[R] Is there a package that can do Fuzzy name matching to standardize names in a single column

[Bert Gunter]

As these are English names and appear to be present always as **first
?? last** (you didn't specify but that's how your example shows it),
maybe something like the following might be a start:

1. Use strsplit() to split the names into their constituent parts.
2. Find the last *meaningful* part in each vector (e.g. Joe Smith Jr.
should exclude Jr. and choose Smith)
3. Split the names into the groups of identical unique last parts
4. Split each of the groups of last names into subgroups based on the
first one or more letters of first name so that, e.g. Joe and Joseph
would be in the same subgroup of Smith. Of course Joe and John would
be also, so you see the problem...

Other Issues:
Are Joe Smith and Joe Smith Jr. the same person?
Misspellings? Typos?  Is Arlene Smith the same as Alene Smith?

Some sort of clustering of the names might also be appropriate. See
https://cran.r-project.org/web/views/Cluster.html  for ideas.

Cheers,
Bert

On Wed, Jun 15, 2022 at 7:58 AM Gregg Powell via R-help
<r-help using r-project.org> wrote:
>
> Have data sets where there are names, in the first column, client names in the second, and Client start date in the third.
>
> There are thousands of these records with thousands of names/clients/client start dates. The name is entered each time the person begins with a new client such that each person has many entries in the name column. Often the names were not entered in a consistent way. With and without middle initial, middle name, or various abbreviations such as ",RN" at the end of the name.
>
> Is there a package that can do fuzzy name matching so that the names in name column get replaced with a "standardized" format - where some type of machine learning can pick the most common spelling of each repeat name and replace the different variations with the common spelling?
>
> I included an example below. First table includes the names with the various spellings. Second table depicts what I hope to achieve.
>
> Again - this is on a large scale - there are something like 10,000 records with names that need to be standardized.
>
>
> Name
>
> Client
>
> Client Start Date
>
> John Good
>
> Client 1
>
> 1/1/2020
>
> Joe Jackson
>
> Client 2
>
> 6/1/2020
>
> Bob A. Barker
>
> Client 3
>
> 8/1/2020
>
> John B. Good
>
> Client 4
>
> 10/1/2020
>
> Joe J. Jackson
>
> Client 5
>
> 12/1/2020
>
> Bob Allen Barker
>
> Client 6
>
> 1/1/2021
>
> John Good
>
> Client 7
>
> 5/1/2021
>
> Joe Jack Jackson
>
> Client 8
>
> 8/1/2021
>
> Bob Barker
>
> Client 9
>
> 12/1/2021
>
>
>
>
>
>
>
> Name
>
> Client
>
> Client Start Date
>
> John Good
>
> Client 1
>
> 1/1/2020
>
> Joe J. Jackson
>
> Client 2
>
> 6/1/2020
>
> Bob A. Barker
>
> Client 3
>
> 8/1/2020
>
> John Good
>
> Client 4
>
> 10/1/2020
>
> Joe J. Jackson
>
> Client 5
>
> 12/1/2020
>
> Bob A. Barker
>
> Client 6
>
> 1/1/2021
>
> John Good
>
> Client 7
>
> 5/1/2021
>
> Joe J. Jackson
>
> Client 8
>
> 8/1/2021
>
> Bob A. Barker
>
> Client 9
>
> 12/1/2021
>
>
>
> THANKS!
>
> Gregg Powell
>
> Arizona, USA______________________________________________
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