stringdist

I'm looking for a way to speed up the following approach. Any pointers are very welcome. Where are the bottlenecks? Say I have the following data.frame : df <- data.frame(names=c("A ADAM", "S BEAN", "A APPLE", "J BOND", "J BOND"), v1=c("Test_a", "Test_b", "Test_a", "Test_b", "Test_b"), v2=c("Test_c", "Test_c", "Test_d", "Test_d", "Test_d")) I want to compare each pair of rows in df on their JaroWinkler similarity. With some help of others ( see this post ), I've been able to construct this code: #columns to compare testCols <- c("names", "v1", "v2") #compare pairs RowCompare= function(x){ comp

I was answering these two questions and got an adequate solution, but I had trouble passing arguments using fuzzy_join into the match_fun that I extracted from fuzzyjoin::stringdist_join . In this case, I'm using a mix of multiple match_fun's, including this customized match_fun_stringdist and also == and <= for exact and criteria matching. The error message I'm getting is: # Error in mf(rep(u_x, n_y), rep(u_y, each = n_x), ...): object 'ignore_case' not found # Data: library(data.table, quietly = TRUE) Address1 <- c("786, GALI NO 5, XYZ","rambo, 45, strret 4, atlast, pqr","23/4, 23RD FLOOR,

I have a data.table dt with 3 columns: id name as string threshold as num A sample is: dt <- <- data.table(nid = c("n1","n2", "n3", "n4"), rname = c("apple", "pear", "banana", "kiwi"), maxr = c(0.5, 0.8, 0.7, 0.6)) nid | rname | maxr n1 | apple | 0.5 n2 | pear | 0.8 n3 | banana | 0.7 n4 | kiwi | 0.6 I have a second table dt.ref with 2 columns: id name as string A sample is: dt.ref <- <- data.table(cid = c("c1", "c2", "c3", "c4", "c5", "c6"), cname = c("apple", "maple", "peer", "dear", "bonobo", "kiwis")) cid | cname c1 | apple c2 | maple c3 | peer c4 | dear c5 | bonobo c6 | kiwis For each

I have two large datasets, one around half a million records and the other one around 70K. These datasets have address. I want to match if any of the address in the smaller data set are present in the large one. As you would imagine address can be written in different ways and in different cases so it is quite annoying to see that there is not a match when it should have matched and there is a match when it should not have matched. I did some research and figured out the package stringdist that can be used. However I am stuck and I feel I am not using to its fullest capabilities and some

Is there a package that contains Levenshtein distance counting function which is implemented as a C or Fortran code? I have many strings to compare and stringMatch from MiscPsycho is too slow for this. George Dontas levenshteinDist (from the RecordLinkage package) calls compiled C code. Give it a try. And stringdist in the stringdist package does it too, even faster than levenshteinDist under certain conditions ( 1 ) Aaron Statham You could try stringDist from Biostrings as well 来源： https://stackoverflow.com/questions/3182091/fast-levenshtein-distance-in-r