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:

Import your data into R

Tidy it

Understand your data by iteratively
 visualizing
 tranforming and
 modeling your data

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

Communicate your results to an audience, or

Automate your analysis for easy reuse

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  a fast, lightweight file format used by both R and Python
 readr  reads tabular data
 readxl  reads Microsoft Excel spreadsheets
 openxlsx  reads Microsoft Excel spreadsheets
 googlesheets  reads Google spreadsheets
 haven  reads SAS, SPSS, and Stata files
 httr  reads data from web APIs
 rvest  scrapes data from web pages
 xml2  reads HTML and XML data
 webreadr  reads common web log formats
 DBI  a universal interface to database management systems (DBMS)
 PivotalR  reads data from and interfaces with Postgres, Greenplum, and HAWQ
 dplyr  contains an interface to common databases
 data.table 
fread()
for fast table reading  git2r  tools to access git repositories
 BioInstaller  Downloader for biological software and database.
Tidy
These packages help you wrangle your data into a form that is easy to analyze in R.
 tidyr  tools for tidying layout of tabular data
 dplyr  tools for joining multiple tables into a tidy data set
 purrr  tools for applying R functions to data structures, very useful when tidying
 broom  tools for tidying statistical models into data frames
 zoo  data structures for time series data
 PivotalR  R wrappers for indatabase SQL operations (i.e. join, group by)
Visualize
These packages help you visualize your data.
 ggplot2 with extensions  a versatile system for making plots
 ggthemes  plot style themes
 ggmap  maps with Google Maps, Open Street Maps, etc.
 ggiraph  interactive ggplots
 ggstance  horizontal versions of common plots
 GGally  scatterplot matrices
 ggalt  additional coordinate systems, geoms, etc.
 ggforce  additional geoms, etc.
 ggrepel  prevent plot labels from overlapping
 ggraph  graphs, networks, trees and more
 ggpmisc  photobiology related extensions
 geomnet  network visualization
 ggExtra  marginal histograms for a plot
 gganimate  animations
 plotROC  interactive ROC plots
 ggspectra  tools for plotting light spectra
 ggnetwork  geoms to plot networks
 ggtech  style themes for plots
 ggradar  radar charts
 ggTimeSeries  time series visualizations
 ggtree  tree visualizations
 ggseas  seasonal adjustment tools
 lattice  Trellis graphics
 rgl  interactive 3D plots
 ggvis  versatile system for interactive graphs
 htmlwidgets  framework for creating JavaScript widgets with R
 leaflet  Interactive maps
 dygraphs  Interactive time series plots
 plotly  Interactive plots
 rbokeh  Interactive Bokeh plots
 Highcharter  Interactive Highcharts plots
 visNetwork  Interactive network graphs
 networkD3  Interative d3 network graphs
 d3heatmap  Interactive d3 heatmaps
 DT  Interactive tables
 threejs  Interactive 3d plots and globes
 rglwidget  Interactive 3d plot
 DiagrammeR  Interactive diagrams
 MetricsGraphics  Interactive MetricsGraphics plots
 rCharts  many interactive JavaScript visualizations
 coefplot  visualizes model statistics
 quantmod  candlestick financial charts
 colorspace  HSL based color palettes
 viridis  Matplotlib viridis color pallete for R
 munsell  Munsell color palettes for R.
 RColorBrewer  color palettes for plots. No manual or website.
 dichromat  colorblind friendly palettes. No manual or website.
 igraph  Network Analysis and Visualization
 latticeExtra  Extensions for lattice graphics
 sp  tools for spatial data
Transform
These packages help you transform your data into new types of data.
 dplyr  a grammar of data transformation
 magrittr  a concise syntax for calling sequences of functions
 tibble  efficient display structure for tabular data
 stringr  tools for working with strings and regular expressions
 lubridate  tools for working with dates and times
 xts  tools for time series based data
 data.table  fast data manipulation
 vtreat  tools for preprocessing variables for predictive modeling
 stringi  fast string processing facilities.
 Matrix  LAPACK methods for dense and sparse matrix operations
Model/Infer
These packages help you build models and make inferences. Often the same packages will focus on both topics.
 car  functions from An R Companion to Applied Regression
 Hmisc  miscellaneous functions for data analysis
 multcomp  Simultaneous Inference in General Parametric Models
 pbkrtest  parametric bootstrap test for linear mixed effects models
 mvtnorm  Multivariate Normal and t Distributions
 MatrixModels  Modelling with Sparse And Dense Matrices
 SparseM  linear algebra for sparse matrices
 lme4  Linear MixedEffects Models using Eigen C++ library
 broom  tools for tidying statistical models into data frames
 caret  tools for Classification And REgression Training
 glmnet  generalized linear models via penalized maximum likelihood
 mosaic  Tools for teaching mathematics, statistics, computation and modeling
 gbm  gradient boosted regression models
 xgboost  Extreme Gradient Boosting
 randomForest  Random Forests for Classification and Regression
 ranger  a fast implementation of Random Forests
 h2o  parallel distributed machine learning algorithms
 ROCR  plots to visualize classifier performance
 pROC  Tools for visualizing, smoothing and comparing ROC curves
 PivotalR  R wrappers for MADlib's parallel distributed machine learning algorithms
Communicate
These packages help you communicate the results of data science to your audiences.
 rmarkdown  easytouse format for reproducible reports and dynamic documents in R
 knitr  embed R code within pdf and html reports
 flexdashboard  easytocreate dashboards based on rmarkdown
 bookdown  books and long documents built on R Markdown
 rticles  ready to use R Markdown templates
 tufte  Tufte handout R Markdown template
 DT  Interactive data tables
 pixiedust  Customized tables
 xtable  Customized tables
 highr  Syntax Highlighting for R Source Code
 formatR 
tidy_source()
to format R source code  yaml  Methods to convert R data to YAML and back
 pander  renders R objects into Pandoc markdown.
 configr  Integrated and improved configuration file parser (json,ini,yaml,toml).
Automate
These packages help you create data science products that automate your analyses.
 shiny  tools to make interactive web apps with R
 shinydashboard  interactive dashboards with R
 shinythemes  style themes for Shiny apps
 shinyAce  Ace text editor for Shiny apps
 shinyjs  adds common JavaScript operations to Shiny apps
 miniUI  UI elements for Shiny gadgets, interactive apps integrated into the R commandline workflow
 shinyapps.io  hosting service for Shiny apps
 Shiny Server Open Source  OS server to host Shiny apps
 Shiny Server Pro  server to host Shiny apps enhanced with features for business enterprises
 rsconnect  deploys Shiny apps to shinyapps.io
 plumber  converts R code to a web API
 rmarkdown  easytouse format for reproducible reports and dynamic documents in R
 rstudioapi  safely access RStudio IDE's API
Program
These packages make it easier to program with the R language.
 RStudio Desktop IDE  IDE application for R
 RStudio Server Open Source  server based IDE for R
 RStudio Server Professional  server based IDE for R enhanced with features for business enterprises
 devtools  tools that make it easier to develop R packages
 packrat  creates project specific libraries, which handle package versioning and enhance reproducibility
 drat  tools to create and use alternative R package repositories
 testthat  easytouse system for unit testing packages
 roxygen2  easytouse method for documenting packages
 purrr  tools for applying R functions to data structures
 profvis  visualizes code profiling data from R
 Rcpp  C++ API for R
 R6  fast, simple object class that uses reference semantics
 htmltools  Tools for HTML generation and output
 nloptr  interface to NLopt nonlinear optimization library.
 minqa  optimization algorithms.
 rngtools  Utilities for working with Random Number Generators
 NMF  Nonnegative Matrix Factorization
 crayon  Adds color to terminal output
 RJSONIO  convert R objects to JSON notation
 jsonlite  a fast JSON parser and generator for R
 RcppArmadillo  interface to 'Armadillo' Templated Linear Algebra Library
Data
These packages contain data sets to use as training data or toy examples.
 babynames  Names given to US babies 18802014
 neiss  sample of all accidents reported to US emergency rooms 20092014
 yrbss  Youth Risk Behaviour Surveillance System data from 1991 to 2013
 nycflights13  all outbound flights from NYC in 2013
 hflights  flights departing Houston in 2011
 USAboundaries  Historical and Contemporary Boundaries of the United States of America
 rworldmap  country border data
 usdanutrients  USDA nutrient database
 fueleconomy  EPA fuel economy data
 nasaweather  geographic and atmospheric measures on a very coarse 24 by 24 grid covering Central America
 mexicomortality  deaths in Mexico
 datamovies and ggplotmovies  data from the Internet Movie Database (IMDB)
 popflows  Population flows around the USA in 2008
 datahousingcrisis  Clean data related to the 2008 US housing crisis
 gunsales  Statistical analysis of monthly background checks of gun purchases from NY times
 stationaRy  hourly meteorological data from one of thousands of global stations
 gapminder  Excerpt from the Gapminder data
 janeaustenr  Jane Austen's Complete Novels
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.
 The package is developed in the open (e.g. on Github or RForge).
 The package uses tests to ensure that it will be stable and bug free well into the future.
 The package is stable and available from CRAN, or we are personally involved with the package and committed to its development.
For other useful choices, please check out our list of popular packages that did not quite meet these criteria.
You can learn more about packages in R with the CRAN task views.