R Programming

Handouts and Online Books

• S Poetry and R Inferno by Patrick Burns by Patrick J. Burns; a large free book on more advanced S programming in PDF format. A glossary of S and statistical terms is also available, as other links to other sites
• Oxford University's R Programming Course
• Statistical Computing Course
• R Clinic

Common R Programming Techniques That Can Be Improved

Attaching Data Frames

Attaching a data frame makes it easy to reference its variables, but if you have two data frames attached things can get confusing, and users frequently forget to detach an attached object. Better to use data=, (when calling functions that use the statistical modeling language), with or within. within is an alternative to transform and upData. Unlike with, within allows you to change or add variables in the referenced data frame if it was a storable object (e.g., not subscripted). xyplot(y ~ x | g, data=mine) with(mine, { plot(x, y) plot(x, z) }) within(mine, { y <- 2*y x <- x-1 new <- x + y }

Logical Operations

R can subscript using integer vectors consisting of those subscripts meeting a certain condition, or using logical TRUE/FALSE vectors whose lengths are the lengths of the original objects tested. The latter usually leads to more readable and reliable code. Instead of male <- which(sex=='male') al <- which(state=='AL') mean(x[intersect(male,al)]) # mean of male Alabamians use mean(x[sex=='male' & state=='AL']) #or: maleal <- sex=='male' & state=='AL' mean(x[maleal])

Repetitive Statistical Analyses

Instead of spearman2(y ~ x1 + x2, data=mydata, subset=sex=='male') spearman2(y ~ x1 + x2, data=mydata, subset=sex=='female') consider for(sx in levels(mydata$sex)) { cat('\n------------------------------\n', sx, '\n\n') s <- spearman2(y ~ x1 + x2, data=mydata, subset=sex==sx) print(s) }