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Convert R output “on command” to data frame

I am trying to use the R by command to get the column value for subsets of a data frame. For example, consider this data frame:

 > z = data.frame(labels=c("a","a","b","c","c"),data=matrix(1:20,nrow=5)) > z labels data.1 data.2 data.3 data.4 1 a 1 6 11 16 2 a 2 7 12 17 3 b 3 8 13 18 4 c 4 9 14 19 5 c 5 10 15 20

I can use the R by command to get the column value according to the labels column:

 > by(z[,2:5],z$labels,colMeans) z[, 1]: a data.1 data.2 data.3 data.4 1.5 6.5 11.5 16.5 ------------------------------------------------------------ z[, 1]: b data.1 data.2 data.3 data.4 3 8 13 18 ------------------------------------------------------------ z[, 1]: c data.1 data.2 data.3 data.4 4.5 9.5 14.5 19.5

But how can I force the output back to the data frame? as.data.frame does not work ...

 > as.data.frame(by(z[,2:5],z$labels,colMeans)) Error in as.data.frame.default(by(z[, 2:5], z$labels, colMeans)) : cannot coerce class '"by"' into a data.frame
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3 answers

You can use ddply from plyr package

 library(plyr) ddply(z, .(labels), numcolwise(mean)) labels data.1 data.2 data.3 data.4 1 a 1.5 6.5 11.5 16.5 2 b 3.0 8.0 13.0 18.0 3 c 4.5 9.5 14.5 19.5

Or aggregate from stats

 aggregate(z[,-1], by=list(z$labels), mean) Group.1 data.1 data.2 data.3 data.4 1 a 1.5 6.5 11.5 16.5 2 b 3.0 8.0 13.0 18.0 3 c 4.5 9.5 14.5 19.5

Or dcast from reshape2 package

 library(reshape2) dcast( melt(z), labels ~ variable, mean)

Using sapply :

  t(sapply(split(z[,-1], z$labels), colMeans)) data.1 data.2 data.3 data.4 a 1.5 6.5 11.5 16.5 b 3.0 8.0 13.0 18.0 c 4.5 9.5 14.5 19.5
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The output of by is list , so you can use do.call to rbind them and then convert this:

 as.data.frame(do.call("rbind",by(z[,2:5],z$labels,colMeans))) data.1 data.2 data.3 data.4 a 1.5 6.5 11.5 16.5 b 3.0 8.0 13.0 18.0 c 4.5 9.5 14.5 19.5
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Working with the result on can be very annoying. I just found a way to remove what you want in a data frame format and you won’t need extra packages.

So if you do this:

 aux <- by(z[,2:5],z$labels,colMeans)

You can then convert it to a data frame by doing the following:

  aux_df <- as.data.frame(t(aux[seq(nrow(aux)),seq(ncol(aux))]))

I just get all the rows and columns from aux, wrapping them and using as.data.frame.

I hope this helps.

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