R-Tutorial

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
• 《S Poetry》Patrick Burns https://www.burns-stat.com/pages/Spoetry/Spoetry.pdf
• 《The R Inferno》Patrick Burns https://www.burns-stat.com/pages/Tutor/R_inferno.pdf

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》Hadley Wickham and Garrett Grolemund 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》学习统计与 R 语言 https://learningstatisticswithr.com/
• 《R Notes for Professionals book》https://goalkicker.com/RBook/
• 《R for Geographic Data Science》Stefano De Sabbata https://sdesabbata.github.io/r-for-geographic-data-science/
• 《Improving Your Statistical Inferences》Daniël Lakens https://lakens.github.io/statistical_inferences/
• 《数据科学入门》 Introduction to Data Science: Data Analysis and Prediction Algorithms with R Rafael A. Irizarry
• John H. Maindonald Using R for Data Analysis and Graphics 后来演变为 Data Analysis and Graphics Using R-3rd-2010 数据集和函数存放在 DAAG 包里，第四版正在进行中，手稿可在作者主页上获得。
• 应用时间序列分析与R语言 Time Series Analysis with Applications in R
• 时间序列预测：原理与实践 Forecasting: Principles and Practice Rob J Hyndman
• 时间序列分析及其应用 Time Series Analysis and Its Applications: With R Examples
• 线性模型与R语言 Extending the Linear Model with R Julian Faraway
• 量化经济学与R语言 Introduction to Econometrics with R
• 数据建模与 R Modeling with Data: Tools and Techniques for Scientific Computing 下载地址 PDF
• 统计反思 Statistical Rethinking Statistical Rethinking Resources
• 统计做错了 Statistics Done Wrong
• 回归模型策略 Regression Modeling Strategies
• 应用预测建模 Applied Predictive Modeling
• 应用回归分析与广义线性模型 Applied Regression Analysis and Generalized Linear Models
• 数据融合，理论、方法和应用 Data Fusion Theory, Methods, and Applications
• 《学习统计与 R 语言》 Learning Statistics with R 网页版 PDF
• An Introduction to Bayesian Thinking: A Companion to the Statistics with R Course
• 统计计算 李东风
• R语言教程 李东风
• 金融时间序列分析讲义 李东风
• 应用时间序列分析备课笔记 李东风

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/
• 《数据可视化与 R 语言》 Data Visualisation with R: 100 Examples
• 《R 语言绘图手册》 R Graphics Cookbook PDF
• 《数据分析与图形艺术》 ggplot2: Elegant Graphics for Data Analysis
• 《可视化数据分析与 R 语言》 Graphical Data Analysis with R
• The Economist
• BBC
• Gapminder

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/
• R Developer’s Guide https://forwards.github.io/rdevguide/

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

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/

统计推断：

• 《Computer Age Statistical Inference: Algorithms, Evidence and Data Science》 Bradley Efron 和 Trevor Hastie https://web.stanford.edu/~hastie/CASI/
• 《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
• 《Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis》Frank E Harrell Jr https://hbiostat.org/doc/rms.pdf https://hbiostat.org/rms/
• 《Regression and Other Stories》 Andrew Gelman, Jennifer Hill, and Aki Vehtari https://users.aalto.fi/~ave/ROS.pdf
• 《Gaussian process modeling, design and optimization for the applied sciences》 Robert B. Gramacy 网页版 PDF
• 《Tidy Modeling with R》 Max Kuhn and Julia Silge https://www.tmwr.org/
• 《Introduction to Categorical Data Analysis》Alan Agresti https://mregresion.files.wordpress.com/2012/08/agresti-introduction-to-categorical-data.pdf
• 《Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R》Paul Roback and Julie Legler https://bookdown.org/roback/bookdown-BeyondMLR/
• 《All of Statistics: A Concise Course in Statistical Inference》Larry Wasserman https://www.stat.cmu.edu/~larry/all-of-statistics/
• 《Multivariate Kernel Smoothing and Its Applications》José Chacón and Tarn Duong http://www.mvstat.net/mvksa PDF
• 《Theory of Statistics》 James E. Gentle PDF
• 《The Boosttrap and Edgeworth Expansion》蒙特卡洛、Boostrap、非参数分布 Michael I. Jordan PDF
• 《Handbook of mathematical functions》 数学函数手册大全 http://people.math.sfu.ca/~cbm/aands/frameindex.htm
• 《Graphical models, exponential families, and variational inference》
  • Foundations and Trends in Machine Learning. Vol.1
  • 图模型 指数族 变分推断 M. J. Wainwright and M. I. Jordan PDF
• 《High-dimensional statistics: A non-asymptotic viewpoint》 Martin J. Wainwright
• 《The Elements of Statistical Learning: Data Mining, Inference, and Prediction》
  • 书籍主页 https://web.stanford.edu/~hastie/ElemStatLearn/
  • The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答
  • Lijun Wang https://github.com/szcf-weiya/ESL-CN
• 《An Introduction to Statistical Learning with Applications in R》 https://www-bcf.usc.edu/~gareth/ISL/
• 《Statistical Learning with Sparsity: The Lasso and Generalizations》 https://web.stanford.edu/~hastie/StatLearnSparsity/
• 《Statistical Learning from a Regression Perspective》
  • 书籍主页 https://www.springer.com/gp/book/9783319440477
  • John L. Weatherwax 学习笔记
• 《Convergence of Stochastic Processes》 随机过程的收敛性 PDF

空间统计书籍：

• 《Analyzing US Census Data: Methods, Maps, and Models in R》 Kyle Walker https://walker-data.com/census-r
• 《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
• 《An Introduction to Spatial Data Analysis and Statistics: A Course in R》 Antonio Paez https://paezha.github.io/spatial-analysis-r/
• 《Predictive Soil Mapping with R》 Tomislav Hengl and Robert A. MacMillan https://soilmapper.org/
• 《Applied Spatial Data Analysis with R》Roger S. Bivand, Edzer Pebesma and V. Gómez-Rubio https://asdar-book.org/
• 《Introduction to Environmental Data Science》 Jerry Davis, SFSU Institute for Geographic Information Science https://bookdown.org/igisc/EnvDataSci/
• 《Geospatial Data Science With R: Applications in Environmental Geography》Michael C. Wimberly https://bookdown.org/mcwimberly/gdswr-book/
• 《Spatial sampling with R》Dick Brus https://github.com/DickBrus/SpatialSamplingwithR
• 《Spatial Point Patterns: Methodology and Applications with R》Adrian Baddeley, Ege Rubak and Rolf Turner https://book.spatstat.org/
• 《Bayesian Modeling of Spatio-Temporal Data with R》 Sujit K. Sahu https://www.sujitsahu.com/bookbmstdr/

空间统计材料：

• Introduction to Geospatial Concepts 空间统计导论
• Introduction to spatial analysis in R 空间分析导论
• Analysis of Spatial and Temporal Data 课程列表
• A curated list of resources focused on Machine Learning in Geospatial Data Science.
• A list of useful resources for getting started with spatial data in R
• An Introduction to Spatial Econometrics in R 空间计量经济
• 空间广义线性混合效应模型及其应用
• Data wrangling visualisation and spatial analysis: R Workshop
• GIS 应用与实践 Jianghao Wang https://github.com/Jianghao/ucasgis

空间统计领域学者：

• Håvard Rue
• Andrew O. Finley
• Peter J. Diggle
• Emanuele Giorgi
• Adrian Baddeley
• Andrew Zammit-Mangion
• Noel Cressie
• Alan E. Gelfand
• Benjamin M. Taylor
• Zhan Zhao
• Song Qian
• Alain F. Zuur

更多学者见列表。

与生态学相关：

• Ecological Models and Data in R PDF 书稿 生态模型
• Statistics and Probability Primer (for Computational Biologists) 计算生物的统计和概率基础
• Primer of Ecology using R 生态学入门与 R 语言 Hank Stevens
• Spatial Data Analysis in Ecology and Agriculture using R
• Data Analysis and Visualization in R for Ecologists 生态学数据分析与可视化
• Beginner's guide to spatial, temporal and spatial-temporal ecological data analysis with R-INLA
• 《Environmental and Ecological Statistics with R》 Song S. Qian https://github.com/songsqian/eesR
• 《Bayesian Applications in Environmental and Ecological Studies with R and Stan》 Song S. Qian https://github.com/songsqian/BeesRStan

与流行病学相关：

• Statistical Methods in Spatial Epidemiology Second Edition Andrew B. Lawson
• Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology Second Edition Andrew B. Lawson
• Handbook of Spatial Epidemiology Andrew B. Lawson
• Geostatistical mapping examples in R loaloa 数据集
• Geostatistical data: Malaria in The Gambia Paula Moraga
• Areal data. Lung cancer risk in Pennsylvania

与机器学习相关：

• 《Foundations of Machine Learning》 机器学习基础第二版 PDF
• 《Foundations of Data Science》 数据科学基础 PDF
• 《强化学习中文教程（蘑菇书）》https://github.com/datawhalechina/easy-rl
• Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
  • 深度学习 PDF
  • 书籍主页 https://www.deeplearningbook.org/
  • 中译本 https://github.com/exacity/deeplearningbook-chinese
  • 网页版 https://exacity.github.io/deeplearningbook-chinese/
• 动手学深度学习 https://zh.d2l.ai/
• 机器翻译：基础与模型
• Convex Optimization
  • 书籍主页 https://web.stanford.edu/~boyd/cvxbook/
• 《Numerical Optimization》 Jorge NocedalStephen J. Wright
• 《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
• 《机器学习》（西瓜书）公式推导解析 https://github.com/datawhalechina/pumpkin-book
• 《神经网络与深度学习》 Neural Network and Deep Learning 邱锡鹏
  • 网页版 https://nndl.github.io/ PDF
• 《统计学习方法》李航，R 语言实现 https://bookdown.org/lyuchengrui/statisticallearningmethods/
• 《Reinforcement Learning: An Introduction》（第二版）中文翻译网页版
  • https://github.com/qiwihui/reinforcement-learning-an-introduction-chinese
  • 英文原版 PDF
• 《迁移学习简明手册》 https://github.com/jindongwang/transferlearning-tutorial
• 《Tensorflow 内核和实现机制》 https://github.com/horance-liu/tensorflow-internals
• 《神经网络与深度学习》 Neural Networks and Deep Learning Michael Nielsen
  • 网页版和书稿
• 《Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares》
  • 书籍主页 https://web.stanford.edu/~boyd/vmls/ PDF
• 《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
• 《Bayesian Reasoning and Machine Learning》贝叶斯推理与机器学习 David Barber 主页
• 《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
• 《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
• 《Machine Learning: A Bayesian and Optimization 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
• 《Machine Learning Systems: Design and Implementation》机器学习系统：设计和实现 https://github.com/openmlsys/openmlsys-zh
• 《Mathematics for Machine Learning》Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. https://mml-book.github.io/ PDF
• 《Machine Learning: a Probabilistic Perspective》 机器学习：概率视角 补充材料 PDF
  • 书籍主页 https://www.cs.ubc.ca/~murphyk/MLbook/
  • 软件工具 Matlab/Octave
• 《Gaussian Processes for Machine Learning》 书籍主页
• 《Information Theory, Inference, and Learning Algorithms》书籍主页 软件工具 Octave/J
• 《Hands-on Machine Learning with R》 Bradley Boehmke & Brandon Greenwell. https://bradleyboehmke.github.io/HOML/
• 《特征工程与特征选择》 Feature Engineering and Selection: A Practical Approach for Predictive Models. Max Kuhn and Kjell Johnson. http://www.feat.engineering
• 《可解释的机器学习》 Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Christoph Molnar. https://christophm.github.io/interpretable-ml-book/
• 《机器学习与R语言》Machine Learning in R. https://mlr.mlr-org.com
• 《分类与回归：caret 包》 Classification And REgression Training. Max Kuhn. https://topepo.github.io/caret/
• 《Mastering Spark with R》 Javier Luraschi, Kevin Kuo, Edgar Ruiz. https://therinspark.com

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/
• Machine Learning and Computational Statistics
• Machine Learning 卡内基梅隆大学课程
• Deep Learning
• Probabilistic Graphical Models 概率图模型-卡内基梅隆大学
• Learning Deep Learning 课程资料大集合
• Theoretical Machine Learning
• 机器学习算法观
• Introduction to Machine Learning 机器学习导论课程
• 2017-李宏毅深度学习
• 为什么我们需要机器学习 《机器学习中的神经网络》
• In-depth introduction to machine learning in 15 hours of expert videos
• 深入理解 RNNs & LSTM 网络学习资料
• TensorFlow 中文社区
• Deep Learning in Energy Production context (wind, gas and oil)
• Off the convex path
• The Neural Network Zoo
• Time Series Prediction With Deep Learning in Keras - Machine Learning Mastery
• Keras R 迁移学习
• Information Systems Research
• Neural Networks from Scratch, in R
• 李宏毅机器学习笔记
• Center for Machine Learning and Intelligent Systems | University of California, Irvine

统计软件：

1. 道家的小无相功

   • 高级 R 语言编程 Advanced R
   • R 语言编程风格指南 R Style Guide
   • 高效 R 语言编程 Efficient R programming
   • Google R 编程风格
   • R 炼狱
   • Rcpp for everyone

2. 开发 R 包

   • 开发 R 包 R packages https://r-pkgs.org/
   • R 扩展 Writing R Extensions
   • Git Pro Git 中文第二版
   • R 用户愉快地使用 Git/Github Happy Git and GitHub for the useR
   • The drake R Package User Manual

3. 文本挖掘

   • 初识命令行 The Unix Workbench
   • 数据科学与命令行 Data Science at the Command Line
   • 字符串处理 Handling Strings with R
   • 文本挖掘 Text Mining with R: A Tidy Approach
   • Linux 内核揭秘

4. 模型部署

   • Docker 入门 A Docker Tutorial for Beginners
   • Docker 从入门到实践 docker practice
   • R, Databases, and Docker

更多在线书籍合集 https://bookdown.org ，学习 R 语言的天花板不在编程软件而在统计。

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/