[R] Use R in a pipeline as a filter
Hi, how can I use R in a pipline like this $ ./generate-data | R --script-file=Script.R | ./further-analyse-data > result.dat Assume a column based output of ./generate-data, e.g. something like: 1 1 1 2 4 8 3 9 27 4 16 64 The R commands that process the data should come from Script.R and should print to stdout (Script.R could for example calculate the square of every entry or calculate the mean of the columns, ...) The output should be printed to stdout, such that further-analyse-data can use the output. Can some R expert code that for me please? I would be very happy. I am also happy about information how to do that myself although I dont think I know enough to do that myself. Thank you for your consideration, Micha -- GMX FreeMail: 1 GB Postfach, 5 E-Mail-Adressen, 10 Free SMS. Alle Infos und kostenlose Anmeldung: http://www.gmx.net/de/go/freemail __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] automated data processing
I have many files (0.4.dat, 0.5.dat, ...) of which I would like to calculate
mean value and variance and save the output in a new file where each line
shouldlook like: "0.4 mean(0.4.dat) var(0.4.dat)" and so on. Right now I got a
a simple script that makes me unhappy:
1. I run it by "R --no-save < script.r > out.dat" unfortunately out.dat has all
the original commands in it and a "[1]" infront of every output
2. I would love to have a variable running through 0.4, 0.5, ... naming the
datafile to process and the first column in the output.
My script looks like:
data <- read.table("0.4.dat"); E <- data$V1[1000:length(data$V1)];
c(0.4, mean(E), var(E));
data <- read.table("0.5.dat"); E <- data$V1[1000:length(data$V1)];
c(0.5, mean(E), var(E));
And that would be its output:
#[1] 0.400 -1134.402 5700.966
#> data <- read.table("0.5.dat"); E <- data$V1[1000:length(data$V1)];
#> c(0.5, mean(E), var(E));
#[1] 0.500 -1787.232 2973.692
Thanks
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