Here’s an example of fuzzy-matching strings in R that I shared on StackOverflow. 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}