRAN.
See tidytextmining.com for quite a few examples.
Julia Silge & David Robinson
diffobj
information evaluation
Base R’s equivalent() perform tells you whether or not or not two objects are the identical; but when they don’t seem to be, it will not let you know why. diffobj offers you a visible illustration of how two R objects differ. CRAN.
diffObj(x,y)
Brodie Gaslam & Michael B. Allen
prophet
forecasting
I do not do a lot forecasting evaluation; but when I did, I’d begin with this package deal. CRAN.
See the Quick begin information.
Sean Taylor & Ben Letham at Facebook
tidymodels
forecasting
For significantly extra strong forecasting, try this suite of modeling packages. Somewhat steep studying curve. CRAN.
See the Getting began information or this workshop repo from Thomas Mock.
Numerous
arrow
information import, information export
R implementation of the cross-language platform for in-memory information with columns. Includes features to learn and write Parquet and Feather recordsdata in addition to CSV and JSON. CRAN.
write_feather(mydf, tempfile())
Neal Richardson & others
fst
information import, information export
Another different for binary file storage (R-only), fst was constructed for quick storage and retrieval, with entry speeds above 1 GB/sec. It additionally provides compression that does not gradual information entry an excessive amount of, in addition to the power to import a selected vary of rows (by row quantity). CRAN.
write.fst(mydf, “myfile.fst”, 100)
Mark Klik
googleAuthR
information import
If you need to use information from a Google API in an R challenge and there is not but a selected package deal for that API, that is the place to show for authenticating CRAN.
See examples on the package deal web site and this gist to be used with Google Calendars. CRAN.
Mark Edmondson
devtools
package deal improvement, package deal set up
devtools has a slew of features aimed toward serving to you create your personal R packages, similar to routinely working all instance code in your assist recordsdata to ensure every little thing works. Requires Rtools on Windows and XCode on a Mac. CRAN.
run_examples()
Hadley Wickham & others
remotes
package deal set up
remotes is a lighter-weight different to devtools if all you need is to put in packages from GitHub, Bitbucket and another sources. CRAN.
install_github(“mangothecat/franc”)
Gabor Csardi & others
githubinstall
package deal set up
Do you need to set up a package deal from GitHub however cannot bear in mind the creator’s identify — or simply do not feel like typing it out? With githubinstall, merely run githubinstall(“packagename”) and the perform will recommend an account; you simply reply Y to put in or n if it is the mistaken one. It even consists of fuzzy matching in the event you misspell a package deal identify!
githubinstall(“AnomalyDetection”)
Koji Makiyama
installr
misc
Windows solely: Update your put in model of R from inside R. On CRAN.
updateR()
Tal Galili & others
usethis
package deal improvement, programming
Initially aimed toward package deal improvement, usethis now consists of helpful features for any coding challenge. Among its useful options are an edit household that allows you to simply replace your .Renvironment and .Rprofile recordsdata. On CRAN, however set up GitHub model from “r-lib/usethis” for contemporary updates.
edit_r_environ()
Hadley Wickham, Jennifer Bryan & RStudio
right here
misc
This package deal has one perform with a single, helpful function: discover your challenge’s working listing. Surprisingly useful if you would like your code to run on multiple system. CRAN.
my_project_directory <- right here()
Kirill Müller
pacman
misc, package deal set up
This package deal is one other that goals to unravel one downside, and clear up it properly: package deal set up. The primary features will loadi a package deal that is already put in or putting in it first if it is not accessible. While that is definitely potential to do with base R’s require() and an if assertion, p_load() is a lot extra elegant for CRAN packages, or p_load_gh() for GitHub. Other helpful choices embody p_temp(), which permits for a short lived, this-session-only package deal set up. CRAN.
p_load(dplyr, right here, tidycensus)
Tyler Rinker
plumber
information export, programming
Turn any R perform right into a host-able API with a line or two of code. This well-thought-out package deal makes it straightforward to make use of R for information dealing with in different, non-R coding initiatives. CRAN.
See the documentation or my article Create your personal Slack bots — and Web APIs — with R
Jeff Allen, Trestle Technology & others
dataCompareR
information wrangling
A fast and chic strategy to examine two information frames, both row by row or by a specified key. CRAN.
rCompare(mydf1, mydf2)
Rob Noble-Eddy at CapitalOne & others
cloudyR challenge
information import, information export
This is a set of packages aimed toward making it simpler for R to work with cloud platforms similar to Amazon Web Services, Google and Travis-CI. Some are already on CRAN, some will be discovered on GitHub.
See the record of packages.
Various
flyio
information import, information export
This is a bit like rio, however for the cloud: It provides a standard set of features whether or not you are utilizing Amazon’s S3 or Google Cloud. Set your information supply, authenticate along with your credentials (which will be saved in an R environmental variable), set a bucket identify, and off you go. GitHub.
See the GitHub repo or YouTube video of a demo on the Delhi useR meetup.
SocialCops
geofacet
information visualization, mapping
While I hardly ever have to create “geofacets” — maps with same-sized blocks in geospatially acceptable areas — this package deal is so cool that I needed to embody it. The package deal enables you to create your personal geofacet visualizations utilizing ggplot2 and built-in grids similar to US states and EU nations. And, it comes with design-your-own geofacet grid capabilities. CRAN.
grid_design()
Ryan Hafen
reticulate
programming
If you already know Python in addition to R, this package deal provides a set of instruments for calling Python from inside R, in addition to “translating” between R and Python objects similar to Pandas information frames and R information frames. CRAN.
See How to run Python in R or the reticulate package deal web site.
JJ Allaire
beepr
misc
This is just about pure enjoyable. Yes, getting an audible notification when code finishes working or encounters an error might be helpful; however right here, the accessible sounds embody choices like a fanfare flourish, a Mario Brothers tune, and even a scream. CRAN.
beep(“wilhelm”)
Rasmus Bååth
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