# Parse Command-Line Arguments `parseCommandArgs` allows for command line arguments to be passed into R. Arguments may be of the form of simple R objects. This makes running the same R code on multiple different options easy, and possible to run in parallel on a single machine or on a cluster. `parseCommandArgsDF` returns a dataframe with all of the values that were set when the code was executed. ```r parseCommandArgs(evaluate=TRUE) ``` ## Arguments - `evaluate`: If TRUE, then the command-line arguments are assigned to the current namespace, over-riding any default values that may have already been set in software. ## Details Returns a list of the command-line arguments that were set. See the example below for a good example of how to use this function, and how to run things in parallel with it. ## See Also `rbatch`, `mergeCsv`, `msplit` ## Examples ```r ## Not run: ## mainSim.R ## Put the following code in the file 'mainSim.R'. ## ## Try this out by running: ## R --vanilla < mainSim.R > mainSim.Rout1013 ## R --vanilla --args seed 1014 bbeta 0 < mainSim.R > mainSim.Rout1014 ## R --vanilla --args seed 1015 bbeta "c(10,20)" < mainSim.R > mainSim.Rout1015 library(batch) ## Set values of some parameters seed <- 1013 ## default value bbeta <- 5 ## default value, note 'beta' is an R function, so we can't use that ## Overwrite the values of 'seed' and 'bbeta', e.g., if they have been ## passed in from the command prompt. parseCommandArgs() ## Will disply the default values on the first run, ## but bbeta=1014 and bbeta=0 on the second run. print(seed) print(bbeta) ## ... your simualtion code ## Write out your results to a csv file write.csv(data.frame(seed=seed, bbeta=paste(bbeta,collapse="~")), paste("res",seed,".csv",sep=""), row.names=FALSE) ## R.miSniam ## End(Not run) ## Not run: ## run_mainSim_parallel.R ## Put the following code in 'run_mainSim_parallel.R' ## ## Selects a variety of parameter combinations to run ## mainSim.R in parallel on a cluster. ## ## First see the commands that would be run (to make sure they are correct) with ## R --vanilla --args RUN 0 < run_mainSim_parallel.R ## Then run the commands with ## R --vanilla < run_mainSim_parallel.R ## or ## R --vanilla --args RUN 1 < run_mainSim_parallel.R ## These will all default to run locally. ## To run on a mosix cluster, run with ## R --vanilla --args RUN 1 RBATCH mosix < run_mainSim_parallel.R ## And on a LSF cluster, run with ## R --vanilla --args RUN 1 RBATCH lsf < run_mainSim_parallel.R library(batch) parseCommandArgs() ## for overwriting default values; here, 'run' ## Choose a high enough seed for later for pasting the results together ## (1,...,9,10) sorts not the way you want, for example. seed <- 1000 for(i in 1:10) seed <- rbatch("mainSim.R", seed=seed, bbeta=i) ## Only for local (but it does not hurt to run in other situations, ## so suggested in all cases). ## This actually runs all the commands when run on the local system. rbatch.local.run() ## R.lellarap_miSniam_nur ## End(Not run) ## Not run: ## paste_mainSim_results.R ## Put the following code in paste_mainSim_results.R (or just ## type them in), and run ## R --vanilla < paste_mainSim_results.R ## ## Pastes all of the csv files created in 'run_mainSim_parallel' ## together. library(batch) mergeCsv() ## End(Not run) ``` ## References Thomas J. Hoffmann (2011). Passing in Command Line Arguments and Parallel Cluster/Multicore Batching in R with batch. Journal of Statistical Software, Code Snippets, 39(1), 1-11. URL [http://www.jstatsoft.org/v39/c01/](http://www.jstatsoft.org/v39/c01/).

parseCommandArgs function