Variants for Cohort Configuration

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When working with cohortBuilder you can configure filtering steps multiple ways. All the possible ways are defined in this article.

Filtering steps in Source

When filtering steps are configured inside Source object, cohort automatically inherits them.

You can achieve configuring filtering steps in Source using add_step method:

librarian_source <- set_source(
  as.tblist(librarian)
) %>% 
  add_step(
    step(
      filter(
        "discrete", id = "author", dataset = "books", 
        variable = "author", value = "Dan Brown"
      ),
      filter(
        "discrete", id = "program", dataset = "borrowers", 
        variable = "program", value = "premium", keep_na = FALSE
      )
    )
  )

or with %->% pipe operator:

librarian_source <- set_source(
  as.tblist(librarian)
) %->% 
  step(
    filter(
      "discrete", id = "author", dataset = "books", 
      variable = "author", value = "Dan Brown"
    ),
    filter(
      "discrete", id = "program", dataset = "borrowers", 
      variable = "program", value = "premium", keep_na = FALSE
    )
  )

You can also configure filtering steps using add_filter methods, passing step_id inside:

librarian_source <- set_source(
  as.tblist(librarian)
) %>% 
  add_filter(
    filter(
      "discrete", id = "author", dataset = "books", 
      variable = "author", value = "Dan Brown"
    ),
    step_id = 1
  ) %>% 
  add_filter(
    filter(
      "discrete", id = "program", dataset = "borrowers", 
      variable = "program", value = "premium", keep_na = FALSE
    ),
    step_id = 1
  )

Note. When step_id is skipped, the filter is added to the last existing step (or the first one if no steps exist).

Or even simpler using %->% (to put filters in the last existing step):

librarian_source <- set_source(
  as.tblist(librarian)
) %->% 
  filter(
    "discrete", id = "author", dataset = "books", 
    variable = "author", value = "Dan Brown"
  ) %->% 
  filter(
    "discrete", id = "program", dataset = "borrowers", 
    variable = "program", value = "premium", keep_na = FALSE
  )

Then, create cohort with:

librarian_cohort <- cohort(librarian_source)
sum_up(librarian_cohort)
#> >> Step ID: 1
#> -> Filter ID: author
#>    Filter Type: discrete
#>    Filter Parameters:
#>      dataset: books
#>      variable: author
#>      value: Dan Brown
#>      keep_na: TRUE
#>      description: 
#>      active: TRUE
#> -> Filter ID: program
#>    Filter Type: discrete
#>    Filter Parameters:
#>      dataset: borrowers
#>      variable: program
#>      value: premium
#>      keep_na: FALSE
#>      description: 
#>      active: TRUE

Filtering steps in Cohort

When filtering steps are not configured in the Source, you can always achieve it using Cohort methods.

The standard way is to place steps configuration while creating Cohort:

librarian_source <- set_source(
  as.tblist(librarian)
)

librarian_cohort <- librarian_source %>% 
  cohort(
    step(
      filter(
        "discrete", id = "author", dataset = "books", 
        variable = "author", value = "Dan Brown"
      ),
      filter(
        "discrete", id = "program", dataset = "borrowers", 
        variable = "program", value = "premium", keep_na = FALSE
      )
    )
  )

Or if you want to define only one step, place filters directly:

librarian_cohort <- librarian_source %>% 
  cohort(
    filter(
      "discrete", id = "author", dataset = "books", 
      variable = "author", value = "Dan Brown"
    ),
    filter(
      "discrete", id = "program", dataset = "borrowers", 
      variable = "program", value = "premium", keep_na = FALSE
    )
  )

In case when Cohort is already defined, you can repeat any approach we presented while adding filtering steps to source.

Using add_step:

librarian_cohort <- librarian_source %>% cohort()

librarian_cohort %>% 
  add_step(
    step(
      filter(
        "discrete", id = "author", dataset = "books", 
        variable = "author", value = "Dan Brown"
      ),
      filter(
        "discrete", id = "program", dataset = "borrowers", 
        variable = "program", value = "premium", keep_na = FALSE
      )
    )
  )

Using %->% pipe operator:

librarian_cohort <- librarian_source %>% cohort()

librarian_cohort %->% 
  step(
    filter(
      "discrete", id = "author", dataset = "books", 
      variable = "author", value = "Dan Brown"
    ),
    filter(
      "discrete", id = "program", dataset = "borrowers", 
      variable = "program", value = "premium", keep_na = FALSE
    )
  )

You can also configure filtering steps using add_filter methods, passing step_id inside:

librarian_cohort <- librarian_source %>% cohort()

librarian_cohort %>% 
  add_filter(
    filter(
      "discrete", id = "author", dataset = "books", 
      variable = "author", value = "Dan Brown"
    )
  ) %>% 
  add_filter(
    filter(
      "discrete", id = "program", dataset = "borrowers", 
      variable = "program", value = "premium", keep_na = FALSE
    )
  )

Note. When step_id is skipped, the filter is added to the last existing step (or the first one if no steps exist).

Or even simpler using %->% (to put filters in the last existing step):

librarian_cohort <- librarian_source %>% cohort()

librarian_cohort %->% 
  filter(
    "discrete", id = "author", dataset = "books", 
    variable = "author", value = "Dan Brown"
  ) %->% 
  filter(
    "discrete", id = "program", dataset = "borrowers", 
    variable = "program", value = "premium", keep_na = FALSE
  )

As usual we can verify the configuration with sum_up:

sum_up(librarian_cohort)
#> >> Step ID: 1
#> -> Filter ID: author
#>    Filter Type: discrete
#>    Filter Parameters:
#>      dataset: books
#>      variable: author
#>      value: Dan Brown
#>      keep_na: TRUE
#>      description: 
#>      active: TRUE
#> -> Filter ID: program
#>    Filter Type: discrete
#>    Filter Parameters:
#>      dataset: borrowers
#>      variable: program
#>      value: premium
#>      keep_na: FALSE
#>      description: 
#>      active: TRUE

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