两个数据库的模糊匹配和精确匹配

Question

I have two databases. The first one has about 70k rows with 3 columns. the second one has 790k rows with 2 columns. Both databases have a common variable grantee_name . I want to match each row of the first database to one or more rows of the second database based on this grantee_name . Note that merge will not work because the grantee_name do not match perfectly. There are different spellings etc. So, I am using the fuzzyjoin package and trying the following:

library("haven"); library("fuzzyjoin"); library("dplyr")
forfuzzy<-read_dta("/path/forfuzzy.dta")
filings <- read_dta ("/path/filings.dta")
> head(forfuzzy)
# A tibble: 6 x 3
  grantee_name                 grantee_city grantee_state
  <chr>                        <chr>        <chr>        
1 (ICS)2 MAINE CHAPTER         CLEARWATER   FL           
2 (SUFFOLK COUNTY) VANDERBILT~ CENTERPORT   NY           
3 1 VOICE TREKKING A FUND OF ~ WESTMINSTER  MD           
4 10 CAN                       NEWBERRY     FL           
5 10 THOUSAND WINDOWS          LIVERMORE    CA           
6 100 BLACK MEN IN CHICAGO INC CHICAGO      IL   
... 7 - 70000 rows to go

> head(filings)
# A tibble: 6 x 2
  grantee_name                       ein 
  <chr>                             <dbl>               
1 ICS-2 MAINE CHAPTER              123456             
2 SUFFOLK COUNTY VANDERBILT        654321            
3 VOICE TREKKING A FUND OF VOICES  789456            
4 10 CAN                           654987               
5 10 THOUSAND MUSKETEERS INC       789123               
6 100 BLACK MEN IN HOUSTON INC     987321      

rows 7-790000 omitted for brevity

The above examples are clear enough to provide some good matches and some not-so-good matches. Note that, for example, 10 THOUSAND WINDOWS will match best with 10 THOUSAND MUSKETEERS INC but it does not mean it is a good match. There will be a better match somewhere in the filings data (not shown above). That does not matter at this stage.

So, I have tried the following:

df<-as.data.frame(stringdist_inner_join(forfuzzy, filings, by="grantee_name", method="jw", p=0.1, max_dist=0.1, distance_col="distance"))

Totally new to R. This is resulting in an error: cannot allocate vector of size 375GB (with the big database of course). A sample of 100 rows from forfuzzy always works. So, I thought of iterating over a list of 100 rows at a time.

I have tried the following:

n=100
lst = split(forfuzzy, cumsum((1:nrow(forfuzzy)-1)%%n==0))

df<-as.data.frame(lapply(lst, function(df_)
{
(stringdist_inner_join(df_, filings, by="grantee_name", method="jw", p=0.1, max_dist=0.1, distance_col="distance", nthread = getOption("sd_num_thread")))
}
)%>% bind_rows)

I have also tried the above with mclapply instead of lapply . Same error happens even though I have tried a high-performance cluster setting 3 CPUs, each with 480G of memory and using mclapply with the option mc.cores=3 . Perhaps a foreach command could help, but I have no idea how to implement it.

I have been advised to use the purrr and repurrrsive packages, so I try the following:

purrr::map(lst, ~stringdist_inner_join(., filings, by="grantee_name", method="jw", p=0.1, max_dist=0.1, distance_col="distance", nthread = getOption("sd_num_thread")))

This seems to be working, after a novice error in the by=grantee_name statement. However, it is taking forever and I am not sure it will work. A sample list in forfuzzy of 100 rows, with n=10 (so 10 lists with 10 rows each) has been running for 50 minutes, and still no results.

Answer 1

I haven't used foreach before but maybe the variable x is already the individual rows of zz1?

Have you tried:

stringdist_inner_join(x, zz2, by="grantee_name", method="jw", p=0.1, max_dist=0.1, distance_col="distance")

?

Answer 2

If you split (with base::split or dplyr::group_split ) your uniquegrantees data frame into a list of data frames, then you can call purrr::map on the list. ( map is pretty much lapply )

purrr::map(list_of_dfs, ~stringdist_inner_join(., filings, by="grantee_name", method="jw", p=0.1, max_dist=0.1, distance_col="distance"))

Your result will be a list of data frames each fuzzyjoined with filings. You can then call bind_rows (or you could do map_dfr ) to get all the results in the same data frame again.

See R - Splitting a large dataframe into several smaller dateframes, performing fuzzyjoin on each one and outputting to a single dataframe