Table joins with conditional "fuzzy" string matching in R

I'm attempting to join two tables, one is a smaller table with a column of names of common food items (e.g. "Corn", "Peppers", "Squash"...etc...), and the other is a larger table with specific food names (e.g. "Sweet Corn", "Red Corn", "Baby Corn", "Zucchini Squash", "Orange Squash", "Squash , Large"...etc...). I'm trying to join based on a "fuzzy" condition in which I specify to join on the food names and pull the most frequent code in another column of the larger table (the mode) into a new column in the smaller table.

Here is an example of the smaller table:

Here is an example of the larger table:

I've tried the "left_join" function from the dplyr package, but this doesn't seem to work that well with the "fuzzy" string join feature. I know that the tidyverse also has a function to find the mode of grouped variables and I was hoping to use that function, but I am unsure how to incorporate that into the left_join statement. I also discovered the fuzzyjoin package in R, but I am not certain if this is the best option.

My desired output would look like:

I hope this helps you.

In stringdist_join, the max_dist argument is used to constrain the degree of fuzziness.

library(fuzzyjoin)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(knitr)


small_tab = data.frame(Food.Name = c('Corn', 'Squash', 'Peppers'), 
                       Food.Code = c(NA, NA, NA))


large_tab = data.frame(Food.Name = c('Sweet Corn', 'Red Corn', 'Baby Corns', 
                                     'Squash', 'Long Squash', 'Red Pepper', 
                                     'Green Pepper', 'Red Peppers'), 
                       Food.Code = c(532, 532, 944, 111, 123, 654, 655, 654))

joined_tab = stringdist_join(small_tab, large_tab, by = 'Food.Name',
                             ignore_case = TRUE, method = 'cosine', 
                             max_dist = 0.5, distance_col = 'dist') %>%
  
  # Tidy columns 
  select(Food.Name = Food.Name.x, -Food.Name.y, 
         Food.Code = Food.Code.y, -dist) %>%
  
  # Only keep most frequent food code per food name
  group_by(Food.Name) %>% count(Food.Name, Food.Code) %>% 
  slice(which.max(n)) %>% select(-n) %>%
  
  # Order food names as in the small table
  arrange(factor(Food.Name, levels = small_tab$Food.Name))

# Show table with columns renamed
joined_tab %>%
  rename('Food Name' = Food.Name, 
         'Food Code' = Food.Code) %>%
  kable()

^{Created on 2023-05-31 with reprex v2.0.2}