R Statistical Programming Software Resources

November 14, 2017

 

Greetings,

 

Recently colleagues have been inquiring about my experiences in learning how to use the R statistical programming software. Given the interest in some of the resources that I found helpful in learning how to use it, and as promised to some, I have provided links to some of these resources below.

 

As most probably know, R can be downloaded and installed for free from the following website:

 

https://www.r-project.org/

 

However, as some may not be aware there is also another add on which some find makes the program a little bit more user friendly names R Studio. It is free as well, and some find it a little bit more familiar and less intimidating because it relies a little bit less on programming language than the R console itself. I have heard that Mac users have had problems with it crashing though. The link to the website is below:

 

https://www.rstudio.com/

 

While the base software can do several basic analytical procedures, there are also a ton of user written packages that can be added to do more advanced techniques (e.g. Multilevel Modeling, Propensity Score Matching, etc.) While most can be found on the Cran website below, others that may not be available there can be found at github.com. The links are both below:

 

https://cran.r-project.org/web/packages/

 

https://github.com/

 

Learning how to use R can take some time, and it is considered to have a very steep learning curve. However, there are some online resources that can be useful. First, the R-bloggers website has some tutorials that can help guide you through the process. As well, you can find information on specific issues on specific packages you may have by searching the blogs too.

 

https://www.r-bloggers.com/how-to-learn-r-2/

 

The Institute for Digital Research and Education at UCLA offers free online workshops throughout the year.

 

https://stats.idre.ucla.edu/other/mult-pkg/seminars/

 

Datacamp also allows you to access several introductory chapters from their online program for free, however they want you to subscribe for the more advanced tutorials but this can be a good way to get familiar with the software.

 

https://www.datacamp.com/

 

There are also two interactive resources that really help people in climbing the R learning curve.

 

tryR offers an introduction to the basics. It takes about an hour to get through and is completely interactive. It'll walk you through basics in R Syntax, different data formats, and running basic summary statistics. It's a great place to start, especially for those who have never touched R before.

 

http://tryr.codeschool.com/

 

Swirl is a package that you download in R (see here for instructions on how) that contains a wide array of tutorials on how to do different things in R. These tutorials are quite extensive and are all interactive. The point of swirl is to learn R in R. 

 

http://swirlstats.com/

 

One of my instructors from the ICPSR has a website with a lot of very useful material. These include powerpoints, handouts, and even programming code for various methods. He taught my advanced regression III course. His website has a lot of information on advanced methods in R such as Box-Tidewell transformations, model selection, non-parametric models (e.g. Lowess, Splines, etc.), flexible models (e.g. regression trees and my new favorite technique - MARS), regression discontinuity, finite mixture models, multiple imputation, heteroskedastic regression, and many more. Although this stuff is pretty advanced, he has also taught the introduction to R course and has introductory material as well.

 

https://quantoid.net/teachicpsr/

 

Many universities also have access to Lynda, and even if you don't have access you can sign up for a free 30 day trial. This website provides several hours of tutorials on R and a couple of links are below. The first link is a beginner level tutorial, and the second is intermediate. However, there are also many others as well.

 

https://www.lynda.com/R-tutorials/Up-Running-R/120612-2.html

 

https://www.lynda.com/R-tutorials/R-Statistics-Essential-Training/142447-2.html

 

The department of Statistics at Penn State also has some beginner and intermediate level online course content that is also helpful and can be found below.

 

https://onlinecourses.science.psu.edu/stat484/

 

https://onlinecourses.science.psu.edu/stat485/

 

Below are links to a few other resources I have come across that may be helpful as well.

 

Texts

 

Short Reference Card

 

An Introduction to R

 

The Undergraduate Guide to R

 

R: A Self Learn Tutorial

 

Online Tutorials

 

R Tutorial

 

Intro To R YouTube Channel

 

Quick R

 

I hope that all of my fellow scholars find these resources useful, and of course feel free to reach out to me if I can be of any help. While I think these are great online resources, I am happy to share any other information on the software and various techniques that may not be found online.

 

~Richard

 

 

 

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