Language of the strongest in the history of R - Resources (free courses, books, tutorials and a variety of advanced chart)

Language of the strongest in the history of R - Resources (free courses, books, tutorials and a variety of advanced chart)

Here are some free R books, tutorials, software packages, 2 single charts and other materials, can learn programming and improve workflow online. There are separate overview of  Python Resources ,  SQL Resources , as well as major  the Data Science, Machine Learning, Statistics & Resources . If you have additional comments, please below or  Business Card Me !

To r is new? →  Start here Wallpaper!

Last updated: 26-03-2018

r basis

chart:

• Base R cheat sheet by Mhairi McNeill***
• Base R Functions cheat sheet by Tom Short
• Basic R cheat sheet by Quandl.com
• R Function Abbreviations Explained by Jeromy Anglim
• RStudio cheat sheet by RStudio
• RStudio Keyboard Shortcuts by RStudio***
• Data Management in R cheat sheet
• data.table wide cheat sheet by DataCamp.com
• data.table long cheat sheet by DataCamp.com
• Advanced R cheat sheet by Arianne Colton & Sean Chen
• tidyverse cheat sheet by DataCamp.com
• Data Import cheat sheet by RStudio with readr, tibble, and tidyr
• Factor Manipulation with forcats cheat sheet by Lise Vaudor
• Data Transformation cheat sheet by Rstudio with dplyr
• Data Transformation cheat sheet 2 by Daniel Lüdecke with dplyr and sjmisc
• Data Wrangling cheat sheet by RStudio with dplyr and tidyr
• Character String Manipulation cheat sheet by RStudio with stringr
• Dates and Times cheat sheet by RStudio with lubridate
• Split-Apply-Combine cheat sheet by Ernest Adrogue Calvera
• purr Functional Programming cheat sheet by RStudio
• Tidy Evaluation by Edwin Thoen
• R Syntax Comparison cheat sheet by Amelia McNamara
• RStudio cheat sheet GitHub

Getting books:

• Introduction to R (R Core Team, 1999)
• R Language Definition (Manual) (R Core Team, 2000)
• Data Import/Export (R Core Team, 2000)
• SimpleR (Verzani, 2001-2)
• R for Beginners (Paradis, 2002)
• Introduction to R (Spector, 2004)
• Ecological Models and Data in R (Bolker, 2007)
• Software for Data Analysis: Programming with R (Chambers, 2008)
• Econometrics in R (Farnsworth, 2008)
• The Art of R Programming (Matloff, 2009)
• R in a Nutshell (Adler, 2010)
• R in Action: Data Analysis and Graphics with R (Kabacoff, 2011)
• R for Psychology Experiments and Questionnaires (Baron, 2011)
• The R Inferno (Burns, 2011)
• Cookbook for R (Chang, ???)
• The R Book (Crawley, 2013)
• Introduction to Statistical Thought (Lavine, 2013)
• A (very) short introduction to R (Torfs & Bauer, 2014)***
• Advanced R (Wickham, 2014)
• Introduction to R (Vaidyanathan, 2014)
• Learning statistics with R: A tutorial for psychology students and other beginners (Navarro, 2014)
• Introduction to R (Venables, Smith, & R Core Team, 2017)
• The R Language Definition (R Core Team, 2017)
• YaRrr! The Pirate’s Guide to R (Phillips, 2017)***

Online courses: introductory and advanced:

• swirl()***
• Try R by Code School
• Learn R by R-Exercises
• R Tutorial by Cyclismo & DataCamp
• 100 Tutorials for Learning R
• Introduction to R by DataCamp
• YaRrr! The Pirate’s Guide to R (Video)
• R for Cats
• A Psychologist’s Guide to R (pdf) by Sean Chris Murphy
• Social Sciences: Critically Analyze Research and Results Using R by Coursera
• A Hands-on Introduction to Statistics with R by DataCamp.com
• R Course in Statistics by PagePiccini.com
• Mini courses in R by UCLA
• Data Science: R Basics @edX
• Introduction to R for Data Science @edX
• Data Analysis and Visualization Using R @VarianceExplained
• R Programming @Coursera*** by Roger Peng, Jeff Leek, & Brian Caffo
• DIY: 37 Youtube R Tutorials Overview by Flavio Azevedo***

Style Guide:

• Google’s R Style Guide
• Advanced R Style Guide by Hadley Wickham
• A guide for switching from base R to the tidyverse.

Data visualization R

• R Graph Gallery & Code Examples***
• Fundamentals of Data Visualization (Wilke, 2018)
• R Base Plots Wiki Reference Guide
• CRAN Task View – Graphics & Visualization
• R Graphical Parameters Cheatsheet by Flowingdata.com

colour

• R Color Guide***
• colourpicker – widget that allows users to choose colours
• ggplot2 colour guide***
• multicolored annotated text in ggplot2 by Andrew Whitby & Visuelle Data

Interactive / html components:

• R HTML Widgets Gallery***
• plotly – interactive plots
• d3heatmap – interactive d3 heatmaps
• DT – interactive tables
• DiagrammeR – interactive diagrams
• dygraphs – interactive time series plots
• formattable – formattable data structures
• ggvis – interactive ggplot2
• Highcharter – interactive Highcharts plots
• leaflet – interactive maps
• metricsgraphics – interactive JavaScript bare-bones line, scatterplot and bar charts
• networkD3 – interative d3 network graphs
• scatterD3 – interactive scatterplots with d3
• rbokeh – interactive Bokeh plots
• rCharts – interactive Javascript charts
• rcdimple – interactive JavaScript bar charts and others
• rglwidget – interactive 3d plots
• threejs – interactive 3d plots and globes
• visNetwork – interactive network graphs
• wordcloud2 – interface to wordcloud2.js.
• hours  - interactive timelines

ggplot2

• Code examples of top-50 ggplot2 visualizations***
• ggplot2 Cheatsheet by RStudio
• ggplot2 Quick Reference Guide 
• ggplot2 Code Snippets
• ggplot2 Code Snippets 2
• A practical introduction with R and ggplot2 (Healy, 2017)
• Data Vizualization: A practical introduction (Healy, 2018)
• Hitchhiker’s Guide to ggplot2 in R (Burchell & Vargas, 2016)
• Setting custom ggplot themes with ggthemr
• Creating custom, reproducible color palettes by Simon Jackson
• Rearranging values within ggplot2 facets
• Combine plots using patchwork or cowplot
• ggplot2 extensions overview***
  • ggthemes – plot style themes
  • hrbrthemes – opinionated, typographic-centric 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 – photo-biology related extensions
  • geomnet – network visualization
  • ggExtra – marginal histograms for a plot
  • gganimate – animations
  • ggspectra – tools for plotting light spectra
  • ggnetwork – geoms to plot networks
  • ggradar – radar charts
  • ggsurvplot (survminer) – survival curves
  • ggseas – seasonal adjustment tools
  • ggtech – style themes for plots
  • ggtern – ternary diagrams
  • ggTimeSeries – time series visualizations
  • ggtree – tree visualizations
  • treemapify – wilcox’s treemaps

Miscellaneous

• coefplot – visualizes model statistics
• circlize – circular visualizations for categorical data
• quantmod – candlestick financial charts
• colorspace – HSL based color palettes
• viridis – Matplotlib viridis color pallete for R
• munsell – Munsell color palettes for R.
• Cairo – high-quality display output
• igraph – Network Analysis and Visualization
• lattice – Trellis graphics
• tmap – thematic maps
• trelliscopejs – interactive alternative for ggplot2::facet_wrap
• rgl – interactive 3D plots
• corrplot – graphical display of a correlation matrix
• googleVis – Google Charts API
• plotROC – interactive ROC plots
• extrafont – fonts in R graphics
• showtext – text using system fonts
• animation – animated graphics using ImageMagick.
• misc3d – 3d plots, isosurfaces, etc.
• xkcd – xkcd style graphics
• imager – CImg library to work with images

Dashboards & Shiny

• Shiny Cheat Sheet by RStudio
• More Shiny Resources by Rob Gilmore
• More Shiny Resources for Statistics by Yingjie Hu
• Shiny Tutorial
• Building Shiny apps – an interactive tutorial by Dean Attali
• flexdashboard – dashboard creation simplified
• colourpicker – widget that allows users to choose colours.

The regular expression R

• • R Regular Expression Cheatsheet by Lise Vaudor
  • R Regular Expression Cheatsheet
  • R Regular Expression Cheatsheet (page 2) by RStudio
  • Regular Expressions in R – Part 1: Introduction and base R functions
  • R Regular Expressions by Jon M. Calder in swirl()
  • R Regular Expression Video Tutorial by Roger Peng
  • General Regular Expression Cheatsheet
  • General Regular Expression Video Tutorial by Roger Peng
  • General Regular Expression Cheatsheet by OverAPI.com

R Markdown & Documentation

• R Markdown Cheatsheet by RStudio
• R Markdown Reference Guide by RStudio
• R Markdown Basics
• Markdown Tutorial by RStudio
• Markdown Gallery by RStudio
• The knitr book (Xie, 2015)
• Creating slides with R Markdown (Video) by Brian Caffo
• General Markdown Cheatsheet
• blogdown: Creating Websites with R Markdown (Xie, Thomas, & Hill, 2018)
• blogdown tutorials
• Packages:
  • tidystats – automating updating of model statistics
  • papaja – preparing APA journal articles
  • blogdown – build websites with Markdown & Hugo
  • huxtable – create Excel, html, & LaTeX tables

R database link

• Tutorial: Database Queries with R
• Introduction to sparklyr by DataCamp
• dbplot – leverages dplyr to process calculations of plot inside database

Functional programming

• Functional Programming lecture by Duke University
• Functional Programming: purrr tutorial by Jenny Bryan
• purr Functional Programming cheat sheet by RStudio
• Writing Function in R by DataCamp.com

Statistical learning, machine learning and data science

• Machine Learning with R: An Irresponsibly Fast Tutorial by Will Stanton***
• CRAN Task View – Machine Learning & Statistical Learning
• R Packages for Machine Learning by Joseph Misiti
• Introduction to Data Science with R (Video)
• Course: Introduction to R for Data Science @Microsoft
• Course: Introduction to R for Data Science @FutureLearn by Hadley Wickham
• Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani, 2013)
• Course: Introduction to Statistical Learning (Hastie & Tibshirani, 2014)***
• Data Science Live Book (Casas, 2017)
• 100 Tutorials for Learning R
• R Programming for Data Science (Peng, 2016)
• R for Data Science (Grolemund & Wickham, 2017)***
• Elements of Statistical Learning (Hastie, Tibshirani, & Friedman, 2001)
• Statistical Foundations of Machine Learning (Bontempi & Taieb, 2017)
• Machine Learning with R (Lantz, 2013)
• R Functions for Regression Analysis Cheatsheet by Vito Ricci
• Regression Models for Data Science in R (Caffo, 2015)
• Applied Biostatistical Analyses using R (Cox, 2017)
• Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Williams, 2011)
• Machine Learning Algorithms R Implementation by Ajitesh Kumar
• R Data Mining Reference Card
• R Data Mining: Examples & Case Studies (Zhao, 2015)
• Statistical modelling in R (Zhao, 2015) @RDataMining
• Predictive modelling in R with caret
• Machine Learning with caret cheat sheet by Max Kuhn
• R sparklyr cheat sheet by RStudio
• Machine Learning modelling cheat sheet by Arnaud Amsellem
• Machine Learning with mlr cheat sheet by Aaron Coley
• Deep Learning with keras cheat sheet by RStudio
• R Interface to Keras

Natural language processing and text mining

• Text Mining Tutorial with tm
• Tidy Text Mining (Silges & Robinson, 2017) with tidytext
• Text Analysis with R for Students of Literature (Jockers, 2014)
• 100 Tutorials for Learning R
• 21 Recipes for Mining Twitter Data (Rudis, 2017) with rtweet
• Course: Introduction to Text Analytics with R @DataScienceDojo
• Course: Twitter Text Mining and Social Network Analysis (Zhoa, 2016) @RDataMining with twitteR
• List of resources for NLP & Text Mining by Stephen Thomas
• Packages — for an overview: CRAN Task View – Natural Language Processing:
  • tm – text mining.
  • tidytext – text mining using tidyverse principles
  • quanteda –  framework for quantitative text analysis
  • gutenbergr – public domain works (free books to practice on)
  • corpora – statistics and data sets for corpus frequency data.
  • tau – Text Analysis Utilities
  • Sentiment140 – headache-free sentiment analysis
  • sentimentr – sentiment analysis using text polarity
  • openNLP – sentence detector, tokenizer, pos-tagger, shallow and full syntactic parser, named-entity detector, and maximum entropy models with OpenNLP.
  • cleanNLP – natural language processing via tidy data models
  • RSentiment – English lexicon-based sentiment analysis with negation and sarcasm detection functionalities.
  • RWeka – data mining tasks with Weka
  • wordnet – a large lexical database of English with WordNet .
  • stringi – language processing wrappers
  • textcat – provides support for n-gram based text categorization.
  • text2vec – text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), and similarities.
  • lsa – Latent Semantic Analysis
  • topicmodels -Latent Dirichlet Allocation (LDA) and Correlated Topics Models (CTM)
  • lda -Latent Dirichlet Allocation and related models

sequentially

• CRAN Task View – TimeSeries
• R xts Cheatsheet
• Forecasting: Principles and Practice (Hyndman & Athanasopoulos, 2017)
• A little book of R for time series (tutorial)
• Tutorials: Part 1, Part 2, Part 3, & Part 4 of tidy time series @Business-Science.io with tidyquant

subsistence analysis

• CRAN Task View – Survival
• R Survival Analysis Cheatsheet by Przemysław Biecek
• survival – functionality for survival and hazard models
• ggsurvplot (survminer) – survival curves

Geography, cartography space

• Making Maps with R (tutorial) with ggmaps, maps, and mapdata
• Importing OpenStreetMap data (tutorial) with osmar 

other apps

• Image featurization
• Introduction to Empirical Bayes (Robinson, ???)
• CRAN Task View – Bayesian Inference

R development, optimization and other

• R Package Development Cheatsheet with devtools
• Mastering Software Development in R (Peng, Kross, & Anderson, 2017)
• Efficient R Programming (Gillespie & Lovelace, 2017)
• Happy Git and GitHub for the useR (Jenny Bryan, 2017)
• RStudio Addins by Dean Attali

R assistance, connection, inspiration

• R Help Mailing List
• R Seek – search engine for R-related websites
• R site search – search engine for help files, manuals, and mailing lists
• Nabble – mailing list archive and forum
• R User Groups & Conferences
• Stack Overflow – a FAQ for all your R struggles (programming)
• Cross Validated – a FAQ for all your R struggles (statistics)
• CRAN Task Views – discover new packages per topic
• The R Journal – open access, refereed journal of R
• Twitter: #rstats, RStudio, Hadley Wickham, Yihui Xie, Mara Averick, Julia Silge, Jenny Bryan, David Smith, Hilary Parker, R-bloggers
• Facebook: R Users Psychology
• Youtube:  Ben Lambert ,  Roger Peng
• Reddit: rstats, rstudio, statistics, machinelearning, dataisbeautiful

R blog

• http://adamleerich.com
• http://njtierney.github.io/
• https://trinkerrstuff.wordpress.com
• https://rollingyours.wordpress.com
• https://www.r-statistics.com
• https://beckmw.wordpress.com
• http://rgraphgallery.blogspot.com
• http://onertipaday.blogspot.com
• https://learnr.wordpress.com
• http://padamson.github.io
• http://www.r-datacollection.com/blog/
• http://www.thertrader.com
• https://fronkonstin.com
• https://nicercode.github.io
• http://www.rblog.uni-freiburg.de
• https://advanceddataanalytics.net
• http://r4stats.com/blog/
• http://blog.revolutionanalytics.com/
• http://www.r-bloggers.com/
• http://kbroman.org/blog/
• https://juliasilge.com/blog/
• http://andrewgelman.com/
• http://www.statsblogs.com/author/eric-cai-the-chemical-statistician/
• https://www.statmethods.net/
• http://www.stats-et-al.com/search/label/R

 

R work

• R Jobs on StackOverflow.com
• R Jobs on R-users.com
• Jobs on Kaggle