RStartHere

A guide to some of the most useful R Packages that we know about, organized by their role in data science.

Click here to suggest packages.

Data Science Workflow

Each data science project is different, but each follows the same general steps. You:

1. Import your data into R

2. Tidy it

3. Understand your data by iteratively

   1. visualizing
   2. tranforming and
   3. modeling your data

4. Infer how your understanding applies to other data sets (including future data, i.e. predictions)

5. Communicate your results to an audience, or

6. Automate your analysis for easy reuse

7. Program the whole way through, since you do each of these things on a computer

Below we list the most useful R packages that we know of for each step.

Import

These packages help you import data into R and save data.

• feather
• readr
• readxl
• cellranger
• haven
• httr
• rvest
• xml2
• webreadr
• DBI
  • RMySQL
  • RPostgres
  • RSQLite
  • bigrquery
• PivotalR
• dplyr
• gdata

Tidy

These packages help you wrangle your data into a form that is easy to analyze in R.

• tidyr
• dplyr
• broom
• zoo

Visualize

These packages help you visualize your data.

• ggplot2 with extensions
  • scales
  • ggthemes
  • ggmap
  • ggiraph
  • ggstance
  • ggalt
  • ggforce
  • ggrepel
  • ggraph
  • ggpmisc
  • geomnet
  • ggExtra
  • gganimate
  • plotROC
  • ggspectra
  • ggnetwork
  • ggtech
  • ggradar
  • ggTimeSeries
  • ggtree
  • ggseas
• lattice
• rgl
• extrafont
• ggvis
  • ggstat
  • gggeom
• manipulate
• htmlwidgets
  • leaflet
  • dygraphs
  • plotly
  • rbokeh
  • Highcharter
  • visNetwork
  • networkD3
  • d3heatmap
  • DT
  • threejs
  • rglwidget
  • DiagrammeR
  • MetricsGraphics
• rCharts
• quantmod

Transform

These packages help you transform your data into new types of data.

• dplyr
• data.table
• stringr
• lubridate
• tibble
• xts
• vtreat

Model/Infer

These packages help you build models and make inferences. Often the same packages will focus on both topics.

• stats
• mgcv
• lme4
• broom
• caret
• glmnet
• mosaic
• gbm
• xgboost
• randomForest
• ranger
• h2o
• kernlab
• nlme
• ROCR
• pROC

Communicate

These packages help you communicate the results of data science to your audiences.

• rmarkdown
• knitr
• flexdashboard
• bookdown
• rticles
• tufte
• DT
• pander
• xtable
• stargazer

Automate

These packages help you create data science products that automate your analyses.

• shiny
  • shinydashboard
  • shinythemes
  • shinyAce
  • shinyjs
  • miniUI
  • shinyapps.io
  • Shiny Server Open Source
  • Shiny Server Pro
• rsconnect
• plumber
• countdown
• rstudioapi

Program

These packages make it easier to program with the R language.

• RStudio Desktop IDE
• RStudio Server Open Source
• RStudio Server Professional
• devtools
• magrittr
• packrat
• testthat
• roxygen2
• purrr
• profvis
• rcpp
• R6
• htmltools
• snow
• Rth
• MKL by Microsoft/Revolution Analytics
• MRS by Microsoft/Revolution Analytics

Data

These packages contain data sets to use as training data or toy examples.

• datasets
• babynames
• neiss
• yrbss
• nycflights13
• hflights
• USAboundaries
• rworldmap
• usdanutrients
• fueleconomy
• nasaweather
• mexico-mortality
• data-movies
• pop-flows
• data-housing-crisis
• gun-sales
• stationaRy
• ggenealogy

Criteria

What makes an R Package useful? A useful R package should perform a useful task, and it should do it well. Here are some criteria that we used to make the list.

• The code in the package runs fast, with few errors.
• The code in the package has an intuitive syntax that is easy to remember.
• The package plays well with other packages; you do not need to munge your data into new forms to use the package.
• The package is widely used and recommended by its users.
• The package has a development website, or series of vignettes, that make the package easy to learn.

You can learn more about packages in R with the CRAN task views.