Below is a list of some highly recommended books that either partially overlap with the content in this book or serve as a natural next step after you finish reading this book. All of these are available for free online.
- The R Cookbook (https://rc2e.com/) by Long & Teetor (2019) contains tons of examples of how to perform common tasks in R.
- R for Data Science (https://r4ds.had.co.nz/) by Wickham & Grolemund (2017) is similar in scope to Chapters 2-6 of this book, but with less focus on statistics and greater focus on tidyverse functions.
- Advanced R (http://adv-r.had.co.nz/) by Wickham (2019) deals with advanced R topics, delving further into object-oriented programming, functions, and increasing the performance of your code.
- R Packages (https://r-pkgs.org/) by Wickham and Bryan describes how to create your own R packages.
- ggplot2: Elegant Graphics for Data Analysis (https://ggplot2-book.org/) by Wickham, Navarro & Lin Pedersen is an in-depth treatise of
- Fundamentals of Data Visualization (https://clauswilke.com/dataviz/) by Wilke (2019) is a software-agnostic text on data visualisation, with tons of useful advice.
- R Markdown: the definitive guide (https://bookdown.org/yihui/rmarkdown/) by Xie et al. (2018) describes how to use R Markdown for reports, presentations, dashboards, and more.
- An Introduction to Statistical Learning with Applications in R (https://www.statlearning.com/) by James et al. (2013) provides an introduction to methods for regression and classification, with examples in R (but not using
- Hands-On Machine Learning with R (https://bradleyboehmke.github.io/HOML/) by Boehmke & Greenwell (2019) covers a large number of machine learning methods.
- Forecasting: principles and practice (https://otexts.com/fpp2/) by Hyndman & Athanasopoulos, G. (2018) deals with forecasting and time series models in R.
- Deep Learning with R (https://livebook.manning.com/book/deep-learning-with-r/) by Chollet & Allaire (2018) delves into neural networks and deep learning, including computer vision and generative models.
- A number of reference cards and cheat sheets can be found online. I like the one at https://cran.r-project.org/doc/contrib/Short-refcard.pdf
- R-bloggers (https://www.r-bloggers.com/) collects blog posts related to R. A great place to discover new tricks and see how others are using R.
- RSeek (http://rseek.org/) provides a custom Google search with the aim of only returning pages related to R.
- Stack Overflow (https://stackoverflow.com/questions/tagged/r) and its sister-site Cross Validated (https://stats.stackexchange.com/) are questions-and-answers sites. They are great places for asking questions, and in addition, they already contain a ton of useful information about all things R-related. The RStudio Community (https://community.rstudio.com/) is another good option.
- The R Journal (https://journal.r-project.org/) is an open-access peer-reviewed journal containing papers on R, mainly describing new add-on packages and their functionality.
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