This is a quick way to use levenshtein distance to match two sets of entries in a 'fuzzy' way. The key is to match the entry in one set with its nearest neighbor in the other set, measuring distance as 'edit distance'.

distance match.r

my_match<-function(m,sp,sp_match='name_spelling',m_match='team',robust=T){
require(RecordLinkage)
require(plyr)
matches<-ldply(m[,m_match],.fun=function(x){
  distances<-levenshteinDist(x,sp[,c(sp_match)])
  most_likely<-sp[which(distances==min(distances)),]
  if(robust==T){
    if(length(most_likely[,1])>1){
      most_likely<-cbind(x,most_likely[],1,min(distances))
    }else{
      most_likely<-cbind(x,most_likely[],0,min(distances))
    }
  }else{
      if(length(most_likely[,1])>1){
        most_likely<-cbind(x,most_likely[1,],1,min(distances))
      }else{
        most_likely<-cbind(x,most_likely[1,],0,min(distances))
      }
    }
  most_likely<-data.frame(most_likely)
  names(most_likely)[1]<-"m_match"
  names(most_likely)[length(most_likely[1,])-1]<-"fuzzy"
  names(most_likely)[length(most_likely[1,])]<-"dist"
  

  return(most_likely[])
})
return(matches)
}

# example:

m<-data.frame(team=c("apple","Peach","orange","watermelon"))
sp<-data.frame(spelling=c("ap","peac","orang","range","water melon","Water melon"),id=c(1:6))
my_match(m,sp,sp_match='spelling',m_match='team',robust=T) # gets all matches for orange
my_match(m,sp,sp_match='spelling',m_match='team',robust=F) # only pulls first match, 'orang', for orange