Pick rows where score is 1 and level per loan is of highest priority

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).

prioritize(data, priority = NULL)

Arguments

data	A data frame like the validated output of match_name(). See Details on how to validate data.
priority	One of: NULL: defaults to the default level priority as returned by prioritize_level(). A character vector giving a custom priority. A function to apply to the output of prioritize_level(), e.g. rev. A quosure-style lambda function, e.g. ~ rev(.x).

Value

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

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_ald 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")

Handling grouped data

This function ignores but preserves existing groups.

See also

match_name(), prioritize_level().

Other main functions: match_name()

Examples

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

# styler: off
matched <- tribble(
  ~sector, ~sector_ald,  ~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)
#> [1] "direct_loantaker"    "intermediate_parent" "ultimate_parent"    

# Using default priority
prioritize(matched)
#> # A tibble: 2 x 5
#>   sector sector_ald score id_loan level              
#>   <chr>  <chr>      <dbl> <chr>   <chr>              
#> 1 coal   coal           1 aa      direct_loantaker   
#> 2 coal   coal           1 bb      intermediate_parent

# Using the reverse of the default priority
prioritize(matched, priority = rev)
#> # A tibble: 2 x 5
#>   sector sector_ald score id_loan level          
#>   <chr>  <chr>      <dbl> <chr>   <chr>          
#> 1 coal   coal           1 aa      ultimate_parent
#> 2 coal   coal           1 bb      ultimate_parent

# Same
prioritize(matched, priority = ~ rev(.x))
#> # A tibble: 2 x 5
#>   sector sector_ald score id_loan level          
#>   <chr>  <chr>      <dbl> <chr>   <chr>          
#> 1 coal   coal           1 aa      ultimate_parent
#> 2 coal   coal           1 bb      ultimate_parent

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

prioritize(matched, priority = bad_idea)
#> # A tibble: 2 x 5
#>   sector sector_ald score id_loan level              
#>   <chr>  <chr>      <dbl> <chr>   <chr>              
#> 1 coal   coal           1 bb      intermediate_parent
#> 2 coal   coal           1 aa      ultimate_parent