Add category column to a data set - r

Question

I've a data table like this
+------------+-------+
| Model | Price |
+------------+-------+
| Apple-1 | 10 |
+------------+-------+
| New Apple | 11 |
+------------+-------+
| Orange | 13 |
+------------+-------+
| Orange2019| 15 |
+------------+-------+
| Cat | 19 |
+------------+-------+
I'want to define a list of base model tags that I want to add to any single row that matches certain condition/value. So for example defined a data frame for tagging like this
+------------+--------+
| Model | Tag |
+------------+------ -+
| Apple-1 | A |
+------------+------ -+
| New Apple | A |
+------------+------ -+
| Orange | B |
+------------+------ -+
| Cat | B |
+------------+--------+
I would like to find some way to get this results:
+------------+-------+--------+
| Model | Price | Tag |
+------------+-------+--------+
| Apple-1 | 10 | A |
+------------+-------+--------|
| New Apple | 11 | A |
+------------+-------+--------|
| Orange | 13 | B |
+------------+-------+--------|
| Orange2019| 15 | B |
+------------+-------+--------|
| Cat | 19 | B |
+------------+-------+--------|
I'm don't mind to use a table to managed the tagging data, and I know that I could write very "ad-hoc" mutate statement to achieve the results I want, just wondering if there is more elegant way to tagging a string based on a pattern match.

Answer 1

One idea is to use the Levenshtein distances to cluster the words you have. You would need to provide with a number of clusters. Once you have this clusters, just add the number of each one as a category tag to your table. Check out this answer which goes into detail of Levenshtein distance clustering. Text clustering with Levenshtein distances
edit
I think I totally misunderstood your question... try this
df=data.frame("Model"=c("Apple-1","New Apple","Organe","Orange2019","Cat"),
"Price"=c(10,11,13,15,19),stringsAsFactors = FALSE)
tags=data.frame("Model"=c("Apple-1","New Apple","Orange","Cat"),
"Tag"=c("A","A","B","B"),stringsAsFactors = FALSE)
df%>%rowwise()%>%mutate(Tag=if_else(!is.na(tags$Tag[which(!is.na(str_extract(Model,tags$Model)))[1]]),
tags$Tag[which(!is.na(str_extract(Model,tags$Model)))[1]],false="None"))
Model Price Tag
<chr> <dbl> <chr>
1 Apple-1 10 A
2 New Apple 11 A
3 Organe 13 None
4 Orange2019 15 B
5 Cat 19 B
I actually changed Orange for Organe so that you see what happens if there is not match ( none is returned)