Get cohort counts from a cohort_table object.
Examples
# \donttest{
library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)
person <- tibble(
person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- tibble(
observation_period_id = 1, person_id = 1,
observation_period_start_date = as.Date("2000-01-01"),
observation_period_end_date = as.Date("2023-12-31"),
period_type_concept_id = 0
)
cohort <- tibble(
cohort_definition_id = c(1, 1, 1, 2),
subject_id = 1,
cohort_start_date = as.Date(c(
"2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01"
)),
cohort_end_date = as.Date(c(
"2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01"
)),
)
cdm <- cdmFromTables(
tables = list("person" = person, "observation_period" = observation_period),
cdmName = "my_example_cdm",
cohortTables = list("cohort1" = cohort)
)
#> Warning: ! 5 column in person do not match expected column type:
#> • `person_id` is numeric but expected integer
#> • `gender_concept_id` is numeric but expected integer
#> • `year_of_birth` is numeric but expected integer
#> • `race_concept_id` is numeric but expected integer
#> • `ethnicity_concept_id` is numeric but expected integer
#> Warning: ! 3 column in observation_period do not match expected column type:
#> • `observation_period_id` is numeric but expected integer
#> • `person_id` is numeric but expected integer
#> • `period_type_concept_id` is numeric but expected integer
#> Warning: ! 2 column in cohort1 do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> • `subject_id` is numeric but expected integer
cohortCount(cdm$cohort1)
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 3 1
#> 2 2 1 1
# }