name_* columnsR/match_name.R
match_name.Rdmatch_name() scores the match between names in a loanbook dataset (columns
can be name_direct_loantaker, name_intermediate_parent* and
name_ultimate_parent) with names in an asset-level dataset (column
name_company). The raw names are first internally transformed, and aliases
are assigned. The similarity between aliases in each of the loanbook and ald
datasets is scored using stringdist::stringsim().
match_name( loanbook, ald, by_sector = TRUE, min_score = 0.8, method = "jw", p = 0.1, overwrite = NULL, ... )
| loanbook, ald | data frames structured like r2dii.data::loanbook_demo and r2dii.data::ald_demo. |
|---|---|
| by_sector | Should names only be compared if companies belong to the
same |
| min_score | A number between 0-1, to set the minimum |
| method | Method for distance calculation. One of |
| p | Prefix factor for Jaro-Winkler distance. The valid range for
|
| overwrite | A data frame used to overwrite the |
| ... | Arguments passed on to |
A data frame with the same groups (if any) and columns as loanbook,
and the additional columns:
id_2dii - an id used internally by match_name() to distinguish
companies
level - the level of granularity that the loan was matched at
(e.g direct_loantaker or ultimate_parent)
sector - the sector of the loanbook company
sector_ald - the sector of the ald company
name - the name of the loanbook company
name_ald - the name of the ald company
score - the score of the match (manually set this to 1
prior to calling prioritize() to validate the match)
source - determines the source of the match. (equal to loanbook
unless the match is from overwrite
The returned rows depend on the argument min_value and the result of the
column score for each loan: * If any row has score equal to 1,
match_name() returns all rows where score equals 1, dropping all other
rows. * If no row has score equal to 1,match_name() returns all rows
where score is equal to or greater than min_score. * If there is no
match the output is a 0-row tibble with the expected column names -- for
type stability.
r2dii.match.sector_classifications: Allows you to use your own
sector_classififications instead of the default. This feature is
experimental and may be dropped and/or become a new argument to
match_name().
The transformation process used to compare names between loanbook and ald datasets applies best practices commonly used in name matching algorithms:
Remove special characters.
Replace language specific characters.
Abbreviate certain names to reduce their importance in the matching.
Spell out numbers to increase their importance.
This function ignores but preserves existing groups.
Other main functions:
prioritize()
library(r2dii.data) library(tibble) # Small data for examples loanbook <- head(loanbook_demo, 50) ald <- head(ald_demo, 50) match_name(loanbook, ald)#> # A tibble: 2 × 28 #> id_loan id_direct_loantaker name_direct_loa… id_intermediate… name_intermedia… #> <chr> <chr> <chr> <chr> <chr> #> 1 L14 C296 Yuasfnjiang Ele… NA NA #> 2 L15 C295 Yuanbsaoshan Po… NA NA #> # … with 23 more variables: id_ultimate_parent <chr>, #> # name_ultimate_parent <chr>, loan_size_outstanding <dbl>, #> # loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>, #> # loan_size_credit_limit_currency <chr>, sector_classification_system <chr>, #> # sector_classification_input_type <chr>, #> # sector_classification_direct_loantaker <dbl>, fi_type <chr>, #> # flag_project_finance_loan <chr>, name_project <lgl>, …match_name(loanbook, ald, min_score = 0.9)#> # A tibble: 1 × 28 #> id_loan id_direct_loantaker name_direct_loa… id_intermediate… name_intermedia… #> <chr> <chr> <chr> <chr> <chr> #> 1 L14 C296 Yuasfnjiang Ele… NA NA #> # … with 23 more variables: id_ultimate_parent <chr>, #> # name_ultimate_parent <chr>, loan_size_outstanding <dbl>, #> # loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>, #> # loan_size_credit_limit_currency <chr>, sector_classification_system <chr>, #> # sector_classification_input_type <chr>, #> # sector_classification_direct_loantaker <dbl>, fi_type <chr>, #> # flag_project_finance_loan <chr>, name_project <lgl>, …# Use your own `sector_classifications` your_classifications <- tibble( sector = "power", borderline = FALSE, code = "3511", code_system = "XYZ" ) restore <- options(r2dii.match.sector_classifications = your_classifications) loanbook <- tibble( sector_classification_system = "XYZ", sector_classification_direct_loantaker = "3511", id_ultimate_parent = "UP15", name_ultimate_parent = "Alpine Knits India Pvt. Limited", id_direct_loantaker = "C294", name_direct_loantaker = "Yuamen Xinneng Thermal Power Co Ltd" ) ald <- tibble( name_company = "alpine knits india pvt. limited", sector = "power", alias_ald = "alpineknitsindiapvt ltd" ) match_name(loanbook, ald)#> # A tibble: 1 × 15 #> sector_classificat… sector_classificatio… id_ultimate_par… name_ultimate_pare… #> <chr> <chr> <chr> <chr> #> 1 XYZ 3511 UP15 Alpine Knits India… #> # … with 11 more variables: id_direct_loantaker <chr>, #> # name_direct_loantaker <chr>, id_2dii <chr>, level <chr>, sector <chr>, #> # sector_ald <chr>, name <chr>, name_ald <chr>, score <dbl>, source <chr>, #> # borderline <lgl>