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update README
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@jacobvjk
jacobvjk committed Apr 19, 2024
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Expand Up @@ -58,26 +58,26 @@ and run fuzzy matching against all company names in the `abcd`:
``` r
match_result <- match_name(loanbook_demo, abcd_demo)
match_result
#> # A tibble: 329 × 28
#> id_loan id_direct_loantaker name_direct_loantaker id_intermediate_pare…¹
#> <chr> <chr> <chr> <chr>
#> 1 L1 C294 Vitale Group <NA>
#> 2 L3 C292 Rowe-Rowe IP5
#> 3 L5 C305 Ring AG & Co. KGaA <NA>
#> 4 L6 C304 Kassulke-Kassulke <NA>
#> 5 L6 C304 Kassulke-Kassulke <NA>
#> 6 L7 C227 Morissette Group <NA>
#> 7 L7 C227 Morissette Group <NA>
#> 8 L8 C303 Barone s.r.l. <NA>
#> 9 L9 C301 Werner Werner AG & Co. KG… IP10
#> 10 L9 C301 Werner Werner AG & Co. KG… IP10
#> # ℹ 319 more rows
#> # ℹ abbreviated name: ¹​id_intermediate_parent_1
#> # ℹ 24 more variables: name_intermediate_parent_1 <chr>,
#> # 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>, …
#> # A tibble: 326 × 22
#> id_loan id_direct_loantaker name_direct_loantaker id_ultimate_parent
#> <chr> <chr> <chr> <chr>
#> 1 L1 C294 Vitale Group UP15
#> 2 L3 C292 Rowe-Rowe UP288
#> 3 L5 C305 Ring AG & Co. KGaA UP104
#> 4 L6 C304 Kassulke-Kassulke UP83
#> 5 L6 C304 Kassulke-Kassulke UP83
#> 6 L7 C227 Morissette Group UP134
#> 7 L7 C227 Morissette Group UP134
#> 8 L8 C303 Barone s.r.l. UP163
#> 9 L9 C301 Werner Werner AG & Co. KGaA UP138
#> 10 L9 C301 Werner Werner AG & Co. KGaA UP138
#> # ℹ 316 more rows
#> # ℹ 18 more variables: 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_direct_loantaker <chr>, lei_direct_loantaker <chr>,
#> # isin_direct_loantaker <chr>, id_2dii <chr>, level <chr>, sector <chr>,
#> # sector_abcd <chr>, name <chr>, name_abcd <chr>, score <dbl>, …
```

### 2. Prioritize validated matches
Expand All @@ -92,26 +92,26 @@ matches, prioritizing (by default) `direct_loantaker` matches over

``` r
prioritize(match_result)
#> # A tibble: 177 × 28
#> id_loan id_direct_loantaker name_direct_loantaker id_intermediate_pare…¹
#> <chr> <chr> <chr> <chr>
#> 1 L6 C304 Kassulke-Kassulke <NA>
#> 2 L13 C297 Ladeck <NA>
#> 3 L20 C287 Weinhold <NA>
#> 4 L21 C286 Gallo Group <NA>
#> 5 L22 C285 Austermuhle GmbH <NA>
#> 6 L24 C282 Ferraro-Ferraro Group <NA>
#> 7 L25 C281 Lockman, Lockman and Lock… <NA>
#> 8 L26 C280 Ankunding, Ankunding and … <NA>
#> 9 L27 C278 Donati-Donati Group <NA>
#> 10 L28 C276 Ferraro, Ferraro e Ferrar… <NA>
#> # A tibble: 177 × 22
#> id_loan id_direct_loantaker name_direct_loantaker id_ultimate_parent
#> <chr> <chr> <chr> <chr>
#> 1 L6 C304 Kassulke-Kassulke UP83
#> 2 L13 C297 Ladeck UP69
#> 3 L20 C287 Weinhold UP35
#> 4 L21 C286 Gallo Group UP63
#> 5 L22 C285 Austermuhle GmbH UP187
#> 6 L24 C282 Ferraro-Ferraro Group UP209
#> 7 L25 C281 Lockman, Lockman and Lockman UP296
#> 8 L26 C280 Ankunding, Ankunding and Anku… UP67
#> 9 L27 C278 Donati-Donati Group UP45
#> 10 L28 C276 Ferraro, Ferraro e Ferraro SPA UP195
#> # ℹ 167 more rows
#> # ℹ abbreviated name: ¹​id_intermediate_parent_1
#> # ℹ 24 more variables: name_intermediate_parent_1 <chr>,
#> # 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>, …
#> # ℹ 18 more variables: 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_direct_loantaker <chr>, lei_direct_loantaker <chr>,
#> # isin_direct_loantaker <chr>, id_2dii <chr>, level <chr>, sector <chr>,
#> # sector_abcd <chr>, name <chr>, name_abcd <chr>, score <dbl>, …
```

The result is a dataset with identical columns to the input loanbook,
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