Modular Cohort-Building Framework for Analytical Dashboards.

shinyCohortBuilder

Move your cohortBuilder workflow to Shiny.

Installation

# CRAN version
install.packages("shinyCohortBuilder")

# Latest development version
remotes::install_github("https://github.com/r-world-devs/shinyCohortBuilder")

Overview

With shinyCohortBuilder you can use cohortBuilder features within your shiny application.

Configure Source and Cohort filters with cohortBuilder (set value/range to NA to select all the options / the whole range, and active = FALSE to collapse filter in GUI):

librarian_source <- set_source(as.tblist(librarian))
librarian_cohort <- cohort(
  librarian_source,
  filter(
    "discrete", id = "author", dataset = "books", 
    variable = "author", value = "Dan Brown", 
    active = FALSE
  ),
  filter(
    "range", id = "copies", dataset = "books", 
    variable = "copies", range = c(5, 10), 
    active = FALSE
  ),
  filter(
    "date_range", id = "registered", dataset = "borrowers", 
    variable = "registered", range = c(as.Date("2010-01-01"), Inf),
    active = FALSE
  )
)

And apply in your application with cb_ui and cb_server:

library(shiny)

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      cb_ui("librarian")
    ),
    mainPanel()
  )
)

server <- function(input, output, session) {
  cb_server("librarian", librarian_cohort)
}

shinyApp(ui, server)

You may listen to cohort data changes with input[[<cohort-id>-data-updated]]:

library(shiny)

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      cb_ui("librarian")
    ),
    mainPanel(
      verbatimTextOutput("cohort_data")
    )
  )
)

server <- function(input, output, session) {
  cb_server("librarian", librarian_cohort)
  
  output$cohort_data <- renderPrint({
    input[["librarian-data-updated"]]
    get_data(librarian_cohort)
  })
  
}

shinyApp(ui, server)

Or run filtering panel locally what just makes your work with cohortBuilder easier:

gui(librarian_cohort)

If you’re interested in more features of shinyCohortBuilder please visit the package website.

Acknowledgement

Special thanks to:

• Kamil Wais for highlighting the need for the package and its relevance to real-world applications.
• Adam Foryś for technical support, numerous suggestions for the current and future implementation of the package.
• Paweł Kawski for indication of initial assumptions about the package based on real-world medical data.

Getting help

In a case you found any bugs, have feature request or general question please file an issue at the package Github. You may also contact the package author directly via email at [email protected].