Generate filters definition based on the Source data

The method should analyze source data structure, generate proper filters based on the data (e.g. column types) and attach them to source.

Usage

autofilter(source, attach_as = c("step", "meta"), ...)

# S3 method for default
autofilter(source, ...)

# S3 method for tblist
autofilter(source, attach_as = c("step", "meta"), ...)

Arguments

source

Source object.

attach_as

Choose whether the filters should be attached as a new step, or list of available filters (used in filtering panel when `new_step = "configure"`). By default in step.

...

Extra arguments passed to a specific method.

Value

Source object having step configuration attached.

See also

source-gui-layer

Examples

library(magrittr)
library(cohortBuilder)
#> 
#> Attaching package: ‘cohortBuilder’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, step
library(shinyCohortBuilder)

iris_source <- set_source(tblist(iris = iris)) %>%
  autofilter()
iris_cohort <- cohort(iris_source)
sum_up(iris_cohort)
#> >> Step ID: 1
#> -> Filter ID: PFUUH1655973313980
#>    Filter Type: range
#>    Filter Parameters:
#>      dataset: iris
#>      variable: Sepal.Length
#>      range: NA
#>      keep_na: TRUE
#>      description: 
#>      active: TRUE
#> -> Filter ID: QJLCH1655973313980
#>    Filter Type: range
#>    Filter Parameters:
#>      dataset: iris
#>      variable: Sepal.Width
#>      range: NA
#>      keep_na: TRUE
#>      description: 
#>      active: TRUE
#> -> Filter ID: PUCBE1655973313980
#>    Filter Type: range
#>    Filter Parameters:
#>      dataset: iris
#>      variable: Petal.Length
#>      range: NA
#>      keep_na: TRUE
#>      description: 
#>      active: TRUE
#> -> Filter ID: SOGRZ1655973313980
#>    Filter Type: range
#>    Filter Parameters:
#>      dataset: iris
#>      variable: Petal.Width
#>      range: NA
#>      keep_na: TRUE
#>      description: 
#>      active: TRUE
#> -> Filter ID: WCLBR1655973313982
#>    Filter Type: discrete
#>    Filter Parameters:
#>      dataset: iris
#>      variable: Species
#>      value: NA
#>      keep_na: TRUE
#>      description: 
#>      active: TRUE

if (interactive()) {
  library(shiny)

  ui <- fluidPage(
    cb_ui("mycoh")
  )

  server <- function(input, output, session) {
    cb_server("mycoh", cohort = iris_cohort)
  }

  shinyApp(ui, server)
}