6 Resources

We’ve only scratched the surface of what is possible using R, but what we’ve tried to cover in these materials are some practical steps to approaching data analysis following these ideas:

"There are 5 core activities of data analysis:

1. Stating and refining the question
2. Exploring the data
3. Building formal statistical models
4. Interpreting the results
5. Communicating the results"

These are the epicycles of data analysis.

"More specifically, for each of the five core activities, it is critical that you engage in the following steps:

1. Setting Expectations,
2. Collecting information (data), comparing the data to your expectations, and if the expectations don’t match,
3. Revising your expectations or fixing the data so your data and your expectations match."

Epicycle of Analysis, The Art of Data Science, Peng & Matsui

In terms of these materials and R skills, this translates to:

1. Importing data.
2. Tidying and cleaning the data.
3. Transforming the data.
4. Plotting the data.
5. Exporting data and plots.

Those processes will enable you (indeed force you) to better state and refine your questions, putting you into a good postion to consider whether you need to revise your experimental/data collection plan, and give you the foundation to go onto learning how to build statistical or other models R, or indeed in other languages. And to these things in reproducible ways.

6.1 The R community

I mostly learn R through the online community, initially through the Simply Statistics blog which introduced me to R. Here’s an entirely arbitrary collection of community people and links I discovered:

6.1.1 Twitter

• Mara Averick
• Hadley Wickham
• Charlotte Wickham
• Jesse Mostipak
• Jenny Bryan
• Alison Hill
• Allison Horst
• David Robinson
• Malcolm Barrett
• Nicholas Tierney
• Julia Silge
• Colin Fay
• Greg Wilson
• Thomas Lin Pedersen
• Garrett Grolemund
• Yihui Xie

6.1.2 R4DS and rOpenSci and The Carpentries

These are useful places to go for advice, materials, and packages

• R for data science and R for data science twitter
• The Carpentries
• rOpenSci and rOpenSci twitter

6.2 Online books and tutorials

• Swirl
• Introduction to Data Science, Rafael A. Irizarry
• Hands-On Programming with R, Garrett Grolemund
• The Art of Data Science,Roger D. Peng and Elizabeth Matsui
• How to name files, Jenny Bryan
• Effective presentation design, Melinda Seckington
• Fundamentals of Data Visualization, Claus O. Wilke

6.2.1 A list of R conferences and meetings

Find R people in real life wherever you are: A list of R conferences and meetings

6.3 References

Ihaka, Ross, and Robert Gentleman. 1996. “R: A Language for Data Analysis and Graphics.” Journal of Computational and Graphical Statistics 5 (3): 299–314.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

RStudio Team. 2018. RStudio: Integrated Development Environment for R. Boston, MA: RStudio, Inc. http://www.rstudio.com/.

Wickham, Hadley. 2019. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.

Wilson, Greg, ed. 2018. Teaching Tech Together. 2018,, Http://Teachtogether.tech/. Lulu.com. http://teachtogether.tech/.

Xie, Yihui. 2019. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.