Summarise variables using a set of estimate functions. The output will be a formatted summarised_result object.

Usage

summariseResult(
  table,
  group = list(),
  includeOverallGroup = FALSE,
  strata = list(),
  includeOverallStrata = TRUE,
  variables = NULL,
  estimates = NULL,
  counts = TRUE,
  weights = NULL,
  customEstimates = list()
)

Arguments

table: A table to process.
group: List of groups to be considered.
includeOverallGroup: TRUE or FALSE. If TRUE, results for an overall group will be reported when a list of groups has been specified.
strata: List of the stratifications within each group to be considered.
includeOverallStrata: TRUE or FALSE. If TRUE, results for an overall strata will be reported when a list of strata has been specified.
variables: Variables to summarise, it can be a list to point to different set of estimate names.
estimates: Estimates to obtain, it can be a list to point to different set of variables.
counts: Whether to compute number of records and number of subjects.
weights: Name of the column in the table that contains the weights to be used when measuring the estimates.
customEstimates: Named list of custom functions. Each function must accept a variable vector as its first argument and return one numeric value. If weights are supplied, they are passed as the second argument when the function provides one; otherwise the estimate is calculated without weights.

Value

A summarised_result object with the summarised data of interest.

Examples

# \donttest{
library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

x <- cdm$cohort1 |>
  addDemographics()

# summarise all variables with default estimates
result <- summariseResult(x)
#> ℹ The following estimates will be calculated:
#> • age: min, q25, median, q75, max
#> • cohort_end_date: min, q25, median, q75, max
#> • cohort_start_date: min, q25, median, q75, max
#> • future_observation: min, q25, median, q75, max
#> • prior_observation: min, q25, median, q75, max
#> • sex: count, percentage
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side.
#> → Start summary of data, at 2026-07-24 17:30:36.579926
#> ✔ Summary finished, at 2026-07-24 17:30:36.656868
result
#> # A tibble: 31 × 13
#>    result_id cdm_name group_name group_level strata_name strata_level
#>        <int> <chr>    <chr>      <chr>       <chr>       <chr>       
#>  1         1 PP_MOCK  overall    overall     overall     overall     
#>  2         1 PP_MOCK  overall    overall     overall     overall     
#>  3         1 PP_MOCK  overall    overall     overall     overall     
#>  4         1 PP_MOCK  overall    overall     overall     overall     
#>  5         1 PP_MOCK  overall    overall     overall     overall     
#>  6         1 PP_MOCK  overall    overall     overall     overall     
#>  7         1 PP_MOCK  overall    overall     overall     overall     
#>  8         1 PP_MOCK  overall    overall     overall     overall     
#>  9         1 PP_MOCK  overall    overall     overall     overall     
#> 10         1 PP_MOCK  overall    overall     overall     overall     
#> # ℹ 21 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>

# get only counts of records and subjects
result <- summariseResult(x, variables = character())
#> → Start summary of data, at 2026-07-24 17:30:36.924278
#> ✔ Summary finished, at 2026-07-24 17:30:37.00603
result
#> # A tibble: 2 × 13
#>   result_id cdm_name group_name group_level strata_name strata_level
#>       <int> <chr>    <chr>      <chr>       <chr>       <chr>       
#> 1         1 PP_MOCK  overall    overall     overall     overall     
#> 2         1 PP_MOCK  overall    overall     overall     overall     
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>

# specify variables and estimates
result <- summariseResult(
  table = x,
  variables = c("cohort_start_date", "age"),
  estimates = c("mean", "median", "density")
)
#> ℹ The following estimates will be calculated:
#> • cohort_start_date: mean, median, density
#> • age: mean, median, density
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side.
#> → Start summary of data, at 2026-07-24 17:30:37.599498
#> ✔ Summary finished, at 2026-07-24 17:30:37.694855
result
#> # A tibble: 2,054 × 13
#>    result_id cdm_name group_name group_level strata_name strata_level
#>        <int> <chr>    <chr>      <chr>       <chr>       <chr>       
#>  1         1 PP_MOCK  overall    overall     overall     overall     
#>  2         1 PP_MOCK  overall    overall     overall     overall     
#>  3         1 PP_MOCK  overall    overall     overall     overall     
#>  4         1 PP_MOCK  overall    overall     overall     overall     
#>  5         1 PP_MOCK  overall    overall     overall     overall     
#>  6         1 PP_MOCK  overall    overall     overall     overall     
#>  7         1 PP_MOCK  overall    overall     overall     overall     
#>  8         1 PP_MOCK  overall    overall     overall     overall     
#>  9         1 PP_MOCK  overall    overall     overall     overall     
#> 10         1 PP_MOCK  overall    overall     overall     overall     
#> # ℹ 2,044 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>

# different estimates for each variable
result <- summariseResult(
  table = x,
  variables = list(c("age", "prior_observation"), "sex"),
  estimates = list(c("min", "max"), c("count", "percentage"))
)
#> ℹ The following estimates will be calculated:
#> • age: min, max
#> • prior_observation: min, max
#> • sex: count, percentage
#> → Start summary of data, at 2026-07-24 17:30:38.1392
#> ✔ Summary finished, at 2026-07-24 17:30:38.358251

# add a custom estimate
ess <- function(x) sum(x^2) / sum(x)
result <- summariseResult(
  table = x,
  variables = "age",
  estimates = "ess",
  customEstimates = list(ess = ess)
)
#> ℹ The following estimates will be calculated:
#> • age: ess
#> ! Table is collected to memory because custom estimates are evaluated in R.
#> → Start summary of data, at 2026-07-24 17:30:38.869762
#> ✔ Summary finished, at 2026-07-24 17:30:38.920155

# }