faster way to create variable that aggregates a column by id
aggregate, performance, plyr, r
Solution
For any kind of aggregation where you want a resulting vector the same length as the input vector with replicates grouped across the grouping vector `ave` is what you want.
df$perc.total <- ave(df$cand.perc, df$id, FUN = sum)
Problem
Is there a faster way to do this? I guess this is unnecessary slow and that a task like this can be accomplished with base functions. ``` df <- ddply(df, "id", function(x) cbind(x, perc.total = sum(x$cand.perc))) ``` I'm quite new to R. I have looked at `by()`, `aggregate()` and `tapply()`, but didn't get them to work at all or in the way I wanted. Rather than returning a shorter vector, I want to attach the sum to the original dataframe. What is the best way to do this? Edit: Here is a speed comparison of the answers applied to my data. ``` > # My original solution > system.time( ddply(df, "id", function(x) cbind(x, perc.total = sum(x$cand.perc))) ) user system elapsed 14.405 0.000 14.479 > # Paul Hiemstra > system.time( ddply(df, "id", transform, perc.total = sum(cand.perc)) ) user system elapsed 15.973 0.000 15.992 > # Richie Cotton > system.time( with(df, tapply(df$cand.perc, df$id, sum))[df$id] ) user system elapsed 0.048 0.000 0.048 > # John > system.time( with(df, ave(cand.perc, id, FUN = sum)) ) user system elapsed 0.032 0.000 0.030 > # Christoph_J > system.time( df[ , list(perc.total = sum(cand.perc)), by="id"][df]) user system elapsed 0.028 0.000 0.028 ```