Question

I have a dataframe, Call it A, that looks something like this:

GroupID  Dist1   Dist2 ...
1        4       4 
1        5       4 
1        3       16 
2        0       4 
2        7       2 
2        8       0 
2        6       4 
2        7       4 
2        8       2 
3        7       4 
3        5       6
...

GroupID is a factor, Dist1, Dist2 are integers.

I have a derived dataframe, SummaryA

GroupID  AveD1  AveD2 ...
1        4       8 
2        6       2
3        6       5
...

For each groupID, I need to find the ROW NUMBER that has the minimum, to do further manipulation, and to extract data to my summary set. For instance, I need:

GroupID  MinRowD1  
1        1 
2        4 
3        11 

On matches, it doesn’t matter which I choose, but I’m stuck as to how I get this. I can’t use which(), because it doesn’t operate over factors nicely, I can’t use ave(Fun=min), because I need the location, not the minimum value.
If I do something with matching to the minimum for each group, I can have multiple matches, which screws stuff up.

Any suggestions for how to do this?

Answer 1

Here’s a base R solution; the basic idea is to split the data by GroupID, get the row with the minimum value for each, and then put it back together. Some think the plyr functions are a more intuitive way to do this; I’m sure a solution using one of them will appear shortly…

A$row <- 1:nrow(A)
As <- split(A, A$GroupID)
sapply(As, function(Ai) {Ai$row[which.min(Ai$Dist1)]})

For large data sets, split is faster when performed on a scalar, not a data frame, like this.

rows <- split(1:nrow(A), A$GroupID)
sapply(rows, function(rowi) {rowi[which.min(A$Dist1[rowi])]})