R is a program, a computing language, and an accumulation of statistical idioms.  It can be difficult to get started.

It seems that users face a couple of separate challenges. First, although R is free and easy to install, it takes most users some time to become comfortable with it. In fact, after a brief time, most users conclude they need to find a better "integrated development environment" in which to prepare R code, such as Emacs or R-Studio. Second, most R users have already had a statistics course in which they used some other software, such as SPSS, SAS, or Stata, and the users need to translate ideas from one program to another. Third, while other statistical packages allow users to prepare subroutines,  R basically requires that they do so. Beyond the most elementary chores, using R is uncomfortably close to writing a program for many newcomers.

Background Reading

Introductions to R from the R Core Team Members

1. Venables, W.N., Smith D.M.R, and the R Core Team, An Introduction to R. A copy of this book is provided with R, but it is also available online in HTML and PDF.

2. Gentleman, R. (2009). "Chapter 2, R Language Fundamentals", R programming for Bioinformatics. Boca Raton: CRC Press, pp 5-66

3. Chambers, John M. "Chapter 2, Using R", "Chapter 3, Programming with R: The Basics". (2008). Software for Data Analysis: Berlin: Springer, pp. 11-78

Other helpful introductions.

1. Long, Jeffrey D. (2011). "2, Brief Introduction to R", Longitudinal Data Analysis for the Behavioral Sciences Using R. Thousand Oaks, Calif: SAGE Publications, Inc., pp. 33-62

2. Schumacker, Randall E. (2014). "1, R Basics", Learning Statistics Using R (Vol. 1). Beaverton, United States: Ringgold Inc, pp 2-33

3. Fox, John, & Weisberg, Sanford. (2010). "2, Reading and Manipulating Data", An R Companion to Applied Regression (Second Edition edition). Thousand Oaks, Calif: SAGE Publications, Inc, pp. 43-105

4. Paradis, Emmanuel. (2005). R for Beginners. R Project. 30 Dec. 2015. Retrieved from https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf.

5. Baron, Jonathan. "R reference card" https://cran.r-project.org/doc/contrib/refcard.pdf.

6. Peng, Roger, D. (2014). R Programming for Data Science. Leanpub. Retrieved from https://leanpub.com/rprogramming

7. Peng, Roger, D. (2015). Exploratory Data Analysis with R. Leanpub. Retrieved from https://leanpub.com/exdata

8.  Eubank, Randall L., & Kupresanin, Ana. (2012). Statistical Computing in C++ and R. Boca Raton, FL: CRC Press.

How Can CRMDA Help?

1. Workshops

The Summer Statistical Institute (http://crmda.ku.edu/statscamp) includes sessions on R

2. CRMDA Guides

The full list of CRMDA guides is available at guides-index and the ones we've tagged with the R software keyword should be found at http://crmda.ku.edu/software/r.

Some of the highlights are

  1.  Guide 20 | Introduction to R: A Syntax Overview Basic Syntax
  2.  Guide 21 | SEM with Lavaan: A guide introducing the R package Lavaan for structural equation modeling with the accompanying data file
  3.  Guide 25ANOVA and Regression in R: An introductory guide to general linear model (GLM) analyses using Rdemonstrating how to perform ANOVA and Regression in R.
  4.  Guide 40Data Table Guide: Basic Usage and Advanced Topics
  5.  Guide 38Dates and Time Information in R

3. Open Consulting

Many of the CRMDA staffers are experts in R and they are able to review your input commands and help to interpret the output. This is a suitable request for the "walk in" Open Consulting period. 


It may be that your project requires some functions with which we are unfamiliar, especially if they are not provided with the base distribution of R. We can sometimes help to decipher problems, but it helps if you bring whatever documentation you have about the functions you are trying to use.


4. Project Consulting

Quite a few of the researchers we meet are convinced that they want to use R, but they don't have enough experience with programming to get the project finished on their own. The CRMDA offers hourly fee-for-service consulting.

R Packages from CRMDA

  • Johnson, Paul E. (2015) rockchalk: Regression Presentation and Interpretation (on CRAN, testing on KRAN). Development on GitHub »
  • Johnson, Paul E. (2015) portableParallelSeeds (testing on KRAN)
  • Pornprasertmanit, S., Miller, P., & Schoemann, A. M. (2012). simsem: SIMulated Structural Equation Modeling. (on CRAN, testing on KRAN)
  • Pornprasertmanit, S., Miller, P., Schoemann, A. M., & Rosseel, Y. (2012). SEMtools. R package (on CRAN, testing on KRAN)
For testing development, we upload packages to a separate CRAN mirror for community testing. This is a valid repository for installing packages ».


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