Efficient way of creating a new variable via fuzzy string matching and grouped summarization in R

I'm trying to use fuzzy string matching to convert strings to specific ids and perform grouped summarization using dplyr. The basic idea is combining imperfect gene sequences into a single gene name via a dictionary lookup approach and counting how many times the gene is detected. This way, counts for sequences aaaaaa and aaaxaa match to gene1 and get added together.

I can do what I want using for and if statements via a row-by-row comparison of the raw data against the dictionary but I find this will be inefficient when I scale up (raw data files have 15k rows on average, the dictionary has 200 rows). Please see my solution below I'm trying to improve and let me know if you can think of a more efficient and elegant way of doing this.

df <- data.frame(str_var = rep(c("aaaaaa", "aXaaaa", "bbbbbb", "bbbXbb"), 3),
                 grp_var = rep(c("grp1","grp2"), each=6),
                 num_var = rep(c(1,2), 6))

df
#>    str_var grp_var num_var
#> 1   aaaaaa    grp1       1
#> 2   aXaaaa    grp1       2
#> 3   bbbbbb    grp1       1
#> 4   bbbXbb    grp1       2
#> 5   aaaaaa    grp1       1
#> 6   aXaaaa    grp1       2
#> 7   bbbbbb    grp2       1
#> 8   bbbXbb    grp2       2
#> 9   aaaaaa    grp2       1
#> 10  aXaaaa    grp2       2
#> 11  bbbbbb    grp2       1
#> 12  bbbXbb    grp2       2


dictionary <- data.frame(string = c("aaaaaa","bbbbbb", "cccccc", "dddddd"),
                         id = c("gene1", "gene2", "gene3", "gene4"))

dictionary
#>   string    id
#> 1 aaaaaa gene1
#> 2 bbbbbb gene2
#> 3 cccccc gene3
#> 4 dddddd gene4

for(i in 1:nrow(df)){
    
    
    for(j in 1:nrow(dictionary)){
        
        match_found <- agrepl(dictionary$string[j], df$str_var[i],
                              max.distance = list(sub=1, ins=0, del=0, all=1-1e-9))
        
        if(match_found == TRUE){
            
            gene = dictionary[j, "id"]
            
            df$gene_id[i] <- gene
            
            break
            
        }
        
    }
    
}

suppressPackageStartupMessages(library(dplyr))

new_df <- df %>%
    group_by(grp_var, gene_id) %>%
    summarize(gene_count=sum(num_var))
#> `summarise()` has grouped output by 'grp_var'. You can override using the `.groups` argument.

new_df
#> # A tibble: 4 x 3
#> # Groups:   grp_var [2]
#>   grp_var gene_id gene_count
#>   <chr>   <chr>        <dbl>
#> 1 grp1    gene1            6
#> 2 grp1    gene2            3
#> 3 grp2    gene1            3
#> 4 grp2    gene2            6

^{Created on 2021-06-08 by the reprex package (v2.0.0)}