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--- cluster:r [2018/10/01 17:48] – [Parallel] mcloughlin
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-====== R ======
-R is available on the cluster. R can also be installed on your computer for free by visiting the [[http://r-project.org/|R-project main page]].
-For more information about R, you might want to use the [[http://cran.r-project.org/doc/manuals/R-intro.html|R manual]] or [[http://rseek.org|RSeek]], the search engine for R related resources.
-===== Running R on the Cluster =====
-You can run R interactively on the cluster with:<code>R</code>
-You can also run a .R file in batch mode with<code>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")</code> and general methods <code>dat <- read.table("MDemp.csv",sep=",")</code>
-==== Getting Help ====
-  * You can use the following command to get the help page for a command: <code>?</code>
-  * To search for text in help text use the following command: <code>??</code>
-====Summary====
-  * Getting summaries is easy: <code>summary(dat)</code>
-  * You can also focus on one variable
-<code>
-summary(dat$num_child)
-table(dat$num_child)
-</code>
-====Subset Data====
-  * When you reference something with <code>[condition,]</code> you can select rows:
-<code>
-dat.lf <- dat[dat$emp %in% c("emp","unemp"),]
-dat.hs <- dat.lf[dat.lf$educ==39,]
-</code>
-==== Linear Models ====
-  * The **lm** function fits linear models with a formula:
-<code>
-lm1 <- lm(weekly_earn ~ age + year,data=dat)
-summary(lm1)
-</code>
-  * You can also treat a variable as a factor:
-<code>
-dat$yearf <- as.factor(dat$year)
-lm2 <- lm(weekly_earn ~ age + yearf,data=dat)
-summary(lm2)
-</code>
-  * And change constraints:
-<code>
-contrasts(dat$yearf) <- "contr.sum"
-lm3 <- lm(weekly_earn ~ age + yearf,data=dat)
-summary(lm3)
-</code>
-==== Aggregate ====
-  * Allows you to create summary statistics for groups
-  * First argument is what you want to summarize
-  * Second argument is what you want to group by
-  * Their argument is what to do to the groups
-<code>
-agg.hs <- aggregate(dat.hs$emps,by=list(dat.lf$yq),mean)
-</code>
-  * Results names a little odd.
-  *
-==== Merge ====
-  * Groups two datasets by shared columns
-<code>
-merged <- merge(data.a,data.b)
-</code>
-* Lots of options for this one
-==== Parallel ====
-Some basic info can be found at the [[http://cran.r-project.org/web/views/HighPerformanceComputing.html|High Performance Computing CRAN view]]. You can use the "parallel" package (which merges both "snow" and "multicore").
-You can also use [[http://cran.r-project.org/web/packages/Rmpi/index.html|Rmpi]] and [[http://cran.r-project.org/web/packages/npRmpi/index.html|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).
-^^OpenMPI^MPICH2^
-|Before anything (installation or usage)|>module load openmpi-x86_64|>module load mpich2-x86_64|
-|Installation|	R> install.packages("<package>", configure.args="--with-Rmpi-include=/usr/lib64/openmpi/1.4-gcc/include --with-Rmpi-libpath=/usr/lib64/openmpi/1.4-gcc/lib --with-Rmpi-type=OPENMPI")|R> install.packages("<package>", configure.args="-with-Rmpi-include=/usr/include/mpich2-x86_64 --with-Rmpi-libpath=/usr/lib64/mpich2/lib --with-Rmpi-type=MPICH")|
-A good intro guide is [[http://onlinelibrary.wiley.com/doi/10.1002/jae.1221/pdf|npRmpi: A package for parallel distributed kernel estimation in R]].
-==== Other functions ====
-  * Merge datasets with: <code>merge</code>
-  * Fit limited dependent variable models with <code>glm</code>
-  * Minimizes / finds zeros with <code>optim</code>
-  * [http://cran.r-project.org/web/views/Econometrics.html contributed econometrics packages]