Package 'r2dii.match'

Description

This is a helper to select the minimum loanbook columns you need to run match_name(). Using more columns may use too much time and memory.

Usage

crucial_lbk()

Value

A character vector.

See Also

Other helpers: prioritize_level()

Examples

crucial_lbk()

Description

A table of column names and descriptions of data frames used or exported by the functions in this package.

Usage

data_dictionary

Format

data_dictionary

dataset

Name of the dataset

column

Name of the column

typeof

Type of the column

definition

Definition of the column

Examples

data_dictionary

Description

match_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-based company data (column name_company). The raw names are first internally transformed, and aliases are assigned. The similarity between aliases in each of the loanbook and abcd is scored using stringdist::stringsim().

Usage

match_name(
  loanbook,
  abcd,
  by_sector = TRUE,
  min_score = 0.8,
  method = "jw",
  p = 0.1,
  overwrite = NULL,
  join_id = NULL,
  sector_classification = default_sector_classification(),
  ...
)

Arguments

Value

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_abcd - the sector of the abcd company

• name - the name of the loanbook company

• name_abcd - the name of the abcd 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.

Assigning aliases

The transformation process used to compare names between loanbook and abcd 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.

Handling grouped data

This function ignores but preserves existing groups.

See Also

Other main functions: prioritize()

Examples

## Not run: 
library(r2dii.data)
library(tibble)

# Small data for examples
loanbook <- head(loanbook_demo, 50)
abcd <- head(abcd_demo, 50)

match_name(loanbook, abcd)

match_name(loanbook, abcd, min_score = 0.9)

# match on LEI
loanbook <- tibble(
  sector_classification_system = "NACE",
  sector_classification_direct_loantaker = "D35.11",
  id_ultimate_parent = "UP15",
  name_ultimate_parent = "Won't fuzzy match",
  id_direct_loantaker = "C294",
  name_direct_loantaker = "Won't fuzzy match",
  lei_direct_loantaker = "LEI123"
)

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

match_name(loanbook, abcd, join_id = c(lei_direct_loantaker = "lei"))

# Use your own `sector_classifications`
your_classifications <- tibble(
  sector = "power",
  borderline = FALSE,
  code = "D35.11",
  code_system = "XYZ"
)

loanbook <- tibble(
  sector_classification_system = "XYZ",
  sector_classification_direct_loantaker = "D35.11",
  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"
)

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

match_name(loanbook, abcd, sector_classification = your_classifications)

# Cleanup
options(restore)

## End(Not run)

Description

When multiple perfect matches are found per loan (e.g. a match at direct_loantaker level and ultimate_parent level), we must prioritize the desired match. By default, the highest priority is the most granular match (i.e. direct_loantaker).

Usage

prioritize(data, priority = NULL)

Arguments

Details

How to validate data Write the output of match_name() into a .csv file with:

# Writting to current working directory
matched %>%
  readr::write_csv("matched.csv")

Compare, edit, and save the data manually:

• Open matched.csv with any spreadsheet editor (Excel, Google Sheets, etc.).

• Compare the columns name and name_abcd manually to determine if the match is valid. Other information can be used in conjunction with just the names to ensure the two entities match (sector, internal information on the company structure, etc.)

• Edit the data:

  • If you are happy with the match, set the score value to 1.

  • Otherwise set or leave the score value to anything other than 1.

• Save the edited file as, say, valid_matches.csv.

Re-read the edited file (validated) with:

# Reading from current working directory
valid_matches <- readr::read_csv("valid_matches.csv")

Value

A data frame with a single row per loan, where score is 1 and priority level is highest.

Handling grouped data

This function ignores but preserves existing groups.

See Also

match_name(), prioritize_level().

Other main functions: match_name()

Examples

library(dplyr)

# styler: off
matched <- tribble(
  ~sector, ~sector_abcd,  ~score, ~id_loan,                ~level,
   "coal",      "coal",       1,     "aa",     "ultimate_parent",
   "coal",      "coal",       1,     "aa",    "direct_loantaker",
   "coal",      "coal",       1,     "bb", "intermediate_parent",
   "coal",      "coal",       1,     "bb",     "ultimate_parent",
)
# styler: on

prioritize_level(matched)

# Using default priority
prioritize(matched)

# Using the reverse of the default priority
prioritize(matched, priority = rev)

# Same
prioritize(matched, priority = ~ rev(.x))

# Using a custom priority
bad_idea <- c("intermediate_parent", "ultimate_parent", "direct_loantaker")

prioritize(matched, priority = bad_idea)

Description

Arrange unique level values in default order of priority

Usage

prioritize_level(data)

Arguments

Value

A character vector of the default level priority per loan.

See Also

Other helpers: crucial_lbk()

Examples

matched <- tibble::tibble(
  level = c(
    "intermediate_parent_1",
    "direct_loantaker",
    "direct_loantaker",
    "direct_loantaker",
    "ultimate_parent",
    "intermediate_parent_2"
  )
)
prioritize_level(matched)