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Vectorize data.table like, grepl or similar to compare large rows of data

I need to check if a row in one column contains the corresponding (numeric) value from the same row in another column for all rows.

If I only checked the string for one template, that would be simple using data.table likeor grepl. However, my template value is different for each row.

There is a somewhat related question here , but unlike this question, I need to create a boolean flag indicating whether a template is present.

Let's say this is my data set;

DT <- structure(list(category = c("administration", "nurse practitioner", 
                                  "trucking", "administration", "warehousing", "warehousing", "trucking", 
                                  "nurse practitioner", "nurse practitioner"), industry = c("admin", 
                                                                                            "truck", "truck", "admin", "nurse", "admin", "truck", "nurse", 
                                                                                            "truck")), .Names = c("category", "industry"), class = "data.frame", row.names = c(NA, 
                                                                                                                                                                               -9L))
setDT(DT)
> DT
             category industry
1:     administration    admin
2: nurse practitioner    truck
3:           trucking    truck
4:     administration    admin
5:        warehousing    nurse
6:        warehousing    admin
7:           trucking    truck
8: nurse practitioner    nurse
9: nurse practitioner    truck

My desired result would be the same:

> DT
   matches
1: TRUE
2: FALSE
3: TRUE
4: TRUE
5: FALSE
6: FALSE
7: TRUE
8: TRUE
9: FALSE

Of course, 1 and 0 will be as good as TRUE and FALSE.

Here are some things I tried that didn't work:

apply(DT,1,grepl, pattern = DT[,2], x = DT[,1])
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

> apply(DT,1,grepl, pattern = DT[,1], x = DT[,2])
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

> grepl(DT[,2], DT[,1])
[1] FALSE

> DT[Vectorize(grepl)(industry, category, fixed = TRUE)]
             category industry
1:     administration    admin
2:           trucking    truck
3:     administration    admin
4:           trucking    truck
5: nurse practitioner    nurse

> DT[stringi::stri_detect_fixed(category, industry)]
             category industry
1:     administration    admin
2:           trucking    truck
3:     administration    admin
4:           trucking    truck
5: nurse practitioner    nurse

> for(i in 1:nrow(DT)){print(grepl(DT[i,2], DT[i,1]))}
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE

> for(i in 1:nrow(DT)){print(grepl(DT[i,2], DT[i,1], fixed = T))}
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE

> DT[category %like% industry]
         category industry
1: administration    admin
2: administration    admin
Warning message:
In grepl(pattern, vector) :
  argument 'pattern' has length > 1 and only the first element will be used
+4
3

OP- , . , data.table , i.

, ,, j,

DT[, stri_detect_fixed(category, industry)]
#[1]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE

, list, a data.table

DT[, list(match=stri_detect_fixed(category, industry))]
+6

:

apply(DT, 1, function(x) grepl(x[2], x[1],fixed=T))
+3

I usually do:

DT[, flag := grepl(industry, category, fixed = TRUE), by = industry]
+3
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