Month: December 2008

While working with a large number of files for data processing, I used the following R commands for data processing. Given that everyone needs to split as well merge and append data – I am just giving some code on splitting data based on parameters , and appending data as well as merging data.

Splitting Data Based on a Parameter.

The following divides the data into subsets which contain either Male or anything else in different datasets.

Input and Subset

Note the read.table command assigns the dataset name X in R environment from the file reference (path denoted by ….)

x <- read.table(....)
rowIndx <- grep("Male", x$col)
write.table(x[rowIndx,], file="match")
write.table(x[-rowIndx,], file="nomatch")

Suppose we need to divide the dataset into multiple data sets.

X17 <- subset(X, REGION == 17)

This is prefered to the technique -

attach(X)
X17 = X[REGION == 17,]

Output

For putting the files back to the Windows environment you can use-

write.table(x,file="",row.names=TRUE,col.names=TRUE,sep=" ")

Append

Lets say you have a large number of data files ( say csv files )

that you need to append (assuming the files are in same structure)

after performing basic operations on them.

>setwd("C:\\Documents and Settings\\admin\\My Documents\\Data")

Note this changes the working folder to folder you want it to be,

note the double slashes which are needed to define the path

>list.files(path = ".", pattern = NULL, all.files = FALSE, full.names = FALSE,recursive = FALSE, ignore.case = FALSE)

The R output would be something like below

[1] "calk.csv"                                            "call.csv"                                           
[2]"calm.csv"                                            "caln.csv"                                           
[3]"calo.csv"                                            "calp.csv"                                           

For appending one file repeatedly (like ten times) you can use the command

file.append("A", rep("B", 10))

For Refcards on learning R , the best ones are –

http://cran.r-project.org/doc/contrib/Short–refcard.pdf

and

http://disinterested.googlepages.com/RQuickReference.pdf