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
- 《R 和 tidyverse 快速入门》 https://github.com/saghirb/Getting-Started-in-R Saghir Bashir
- 《R 和 tinyverse 快速入门》 https://github.com/eddelbuettel/gsir-te Dirk Eddelbuettel
- 《数据科学卡片》https://github.com/aaronwangy/Data-Science-Cheatsheet Aaron Wang
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/
- 《Learning statistics with R》https://learningstatisticswithr.com/ 学习统计与 R 语言
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/
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《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/
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《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/
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《Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny》 Paula Moraga https://www.paulamoraga.com/book-geospatial/
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《Spatial Data Science》 Edzer Pebesma and Roger Bivand https://www.r-spatial.org/book
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《All Models Are Wrong: Concepts of Statistical Learning》Gaston Sanchez and Ethan Marzban https://allmodelsarewrong.github.io/
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《Statistical Inference via Data Science: A ModernDive into R and the Tidyverse》 Chester Ismay and Albert Y. Kim https://moderndive.com/
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《Statistical Learning From A Regression Perspective》 Richard A. Berk
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计算生物的统计和概率基础 Statistics and Probability Primer (for Computational Biologists)
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生态学入门与 R 语言 Primer of Ecology using R Hank Stevens
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/
- 《Bayesian Data Analysis, 3rd》贝叶斯数据分析第三版 Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari and Donald Rubin https://github.com/avehtari/BDA_course_Aalto PDF 下载 https://users.aalto.fi/~ave/BDA3.pdf
- 《Foundations of Machine Learning, 2nd》机器学习基石 Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar https://cs.nyu.edu/~mohri/mlbook/ PDF 下载 https://www.dropbox.com/s/7voitv0vt24c88s/10290.pdf
- 《Pattern Recognition and Machine Learning》模式识别与机器学习 Christopher Bishop https://www.microsoft.com/en-us/research/people/cmbishop/ PDF 下载 https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
- 《Machine Learning: A Bayesian and Optimization Perspective》 https://iie.fing.edu.uy/~nacho/docs/libros/machine_learning_a_bayesian_perspective.pdf
- 《Probabilistic Machine Learning: An Introduction》 https://github.com/probml/pml-book 提供 PDF 电子版下载
- 《线性规划》https://github.com/Operations-Research-Science/Ebook-Linear_Programming
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/
- 《AWK 程序设计语言》https://github.com/wuzhouhui/awk
以及了解一些大数据处理工具