This function will evaluate the number of distinct values in the site column in
the provided cohort table and determine how that compares to the provided
multi_or_single_site designation.
Arguments
- cohort
tabular input || required
The cohort to be used for data quality testing. This table should contain, at minimum:
site| character | the name(s) of institutions included in your cohortperson_id/patid| integer / character | the patient identifierstart_date| date | the start of the cohort periodend_date| date | the end of the cohort period
- multi_or_single_site
string || defaults to
singleA string, either
singleormulti, indicating whether a single-site or multi-site analysis should be executed
Value
If multi_or_single_site = single but multiple sites are provided, the cohort table
is returned with a summary site column set to combined so all sites will be treated
as one group. Otherwise, the existing site column is returned as-is.
If an illogical parameter combination is supplied, the function will
return an error with recommendations on how to remedy the issue.
Examples
## Create sample cohort
cohort_sample <- dplyr::tibble(site = c('Site A', 'Site B', 'Site C'),
person_id = c(1,2,3))
## If number of sites & indicated multi/single site match, output same table
check_site_type(cohort = cohort_sample,
multi_or_single_site = 'multi')
#> $cohort
#> # A tibble: 3 × 2
#> site person_id
#> <chr> <dbl>
#> 1 Site A 1
#> 2 Site B 2
#> 3 Site C 3
#>
#> $grouped_list
#> [1] "site"
#>
#> $site_list_adj
#> [1] "Site A" "Site B" "Site C"
#>
## If multiple sites but single site indicated, create site_summ column
check_site_type(cohort = cohort_sample,
multi_or_single_site = 'single')
#> $cohort
#> # A tibble: 3 × 3
#> site person_id site_summ
#> <chr> <dbl> <chr>
#> 1 Site A 1 combined
#> 2 Site B 2 combined
#> 3 Site C 3 combined
#>
#> $grouped_list
#> [1] "site_summ"
#>
#> $site_list_adj
#> [1] "combined"
#>