Finding and matching typos in strings in a dataframe in R. See the question at https://stackoverflow.com/questions/45990947/how-to-find-a-typo-in-a-data-frame-and-replace-it/

matchTypos.R

library(stringdist)
library(dplyr)

#Example Data Frame to find and correct typos in
my_df<-data.frame(BIRTH = c(1,1,2,3,1,5,3,3,1),
           NAME = c("Luke","Luke","Leia","Han","Ben","Lando","Han","Ham","Luke"),
           SURNAME = c("Skywalker","Skywalker","Organa","Solo","Solo","Calrissian","Solo","Solo","Wkywalker"),
           random_value = c(1,2,3,7,1,3,4,4,9))

#Concatenate the birthday and name columns
my_df$birth_and_names <- do.call(paste, c(my_df[c("BIRTH", "NAME", "SURNAME")], sep = " ")) 

#Work out the string distances between each possible pair of entries
dist.matrix<-stringdistmatrix(my_df$birth_and_names,my_df$birth_and_names,method='jw',p=0.1)
row.names(dist.matrix)<-my_df$birth_and_names
names(dist.matrix)<-my_df$birth_and_names
dist.matrix<-as.dist(dist.matrix)

#Hierarchical clustering to find closest
clusts<-hclust(dist.matrix,method="ward.D2")

plot(clusts)

#Cut into appropriate clusters based upon height in the dendrogram
my_df$LikelyGroup<-cutree(clusts,h=0.2)

#Define "mode" function which only selects one mode even in bimodal cases.
Mode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}

#Select modal name for each group
my_df<-my_df%>%
  group_by(LikelyGroup)%>%
  mutate(Group_Birth=Mode(BIRTH),
         Group_Name=Mode(NAME),
         Group_Surname=Mode(SURNAME),
         birth_and_names=NULL)