Filter a <summarised_result> using the settings
Arguments
- result
A
<summarised_result>object.- ...
Expressions that return a logical value (columns in settings are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.
Value
A <summarised_result> object with only the result_id rows that fulfill
the required specified settings.
Examples
library(dplyr)
library(omopgenerics)
x <- tibble(
"result_id" = as.integer(c(1, 2)),
"cdm_name" = c("cprd", "eunomia"),
"group_name" = "cohort_name",
"group_level" = "my_cohort",
"strata_name" = "sex",
"strata_level" = "male",
"variable_name" = "Age group",
"variable_level" = "10 to 50",
"estimate_name" = "count",
"estimate_type" = "numeric",
"estimate_value" = "5",
"additional_name" = "overall",
"additional_level" = "overall"
) |>
newSummarisedResult(settings = tibble(
"result_id" = c(1, 2), "custom" = c("A", "B")
))
#> `result_type`, `package_name`, and `package_version` added to settings.
x
#> # A tibble: 2 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 cprd cohort_name my_cohort sex male
#> 2 2 eunomia cohort_name my_cohort sex male
#> # 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>
x |> filterSettings(custom == "A")
#> # A tibble: 1 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 cprd cohort_name my_cohort sex male
#> # 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>