I\'m trying to create new variables based on a procedure code variable with 2500+ values in a medical dataset to pull out the antibiotics, their dose, and route. I\'ve been abl
A base R approach for a simple option:
# my dummy data
df1 <- data.frame("v1" = c(LETTERS[1:10]), "v2" = rep(NA, 10))
# step 1, fill the column with 0 (the else part of your code)
df1[,'v2'] <- 0
# step 2, create a vector containing ids you want to change
change_vec <- c("A", "C", "D", "F")
# step 3, use %in% to index and replace with 1
df1[,'v2'][df1[,'v1'] %in% change_vec] <- 1
In most cases this will be adequate, but be aware of the risks of using indexing vectors that contain numeric values.
https://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-doesn_0027t-R-think-these-numbers-are-equal_003f
Two alternative methods, both using merges/joins. One advantage of this approach is that it is much easier to maintain: you have well-structured and manageable tables of procedures instead of (potentially really-long) lines of code with your ifelse
statement. The comments suggesting %in%
also reduce this problem, though you'll deal with manageable vectors instead of mangeable frames.
Fake data:
library(dplyr)
library(tidyr)
vet <- data_frame(ProcedureCode = c('6160', '2028', '2029'))
One frame per procedure type. This is manageable, but might be annoying if you have a lot of different types. Repeat the left_join
for each type.
abs <- data_frame(ab=TRUE, ProcedureCode = c('6160', '2028'))
antis <- data_frame(antibiotic=TRUE, ProcedureCode = c('2029'))
vet %>%
left_join(abs, by = "ProcedureCode") %>%
left_join(antis, by = "ProcedureCode") %>%
mutate_at(vars(ab, antibiotic), funs(!is.na(.)))
# # A tibble: 3 × 3
# ProcedureCode ab antibiotic
# <chr> <lgl> <lgl>
# 1 6160 TRUE FALSE
# 2 2028 TRUE FALSE
# 3 2029 FALSE TRUE
The use of ab=TRUE
(etc) is so that there is a column to merge. The rows that do not match will have an NA
, which mandates the need for !is.na(.)
to convert T,NA,T
to T,F,T
.
You could even use vectors of procedure codes instead, something like:
vet %>%
left_join(data_frame(ab=TRUE, ProcedureCode=vector_of_abs), by = "ProcedureCode") %>%
...
Though that really only helps if you already have the codes as vectors, otherwise it seems to be solely whichever is easier for you to maintain.
One frame with all procedures, requiring only a single frame for types and a single left_join
.
procedures <- tibble::tribble(
~ProcedureCode, ~procedure,
'6160' , 'ab',
'2028' , 'ab',
'2029' , 'antibiotic'
)
left_join(vet, procedures, by = "ProcedureCode")
# # A tibble: 3 × 2
# ProcedureCode procedure
# <chr> <chr>
# 1 6160 ab
# 2 2028 ab
# 3 2029 antibiotic
You can either keep it as-is (if it makes sense to store it that way) or spread
it to be like the others:
left_join(vet, procedures, by = "ProcedureCode") %>%
mutate(ignore=TRUE) %>%
spread(procedure, ignore) %>%
mutate_at(vars(ab, antibiotic), funs(!is.na(.)))
# # A tibble: 3 × 3
# ProcedureCode ab antibiotic
# <chr> <lgl> <lgl>
# 1 2028 TRUE FALSE
# 2 2029 FALSE TRUE
# 3 6160 TRUE FALSE
(Order after the join/merge is different here, but the data remains the same.)
(I used logical
s, it's easy enough to convert them to 1s and 0s, perhaps mutate(ab=1L*ab)
or mutate(ab=as.integer(ab))
.)