If/else if on a text column in R - r

Question

I'm a new to R and need help with the following.
Have
Need
Male_18_24_pn
18_24
Male_25_39_pn
25_39
Male_40_64_pn
40_64
Male_65_84_pn
65_84
Male_85_plus_pn
85_plus
Female_18_24_pn
18_24
I need to create the "Need" column using the "Have" column, wondering how I can achieve this in R. As a initial effort, I tried the following code to test but got warning message and every cell of "Need" populated with "18_24":
if (str_detect(pe_1P_new$Have,"18_24")) {pe_1P_new$Need= "18_24"}
Warning message:
In if (str_detect(pe_1P_new$Have, "18_24")) { :
the condition has length > 1 and only the first element will be used
Your help is greatly appreciated. Thanks in advance!

Answer 1

You can do:
require(data.table)
dt = data.table(
have = c("Male_18_24_pn", "Male_25_39_pn",
"Male_40_64_pn", "Male_65_84_pn",
"Male_85_plus_pn", "Female_18_24_pn")
)
dt[ , need := gsub('(Male|Female)_(.+)(_pn)', '\\2', have) ]
Base R solution:
dt$need = gsub('(Male|Female)_(.+)(_pn)', '\\2', dt$have)
No need for a loop or any conditional statements. You can extract the needed information with the help of a vectorized function, such as gsub() and a simple regex.
[Volunteer edit] This is an attempt to explain the regular expression logic of that substitution pattern (see ?regex for a very terse but complete description). You need to understand what a capture class can do.
gsub('(Male|Female)#This matches "Male" or "Female", the first "capture classes"
_(.+)# Second capture class, matching anything after an underscore
(_pn)',# ... up to but not including an "_pn"
'\\2', # replace anything matched with only the second capture class
dt$have)
You will not be able to run this version because the carriage retruns and spaces get in the way of the regex engine process.

Answer 2

We could use trimws as well
dt[, need := trimws(have, whitespace = '[[:alpha:]]+_|_[[:alpha:]]+')]

Answer 3

We can try gsub like below
> dt[, need := gsub(".*?_(.*)_.*", "\\1", have)][]
have need
1: Male_18_24_pn 18_24
2: Male_25_39_pn 25_39
3: Male_40_64_pn 40_64
4: Male_65_84_pn 65_84
5: Male_85_plus_pn 85_plus
6: Female_18_24_pn 18_24

Answer 4

You can use the package {unglue}
df <- data.frame(
Have = c("Male_18_24_pn", "Male_25_39_pn",
"Male_40_64_pn", "Male_65_84_pn",
"Male_85_plus_pn", "Female_18_24_pn")
)
library(unglue)
unglue_unnest(df, Have, "{}_{Need}_pn", remove = FALSE)
#> Have Need
#> 1 Male_18_24_pn 18_24
#> 2 Male_25_39_pn 25_39
#> 3 Male_40_64_pn 40_64
#> 4 Male_65_84_pn 65_84
#> 5 Male_85_plus_pn 85_plus
#> 6 Female_18_24_pn 18_24
Created on 2021-07-20 by the reprex package (v0.3.0)