aggregating output from multiple input files in R - r

Question

Right now I have the R code below. It reads in data that looks like this:
track_id day hour month year rate gate_id pres_inter vmax_inter
9 10 0 7 1 9.6451E-06 2 97809 23.545
9 10 0 7 1 9.6451E-06 17 100170 13.843
10 3 6 7 1 9.6451E-06 2 96662 31.568
13 22 12 8 1 9.6451E-06 1 94449 48.466
13 22 12 8 1 9.6451E-06 17 96749 30.55
16 13 0 8 1 9.6451E-06 4 98702 19.205
16 13 0 8 1 9.6451E-06 16 98585 18.143
19 27 6 9 1 9.6451E-06 9 98838 20.053
19 27 6 9 1 9.6451E-06 17 99221 17.677
30 13 12 6 2 9.6451E-06 2 97876 27.687
30 13 12 6 2 9.6451E-06 16 99842 18.163
32 20 18 6 2 9.6451E-06 1 99307 17.527
##################################################################
# Input / Output variables
##################################################################
for (N in (59:96)){
if (N < 10){
# TrackID <- "000$N"
TrackID <- paste("000",N, sep="")
}
else{
# TrackID <- "00$N"
TrackID <- paste("00",N, sep="")
}
print(TrackID)
# For 2010_08_24 trackset
# fname_in <- paste('input/2010_08_24/intersections_track_calibrated_jma_from1951_',TrackID,'.csv', sep="")
# fname_out <- paste('output/2010_08_24/tracks_crossing_regional_polygon_',TrackID,'.csv', sep="")
# For 2012_05_01 trackset
fname_in <- paste('input/2012_05_01/intersections_track_param_',TrackID,'.csv', sep="")
fname_out <- paste('output/2012_05_01/tracks_crossing_regional_polygon_',TrackID,'.csv', sep="")
fname_out2 <- paste('output/2012_05_01/GateID_',TrackID,'.csv', sep="")
#######################################################################
# we read the gate crossing track date
cat('reading the crosstat output file', fname_in, '\n')
header <- read.table(fname_in, nrows=1)
track <- read.table(fname_in, sep=',', skip=1)
colnames(track) <- c("ID", "day", "month", "year", "hour", "rate", "gate_id", "pres_inter", "vmax_inter")
# track_id=track[,1]
# pres_inter=track[,15]
# Function to select maximum surge by stormID
ByTrack <- ddply(track, "ID", function(x) x[which.max(x$vmax_inter),])
ByGate <- count(track, vars="gate_id")
# Write the output file with a single record per storm
cat('Writing the full output file', fname_out, '\n')
write.table(ByTrack, fname_out, col.names=T, row.names=F, sep = ',')
# Write the output file with a single record per storm
cat('Writing the full output file', fname_out2, '\n')
write.table(ByGate, fname_out2, col.names=T, row.names=F, sep = ',')
}
My output for the final section of code is a file the groups by GateID and outputs the frequency of occurrence. It looks like this:
gate_id freq
1 935
2 2096
3 1363
4 963
5 167
6 17
7 43
8 62
9 208
10 267
11 64
12 162
13 178
14 632
15 807
16 2003
17 838
18 293
The thing is that I output a file that looks just like this for 96 different input files. Instead of outputting 96 separate files, I'd like to calculate these aggregations per input file, and then sum the frequency across all 96 inputs and print out one SINGLE output file. Can anyone help?
Thanks,
K

Answer 1

You are going to need to do something like the function below. This would grab all the .csv files in one directory, so that directory would have to have only the files you want to analyze in it.
myFun <- function(out.file = "mydata") {
files <- list.files(pattern = "\\.(csv|CSV)$")
# Use this next line if you are going use the file name as a variable/output etc
files.noext <- substr(basename(files), 1, nchar(basename(files)) - 4)
for (i in 1:length(files)) {
temp <- read.csv(files[i], header = FALSE)
# YOUR CODE HERE
# Use the code you have already written but operate on files[i] or temp
# Save the important stuff into one data frame that grows
# Think carefully ahead of time what structure makes the most sense
}
datafile <- paste(out.file, ".csv", sep = "")
write.csv(yourDataFrame, file = datafile)
}