Can a stemming dictionary be used as rejection criteria in R?

I am struggling through some text analysis, and I'm not sure I'm doing the stemming correctly. Right now, my command for single-term stemming is

text_stem <- text_clean %>% mutate(stem = wordStem(word, language = "english"))

Is it possible to use this not only as a stemmer, but as a filter? For example, if "text_clean" contains the word aksdjhgla and that word is not in whatever SnowballC uses as a dictionary, the stemmed text would reject it? Maybe there's another command that does this kind of filtering?

wordStem does not employ a dictionary but uses grammatical rules to do stemming (which is a rather crude approximation to lemmatisation btw). Here is an example:

words <- c("win", "winning")
words2 <- c("aksdjhglain", "aksdjhglainning")

SnowballC::wordStem(words, language = "english")
#> [1] "win"    "win"
SnowballC::wordStem(words2, language = "english")
#> [1] "aksdjhglain"  "aksdjhglain"

As you can see, wordStem does exactly the same, no matter if the words actually exist or are complete rubbish. All that matters are the word endings (ie stems). As @Kat suggested, you probably want to look at the hunspell package which actually uses dictionaries. To find out which words exist in the dictionary, use hunspell_check:

hunspell::hunspell_check(c(words, words2))
#> [1]  TRUE  TRUE  FALSE FALSE

Inside your existing code, you could use this to remove misspelled words:

text_stem <- text_clean %>% 
  mutate(stem = wordStem(word, language = "english")) %>% 
  filter(hunspell::hunspell_check(word), dict = dictionary("en_US"))