R: for-loop with ddply

Question

I'm new to R and to stackoverflow so I'm sorry if the question or it's format isn't ideal...

I'm trying to get some basic statistics from a matrix by using ddply and I wanted to make a process a bit faster by using for -loop. Unfortunately this wasn't as easy as I had thought...

Strain  gene1         gene2      gene3  .   .   .
 A    2.6336700     1.42802     0.935742
 A    2.0634700     2.31232     1.096320
 A    2.5798600     2.75138     0.714647
 B    2.6031200     1.31374     1.214920
 B    2.8319400     1.30260     1.191770
 B    1.9796000     1.74199     1.056490
 C    2.4030300     1.20324     1.069800
 .
 .
 .
----------

for (n in c("gene1","gene2","gene3","gene4")) {
  summary <- ddply(Data, .(Strain), summarise,
                mean = mean(n),
                sd   = sd(n),
                se   = sd(n) / sqrt(length(n)) )
}

In results it reads that mean = 6 and both sd and se are "NA" ... obviously not what I had in mind.

If I get rid of the for -loop and manually insert the column name ("gene1"):

summary <- ddply(Data, .(Strain), summarise,
              mean = mean(gene1),
              sd   = sd(gene1),
              se   = sd(gene1) / sqrt(length(gene1)) )

Now it seems to give me the correct result. Can someone enlighten me on this matter and tell me what I'm doing wrong?

Answer 1

Just use colwise (mean) , colwise(sd) , and colwise(length) . No need for for loop

library(plyr)

ddply(mtcars,.(cyl), colwise(mean))



cyl      mpg     disp        hp     drat       wt     qsec        vs        am     gear     carb
1   4 26.66364 105.1364  82.63636 4.070909 2.285727 19.13727 0.9090909 0.7272727 4.090909 1.545455
2   6 19.74286 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286 0.4285714 3.857143 3.428571
3   8 15.10000 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000 0.1428571 3.285714 3.500000

For you example,

ddply(df,.(Strain),colwise(mean))

Answer 2

I know you didn't ask for it, but here is a solution with aggregate in base .

# One line in base.
aggregate(Data[paste0('gene',1:3)],by=Data['Strain'],
     function(x) c(mean=mean(x),sd=sd(x),se=sd(x)/sqrt(length(x))))

Answer 3

You can do it using ddply, but then you just have to create a work-around by first turning your command into a string, and then by evaluating the string.

all.genes <- c("gene1","gene2","gene3","gene4")
for (i in 1:length(all.genes) {
    string_eval <- sprintf("summary <- ddply(Data, .(%s), summarise, 
                mean = mean(n),
                sd   = sd(n),
                se   = sd(n) / sqrt(length(n)))", 
                all.genes[i]) 
    eval(parse(text = string_eval)) 
}

I just used your code, but this loop would overwrite summary every round. I had the same problem as you, so I just wanted to let you know the solution I ended up using.