97_where-to-learn-more.Rmd

Where to Learn More {-}
=======================

This reader provides an introduction to the basics of R, but there's lots more
to learn. Fortunately, many R and data science learning resources are available
for free online or through the library.

Here are a few readers and notes created by DataLab staff:

* [README, Write Me!][readme], our workshop about how to organize and document
  computing projects.
* [Principles of Data Visualization][data-viz], our workshop about how to
  design clear and effective data visualizations.
* [Adventures in Data Science][adventures], our course introducing humanities
  undergraduates to data science techniques.
* [My personal teaching notes][notes] from several years of teaching
  statistical computing.

[readme]: https://ucdavisdatalab.github.io/workshop_how-to-data-documentation/
[data-viz]: https://ucdavisdatalab.github.io/workshop_data_viz_principles/
[adventures]: https://ucdavisdatalab.github.io/adventures_in_data_science/
[notes]: https://github.com/nick-ulle/teaching-notes

And here are a few books created by others that we've found useful:

* [R for Data Science][r4ds] by Wickham & Grolemund. An introduction to using R
  for data science, but with a very heavy focus on Tidyverse packages.
* [The Art of R Programming][art-of-r] by Matloff. A general reference on R
  programming.
* [Advanced R][adv-r] by Wickham. A description of how R works at a deeper
  level, with many examples of R features that are important for
  package/software development.
* [The R Inferno][r-inferno] by Burns. A discussion of the most difficult and
  confusing parts of R.

[r4ds]: http://r4ds.had.co.nz/
[art-of-r]: https://ebookcentral.proquest.com/lib/ucdavis/detail.action?docID=1137514
[adv-r]: https://adv-r.hadley.nz/
[r-inferno]: http://www.burns-stat.com/documents/books/the-r-inferno/

Finally, here are some websites popular in the R community:

* [R Graph Gallery](http://www.r-graph-gallery.com/). Examples of graphs you
  can make in R, with code.
* [RStudio Cheat Sheets](https://www.rstudio.com/resources/cheatsheets/). Cheat
  sheets for a variety of R tools and packages.
* [R Weekly](https://rweekly.org/). Weekly updates about what's happening in
  the R community.
* [R-bloggers](https://www.r-bloggers.com/). An aggregator for blog posts
  about R.