Suppose I have a formula and some values. I am trying to do the following mutate statement, which is in the form result{value}=my_formula({value},col1,col2,col3):
library(dplyr)
my_formula <- function(value,col1,col2,col3){
step1 <- value*col1*col2+col3
step2 <- 50000<step1 & step1<100000
return(step2)
}
values=c(2,11,14)
mtcars%>%
mutate(result2=my_formula(2,disp,hp,carb),
result11=my_formula(11,disp,hp,carb),
result14=my_formula(14,disp,hp,carb)) %>%
head
mpg cyl disp hp drat wt qsec vs am gear carb result2 result11 result14
1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 FALSE FALSE FALSE
2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 FALSE FALSE FALSE
3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 FALSE FALSE FALSE
4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 TRUE FALSE FALSE
5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 FALSE FALSE FALSE
6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 FALSE FALSE FALSE
Is it possible to automate this mutate statement? I am working with 10~1000 values in practice. I am also open to other approaches to generate these columns.
Similar to @JilberUrbina's answer, but putting an lapply(values, ..) directly in a mutate:
library(dplyr)
values <- c(2,11,14)
values <- setNames(values, paste0("res", values))
mtcars |>
mutate(data.frame(lapply(values, my_formula, disp, hp, carb))) |>
head()
# mpg cyl disp hp drat wt qsec vs am gear carb res2 res11 res14
# Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 FALSE FALSE FALSE
# Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 FALSE FALSE FALSE
# Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 FALSE FALSE FALSE
# Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 TRUE FALSE FALSE
# Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 FALSE FALSE FALSE
# Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 FALSE FALSE FALSE
This uses the under-referenced feature of mutate (and summarize) that if the unnamed argument returns a data.frame, then those columns are cbind'ed to the original data.