fuzzy matching in R

I am trying to detect matches between an open text field (read: messy!) with a vector of names. I created a silly fruit example that highlights my main challenges.

df1 <- data.frame(id = c(1, 2, 3, 4, 5, 6),
              entry = c("Apple", 
                        "I love apples", 
                        "appls",
                        "Bannanas",
                        "banana", 
                        "An apple a day keeps..."))
df1$entry <- as.character(df1$entry)

df2 <- data.frame(fruit=c("apple",
                          "banana",
                          "pineapple"),
                  code=c(11, 12, 13))
df2$fruit <- as.character(df2$fruit)

df1 %>%
  mutate(match = str_detect(str_to_lower(entry), 
                            str_to_lower(df2$fruit)))

My approach grabs the low hanging fruit, if you will (exact matches for "Apple" and "banana").

#  id                   entry match
#1  1                   Apple  TRUE
#2  2           I love apples FALSE
#3  3                   appls FALSE
#4  4                Bannanas FALSE
#5  5                  banana  TRUE
#6  6 An apple a day keeps... FALSE

The unmatched cases have different challenges:

1. The target fruit in cases 2 and 6 are embedded in larger strings.
2. The target fruit in 3 and 4 require a fuzzy match.

The fuzzywuzzyR package is great and does a pretty good job (see page for details on installing python modules).

library(fuzzywuzzyR)
choices <- df2$fruit
word <- df1$entry[3]  # "appls"

init_proc = FuzzUtils$new()      
PROC = init_proc$Full_process    
PROC1 = tolower                  

init_scor = FuzzMatcher$new()    
SCOR = init_scor$WRATIO          

init <- FuzzExtract$new()        

init$Extract(string = word, 
             sequence_strings = choices, 
             processor = PROC, 
             scorer = SCOR)

This setup returns a score of 80 for "apple" (the highest).

Is there another approach to consider aside from fuzzywuzzyR? How would you tackle this problem?

Adding fuzzywuzzyR output:

[[1]]
[[1]][[1]]
[1] "apple"

[[1]][[2]]
[1] 80


[[2]]
[[2]][[1]]
[1] "pineapple"

[[2]][[2]]
[1] 72


[[3]]
[[3]][[1]]
[1] "banana"

[[3]][[2]]
[1] 18