Need help with the categorical variable

[api]

I'm cleaning data that is originally a character.

head(bil$location, 20)
[1] "Right parietal lobe tumor" "Right frontal lobe tumor" "Rt. Frontal Astrocytoma" "Right Parietal Tumor"
[5] "Right Frontal Parietal Tumor" "Right Parietal Tumor" "Left Frontal Mass" "Left frontal tumor"
[9] "Right frontal tumor" "Left parietal lesion" "Right Frontal Lobe Tumor" "Left Frontal Lobe Astrocytoma"
[13] "Left Temporal Lobe Tumor" "Left Frontal Lobe Tumor" "Left Frontal Low Grade Glioma" "Right sided Tumor"
[17] "Left sided glioma" "Left Frontal Lesion" "Left Frontal Lesion" "Left Frontal Lesion"

I want to create another variable as a factor with 11 levels;
1-Frontal
2-Parietal
3-Fronto-parietal
4-Temporal
5-Fronto-temporal
6-Parietal
7-Parieto-occipital
8-Temporo-occipital
9-Insula
10-Temporo-insula
11-multiple

The new variable should grab the information from the original variable. For example, if the observation is "Right Frontal Tumor", it should be in the level 1 "Frontal". If the observation is "Right Fronto-parietal astrocytoma", it should be in the level 3 "Fronto-parietal". If the observation is "Recurrent Right Frontal PNET", it should be in the level 1 "Frontal".

If the observation in the bil$location cannot be defined at any level, it should be defined as NA. For example, if the information in bil$location is "Left Hemisphere Astrocytoma", it should return "NA".

Can anyone suggest to me the approach to do this or the appropriate package to tackle this problem?

[nirgrahamuk]

The general approach will involve regular expressions for pattern matching in text, a good package in that area is stringr.

[rene_at_coco]

I would make use of stringr::str_detect and dplyr::case_when. Maybe something like:

bil_coded <- bil %>%
    new_variable = factor(case_when(
        stringr::str_detect(location, "frontal") ~ "1-Frontal", # create as many criteria as needed to d
        stringr::str_detect(location, "parietal") ~ "2-Parietal",
        TRUE ~ NA_character_ # this should create an NA observation for everything else that doesn't fit your criteria
    ))