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Asked: 2026-06-05T12:22:22+00:00

I have a data.frame with 1,000 rows and 3 columns. It contains a large

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I have a data.frame with 1,000 rows and 3 columns. It contains a large number of duplicates and I’ve used plyr to combine the duplicate rows and add a count for each combination as explained in this thread.

Here’s an example of what I have now (I still also have the original data.frame with all of the duplicates if I need to start from there):

   name1    name2    name3     total
1  Bob      Fred     Sam       30
2  Bob      Joe      Frank     20
3  Frank    Sam      Tom       25
4  Sam      Tom      Frank     10
5  Fred     Bob      Sam       15

However, column order doesn’t matter. I just want to know how many rows have the same three entries, in any order. How can I combine the rows that contain the same entries, ignoring order? In this example I would want to combine rows 1 and 5, and rows 3 and 4.

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1 Answer

  1. Editorial Team

    Define another column that’s a “sorted paste” of the names, which would have the same value of “Bob~Fred~Sam” for rows 1 and 5. Then aggregate based on that.

    Brief code snippet (assumes original data frame is dd): it’s all really intuitive. We create a lookup column (take a look and should be self explanatory), get the sums of the total column for each combination, and then filter down to the unique combinations…

    dd$lookup=apply(dd[,c("name1","name2","name3")],1,
                                  function(x){paste(sort(x),collapse="~")})
tab1=tapply(dd$total,dd$lookup,sum)
ee=dd[match(unique(dd$lookup),dd$lookup),]
ee$newtotal=as.numeric(tab1)[match(ee$lookup,names(tab1))]

    You now have in ee a set of unique rows and their corresponding total counts. Easy – and no external packages needed. And crucially, you can see at every stage of the process what is going on!

    (Minor update to help OP:) And if you want a cleaned-up version of the final answer:

    outdf = with(ee,data.frame(name1,name2,name3,
                           total=newtotal,stringsAsFactors=FALSE))

    This gives you a neat data frame with the three all-important name columns, and with the aggregated totals in a column called total rather than newtotal.

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