└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Learning R - Resources 2 | 3 | _Last Update_ : 25-July/2020 4 | 5 | ## Cursos 6 | * Listas de cursos https://www.learnr4free.com/en/index.html 7 | 8 | ## R Programming 9 | * CRAN Contributed Documentation https://cran.r-project.org/ 10 | * What they forgot to teach you about R _Jenny Bryan_ https://rstats.wtf/ 11 | 12 | ### Books 13 | * R Programming for Data Science _Roger D. Peng_ 2016-12-22 http://bit.ly/2AbQRhd 14 | * R for Data Science _Garrett Grolemund & Hadley Wickham_ http://bit.ly/2AaFWEw 15 | * Efficient R programming _Colin Gillespie & Robin Lovelace_ http://bit.ly/2AaGKcw 16 | * Hands-On Programming with R _Garrett Grolemund_ http://bit.ly/2QYJRJZ 17 | * Advanced R _Hadley Wickham_ http://bit.ly/2AapVhT 18 | * Wikibook R Programming https://en.wikibooks.org/wiki/R_Programming 19 | * Learning statistics with R: A tutorial for psychology students and other beginners _Danielle Navarro_ http://bit.ly/2DaYoig 20 | * The Tidynomicon A Brief Introduction to R for Python Programmers _Greg Wilson_ http://bit.ly/2IEh4t8 21 | * Rad _R for academics_ http://bit.ly/2UufM8b 22 | 23 | #### _Español_ 24 | * El arte de programar en R _Julio Sergio Santana & Efraín Mateos Farfán_ http://bit.ly/2N2Y1Y8 25 | * R para Principiantes _Juan Bosco Mendoza Vega_ http://bit.ly/2Zg0I3M 26 | 27 | ## Rstudio [webinars](https://rstudio.com/resources/webinars/) 28 | 29 | ## Data Manipulation 30 | ### tidyr and dplyr Packages 31 | * Tidy evaluation is one of the major feature of the latest versions of dplyr and tidyr [Video] http://bit.ly/2AbXJLs 32 | * Tidy eval: Programming with dplyr, tidyr, and ggplot2 _Hadley Wickham_ [Video] http://bit.ly/2QR07N5 33 | * Data wrangling with R and RStudio [Video] http://bit.ly/2AaocZX slides: http://bit.ly/2QSr7vS 34 | * Wrangling data in the Tidyverse [Video] (useR! 2018 Conf) [Part 1] http://bit.ly/2SEHDBc [Part 2] http://bit.ly/2SK9EHt 35 | * dplyr tutorials _Suzan Baert_ http://bit.ly/2AbSHi2 36 | * Getting more out of dplyr SatRday Amsterdam 2018 slides _Suzan Baert_ http://bit.ly/2QXf28I 37 | * dplyr 10 tips and tricks _Suzan Baert (RoCur WeAreRLadies)_ http://bit.ly/2AaWb4k 38 | * STAT 545 Course https://stat545.com/tidy-data.html 39 | * Let the Data Flow: Pipelines in R with dplyr and magrittr http://bit.ly/2AaVDvz 40 | * Data Processing with dplyr & tidyr (Rpubs) http://bit.ly/2Aah7Zd 41 | * Introducción a tidyr: Datos ordenados en R (Rpubs) [español] http://bit.ly/2AaWV9T 42 | * dplyr Rstudio cheatsheet http://bit.ly/2IEwRcM 43 | * tidylog _Tidylog provides feedback about basic dplyr operations_ http://bit.ly/2MJJUvq 44 | 45 | #### Joins 46 | * Vignettes for Joins - http://bit.ly/2Zhdsaj 47 | * Join Functions _Jenny Bryan_ http://bit.ly/2AbUZ0C 48 | * Joining Data in R with dplyr (Rpubs) http://bit.ly/2ZjTwnm 49 | * Gif for differrent types of Joins http://bit.ly/2ZixS2L 50 | 51 | ### data.table Package 52 | * Intro to data.table Package http://bit.ly/2Aa6Yf3 53 | * Wrangling with data.table http://bit.ly/2QQfLIy 54 | * R studio cheatsheet (data.table) http://bit.ly/2IEwRcM 55 | * Data crunching with data.table (Rpubs) http://bit.ly/2AbNCGz 56 | * Best packages for data manipulation in R (dplyr & data.table) http://bit.ly/2AenZox 57 | * A data.table and dplyr tour http://bit.ly/2IDlIYd 58 | 59 | ### String manipulation and stringr package 60 | * String Manipulation in R with stringr (Rpubs) http://bit.ly/2SzLyiR 61 | * Regular Expression in R _Gloria Li and Jenny Bryan_ http://bit.ly/2SD74Dg 62 | 63 | ## Data Visualization 64 | ### ggplot2 Package 65 | * R Graph Gallery http://bit.ly/2UmD3ZN 66 | * DataCarpentry resources: http://bit.ly/2Aaiwz2 67 | * Visualización estática e interactiva con ggplot2 y plotly [español] http://bit.ly/2xI2dqH 68 | * Data Visualization in R http://bit.ly/2AaKzy9 69 | * R graphics with ggplot2 workshop notes http://bit.ly/2AavgG4 70 | * Data visualization using ggplot2 http://bit.ly/2Aal7ZT 71 | * ggplot2 package by Hadley Wickham (Rpubs) http://bit.ly/2AaaeqN 72 | * 7 Visualizations You Should Learn in R http://bit.ly/2NwhCBf 73 | * How to make fancy graphs with ggplot2 (Medium post) http://bit.ly/2PTV51W 74 | * Designing ggplots making clear figures that communicate bit.ly/ggplots 75 | * Drawing anything with ggplot2 https://github.com/thomasp85/ggplot2_workshop 76 | 77 | #### Books 78 | * Data Visualization A practical introduction _Kieran Healy_ http://bit.ly/2AaF9n2 79 | * Data Visualization with R. _Rob Kabacoff_ http://bit.ly/2A9pLaj 80 | * ggplot2: Elegant Graphics for Data Analysis _Hadley Wickham_ https://ggplot2-book.org/ 81 | 82 | 83 | #### Visualization Courses 84 | * CS 448B Visualization. Stanford CS course on data visualization techniques (Fall 2018) http://bit.ly/2IDzfyW 85 | 86 | ## Modeling 87 | ### Broom 88 | * Broom vignette http://bit.ly/2M42z5y 89 | * Convenient analysis with broom - Alex Hayes - http://bit.ly/2ZdV7e4 90 | * broom: a package for tidying statistical models into data frames http://bit.ly/2Wi0FBZ 91 | ### Tidymodels 92 | * A gentle introduction to tidymodels http://bit.ly/2G176QI 93 | * Tutorial on tidymodels for Machine Learning https://bit.ly/37iyQwC 94 | 95 | 96 | ### H2o.ai 97 | * Auto Machine Learning with H2o.ai #LatinR2019 _Erin Ledell_ http://bit.ly/35nDEQ7 98 | * Youtube Channel http://bit.ly/2ogLiep 99 | 100 | ### Data Modeling 101 | * Hands-on Machine Learning with R http://bit.ly/2IBxTEM 102 | * Feature Engineering and Selection: A Practical Approach for Predictive Models http://bit.ly/2IEf2Jw 103 | 104 | 105 | ## Shiny Web Application 106 | * Rstudio Resources http://bit.ly/2QOovPq 107 | * Introduction to Shiny [video] http://bit.ly/2Aat9BQ 108 | * Testing Shiny applications with Shinytest - Shiny developers now have tools for automated testing of complete applications [video] http://bit.ly/2AauJUq 109 | * Understanding PCA using Shiny and Stack Overflow data _Julia Silge_ [video] http://bit.ly/2QLmG5K 110 | * Developing and deploying large scale Shiny applications _Herman Sontrop_ [video] http://bit.ly/2QT8rMx 111 | * Understanding Shiny Modules [video] http://bit.ly/2AaTuzS 112 | * Interactive Graphics with Shiny [video] http://bit.ly/2Aau45h 113 | * Interactive web-based data visualization with R, plotly, and shiny https://plotly-r.com/ 114 | * Javascript for Shiny Users https://github.com/rstudio-conf-2020/js-for-shiny 115 | * Interactive web applications with Shiny - meetup material https://bit.ly/2B0Eacq 116 | * Production-grade Shiny Apps with golem - rstudio::conf2019 talk https://bit.ly/30G685J 117 | * Building Big Shiny Apps — A Workflow – [1/2](https://bit.ly/32Hr0MN) [2/2](https://bit.ly/3jvlxP8) 118 | * Building a Shiny App as a Package https://bit.ly/30EgTpb 119 | * Testing shiny Apss https://speakerdeck.com/colinfay/erum-2020-testing-shiny-why-what-and-how 120 | * A gRadual introduction to Shiny. https://laderast.github.io/gradual_shiny/index.html 121 | 122 | #### Books 123 | * Mastering Shiny _Hadley Wickham_ http://bit.ly/2z89f9l 124 | * Interactive web-based data visualization with R, plotly, and shiny http://bit.ly/2IBuR3m 125 | * Engineering Production-Grade Shiny Apps https://engineering-shiny.org/ 126 | 127 | ## R Markdown 128 | * R Markdown Gallery http://bit.ly/2QPHxoI 129 | * R Markdown articles http://bit.ly/2A9LfEe 130 | * R Markdown Rstudio lessons http://bit.ly/2A9Ln6G 131 | * 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 132 | * Beyond static reports with R Markdown [video] http://bit.ly/2Ac2jtd 133 | * Introducing Notebooks with R Markdown [video] http://bit.ly/2AaYPXH 134 | * RMarkdown Tips and Tricks - An Introduction to RMarkdown http://bit.ly/2P1NjaA 135 | * RMarkdwon Workshop http://bit.ly/2P3kYkt 136 | * Reproducible Reporting http://bit.ly/2A9MH9E 137 | * 15 Tips on Making Better Use of R Markdown _Yihui Xie_ https://slides.yihui.org/2019-dahshu-rmarkdown#1 138 | 139 | ### Books 140 | * R Markdown: The Definitive Guide _Yihui Xie, J. J. Allaire, Garrett Grolemund_ http://bit.ly/2QNTISX 141 | * Introduction to RMarkdown http://bit.ly/2P59GMo 142 | * RMarkdown for Scientists http://bit.ly/2T2Uca8 143 | 144 | 145 | ## Bookdown & Blogdown 146 | * Introducing bookdown [video] http://bit.ly/2AbArpc 147 | * Introducing blogdown, a new R package to make blogs and websites with R Markdown [video] http://bit.ly/2AamVSt 148 | * A week of blogdown for RStudio's summer 2019 interns _Alison Hill_ https://summer-of-blogdown.netlify.com/ 149 | ### Books 150 | * bookdown: Authoring Books and Technical Documents with R Markdown _Yihui Xie_ http://bit.ly/2QLTZWq 151 | * blogdown: Creating Websites with R Markdown _Yihui Xie, Amber Thomas, Alison Presmanes Hill_ http://bit.ly/2QPjCpm 152 | 153 | ## R code Best Practices 154 | * R Best Practices: R you writing the R way! http://bit.ly/2P2TkE3 155 | * R Code – Best practices http://bit.ly/2P13Mfq 156 | * Best Practices for Writing R Code [The Carpentries] http://bit.ly/2P3485h 157 | 158 | ## R Package Development 159 | * Write your first R Package (STAT 545 Course) _Jenny Bryan_ http://bit.ly/2OjiBs2 160 | * You can make a package in 20 minutes _Jim Hester_ [Video] http://bit.ly/2QR3K5D 161 | * What makes a great R package? _Joseph Rickert_ [Video] http://bit.ly/2QLS9Vw 162 | * How to develop good R packages (for open science) _Maëlle Salmon_ http://bit.ly/2QTXgmP 163 | * Writing an R package from scratch (Not so Standard deviations blogpost) _Hilary Parker_ http://bit.ly/2QOlONO 164 | * R Package Development Pictorial http://bit.ly/2QP5tbW 165 | * Developing Packages with RStudio http://bit.ly/2QOav8v 166 | * Writing an R package from scratch http://bit.ly/2QTWZAj 167 | * Reproducible Research: Writing an R Package. http://bit.ly/2AarXi0 168 | * Advanced R Course (Chapter 6: R Packages) _Florian Privé_ http://bit.ly/2QT53kN 169 | * rOpenSci Packages: Development, Maintenance, and Peer Review http://bit.ly/2P3k7QN 170 | * R Package Development Tutorial #LatinR2019 _Hadley Wickham_ https://github.com/hadley/pkg-dev 171 | * Make an R Package - the easy way - _Matt Dray_ http://bit.ly/2PCEhQh 172 | * Usethis package development workflow http://bit.ly/2pzuoIg 173 | 174 | ### Books 175 | * R Packages _Hadley Wickham_ http://r-pkgs.had.co.nz/ 176 | 177 | ## purrr Package - Functional Programming 178 | * Happy R Users Purrr – Tutorial _Charlotte Wickham_ [Video] http://bit.ly/2AakkIv 179 | * Purrr tutorial - _Charlotte Wickham_ http://bit.ly/2AaDCNO 180 | * Purrr tutorial - _Jenny Bryan_ http://bit.ly/2QSVoLC 181 | * Package CRAN Documentation http://bit.ly/2zbuSFz 182 | * Purrr as part of the tidyverse http://bit.ly/2z6gFcO 183 | * The joy of Functional Programming _Hadley Wickham_ http://bit.ly/2IC2qCk 184 | * Purrr - tips and tricks https://bit.ly/2AWODF6 185 | * Two examples of iteration with purrr - Class for the R-Studio certification https://bit.ly/3dzWKoK 186 | 187 | 188 | ## How to write functions in R. 189 | * Jenny Bryan's STAT 545 Course http://bit.ly/2QGCtnc 190 | * Jenny Bryan's Talk in RLadies Bs As _Writing R functions for fun and profit_ http://bit.ly/2xMqhsu 191 | 192 | ## R-Spatial 193 | * Spatial Data Analysis and Modeling with R http://rspatial.org/ 194 | * Spatial modelling using ‘raster’ package (useR! Conf 2018) - [Part 1] http://bit.ly/2SJ9PTB [Part 2] http://bit.ly/2SIJgOr 195 | * Spatial Data Science _Edzer Pebesma, Roger Bivand_ https://keen-swartz-3146c4.netlify.com/ 196 | 197 | ## Reproducible Research 198 | * Best Practices for Scientific Computing _Greg Wilson … Paul Wilson_ | *PLoS Biology 2014* http://bit.ly/2SHZqrs 199 | * Good enough practices in scientific computing _Greg Wilson ... Tracy Teal_| *_PlOS Computational Biology 2017_* http://bit.ly/2zhSSXW 200 | * Reproducibility in Science - ROpenSci - http://bit.ly/2P18DgA 201 | * The drake R Package User Manual http://bit.ly/2P4n9nK 202 | * rrtools: Tools for Writing Reproducible Research in R - http://bit.ly/2zhMekA 203 | * Use of an R package to facilitate reproducible research - http://bit.ly/2zhP8G0 204 | 205 | 206 | ## Metaprogramming - TidyEval 207 | * Tidy evaluation _Lionel Henry & Hadley Wickham_ http://bit.ly/2P5oRFy 208 | * Lazy evaluation _Jenny Bryan_ [video Rstudio conf 2019] http://bit.ly/2P82pvn [material] http://bit.ly/2PgphbY 209 | 210 | ## Tutorials from different topics 211 | * The coding club http://bit.ly/2SJzTy7 212 | * The R class _R programming for biologists_ http://bit.ly/2SD71HA 213 | * R for NFL analysis http://bit.ly/2ICmqoo 214 | 215 | ## R Courses with Tidyverse 216 | * Tidy Data Science Workshop (Jun-2019) http://bit.ly/2ID1mhV 217 | * RaukR-2019 http://rstd.io/raukr 218 | * UC Business Analytics R Programming Guide http://uc-r.github.io/ 219 | * STAT 545A/547M: Exploratory Data Analysis - Jenny Bryan - http://bit.ly/31fsz0t 220 | * Data Science in a Box - Mine Çetinkaya-Rundel - http://bit.ly/2PcydPU 221 | * R for learning and teaching R _List of resources_ http://bit.ly/2PaIqwc 222 | * BIMS8382 Spring 2018 https://bims8382.github.io/2018/ 223 | * Course materials for Applied Machine Learning course in 2019 in London https://github.com/topepo/aml-london-2019 224 | --------------------------------------------------------------------------------