Detect string with multiple conditions and mutate column in r - r

Question

I have Lookup_DF which contains dictionary to refer strings and Raw_file which has combination of strings, Lookup_DF is having Types to populate in Result data frame based on Items in raw files.
Item1=c("Banana","Toamto","Potato","Palak")
Item2=c("","Orange","Onion","Mango")
Type1=c("Fruit","Vegetable","Vegetable","Leaves")
Type2=c("","Fruit","Vegetable","Fruit")
DF1=data.frame(Item1,Item2,Type1,Type2)
Items=c("Onion,Potato,Ginger","Tomato","Banana","Palak,Mango","Onion,Capsicum","Orange,Sweet_potato")
Raw_file=data.frame(Items)
Result_Type1=c("Vegetable","Vegetable","Fruit","Leaves","","")
Result_Type2=c("Vegetable","","","Fruit","Vegetable","Fruit")
Result=data.frame(Items,Result_Type1,Result_Type2)
My Output data frame would look like Result.
I tried something with str_detect in case statement but not able to get it. Can someone help me out on this.

Answer 1

Maybe you can do a join between these two tables (similar to your other question).
First would put DF1 in long format. For Raw_file, use separate_rows to have a single item in each row before the join.
library(tidyverse)
DF1_long <- DF1 %>%
pivot_longer(cols = everything(),
names_to = c(".value", "number"),
names_pattern = "(\\w+)(\\d+)$")
Raw_file %>%
mutate(value = Items) %>%
separate_rows(value) %>%
inner_join(DF1_long, by = c("value" = "Item")) %>%
group_by(Items) %>%
distinct(Items, number, .keep_all = TRUE) %>%
pivot_wider(id_cols = Items,
names_from = number,
values_from = Type,
names_prefix = "Result_Type")
Output
Items Result_Type2 Result_Type1
<chr> <chr> <chr>
1 Onion,Potato,Ginger Vegetable Vegetable
2 Tomato NA Vegetable
3 Banana NA Fruit
4 Palak,Mango Fruit Leaves
5 Onion,Capsicum Vegetable NA
6 Orange,Sweet_potato Fruit NA