I have a dataframe with sets of scores, and sets of grouping variables, something like:
s1 s2 s3 g1 g2 g3
4 3 7 F F T
6 2 2 T T T
2 4 9 G G F
1 3 1 T F G
I want to run an aggregate, at the moment I'm doing:
aggregate(df[c("s1","s2","s3")],df["g1"],function(x) c(m =mean(x, na.rm=T), sd = sd(x, na.rm=T), n = length(x)))
I'd like to have just one line of code, so I could aggregate the multiple variables by multiple factors all at once. Note I'm not trying to get a summary of s1-3 by combinations of g1-3 (as per answers here). I've looked at summaryBy in the doBy package, but again that seems to do combinations of each factor rather than just an overall which isn't what I want (useful though!). I've been playing with variants on:
apply(df[c("g1","g2","g3")], 2, function (z) aggregate(df[c("s1","s2","s3")],z,function(x) c(m =mean(x, na.rm=T), sd = sd(x, na.rm=T), n = length(x)))
But I get the error: "'by' must be a list" with that. I think I could work out how to do this with a loop and I know with various versions of ddply or reshape you can get aggregation but the most intuitive way (to me at least) seems to be an apply and aggregate - what am I missing?
Let us name the anonymous function in the question as follows. Then the Map statement at the end applies aggregate to df[1:3] separately by each grouping variable:
mean.sd.n <- function(x) c(m = mean(x, na.rm=T), sd = sd(x, na.rm=T), n = length(x))
Map(function(nm) aggregate(df[1:3], df[nm], mean.sd.n), names(df)[4:6])
giving:
$g1
g1 s1.m s1.sd s1.n s2.m s2.sd s2.n s3.m s3.sd s3.n
1 F 4.000000 NA 1.000000 3.0000000 NA 1.0000000 7.0000000 NA 1.0000000
2 G 2.000000 NA 1.000000 4.0000000 NA 1.0000000 9.0000000 NA 1.0000000
3 T 3.500000 3.535534 2.000000 2.5000000 0.7071068 2.0000000 1.5000000 0.7071068 2.0000000
$g2
g2 s1.m s1.sd s1.n s2.m s2.sd s2.n s3.m s3.sd s3.n
1 F 2.50000 2.12132 2.00000 3 0 2 4.000000 4.242641 2.000000
2 G 2.00000 NA 1.00000 4 NA 1 9.000000 NA 1.000000
3 T 6.00000 NA 1.00000 2 NA 1 2.000000 NA 1.000000
$g3
g3 s1.m s1.sd s1.n s2.m s2.sd s2.n s3.m s3.sd s3.n
1 F 2.000000 NA 1.000000 4.0000000 NA 1.0000000 9.000000 NA 1.000000
2 G 1.000000 NA 1.000000 3.0000000 NA 1.0000000 1.000000 NA 1.000000
3 T 5.000000 1.414214 2.000000 2.5000000 0.7071068 2.0000000 4.500000 3.535534 2.000000
Note: This could be shortened slightly by using fn$ from the gsubfn package. It allows us to specify the anonymous function in the line of code that starts with Map using formula notation as shown:
library(gsubfn)
fn$Map(nm ~ aggregate(df[1:3], df[nm], mean.sd.n), names(df)[4:6])