Question

I have a dataframe that has two sets of data that I need to multiply for a calculation. A simple version would be

sample = data.frame(apples=c(10,20,25,30,40,NA,NA,15))
sample$oranges = c(25,60,90,86,10,67,45,10)
sample$oats = c(65,75,85,95,105,115,125,135)
sample$eggs = c(23,22,21,20,19,18,17,16)
sample$consumer =c('john','mark','luke','paul','peter','thomas','matthew','brian')
sample$mealtime = c('breakfast','lunch','lunch','snack','lunch','breakfast','snack','dinner')

s1 = melt(sample,id.vars=c(5,6),measure.vars=c(1:4))

and what I’m trying to do is something along the lines of

s2 = dcast(s1, mealtime ~ ., function(x) (x[variable == 'oranges'] * x[variable =='apples'])/sum(x[variable == 'apples'])

In practice its a much longer data.frame and a more elaborate calculation but the principle should be the same. Thanks — first post to SO so apologies for any errors.

The output would be a data frame that has mealtimes as the Id var and the apple weighted average of the orange data as the values for each mealtime.

Something along the lines of

    Group.1         x
1 breakfast  1.785714
2    dinner  1.071429
3     lunch 27.500000
4     snack 18.428571

This was calculated using

sample$wa = sample$oranges*sample$apples/sum(sample$apples)
aggregate(sample$wa,by=list(sample$mealtime),sum,na.rm=T)

which feels off mathematically but was meant to be a quick kludgy approximation.

Answer 1

This is a much better task for plyr than it is for reshape.

library(plyr)
s1<-ddply(sample,.(mealtime), function(x) {return(sum(x$apples,x$oranges))})

And now you have clarified the output:

ddply(sample,.(mealtime), summarize,
      wavg.oranges = sum(apples * oranges, na.rm=TRUE) / sum(apples, na.rm=TRUE))
#    mealtime wavg.oranges
# 1 breakfast     25.00000
# 2    dinner     10.00000
# 3     lunch     45.29412
# 4     snack     86.00000