The R way

As we’ve seen R is powerful, flexible, and wide-ranging.

It’s also sprawling, disorganized, and use-at-your-own risk.

Base packages, widely-used packages and packages by frequent R contributors are more trustworthy.

Some of the ‘features’ of R

• R is inconsistent in its naming conventions: Some functions are camelCase; others are dot.separated; others use_underscores. For programmers used to OOP, the use of dots is maddening.
• Multiple OOP paradigms and OOP is not as clean or full-featured as other languages
• R has multiple graphics packages that different functions use for default plot construction (base, grid, lattice, and ggplot2)
• R has multiple packages and functions to do the same analysis
• R obscures some of what happens behind the scenes in a way that can be confusing and lead to errors: type coercion/casting, recycling, etc.

Any other things that you found frustrating and don’t think are well-designed?

R tutorials, materials, and assistance

Some of this material was borrowed/modified from Jared Knowles’ bootcamp

CRAN has manuals and contributed learning material.

The SCF has a series of hands-on tutorials on related topics, some focused on topics in R, others on topics in UNIX/bash, and others on topics in managing workflows and reproducible research.

D-Lab holds workshops on R and other topics, as well as having drop-in office hours.

For grad students and researchers in statistics, economics and biostatistics, Chris Paciorek provides consulting via consult@stat.berkeley.edu and consult@econ.berkeley.edu and drop-in discussions in Evans 495.

For researchers around campus, Berkeley Research Computing offers consulting via brc@berkeley.edu on accessing and using cluster and cloud resources on campus and beyond, including R.

Various online tutorials/courses are offered by DataCamp, Software Carpentry, and Coursera. The DataCamp Introduction to R and Intermediate R courses are free and look pretty good to me. Finally the swirl package provides interactive training.

Some good books

R in general:

• Adler, Joseph; R in a Nutshell (available electronically through OskiCat: https://uclibs.org/PID/151634) (hands-on)
• Wickham, Hadley; Advanced R. https://adv-r.had.co.nz (good coverage of advanced topics in the R language)
• Chambers, John; Software for Data Analysis: Programming with R (available electronically through OskiCat: https://dx.doi.org/10.1007/978-0-387-75936-4) (More conceptual/theoretical)

Specific aspects of R:

• Xie, Yihui; Dynamic documents with R and knitr.
• Nolan, Deborah and and Duncan Temple Lang; XML and Web Technologies for Data Sciences with R (available electronically through OskiCat: https://dx.doi.org/10.1007/978-1-4614-7900-0)
• Murrell, Paul; R Graphics, 2nd ed. https://www.stat.auckland.ac.nz/~paul/RG2e
• Murrell, Paul; Introduction to Data Technologies. https://www.stat.auckland.ac.nz/~paul/ItDT/

Thanks!

• To all of the Statistics presenters and assistants.
• To the Statistics Department and the Statewide Database at Berkeley Law for sponsorship and logistical support.