Hi,
I received several really good suggestions, but the feeling seems to be
to use a for loop. I did this at first, but my curiosity took over --
hence, my question. The loop, coupled with a list of relevant columns,
seems best.
Thanks all,
Walt
________________________
Walter R. Paczkowski, Ph.D.
Data Analytics Corp.
44 Hamilton Lane
Plainsboro, NJ 08536
________________________
(V) 609-936-8999
(F) 609-936-3733
w...@dataanalyticscorp.com
www.dataanalyticscorp.com
_____________________________________________________
On 9/8/2013 10:06 AM, John Fox wrote:
Dear Walt and A.K.,
One shouldn't reflexively avoid loops in R. In this case, it seems to me clearer to use a
loop, and it's no less "efficient" (especially, I would guess, when one takes
into account the time to figure out how to do the computation). I get
system.time({
+ res<-do.call(rbind,lapply(split(colnames(dat1),((seq_len(ncol(dat1))-1)%/%21)+1),function(x)
{x1<- dat1[,x]; colnames(x1)<- paste("V",1:21);x1}))
+ row.names(res)<- 1:nrow(res)
+ })
user system elapsed
0.02 0.00 0.02
dim(res)
[1] 1170 21
system.time({
+ res2 <- as.data.frame(matrix(0, 1170, 21))
+ for (i in 1:9){
+ res2[((i - 1)*130 + 1):(i*130), ] <- dat1[, ((i - 1)*21 + 1):(i*21)]
+ }
+ })
user system elapsed
0.02 0.00 0.01
dim(res2)
[1] 1170 21
all(res == res2)
[1] TRUE
Best,
John
On Sun, 8 Sep 2013 06:29:42 -0700 (PDT)
arun <smartpink...@yahoo.com> wrote:
Hi,
You could try:
set.seed(48)
dat1<- as.data.frame(matrix(sample(1:40,189*130,replace=TRUE),ncol=189))
res<-do.call(rbind,lapply(split(colnames(dat1),((seq_len(ncol(dat1))-1)%/%21)+1),function(x)
{x1<- dat1[,x]; colnames(x1)<- paste("V",1:21);x1}))
row.names(res)<- 1:nrow(res)
dim(res)
#[1] 1170 21
A.K.
----- Original Message -----
From: Data Analytics Corp. <w...@dataanalyticscorp.com>
To: R help <r-help@r-project.org>
Cc:
Sent: Saturday, September 7, 2013 11:33 PM
Subject: [R] melting a data frame
Hi,
Suppose I have a data frame with 189 columns. The columns are actually 9 blocks of 21
columns each, each block representing measures on each of 9 products. There are 130
rows. Suppose I extract the first block of 21 columns and make them into a separate data
frame. I then want to take the second block of 21 columns and rbind it to the first;
then the third set of 21 and rbind it to the first two; etc. The final data frame should
have 1170 (= 9 * 130) rows and 21 columns. Is there an easy way to melt the blocks
comparable to using the melt function in the plyr package (which is why I'm referring to
what I want to do as "melting")? It seems that there should be a simple way to
do this. I used a for loop which worked, but I want to see if there's a more efficient
way.
Thanks,
Walt
________________________
Walter R. Paczkowski, Ph.D.
Data Analytics Corp.
44 Hamilton Lane
Plainsboro, NJ 08536
________________________
(V) 609-936-8999
(F) 609-936-3733
w...@dataanalyticscorp.com
www.dataanalyticscorp.com
______________________________________________
R-help@r-project.org 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-help@r-project.org 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.
------------------------------------------------
John Fox
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/
______________________________________________
R-help@r-project.org 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-help@r-project.org 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.
