select rows with largest value of variable within a group in r

Question

a.2<-sample(1:10,100,replace=T)
b.2<-sample(1:100,100,replace=T)
a.3<-data.frame(a.2,b.2)

r<-sapply(split(a.3,a.2),function(x) which.max(x$b.2))

a.3[r,]

returns the list index, not the index for the entire data.frame

Im trying to return the largest value of b.2 for each subgroup of a.2. How can I do this efficiently?

Answer 1

a.2<-sample(1:10,100,replace=T)
b.2<-sample(1:100,100,replace=T)
a.3<-data.frame(a.2,b.2)

The answer by Jonathan Chang gets you what you explicitly asked for, but I'm guessing that you want the actual row from the data frame.

sel <- ave(b.2, a.2, FUN = max) == b.2
a.3[sel,]

Answer 2

The ddply and ave approaches are both fairly resource-intensive, I think. ave fails by running out of memory for my current problem (67,608 rows, with four columns defining the unique keys). tapply is a handy choice, but what I generally need to do is select all the whole rows with the something-est some-value for each unique key (usually defined by more than one column). The best solution I've found is to do a sort and then use negation of duplicated to select only the first row for each unique key. For the simple example here:

a <- sample(1:10,100,replace=T)
b <- sample(1:100,100,replace=T)
f <- data.frame(a, b)

sorted <- f[order(f$a, -f$b),]
highs <- sorted[!duplicated(sorted$a),]

I think the performance gains over ave or ddply, at least, are substantial. It is slightly more complicated for multi-column keys, but order will handle a whole bunch of things to sort on and duplicated works on data frames, so it's possible to continue using this approach.

Answer 3

library(plyr)
ddply(a.3, "a.2", subset, b.2 == max(b.2))

Answer 4

a.2<-sample(1:10,100,replace=T)
b.2<-sample(1:100,100,replace=T)
a.3<-data.frame(a.2,b.2)
m<-split(a.3,a.2)
u<-function(x){
    a<-rownames(x)
    b<-which.max(x[,2])
    as.numeric(a[b])
    }
r<-sapply(m,FUN=function(x) u(x))

a.3[r,]

This does the trick, albeit somewhat cumbersome...But it allows me to grab the rows for the groupwise largest values. Any other ideas?

Answer 5

> a.2<-sample(1:10,100,replace=T)
> b.2<-sample(1:100,100,replace=T)
> tapply(b.2, a.2, max)
 1  2  3  4  5  6  7  8  9 10 
99 92 96 97 98 99 94 98 98 96 

Answer 6

a.2<-sample(1:10,100,replace=T)
b.2<-sample(1:100,100,replace=T)
a.3<-data.frame(a.2,b.2)

With aggregate, you can get the maximum for each group in one line:

aggregate(a.3, by = list(a.3$a.2), FUN = max)

This produces the following output:

   Group.1 a.2 b.2
1        1   1  96
2        2   2  82
...
8        8   8  85
9        9   9  93
10      10  10  97