Conditionally replacing column values with data.table

I have the following data.table:

dt <- data.table(col1 = rep("a",6), col2 = c(1,1,1,2,3,1))

Now I want to replace all the 1 in col2 with value "bigDog". I can do it using the data.frame spirit:

dt$col2[dt$col2==1,] <- "bigDog"

But I wonder if there is a different way, more "data.table oriented"?

Had you not wanted to change the type of the column, you'd do:

dt[col2 == 1, col2 := 123]

With the type change, you can do:

dt[, col2 := as.character(col2)][col2 == "1", col2 := "bigDog"]

If you don't change the type first, "bigDog" will get coerced to integer, i.e. NA. You'll also get a bunch of warnings about that of course.

Note that besides less cumbersome syntax, using := has the advantage of not making extra copies of data (as <- will) and instead modifies in place.

Aditionally you could use the library plyr

library(data.table)
library(plyr)
dt <- data.table(col1 = rep("a",6), col2 = c(1,1,1,2,3,1))
dt <- mapvalues(dt[,col2], c(1), c("BigDog"))