library(DBI)
library(duckdb)
library(CDMConnector)
library(CohortConstructor)
library(CohortCharacteristics)
library(ggplot2)For this example we’ll use the Eunomia synthetic data from the CDMConnector package.
con <- dbConnect(duckdb(), dbdir = eunomiaDir())
cdm <- cdmFromCon(con, cdmSchema = "main",
writeSchema = "main", writePrefix = "my_study_")Let’s start by creating two drug cohorts, one for users of diclofenac and another for users of acetaminophen.
cdm$medications <- conceptCohort(cdm = cdm,
conceptSet = list("diclofenac" = 1124300,
"acetaminophen" = 1127433),
name = "medications")
cohortCount(cdm$medications)
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 9365 2580
#> 2 2 830 830We can take a sample from a cohort table using the function
sampleCohort(). This allows us to specify the number of
individuals in each cohort.
cdm$medications |> sampleCohorts(cohortId = NULL, n = 100)
#> # Source: table<my_study_medications> [?? x 4]
#> # Database: DuckDB 1.4.0 [unknown@Linux 6.11.0-1018-azure:R 4.5.1//tmp/Rtmp2vF72e/file297611a8a64f.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 938 1975-05-22 1975-05-29
#> 2 1 938 1993-12-21 1994-01-04
#> 3 1 1641 1963-12-05 1963-12-12
#> 4 1 2394 1987-05-27 1987-06-10
#> 5 1 4675 2006-11-11 2006-12-11
#> 6 1 4125 1992-06-14 1992-07-14
#> 7 1 1715 1970-06-20 1970-06-27
#> 8 1 1715 1972-09-20 1972-10-04
#> 9 1 1837 1974-04-30 1974-05-28
#> 10 1 195 1974-04-01 1974-04-15
#> # ℹ more rows
cohortCount(cdm$medications)
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 372 100
#> 2 2 100 100When cohortId = NULL all cohorts in the table are used. Note that this function does not reduced the number of records in each cohort, only the number of individuals.
It is also possible to only sample one cohort within cohort table, however the remaining cohorts will still remain.
cdm$medications <- cdm$medications |> sampleCohorts(cohortId = 2, n = 100)
cohortCount(cdm$medications)
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 9365 2580
#> 2 2 100 100The chosen cohort (users of diclofenac) has been reduced to 100 individuals, as specified in the function, however all individuals from cohort 1 (users of acetaminophen) and their records remain.
If you want to filter the cohort table to only include individuals
and records from a specified cohort, you can use the function
subsetCohorts.
cdm$medications <- cdm$medications |> subsetCohorts(cohortId = 2)
cohortCount(cdm$medications)
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 2 830 830The cohort table has been filtered so it now only includes
individuals and records from cohort 2. If you want to take a sample of
the filtered cohort table then you can use the
sampleCohorts function.
cdm$medications <- cdm$medications |> sampleCohorts(cohortId = 2, n = 100)
cohortCount(cdm$medications)
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 2 100 100