Calculating subtotals in R

Question

I have a data frame with 900,000 rows and 11 columns in R. The column names and types are as follows:

column name: date / mcode / mname / ycode / yname / yissue          


        

Answer 1

if your data is large and speed matters, i would recommend using the R function rowsum, which is a lot faster. i applied the 3 methods (f1 = aggregate, f2 = ddply, f3 = tapply) suggested in the answers to compare it with f4 = rowsum and here is what i find:

   test replications elapsed relative
4 f4()          100   0.033     1.00
3 f3()          100   0.046     1.39
1 f1()          100   0.165     5.00
2 f2()          100   0.605    18.33

i have added my code below if someone wants to explore in more detail.

library(plyr);
library(rbenchmark);

val  = rnorm(50);
name = rep(letters[1:5], each = 10);
data = data.frame(val, name);

f1 = function(){aggregate(data$val, by=list(data$name), FUN=sum)}
f2 = function(){ddply(data, .(name), summarise, sum = sum(val))}
f3 = function(){tapply(data$val, data$name, sum)}
f4 = function(){rowsum(x = data$val, group = data$name)}

benchmark(f1(), f2(), f3(), f4(),
          columns=c("test", "replications", "elapsed", "relative"),
          order="relative", replications=100)