Loops to create new variables in ddply

I am using ddply to aggregate and summarize data frame variables, and I am interested in looping through my data frame's list to create the new variables.

new.data <- ddply(old.data, 
                  c("factor", "factor2"),
                  function(df)
                    c(a11_a10 = CustomFunction(df$a11_a10),
                      a12_a11 = CustomFunction(df$a12_a11),
                      a13_a12 = CustomFunction(df$a13_a12),
                      ...
                      ...
                      ...))

Is there a way for me to insert a loop in ddply so that I can avoid writing each new summary variable out, e.g.

for (i in 11:n) {
  paste("a", i, "_a", i - 1) = CustomFunction(..... )
}开发者_JAVA百科

I know that this is not how it would actually be done, but I just wanted to show how I'd conceptualize it. Is there a way to do this in the function I call in ddply, or via a list?

UPDATE: Because I'm a new user, I can't post an answer to my own question:

My answer involves ideas from Nick's answer and Ista's comment:

func <- function(old.data, min, max, gap) {
  varrange <- min:max
  usenames <- paste("a", varrange, "_a", varrange - gap, sep="")
  new.data <- ddply(old.data,
                    .(factor, factor2),
                    colwise(CustomFunction, c(usenames)))
}

---

Building on the excellent answer by @Nick, here is one approach to the problem

foo <- function(df){
  names   = paste("a", 11:n, "_a", 10:(n-1), sep = "")
  results = sapply(df[,names], CustomFunction)
}

new.data = ldply(dlply(old.data, c("factor", "factor2")), foo)

Here is an example application using the tips dataset in ggplot2. Suppose we want to calculate the average of tip and total_bill by combination of sex and smoker, here is how the code would work

foo = function(df){names = c("tip", "total_bill"); sapply(df[,names], mean)}
new = ldply(dlply(tips, c("sex", "smoker")), foo)

It produces the output shown below

         .id      tip total_bill
1  Female.No 2.773519   18.10519
2 Female.Yes 2.931515   17.97788
3    Male.No 3.113402   19.79124
4   Male.Yes 3.051167   22.28450

Is this what you were looking for?

---

If I understand you correctly, you essentially want to apply a custom function to every column in the ddply data.frame.

The good news is there is a ddply function that does exactly that. This means the solution to your problem boils down to a one liner:

Building on the excellent example of @Ramnath:

library(ggplot2)
customfunction <- mean
ddply(tips, .(sex, smoker), numcolwise(customfunction))

     sex smoker total_bill      tip     size
1 Female     No   18.10519 2.773519 2.592593
2 Female    Yes   17.97788 2.931515 2.242424
3   Male     No   19.79124 3.113402 2.711340
4   Male    Yes   22.28450 3.051167 2.500000

The reason this works is that colwise turns a function that works on a vector into a function that works on a column in a data.frame. There are two variants of colwise: numcolwise works only on numeric columns, and catcolwise works on categorical columns. See?colwise for more information.

EDIT:

I appreciate that you may not wish to apply the function to all columns in your data.frame. Still, I find this syntax so easy, that my general approach would be to modify the data.frame that I pass to ddply. For example, the following modified example subsets tips to exclude some columns. The solution is still a one-liner:

ddply(tips[, -2], .(sex, smoker), numcolwise(customfunction))

     sex smoker total_bill     size
1 Female     No   18.10519 2.592593
2 Female    Yes   17.97788 2.242424
3   Male     No   19.79124 2.711340
4   Male    Yes   22.28450 2.500000

---

In steps:

varrange<-11:n
usenames<-paste("a", varrange, "_a", varrange - 1, sep="")
results<-sapply(usenames, function(curname){CustomFunction(df[,curname])})
names(results)<-usenames

Is this what you want?