feat: match_name gains functionality to automatically join by LEI/ISIN (or other specified identifer)

[maurolepore]

Will it be possible to add the matching by LEI/ISIN? I remember discussing it w/ you Jackson, I believe we had said it wasn't a priority. But since the match.name() function gets so much done, it'll be hard for a user to know where to 'manually' fit their LEI matches in the middle. It'd consist in eg adding an LEI column in the lbk, and in the ald, and having priorityze take those into account as well, and the matchname function not return any stringdist matches for those companies matched via LEIs

--@fpalandri

AB#10175

[jdhoffa]

• Add a handful of ISINs to raw loanbook and ald
• matchname(..., ignore_ISIN = FALSE)
• matching priority: ISIN at direct_loantaker level > everything else

[jdhoffa]

This seems to do it:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(r2dii.data)
library(r2dii.match)

loanbook <- tibble(
  sector_classification_system = "NACE",
  sector_classification_direct_loantaker = "100", # this generally shouldn't match to anything
  id_ultimate_parent = c("UP15", "UP16"),
  name_ultimate_parent = c("Alpine Knits India Pvt. Limited", "Alpine Knits No Match"),
  id_direct_loantaker = c("C294", "C295"),
  name_direct_loantaker = "Yuamen Xinneng Thermal Power Co Ltd",
  lei = c("LEI123", NA_character_)
)

abcd <- tibble(
  name_company = "alpine knits india pvt. limited",
  sector = "power",
  lei = "LEI123"
)

# finds no match using only fuzzy matching
match_name(loanbook, abcd)
#> Warning: Found no match.
#> # A tibble: 0 × 16
#> # ℹ 16 variables: sector_classification_system <chr>,
#> #   sector_classification_direct_loantaker <chr>, id_ultimate_parent <chr>,
#> #   name_ultimate_parent <chr>, id_direct_loantaker <chr>,
#> #   name_direct_loantaker <chr>, lei <chr>, id_2dii <lgl>, level <lgl>,
#> #   sector <lgl>, sector_abcd <lgl>, name <lgl>, name_abcd <lgl>, score <lgl>,
#> #   source <lgl>, borderline <lgl>

# accepts the match using a direct LEI join

match_name_ <- function(loanbook, abcd, join_id = NULL) {
  if (!is.null(join_id)) {
    join_id <- "lei"
    loanbook <-  dplyr::mutate(loanbook, join_id = .data[[join_id]], !!join_id := NULL)
    abcd <- dplyr::mutate(abcd, join_id = .data[[join_id]], !!join_id := NULL)
    
    loanbook_joined <- dplyr::inner_join(loanbook, abcd, by = "join_id")
    loanbook_joined <- dplyr::mutate(loanbook_joined, !!join_id := NULL)
    loanbook <- dplyr::filter(loanbook, !.data[["join_id"]] %in% loanbook_joined[["join_id"]])
  }
  
  out <- suppressWarnings(match_name(loanbook, abcd))
  
  if (nrow(out) != 0 && exists("loanbook_joined")) {
    out <- dplyr::bind_rows(loanbook_joined, out)
  } else if (nrow(out) == 0 && exists("loanbook_joined")) {
    out <- loanbook_joined
  }
  
  out
}

match_name_(loanbook, abcd, join_id = "lei")
#> # A tibble: 1 × 9
#>   sector_classification_system sector_classification_direct…¹ id_ultimate_parent
#>   <chr>                        <chr>                          <chr>             
#> 1 NACE                         100                            UP15              
#> # ℹ abbreviated name: ¹​sector_classification_direct_loantaker
#> # ℹ 6 more variables: name_ultimate_parent <chr>, id_direct_loantaker <chr>,
#> #   name_direct_loantaker <chr>, join_id <chr>, name_company <chr>,
#> #   sector <chr>

^{Created on 2024-03-06 with reprex v2.1.0}