I am looking for a readable alternative to plyr::mapvalues in data.table.
For example, in plyr::mapvalues, if I would like to change the values of carb in mtcars to type1, type2, type3, I would do something like this:
library(tidyverse)
mtcars %>%
mutate(carb = plyr::mapvalues(
carb,
from = c("1", "2", "3", "4", "6", "8"),
to = c("type1", "type1", "type2", "type2", "type3", "type3")))
To get the same thing in data.table, I would do it like this, which doesn't seem to be the conventional method:
library(data.table)
dt <- data.table(mtcars)
dt$carb <- as.character(dt$carb)
dt[which(carb %in% c("1", "2")),
carb := "type1"]
dt[which(carb %in% c("3", "4")),
carb := "type2"]
dt[which(carb %in% c("6", "8")),
carb := "type3"]
Is it possible to change all the values in one condition (dt[...])?
Using base::factor is the easiest method:
library(data.table)
setDT(mtcars)[, carb := factor(carb,
levels = c("1", "2", "3",
"4", "6", "8"),
labels = c("type1", "type1",
"type2", "type2",
"type3", "type3"))][]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 type2
#> 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 type2
#> 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 type1
#> 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 type1
#> 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 type1
#> 6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 type1
#> 7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 type2
#> 8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 type1
#> 9: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 type1
## ...