└── README.md


/README.md:
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 1 | # Books for learning R
 2 | A collection of links to online books to learn R. Generally the ones that are written with the `bookdown` package.
 3 | 
 4 | **[Big Book of R](https://www.bigbookofr.com/)**
 5 | 
 6 | **Newbies start here**
 7 | * [R Programming for Data Science](https://bookdown.org/rdpeng/rprogdatascience/)
 8 | * [R for Data Science](http://r4ds.had.co.nz/)
 9 | * [Practical R for Mass Communication and Journalism](http://www.machlis.com/R4Journalists/)
10 | * [Data Science with R: A Resource Compendium](https://bookdown.org/martin_monkman/DataScienceResources_book/)
11 | * [Introduction to Data Science: Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/)
12 | * [Statistical Inference via Data Science - A ModernDive into R and the tidyverse](https://moderndive.com/index.html)
13 | * [Hands-On Programming with R](https://rstudio-education.github.io/hopr/)
14 | 
15 | **Good practice / Reproducibility**
16 | * [The Turing Way: a how to guide for reproducible data science](https://the-turing-way.netlify.com/)
17 | * [What They Forgot to Teach You About R](https://whattheyforgot.org/)
18 | * [Reproducible Analytical Pipelines Companion](https://ukgovdatascience.github.io/rap_companion/)
19 | * [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/)
20 | * [R Markdown Cookbook](https://bookdown.org/yihui/rmarkdown-cookbook/)
21 | * [Happy Git and GitHub for the useR](http://happygitwithr.com/)
22 | * [Efficient R Programming](https://bookdown.org/csgillespie/efficientR/)
23 | * [The tidyverse style guide](http://style.tidyverse.org/)
24 | * [R Packages](https://r-pkgs.org/)
25 | * [The drake R Package User Manual](https://ropenscilabs.github.io/drake-manual/)
26 | * [The targets R Package User Manual](https://wlandau.github.io/targets-manual/)
27 | * [Licensing R](https://thinkr-open.github.io/licensing-r/)
28 | * [bookdown: Authoring Books and Technical Documents with R Markdown](https://bookdown.org/yihui/bookdown/)
29 | * [GitHub Actions with R](https://ropenscilabs.github.io/actions_sandbox/)
30 | 
31 | **Visualisation**
32 | * [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/)
33 | * [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/)
34 | * [R Graphics Cookbook](https://r-graphics.org/)
35 | * [Interactive web-based data visualization with R, plotly, and shiny](https://plotly-r.com/)
36 | * [Mastering Shiny](https://mastering-shiny.org/)
37 | * [Engineering Production-Grade Shiny Apps](https://engineering-shiny.org/)
38 | 
39 | **Geospatial**
40 | * [Geocomputation with R](https://geocompr.robinlovelace.net/)
41 | * [Spatial Data Science](https://keen-swartz-3146c4.netlify.com/)
42 | 
43 | **Statistics / Machine Learning**
44 | * [An Introduction to Statistical and Data Sciences via R](https://moderndive.com/)
45 | * [Learning Statistics with R](https://learningstatisticswithr.com/book/)
46 | * [Feature Engineering and Selection: A Practical Approach for Predictive Models](http://www.feat.engineering/)
47 | * [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)
48 | * [Interpretable Machine Learning - A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/)
49 | * [The caret Package](http://topepo.github.io/caret/)
50 | * [Tidy Modeling with R](https://www.tmwr.org/)
51 | 
52 | **Other skills**
53 | * [Spreadsheet Munging Strategies](https://nacnudus.github.io/spreadsheet-munging-strategies/)
54 | * [Handling Strings with R](https://www.gastonsanchez.com/r4strings/)
55 | * [Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/)
56 | * [Mastering Spark with R](https://therinspark.com/index.html)
57 | * [21 Recipes for Mining Twitter Data with rtweet](https://rud.is/books/21-recipes/)
58 | * [Twitter for R Programmers](https://www.t4rstats.com/)
59 | * [Forecasting: Principles and Practice](https://otexts.com/fpp2/)
60 | * [Text Mining with R: A Tidy Approach](https://www.tidytextmining.com/)
61 | * [Supervised Machine Learning for Text Analysis in R](https://smltar.com/)
62 | * [JavaScript for R](https://book.javascript-for-r.com/)
63 | * [blogdown: Creating Websites with R Markdown](https://bookdown.org/yihui/blogdown/)
64 | * [Mastering Software Development in R](https://bookdown.org/rdpeng/RProgDA/)
65 | * [Advanced R](https://adv-r.hadley.nz/)
66 | 


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