Clean a specific column

clean.r

# function to substitute matching string and everything after it with nothing
# note leading space if that applies to your dataset
removeCrap <- function(crapThing) {
    gsub(" (0|[1-9][0-9]*)\ ?(GB).*$", "", crapThing)
}

# load data from csv into a dataframe
d <- read.csv("input.csv", sep = ",")

# display a few rows of the dataframe 'd'
# note name of column we want to change in this case is Col_1
head(d)

# apply the removeCrap() function to each row of the correct column
# by referencing dataframe 'd', column 'Col_1' (known together as d$Col_1)
# and output to a separate 'factor' called cleanCol
cleanCol <- sapply(d$Col_1, FUN = removeCrap)

# bind the original data and the clean column
cleanData <- cbind(cleanCol, d)

# output the data to a new csv file, not using any row numbers
# (cos Excel has those already)
write.csv(cleanData, "output.csv", row.names = F)

# APPENDIX
# creating fake data and writing to a file to read in above this
# col1 <- c("some nice text 9GB blah blah", "some other text 10 GB nonsense with a space")
# col2 <- c(12, 24)
# testData <- data.frame(col1, col2)
# colnames(testData) <- c("Col_1", "Col_2")
# write.csv(testData, "input.csv", row.names = F)