Dear R users,?I have fifty two (52) text files with the same dimensions (ie 31
by 13). Three sample of such data files are attached. I want to compute the
rowMeans for each separate file for;(i) all the months
(ii) For January and February
(iii) For March, April and May
(iv) For June, July and august?
(v) For October, November and December
(vi) Plot the single mass curve for each file and season ie. plot(Year,
cumsum(rowMeans([])))
(vii) Plot Time series graphs for each file and per each season.
The code I was trying to use is given below, and I investigated it and find that
it does just for one only one file.How can I loop through all files?
I kindly need your help.
Thanks in advance!!
rm(list = ls())
setwd("/run/media/nwp-tma/+255767090047/analysis/R/R_sessions/R_sessions_prec/Rain_stn_data")#
Import text filesprec_files <- list.files(pattern="*.txt")# Reading
my files
prec_files <- list.files(pattern = ".txt")
for (i in 1:length(prec_files)){ ?
? prec_data = read.delim(prec_files[i], sep="\t",
header = TRUE)? }
#prec_data <- as.numeric(prec_data[, 2:13])
# All years assignments
all_yr <- prec_data[, 2:13]
# Season assignment
jf <- prec_data[, 2:3]
mam <- prec_data[, 4:6]
jja <- prec_data[, 7:9]
ond <- prec_data[, 11:13]
jf_means <- apply(jf, 1, mean)
mam_means <- apply(mam, 1, mean)
jja_means <- apply(jja, 1, mean)
ond_means <-apply(ond, 1, mean)
yr_mean <- apply(all_yr, 1, mean)
?_____________
Peter? E. Tuju
Dar es Salaam
T A N Z A N I A
----------------------
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Hi Peter,
I think your problem is that your calculations occur after the end of
the loop. What you probably want is something like this:
yr_means<-mam_means<-jf_means<-
jja_means<-ond_means<-rep(NA,length(prec_files))
for (i in 1:length(prec_files)) {
prec_data<-read.delim(prec_files[i], sep="\t",header = TRUE)
# All years assignments
all_yr <- prec_data[, 2:13]
# Season assignment
jf <- prec_data[, 2:3]
mam <- prec_data[, 4:6]
jja <- prec_data[, 7:9]
ond <- prec_data[, 11:13]
jf_means[i] <- apply(jf, 1, mean)
mam_means[i] <- apply(mam, 1, mean)
jja_means[i] <- apply(jja, 1, mean)
ond_means[i] <-apply(ond, 1, mean)
yr_means[i] <- apply(all_yr, 1, mean)
}
Jim
On Mon, Aug 24, 2015 at 3:01 AM, Peter Tuju <peterenos at ymail.com>
wrote:> Dear R users, I have fifty two (52) text files with the same dimensions (ie
31 by 13). Three sample of such data files are attached. I want to compute the
rowMeans for each separate file for;(i) all the months
> (ii) For January and February
> (iii) For March, April and May
> (iv) For June, July and august
> (v) For October, November and December
> (vi) Plot the single mass curve for each file and season ie. plot(Year,
cumsum(rowMeans([])))
> (vii) Plot Time series graphs for each file and per each season.
> The code I was trying to use is given below, and I investigated it and find
that it does just for one only one file.How can I loop through all files?
> I kindly need your help.
>
> Thanks in advance!!
>
>
> rm(list = ls())
>
setwd("/run/media/nwp-tma/+255767090047/analysis/R/R_sessions/R_sessions_prec/Rain_stn_data")#
Import text filesprec_files <- list.files(pattern="*.txt")# Reading
my files
> prec_files <- list.files(pattern = ".txt")
> for (i in 1:length(prec_files)){
> prec_data = read.delim(prec_files[i], sep="\t",
> header = TRUE) }
> #prec_data <- as.numeric(prec_data[, 2:13])
> # All years assignments
> all_yr <- prec_data[, 2:13]
> # Season assignment
> jf <- prec_data[, 2:3]
> mam <- prec_data[, 4:6]
> jja <- prec_data[, 7:9]
> ond <- prec_data[, 11:13]
> jf_means <- apply(jf, 1, mean)
> mam_means <- apply(mam, 1, mean)
> jja_means <- apply(jja, 1, mean)
> ond_means <-apply(ond, 1, mean)
> yr_mean <- apply(all_yr, 1, mean)
>
>
> _____________
> Peter E. Tuju
> Dar es Salaam
> T A N Z A N I A
> ----------------------
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
Note that, in general, rowMeans(x) is preferable to apply(x,1,mean) Consult ?rowMeans for details. Cheers, Bert Bert Gunter "Data is not information. Information is not knowledge. And knowledge is certainly not wisdom." -- Clifford Stoll On Mon, Aug 24, 2015 at 2:20 AM, Jim Lemon <drjimlemon at gmail.com> wrote:> Hi Peter, > I think your problem is that your calculations occur after the end of > the loop. What you probably want is something like this: > > yr_means<-mam_means<-jf_means<- > jja_means<-ond_means<-rep(NA,length(prec_files)) > for (i in 1:length(prec_files)) { > prec_data<-read.delim(prec_files[i], sep="\t",header = TRUE) > # All years assignments > all_yr <- prec_data[, 2:13] > # Season assignment > jf <- prec_data[, 2:3] > mam <- prec_data[, 4:6] > jja <- prec_data[, 7:9] > ond <- prec_data[, 11:13] > jf_means[i] <- apply(jf, 1, mean) > mam_means[i] <- apply(mam, 1, mean) > jja_means[i] <- apply(jja, 1, mean) > ond_means[i] <-apply(ond, 1, mean) > yr_means[i] <- apply(all_yr, 1, mean) > } > > Jim > > On Mon, Aug 24, 2015 at 3:01 AM, Peter Tuju <peterenos at ymail.com> wrote: >> Dear R users, I have fifty two (52) text files with the same dimensions (ie 31 by 13). Three sample of such data files are attached. I want to compute the rowMeans for each separate file for;(i) all the months >> (ii) For January and February >> (iii) For March, April and May >> (iv) For June, July and august >> (v) For October, November and December >> (vi) Plot the single mass curve for each file and season ie. plot(Year, cumsum(rowMeans([]))) >> (vii) Plot Time series graphs for each file and per each season. >> The code I was trying to use is given below, and I investigated it and find that it does just for one only one file.How can I loop through all files? >> I kindly need your help. >> >> Thanks in advance!! >> >> >> rm(list = ls()) >> setwd("/run/media/nwp-tma/+255767090047/analysis/R/R_sessions/R_sessions_prec/Rain_stn_data")# Import text filesprec_files <- list.files(pattern="*.txt")# Reading my files >> prec_files <- list.files(pattern = ".txt") >> for (i in 1:length(prec_files)){ >> prec_data = read.delim(prec_files[i], sep="\t", >> header = TRUE) } >> #prec_data <- as.numeric(prec_data[, 2:13]) >> # All years assignments >> all_yr <- prec_data[, 2:13] >> # Season assignment >> jf <- prec_data[, 2:3] >> mam <- prec_data[, 4:6] >> jja <- prec_data[, 7:9] >> ond <- prec_data[, 11:13] >> jf_means <- apply(jf, 1, mean) >> mam_means <- apply(mam, 1, mean) >> jja_means <- apply(jja, 1, mean) >> ond_means <-apply(ond, 1, mean) >> yr_mean <- apply(all_yr, 1, mean) >> >> >> _____________ >> Peter E. Tuju >> Dar es Salaam >> T A N Z A N I A >> ---------------------- >> ______________________________________________ >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> 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-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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.