Last Update : 18-Ago/2019
- CRAN Contributed Documentation https://cran.r-project.org/
- Datacamp (Free) Course Introduction to R http://bit.ly/2QTXMkv
- R Programming for Data Science Roger D. Peng 2016-12-22 http://bit.ly/2AbQRhd
- R for Data Science Garrett Grolemund & Hadley Wickham http://bit.ly/2AaFWEw
- Efficient R programming Colin Gillespie & Robin Lovelace http://bit.ly/2AaGKcw
- Hands-On Programming with R Garrett Grolemund http://bit.ly/2QYJRJZ
- Advanced R Hadley Wickham http://bit.ly/2AapVhT
- El arte de programar en R Julio Sergio Santana & Efraín Mateos Farfán [español] http://bit.ly/2N2Y1Y8
- Wikibook R Programming https://en.wikibooks.org/wiki/R_Programming
- Learning statistics with R: A tutorial for psychology students and other beginners Danielle Navarro http://bit.ly/2DaYoig
- The Tidynomicon A Brief Introduction to R for Python Programmers Greg Wilson http://bit.ly/2IEh4t8
- Programming - Part 1 (Writing code in RStudio) http://bit.ly/2AaZ4Cf
- Programming - Part 2 ((Debugging code in RStudio) http://bit.ly/2AaifvV
- Programming - Part 3 (Package writing in RStudio) http://bit.ly/2AayPfq
- Managing - Part 1 (Projects in RStudio) http://bit.ly/2Abo3We
- Managing - Part 2 (Github and RStudio) http://bit.ly/2A9Pu2D
- Managing - Part 3 (Packrat and RStudio) http://bit.ly/2AazLAs
- Debugging techniques in RStudio Amanda Gadrow [video] http://bit.ly/2QQD4C2
- Tidy evaluation is one of the major feature of the latest versions of dplyr and tidyr [Video] http://bit.ly/2AbXJLs
- Tidy eval: Programming with dplyr, tidyr, and ggplot2 Hadley Wickham [Video] http://bit.ly/2QR07N5
- Data wrangling with R and RStudio [Video] http://bit.ly/2AaocZX slides: http://bit.ly/2QSr7vS
- Wrangling data in the Tidyverse [Video] (useR! 2018 Conf) [Part 1] http://bit.ly/2SEHDBc [Part 2] http://bit.ly/2SK9EHt
- dplyr tutorials Suzan Baert http://bit.ly/2AbSHi2
- Getting more out of dplyr SatRday Amsterdam 2018 slides Suzan Baert http://bit.ly/2QXf28I
- dplyr 10 tips and tricks Suzan Baert (RoCur WeAreRLadies) http://bit.ly/2AaWb4k
- STAT 545 Course Jenny Bryan http://bit.ly/2AajX0o
- Join Functions Jenny Bryan http://bit.ly/2AbUZ0C
- Let the Data Flow: Pipelines in R with dplyr and magrittr http://bit.ly/2AaVDvz
- Data Processing with dplyr & tidyr (Rpubs) http://bit.ly/2Aah7Zd
- Introducción a tidyr: Datos ordenados en R (Rpubs) [español] http://bit.ly/2AaWV9T
- dplyr Rstudio cheatsheet http://bit.ly/2IEwRcM
- Intro to data.table Package http://bit.ly/2Aa6Yf3
- Wrangling with data.table http://bit.ly/2QQfLIy
- R studio cheatsheet (data.table) http://bit.ly/2IEwRcM
- Data crunching with data.table (Rpubs) http://bit.ly/2AbNCGz
- Best packages for data manipulation in R (dplyr & data.table) http://bit.ly/2AenZox
- A data.table and dplyr tour http://bit.ly/2IDlIYd
- String Manipulation in R with stringr (Rpubs) http://bit.ly/2SzLyiR
- Regular Expression in R Gloria Li and Jenny Bryan http://bit.ly/2SD74Dg
- DataCarpentry resources: http://bit.ly/2Aaiwz2
- Visualización estática e interactiva con ggplot2 y plotly [español] http://bit.ly/2xI2dqH
- Data Visualization in R http://bit.ly/2AaKzy9
- R graphics with ggplot2 workshop notes http://bit.ly/2AavgG4
- Data visualization using ggplot2 http://bit.ly/2Aal7ZT
- ggplot2 package by Hadley Wickham (Rpubs) http://bit.ly/2AaaeqN
- 7 Visualizations You Should Learn in R http://bit.ly/2NwhCBf
- How to make fancy graphs with ggplot2 (Medium post) http://bit.ly/2PTV51W
- Data Visualization A practical introduction Kieran Healy http://bit.ly/2AaF9n2
- Data Visualization with R. Rob Kabacoff http://bit.ly/2A9pLaj
- CS 448B Visualization. Stanford CS course on data visualization techniques (Fall 2018) http://bit.ly/2IDzfyW
- Rstudio Resources http://bit.ly/2QOovPq
- Building Web Applications in R with Shiny (Datacamp FREE COURSE) http://bit.ly/2QPEyN2
- Introduction to Shiny [video] http://bit.ly/2Aat9BQ
- Testing Shiny applications with Shinytest - Shiny developers now have tools for automated testing of complete applications [video] http://bit.ly/2AauJUq
- Understanding PCA using Shiny and Stack Overflow data Julia Silge [video] http://bit.ly/2QLmG5K
- Developing and deploying large scale Shiny applications Herman Sontrop [video] http://bit.ly/2QT8rMx
- Understanding Shiny Modules [video] http://bit.ly/2AaTuzS
- Interactive Graphics with Shiny [video] http://bit.ly/2Aau45h
- Interactive web-based data visualization with R, plotly, and shiny https://plotly-r.com/
- Hands-on Machine Learning with R http://bit.ly/2IBxTEM
- Feature Engineering and Selection: A Practical Approach for Predictive Models http://bit.ly/2IEf2Jw
- R Markdown Gallery http://bit.ly/2QPHxoI
- R Markdown articles http://bit.ly/2A9LfEe
- R Markdown Rstudio lessons http://bit.ly/2A9Ln6G
- R Markdown and knitr make it easy to intermingle code and text to generate compelling reports and presentations that are never out of date. [video] http://bit.ly/2A9MH9E
- Beyond static reports with R Markdown [video] http://bit.ly/2Ac2jtd
- Introducing Notebooks with R Markdown [video] http://bit.ly/2AaYPXH
- RMarkdown Tips and Tricks - An Introduction to RMarkdown http://bit.ly/2P1NjaA
- RMarkdwon Workshop http://bit.ly/2P3kYkt
- R Markdown: The Definitive Guide Yihui Xie, J. J. Allaire, Garrett Grolemund http://bit.ly/2QNTISX
- Introduction to RMarkdown http://bit.ly/2P59GMo
- RMarkdown for Scientists http://bit.ly/2T2Uca8
- Introducing bookdown [video] http://bit.ly/2AbArpc
- Introducing blogdown, a new R package to make blogs and websites with R Markdown [video] http://bit.ly/2AamVSt
- bookdown: Authoring Books and Technical Documents with R Markdown Yihui Xie http://bit.ly/2QLTZWq
- blogdown: Creating Websites with R Markdown Yihui Xie, Amber Thomas, Alison Presmanes Hill http://bit.ly/2QPjCpm
- R Best Practices: R you writing the R way! http://bit.ly/2P2TkE3
- R Code – Best practices http://bit.ly/2P13Mfq
- Best Practices for Writing R Code [The Carpentries] http://bit.ly/2P3485h
- Write your first R Package (STAT 545 Course) Jenny Bryan http://bit.ly/2OjiBs2
- You can make a package in 20 minutes Jim Hester [Video] http://bit.ly/2QR3K5D
- What makes a great R package? Joseph Rickert [Video] http://bit.ly/2QLS9Vw
- How to develop good R packages (for open science) Maëlle Salmon http://bit.ly/2QTXgmP
- Writing an R package from scratch (Not so Standard deviations blogpost) Hilary Parker http://bit.ly/2QOlONO
- R Package Development Pictorial http://bit.ly/2QP5tbW
- Developing Packages with RStudio http://bit.ly/2QOav8v
- Writing an R package from scratch http://bit.ly/2QTWZAj
- Reproducible Research: Writing an R Package. http://bit.ly/2AarXi0
- Advanced R Course (Chapter 6: R Packages) Florian Privé http://bit.ly/2QT53kN
- rOpenSci Packages: Development, Maintenance, and Peer Review http://bit.ly/2P3k7QN
- R Packages Hadley Wickham http://r-pkgs.had.co.nz/
- Happy R Users Purrr – Tutorial Charlotte Wickham [Video] http://bit.ly/2AakkIv
- Purrr tutorial - Charlotte Wickham http://bit.ly/2AaDCNO
- Purrr tutorial - Jenny Bryan http://bit.ly/2QSVoLC
- Package CRAN Documentation http://bit.ly/2zbuSFz
- Purrr as part of the tidyverse http://bit.ly/2z6gFcO
- The joy of Functional Programming Hadley Wickham http://bit.ly/2IC2qCk
- Jenny Bryan's STAT 545 Course http://bit.ly/2QGCtnc
- Jenny Bryan's Talk in RLadies Bs As Writing R functions for fun and profit http://bit.ly/2xMqhsu
- Spatial Data Analysis and Modeling with R http://rspatial.org/
- Spatial modelling using ‘raster’ package (useR! Conf 2018) - [Part 1] http://bit.ly/2SJ9PTB [Part 2] http://bit.ly/2SIJgOr
- Spatial Data Science Edzer Pebesma, Roger Bivand https://keen-swartz-3146c4.netlify.com/
- Best Practices for Scientific Computing Greg Wilson … Paul Wilson | PLoS Biology 2014 http://bit.ly/2SHZqrs
- Reproducibility in Science - ROpenSci - http://bit.ly/2P18DgA
- The coding club http://bit.ly/2SJzTy7
- The R class R programming for biologists http://bit.ly/2SD71HA
- R for NFL analysis http://bit.ly/2ICmqoo
- Tidy Data Science Workshop (Jun-2019) http://bit.ly/2ID1mhV
- RaukR-2019 http://rstd.io/raukr
- UC Business Analytics R Programming Guide http://uc-r.github.io/
- STAT 545A/547M: Exploratory Data Analysis http://bit.ly/31fsz0t