R is available on the cluster. R can also be installed on your computer for free by visiting the R-project main page.

For more information about R, you might want to use the R manual or RSeek, the search engine for R related resources.

You can run R interactively on the cluster with R. You can also run a .R file in batch mode with

R CMD BATCH Rfile.R<code> where Rfile.R is your R file. 
To run your R command in the background, see [[cluster:managing_jobs|Managing Jobs]]

===== Introduction to R =====
The following section comes initially from an introductory talk on R given by Paul Bailey in February 2011.  The data used in the examples is located at [[http://terpconnect.umd.edu/~pdbailey/R/MDemp.csv|this link]].

===== R Background =====
  * Based on Bell Labs S
  * Open source software
    * Large group of contributors
    * Most R code is written in R
    * Computationally intensive code written in FORTRAN or C
  * Datasets, matrices are native types
  * Easy, customizable graphics

==== R Pros ====
  * Free
  * Easy to get a sense of what is going on with data
  * Excellent at simulation
  * Interfaces with lots of other software (i.e. WINBUGS, SQL)

==== R Cons ====
  * Uses RAM to store data
  * Support mainly via listserves
  * Difficult to get started

==== Read in Data ====
  * Some type specific methods, and a general method <code>dat <- read.csv("MDemp.csv")

and general methods

dat <- read.table("MDemp.csv",sep=",")

• You can use the following command to get the help page for a command:

  ?

• To search for text in help text use the following command:

  ??

• Getting summaries is easy: summary(dat)
• You can also focus on one variable

summary(dat$num_child)
table(dat$num_child)

• When you reference something with [condition,] you can select rows:

dat.lf <- dat[dat$emp %in% c("emp","unemp"),]
dat.hs <- dat.lf[dat.lf$educ==39,]

• The lm function fits linear models with a formula:

lm1 <- lm(weekly_earn ~ age + year,data=dat)
summary(lm1)

• You can also treat a variable as a factor:

dat$yearf <- as.factor(dat$year)
lm2 <- lm(weekly_earn ~ age + yearf,data=dat)
summary(lm2)

• And change constraints:

contrasts(dat$yearf) <- "contr.sum"
lm3 <- lm(weekly_earn ~ age + yearf,data=dat)
summary(lm3)

• Allows you to create summary statistics for groups
• First argument is what you want to summarize
• Second argument is what you want to group by
• Their argument is what to do to the groups

agg.hs <- aggregate(dat.hs$emps,by=list(dat.lf$yq),mean)

• Results names a little odd.

• Groups two datasets by shared columns

merged <- merge(data.a,data.b)

* Lots of options for this one

Some basic info can be found at the High Performance Computing CRAN view. You can use the “parallel” package (which merges both “snow” and “multicore”).

You can also use Rmpi and npRmpi packages. You have your choice of MPI2 libraries (both OpenMPI and MPICH2). You will have to install the packages in your userspace (requiring compilation).

A good intro guide is npRmpi: A package for parallel distributed kernel estimation in R.

• merge merges datasets
• glm fits limited dependent variable models.
• optim minimizes / finds zeros
• Contributed econometrics packages