I am trying to apply a function to a data.frame column in R that detects whether or not specific string values exist. There are various string patterns that each constitute their own categorization. The function should create a new column that provides said classification (dat$id_class) based off the string in the dat$id column.
I am relying on the stringr and dplyr packages to do this. Specifically, I'm using dplyr::mutate to apply the function.
This code runs & produces the exact results I'm looking for, but I'm looking for a faster solution (if one exists). This is obviously a small-scale example with a limited dataset, and this same approach on my very large dataset is taking much longer than desired.
library(stringi)
library(dplyr)
library(stringr)
dat <- data.frame(
id = c(
sprintf("%s%s%s", stri_rand_strings(1000000, 5, '[A-Z]'),
stri_rand_strings(5, 4, '[0-9]'), stri_rand_strings(5, 1, '[A-Z]'))
))
classify <- function(x){
if(any(stringr::str_detect(x,pattern = c('AA','BB')))){
'class_1'
} else if (any(stringr::str_detect(x,pattern = c('AB','BA')))){
'class_2'
} else {
'class_3'
}
}
dat <- dat %>% rowwise() %>% mutate(id_class = classify(id))
There's a great chance this has already been answered, and that I'm just not looking in the right place, but it's worth a shot.
Any assistance appreciated!
Use case_when which is vectorized and instead of doing rowwise with if/else change the pattern with OR (|)
library(stringr)
library(dplyr)
system.time({
dat1 <- dat %>%
mutate(id_class = case_when(str_detect(id, 'AA|BB') ~'class_1',
str_detect(id, 'AB|BA') ~ 'class_2', TRUE ~ 'class_3'))
})
# user system elapsed
# 0.460 0.036 0.493
The timings based on OP's function
system.time({dat2 <- dat %>%
rowwise() %>%
mutate(id_class = classify(id))
})
# user system elapsed
# 31.927 0.303 32.891
-checking the outputs
> all.equal(dat1, as.data.frame(dat2), check.attributes = FALSE)
[1] TRUE