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Best Mapped Concepts – Processing

Source: R/best_mapped_concepts.R
process_bmc.Rd

Intakes the output of check_bmc in order to apply additional processing. This includes applying the user-specified best/not best labels that were added to the bmc_concepts table, then using those labels to compute proportions of best vs not best concept representation in each check.

Usage

process_bmc(
  bmc_results,
  bmc_concepts_labelled,
  rslt_source = "remote",
  csv_rslt_path = NULL
)

Arguments

bmc_results

tabular input || required

The bmc_counts output of check_bmc. This table should include results for all institutions that should be included in the computation of overall / "network level" statistics.

bmc_concepts_labelled

tabular input || required

The bmc_concepts output of check_bmc, with an additional column called include added with "not best" or non-ideal concepts marked with a 0 (optionally, "best" concepts can also be marked with a 1)

rslt_source

string || defaults to remote

A string that identifies the location of the bmc_results table. Acceptable values are

  • local - table is stored as a dataframe in the local R environment

  • csv - table is stored as a CSV file

  • remote - table is stored on a remote database

csv_rslt_path

string || defaults to NULL

If rslt_source has been set to csv, this parameter should indicate the path to the result file(s). Otherwise, this parameter can be left as NULL

Value

This function will return a list of two dataframes:

  • bmc_output_pp: A table summarizing the proportion of best vs not best concepts for a given check, indicated by the user designation in the bmc_concepts_labelled table

  • bmc_concepts_pp: The bmc_results input table with the best / not best designations added

Examples

# This function should be run after check_bmc has been executed for all
# network institutions and results have been combined into a common table

# All returned concepts should also be labelled to indicate whether they
# should be included as a "best" concept or not:

readr::read_csv(system.file('extdata', 'bmc_concept_examples.csv', package = 'ndq'))
#> Rows: 6 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (2): check_name, concept
#> dbl (1): include
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 6 × 3
#>   check_name    concept                   include
#>   <chr>         <chr>                       <dbl>
#> 1 bmc_race      White                          NA
#> 2 bmc_race      Black or African American      NA
#> 3 bmc_race      Unknown                         0
#> 4 bmc_ethnicity Hispanic                       NA
#> 5 bmc_ethnicity Non-Hispanic                   NA
#> 6 bmc_ethnicity Other                           0

# Once the labels have been applied, the function can be executed
## When results are kept locally:
if (FALSE) { # \dontrun{
my_bmc_process <- process_bmc(bmc_results = my_bmc_rslts,
                              bmc_concepts_labelled = my_bmc_concepts,
                              rslt_source = 'local')
} # }

## When results are kept in CSV files:
if (FALSE) { # \dontrun{
my_bmc_process <- process_bmc(bmc_results = 'my_bmc_rslts',
                              bmc_concepts_labelled = 'my_bmc_concepts',
                              rslt_source = 'csv',
                              csv_rslt_path = 'path/to/my/results')
} # }

## When results are kept on a remote database:
if (FALSE) { # \dontrun{
my_bmc_process <- process_bmc(bmc_results = 'my_bmc_rslts',
                              bmc_concepts_labelled = 'my_bmc_concepts',
                              rslt_source = 'remote')
} # }