Odp: Calculated mean value based on another column bin from dataframe.
Hi r-help-bounces at r-project.org napsal dne 06.04.2011 10:48:04:
Dear list, I have a dataframe with two column as fellow.
head(dat)
V1 V2 0.15624 0.94567 0.26039 0.66442 0.16629 0.97822 0.23474 0.72079 0.11037 0.83760 0.14969 0.91312 I want to get the column V2 mean value based on the bin of column of V1. I write the code as fellow. It works, but I think this is not the elegant way. Any suggestions?
Do you want something like that?
#make data
x<-runif(100)
y<-runif(100)
#cut first column to bins (in your case dat[,1] and ran)
x.c<-cut(x, seq(0,1,.1))
#aggregate column 2 according to bins (in your case dat[,2])
aggregate(y,list(x.c), mean)
Group.1 x
1 (0,0.1] 0.5868734
2 (0.1,0.2] 0.5436263
3 (0.2,0.3] 0.5099366
4 (0.3,0.4] 0.4815855
5 (0.4,0.5] 0.4137687
6 (0.5,0.6] 0.4698156
7 (0.6,0.7] 0.4687639
8 (0.7,0.8] 0.5661048
9 (0.8,0.9] 0.5489297
10 (0.9,1] 0.4812521
Regards
Petr
dat<-read.table("dat.txt",head=F)
ran<-seq(0,0.5,0.05)
mm<-NULL
for (i in c(1:(length(ran)-1)))
{
fil<- dat[,1] > ran[i] & dat[,1]<=ran[i+1]
m<-mean(dat[fil,2])
mm<-c(mm,m)
}
mm
Here is the first 20 lines of my data.
dput(head(dat,20))
structure(list(V1 = c(0.15624, 0.26039, 0.16629, 0.23474, 0.11037,
0.14969, 0.16166, 0.09785, 0.36417, 0.08005, 0.29597, 0.14856,
0.17307, 0.36718, 0.11621, 0.23281, 0.10415, 0.1025, 0.04238,
0.13525), V2 = c(0.94567, 0.66442, 0.97822, 0.72079, 0.8376,
0.91312, 0.88463, 0.82432, 0.55582, 0.9429, 0.78956, 0.93424,
0.87692, 0.83996, 0.74552, 0.9779, 0.9958, 0.9783, 0.92523, 0.99022
)), .Names = c("V1", "V2"), row.names = c(NA, 20L), class =
"data.frame")
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