在当下信息爆炸的时代，唯一不缺的就是各种学习资源：《R 语言资料卡片》中文版 https://github.com/sunbjt/r_reference 《R导论》中文版 https://github.com/DingGuohui/R-intro-cn 《R 语言高频问题》 https://cran.r-project.org/doc/contrib/Liu-FAQ.pdf 由刘思喆收集自中文论坛，非 Kurt Hornik 维护的官方文档适合入门的书籍：《Exploratory Data Analysis with R》探索性分析与 R 语言 Roger D. Peng https://bookdown.org/rdpeng/exdata/ 《R Programming for Data Science》数据科学中的 R 语言 Roger D. Peng https://bookdown.org/rdpeng/rprogdatascience/ 《Efficient R programming》高效的 R 语言编程 Colin Gillespie 和 Robin Lovelace https://csgillespie.github.io/efficientR/ 《An Introduction to R》 R 语言入门 Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto 和 David Lusseau https://intro2r.com/ 《The Book of R》 https://web.itu.edu.tr/ tokerem /The_Book_of_R.pdf 《The Art of R Programming》 http://heather.cs.ucdavis.edu/ matloff /132/NSPpart.pdf 《Hands-On Programming with R》 https://web.itu.edu.tr/ tokerem /Hands-On_R.pdf 《Learning R》 https://web.itu.edu.tr/ tokerem /Learning_R.pdf 《R for Data Science》 http://r4ds.had.co.nz/ 《Data Science for Psychologists》心理学家的数据科学 https://bookdown.org/hneth/ds4psy/ 《Big Book of R》R 语言学习资源集散地 https://bigbookofr.netlify.app/ 《Data Science in Education Using R》数据科学在教育领域中的应用 https://datascienceineducation.com/ 《Statistical Modeling and Computation for Educational Scientists》统计建模和计算在教育科学中的应用 https://zief0002.github.io/modeling/ 与数据可视化相关：《ggplot2: Elegant Graphics for Data Analysis, 3rd》数据分析与图形艺术 Hadley Wickham https://ggplot2-book.org/ 《Fundamentals of Data Visualization》数据可视化精要 Claus O. Wilke https://serialmentor.com/dataviz/ 《Interactive web-based data visualization with R, plotly, and shiny》交互式数据可视化 Carson Sievert https://plotly-r.com/ 《Data Visualization: A Practical Introduction》数据可视化：实践指南 Kieran Healy https://socviz.co/ 与编程开发相关： The tidyverse style guide https://style.tidyverse.org/ Tidyverse design guide https://design.tidyverse.org/ Documentation for R's internal C API https://github.com/hadley/r-internals Advanced R https://adv-r.hadley.nz/ R Packages https://r-pkgs.org/ 与写书、建站相关： bookdown: Authoring Books and Technical Documents with R Markdown https://bookdown.org/yihui/bookdown/ blogdown: Creating Websites with R Markdown https://bookdown.org/yihui/blogdown/ R Markdown: The Definitive Guide https://bookdown.org/yihui/rmarkdown/ Reproducible Research with R and RStudio https://github.com/christophergandrud/Rep-Res-Book 与 shiny 相关：《Engineering Production-Grade Shiny Apps》 Colin Fay, Sébastien Rochette, Vincent Guyader 和 Cervan Girard https://engineering-shiny.org/ 《Mastering Shiny》Hadley Wickham https://mastering-shiny.org/ 与统计推断相关：《Computer Age Statistical Inference: Algorithms, Evidence and Data Science》 Bradley Efron 和 Trevor Hastie https://web.stanford.edu/ hastie /CASI/ 《Spatio-Temporal Statistics with R》 Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie https://spacetimewithr.org/ 《Geocomputation with R》 Robin Lovelace, Jakub Nowosad, Jannes Muenchow https://geocompr.robinlovelace.net/ 《Bayesian inference with INLA》Virgilio Gómez-Rubio https://becarioprecario.bitbucket.io/inla-gitbook/ 《Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA》Elias T. Krainski, Virgilio Gómez-Rubio, Haakon Bakka, Amanda Lenzi, Daniela Castro-Camilo, Daniel Simpson, Finn Lindgren and Håvard Rue https://becarioprecario.bitbucket.io/spde-gitbook/ 《Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny》 Paula Moraga https://www.paulamoraga.com/book-geospatial/ 《Spatial Data Science》 Edzer Pebesma and Roger Bivand https://www.r-spatial.org/book 《All Models Are Wrong: Concepts of Statistical Learning》 Gaston Sanchez and Ethan Marzban https://allmodelsarewrong.github.io/ 《Statistical Inference via Data Science: A ModernDive into R and the Tidyverse》 Chester Ismay and Albert Y. Kim https://moderndive.com/ 《Statistical Learning From A Regression Perspective》 Richard A. Berk 与机器学习相关：《机器翻译：统计建模与深度学习方法》肖桐朱靖波著 https://opensource.niutrans.com/mtbook/ 《南瓜书》 https://datawhalechina.github.io/pumpkin-book 《Data Scientist Handbook》 https://bookdown.org/BaktiSiregar/data-science-for-beginners/ 以及课程资源： CS229: Machine Learning 机器学习课程 http://cs229.stanford.edu/ Statistical Learning 统计学习课程 Trevor Hastie and Rob Tibshirani https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/ Data wrangling, exploration, and analysis with R 数据加工、探索和分析 Jenny Bryan https://stat545.com/ 还有各类食谱：《R Cookbook, 2nd》R 语言食谱 James (JD) Long 和 Paul Teetor https://rc2e.com/ 书稿源码《R Graphics Cookbook, 2nd》 R 绘图食谱 Winston Chang https://r-graphics.org/ 书稿源码《R Markdown Cookbook》R Markdown 食谱 Yihui Xie、 Christophe Dervieux 和 Emily Riederer https://bookdown.org/yihui/rmarkdown-cookbook/ 书稿源码以及中外博客：谢益辉 https://yihui.org/ 于淼 https://yufree.cn/ 谭显英 https://shrektan.com/ 任坤 https://renkun.me/ Andrew Gelman https://andrewgelman.com/ Julia Silge https://juliasilge.com/ David Robinson http://varianceexplained.org/ 除了 R 语言，我们还需要掌握一点和命令行相关的东西，比如 Bash 和 Makefile 等。《Bash 教程》阮一峰 https://github.com/wangdoc/bash-tutorial 《跟我一起写 Makefile》陈浩 https://github.com/seisman/how-to-write-makefile 《快乐的 Linux 命令行》Peter Wang 和 Billie Zhang https://billie66.github.io/TLCL/ 《Linux 就该这么学》刘遄 https://www.linuxprobe.com/docs/LinuxProbe.pdf 《Docker 从入门到实践》 https://vuepress.mirror.docker-practice.com/ 以及了解一些大数据处理工具大数据入门指南 https://github.com/heibaiying/BigData-Notes

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学习资源分享

Cloud2016

在当下信息爆炸的时代，唯一不缺的就是各种学习资源：

《R 语言资料卡片》中文版 https://github.com/sunbjt/r_reference
《R导论》中文版 https://github.com/DingGuohui/R-intro-cn
《R 语言高频问题》https://cran.r-project.org/doc/contrib/Liu-FAQ.pdf 由刘思喆收集自中文论坛，非 Kurt Hornik 维护的官方文档

适合入门的书籍：

《Exploratory Data Analysis with R》探索性分析与 R 语言 Roger D. Peng https://bookdown.org/rdpeng/exdata/
《R Programming for Data Science》数据科学中的 R 语言 Roger D. Peng https://bookdown.org/rdpeng/rprogdatascience/
《Efficient R programming》高效的 R 语言编程 Colin Gillespie 和 Robin Lovelace https://csgillespie.github.io/efficientR/
《An Introduction to R》 R 语言入门 Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto 和 David Lusseau https://intro2r.com/
《The Book of R》https://web.itu.edu.tr/_tokerem/The_Book_of_R.pdf
《The Art of R Programming》http://heather.cs.ucdavis.edu/_matloff/132/NSPpart.pdf
《Hands-On Programming with R》 https://web.itu.edu.tr/_tokerem/Hands-On_R.pdf
《Learning R》 https://web.itu.edu.tr/_tokerem/Learning_R.pdf
《R for Data Science》 http://r4ds.had.co.nz/
《Data Science for Psychologists》心理学家的数据科学 https://bookdown.org/hneth/ds4psy/
《Big Book of R》R 语言学习资源集散地 https://bigbookofr.netlify.app/
《Data Science in Education Using R》数据科学在教育领域中的应用 https://datascienceineducation.com/
《Statistical Modeling and Computation for Educational Scientists》统计建模和计算在教育科学中的应用 https://zief0002.github.io/modeling/

与数据可视化相关：

《ggplot2: Elegant Graphics for Data Analysis, 3rd》数据分析与图形艺术 Hadley Wickham https://ggplot2-book.org/
《Fundamentals of Data Visualization》数据可视化精要 Claus O. Wilke https://serialmentor.com/dataviz/
《Interactive web-based data visualization with R, plotly, and shiny》交互式数据可视化 Carson Sievert https://plotly-r.com/
《Data Visualization: A Practical Introduction》数据可视化：实践指南 Kieran Healy https://socviz.co/

与编程开发相关：

The tidyverse style guide https://style.tidyverse.org/
Tidyverse design guide https://design.tidyverse.org/
Documentation for R's internal C API https://github.com/hadley/r-internals
Advanced R https://adv-r.hadley.nz/
R Packages https://r-pkgs.org/

与写书、建站相关：

bookdown: Authoring Books and Technical Documents with R Markdown https://bookdown.org/yihui/bookdown/
blogdown: Creating Websites with R Markdown https://bookdown.org/yihui/blogdown/
R Markdown: The Definitive Guide https://bookdown.org/yihui/rmarkdown/
Reproducible Research with R and RStudio https://github.com/christophergandrud/Rep-Res-Book

与 shiny 相关：

《Engineering Production-Grade Shiny Apps》 Colin Fay, Sébastien Rochette, Vincent Guyader 和 Cervan Girard https://engineering-shiny.org/
《Mastering Shiny》Hadley Wickham https://mastering-shiny.org/

与统计推断相关：

《Computer Age Statistical Inference: Algorithms, Evidence and Data Science》 Bradley Efron 和 Trevor Hastie https://web.stanford.edu/_hastie/CASI/
《Spatio-Temporal Statistics with R》 Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie https://spacetimewithr.org/
《Geocomputation with R》 Robin Lovelace, Jakub Nowosad, Jannes Muenchow https://geocompr.robinlovelace.net/
《Bayesian inference with INLA》Virgilio Gómez-Rubio https://becarioprecario.bitbucket.io/inla-gitbook/
《Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA》Elias T. Krainski, Virgilio Gómez-Rubio, Haakon Bakka, Amanda Lenzi, Daniela Castro-Camilo, Daniel Simpson, Finn Lindgren and Håvard Rue https://becarioprecario.bitbucket.io/spde-gitbook/
《Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny》 Paula Moraga https://www.paulamoraga.com/book-geospatial/
《Spatial Data Science》 Edzer Pebesma and Roger Bivand https://www.r-spatial.org/book
《All Models Are Wrong: Concepts of Statistical Learning》Gaston Sanchez and Ethan Marzban https://allmodelsarewrong.github.io/
《Statistical Inference via Data Science: A ModernDive into R and the Tidyverse》 Chester Ismay and Albert Y. Kim https://moderndive.com/
《Statistical Learning From A Regression Perspective》 Richard A. Berk

与机器学习相关：

《机器翻译：统计建模与深度学习方法》肖桐朱靖波著 https://opensource.niutrans.com/mtbook/
《南瓜书》 https://datawhalechina.github.io/pumpkin-book
《Data Scientist Handbook》https://bookdown.org/BaktiSiregar/data-science-for-beginners/

以及课程资源：

CS229: Machine Learning 机器学习课程 http://cs229.stanford.edu/
Statistical Learning 统计学习课程 Trevor Hastie and Rob Tibshirani https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
Data wrangling, exploration, and analysis with R 数据加工、探索和分析 Jenny Bryan https://stat545.com/

还有各类食谱：

《R Cookbook, 2nd》R 语言食谱 James (JD) Long 和 Paul Teetor https://rc2e.com/ 书稿源码
《R Graphics Cookbook, 2nd》 R 绘图食谱 Winston Chang https://r-graphics.org/ 书稿源码
《R Markdown Cookbook》R Markdown 食谱 Yihui Xie、 Christophe Dervieux 和 Emily Riederer https://bookdown.org/yihui/rmarkdown-cookbook/ 书稿源码