you should have found a solution for that in the help page of apply.
just run
min.values = apply(the.data,1,min)
the '1' marks the direction (e.g. whether apply is applied to rows or
columns), it could be a 2 as well. Check that yourself in the apply
documentation.
Then run rbind(the.data,min.values) (could be cbind as well, I am not
sure again ;-) ) and you get what you want.
09wkj schrieb:
I am mainly a Java/C++ programmer, so my mind is used to iterating over data with for
loops. After a long break, I am trying to get back into the "R mindset", but I
could not find a solution in the documentation for the applys, aggregate, or by.
I have a data.frame where each row is an entry with 10 groups of measurements.
The first measurement spans 1 column, the second spans 2 columns, third 3, and
so on (55 total columns). What I want to do is add to my data.frame 10 new
columns containing the minimum value of each measurement.
dim(the.data)
[1] 1679 55
colnames(the.data)
[1] "k.1.1" "k.2.1" "k.2.2" "k.3.1" "k.3.2" "k.3.3" "k.4.1"
[8] "k.4.2" "k.4.3" "k.4.4" "k.5.1" "k.5.2" "k.5.3" "k.5.4"
[15] "k.5.5" "k.6.1" "k.6.2" "k.6.3" "k.6.4" "k.6.5" "k.6.6"
[22] "k.7.1" "k.7.2" "k.7.3" "k.7.4" "k.7.5" "k.7.6" "k.7.7"
[29] "k.8.1" "k.8.2" "k.8.3" "k.8.4" "k.8.5" "k.8.6" "k.8.7"
[36] "k.8.8" "k.9.1" "k.9.2" "k.9.3" "k.9.4" "k.9.5" "k.9.6"
[43] "k.9.7" "k.9.8" "k.9.9" "k.10.1" "k.10.2" "k.10.3" "k.10.4"
[50] "k.10.5" "k.10.6" "k.10.7" "k.10.8" "k.10.9" "k.10.10"
I want to add to the.data new columns: min.k.1, min.k.2, ..., min.k.10
This is the section of code I would like to improve, hopefully getting rid of
the eval and the for loop:
for(k in 1:10){
s <- subset(the.data, select=paste("k", k, 1:k, sep="."))
eval(parse(text = paste("the.data$min.k.", k, "<-as.vector(by(s, 1:nrow(s), min))",
sep="")))
}
Thanks for any help,
Bill
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