R code to take JSTOR DfR wordcount CSV files and convert them to the bags of words txt files ready for input to MALLET

JSTOR2MALLET.r

# set working directory, ie. location of JSTOR DfR CSV
# files on the computer
setwd("C:\\some directory with JSTOR DfR CSV files")
 
# create a list of all the CSV files
myFiles <- list.files(pattern="*.csv|CSV")
 
# read in all the CSV files to an R data object
myData <-  lapply(myFiles, read.csv)
 
# assign file names to each dataframe in the list
names(myData) <- myFiles
 
# Here's the step where we turn the JSTOR DfR 'wordcount' into
# the 'bag of words' that's typically needed for topic modelling
# The R process is 'untable-ing' each CSV file into a
# list of data frames, one data frame per file
myUntabledData <- sapply(1:length(myData),
  function(x) {rep(myData[[x]]$WORDCOUNTS, times = myData[[x]]$WEIGHT)})
 
# And here's the step where we create individual txt files
# for each data frame (formerly a CSV file) that should be suitable for
# input into MALLET.
names(myUntabledData) <- myFiles
sapply(myFiles,
  function (x) write.table(myUntabledData[x], file=paste(x, "txt", sep="."),
                          quote = FALSE, row.names = FALSE, eol = " " ))
 
# Look in the working directory to find the txt files