Efficient multiplication of columns in a data fram

问题:

I have a large data frame in which I am multiplying two columns together to get another column. At first I was running a for-loop, like so:

      for(i in 1:nrow(df)){

      df$new_column[i] <- df$column1[i] * df$column2[i]

      }

but this takes like 9 days.

Another alternative was plyr, and I actually might be using the variables incorrectly:

     new_df <- ddply(df, .(column1,column2), transform, new_column = column1 * column2)
     # but this is taking forever

回答1:

As Blue Magister said in comments,

df$new_column <- df$column1 * df$column2

should work just fine. Of course we can never know for sure if we don't have an example of the data.

回答2:

A data.table solution will avoid lots of internal copying while having the advantages of not spattering the code with $.

 library(data.table)
 DT <- data.table(df)
 DT[ , new := column1 * column2]

回答3:

A minor, somewhat less efficient, version of Sacha's Answer is to use transform() or within()

df <- transform(df, new = column1 * column2)

or

df <- within(df, new <- column1 * column2)

(I hate spattering my user code with $.)