Data Exploration & Machine Learning, Hands-on


Recommended free walkthrough, check it out and boost your career:

Boost your data science career


The Peter Norvig Magic Spell Checker in R

Practical walkthroughs on machine learning, data exploration and finding insight.

peter norvig magic spell checker


During a recent Kaggle competition, I was introduced to Peter Norvig’s blog entry entitled How to Write a Spelling Corrector. He offers a clever way for any of us to create a good spell checker with nothing more than a few lines of code and some text data. No complex cascading grammar rules or API calls required! In essence, you create slight but increasing alternations for every one of your words against the large corpus of correctly spelled words until you find a match (or until it gives up).

In this post, I simply translated Peter’s Python code into R as closely to the original as possible. I used (or tried my best) the same functions, variable names and constructs. This isn’t about optimization but clarity, and the good news is that there are plenty of ways of optimizing the R code.

Before we start looking at the code, you will need to download Peter’s big.txt and save it locally. big.txt contains a million or so words from Project Gutenberg and other sources.

Hi there, this is Manuel Amunategui- if you're enjoying the content, find more at

Coding Time

The first part is practically a verbatim copy of Peter’s Python version. It just loads the big.txt into memory, strips it of all non-alphabetic characters, applies tolower, and returns unique words and their frequencies.

# point to local folder containing big.txt

words <- function(text){
     text <- gsub(x=text, pattern="[^[:alpha:]]", replacement = ' ')
     return (trimws(tolower(text)))

train <- function(features){
     features <- strsplit(x=features,split = ' ')
     model <-

NWORDS = train(words(readChar('big.txt','big.txt')$size)))
NWORDS$features <- as.character(NWORDS$features)


##   features   Freq
## 1          303982
## 2        a  21155
## 3      aah      1
## 4    aaron      5
## 5       ab      2
## 6    aback      3

Now we get to the heart of the code - we create variations of the word being checked to help find the best match. We get iterations of the word missing a letter, two letters transpositions, sequential replacement and insertions with every letter of the alphabet - pheww!

edits1 <- function(word) {

     # create copies of word with one letter missing
     deletes <- c()
     for (i in seq(1:nchar(word)))
          deletes <- c(deletes, paste0(substr(x=word,start = 0, stop=i-1), 
                       substr(x=word,start = i+1, stop=nchar(word))))

     # create copies of word with consecutive pair of letters transposed                   
     transposes <- c()
     vec_word <- strsplit(word, split = '')[[1]]
     for (i in seq(1:(nchar(word)-1))) {
          vec_word_tmp <- vec_word
          splice <- rev(vec_word_tmp[i:(i+1)])
          vec_word_tmp[i] <- splice[1]
          vec_word_tmp[i+1] <- splice[2]
          transposes <- c(transposes, paste(vec_word_tmp, collapse = ""))
     # create copies of word with every letter swapped with entire alphabet                     
     replaces <- c()
     for (i in seq(1:nchar(word)))
          replaces <- c(replaces, paste0(substr(x=word,start = 0, stop=i-1), 
                                          substr(x=word,start = i+1, stop=nchar(word))))
     # create copies of word with entire alphabet inserted before and after every letter
     inserts <- c()
     for (i in seq(1:(nchar(word)+1)))
          inserts <- c(inserts, paste0(substr(x=word,start = 0, stop=i-1), 
                                          substr(x=word,start = i, stop=nchar(word))))
     return (unique(c(deletes, transposes, replaces, inserts)))

The above code is a lot faster in Python than my R translation - optimization alert!

Peter gets not only gets the custom permutations for the word being checked, he also gets the permutation of the permutation. That creates a huge amount of words, so we trim the created words by checking if they are actual words from our corpus:

known_edits2 <- function(word) {
     matches <- c()
     for (edt in edits1(word)) matches <- c(matches, intersect(edits1(edt),NWORDS$features))

Function to check if the word exits:

known <- function(words) { 
     return (unique(intersect(words,NWORDS$features)))

All functions are ready and we now create the public function correct. This function finds all correctly spelled words and returns the one with the least alterations and highest frequency count:

correct <- function(word){
     correct_spelling <- known(c(word))
     if (identical(correct_spelling, character(0))) {
          correct_spelling <- known(edits1(word))
          if (identical(correct_spelling, character(0))) {
               correct_spelling <- known_edits2(word)
               if (identical(correct_spelling, character(0))) {
                    correct_spelling <- word
     correct_spelling <- data.frame('features'=correct_spelling)
     correct_spelling <- merge(correct_spelling, NWORDS, all.x=TRUE)
     return(as.character(correct_spelling[order(correct_spelling$Freq, decreasing = TRUE),]$features[1]))


## [1] "seeing"


## [1] "spelling"


## [1] "spelling"


## [1] "spelinggg"


Though Peter states this isn’t as accurate as an industrial spell checker, the ramifications of such a simple approach are big! You can easily focus your spell checking to highly specialized/technical terms as long as you have the corpus - just as you can also localize your spelling to other languages with supporting corpus.

And here is a great two-line implementation in R!

Big thanks to Lucas for the Spelling Wizard art work!

Manuel Amunategui - Follow me on Twitter: @amunategui