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<!-- `r if (knitr::is_html_output()) '# References {-}'` -->
# Bibliography {-#references}
## Further reading {-#references1}
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 `ggplot2`.
* _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 `caret`).
* _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.
## Online resources {-#references2}
* 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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