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Getting Started with teal

NEST CoreDev

2022-11-03

teal.Rmd

Introduction

teal is a shiny-based interactive exploration framework for analyzing data, with particular emphasis on CDISC clinical trial data. teal applications provide their users with:

  • Ability to “pull” in data from external data sources
  • Dynamic filtering of data to be used in the analyses
  • Ability to generate reproducible code to regenerate the on-screen analyses
  • Ability to create and download reports containing results of analyses (for analysis modules which support reporting)

In addition, the teal framework also provides application developers with:

  • A large suite of custom-made standard analysis modules to be included in applications
  • A logging framework to facilitate debugging of applications

More advanced users of the framework can also create new analysis modules which can be added into any teal applications. See the Creating Custom Modules vignette for a brief introduction to creating modules.

Your first teal application:

This simple application takes the iris and mtcars datasets and displays their contents in a teal application:

library(teal)

app <- init(
  data = teal_data(
    dataset("IRIS", iris),
    dataset("MTCARS", mtcars)
  ),
  modules = example_module(),
  header = "My first teal application"
)

if (interactive()) {
  runApp(app)
}

Example application

As shown in the image above, this application consists of several distinct areas:

  • Application header: The title of the application shown at the top.
  • A filter panel (panels on the right hand side): for filtering the data to be passed into all teal modules.
  • teal modules (accessible via a set of tabs): in this case a simple module named “example teal module”.
  • An encoding panel (panel on the left hand side): Module specific UI components, in this case a drop-down to select a dataset name.
  • Main output panel (panel on the middle): The outputs of the module, for this example module the chosen dataset is displayed.

Filter panel

The filter panel allows users to select parts of the datasets they wish to use in the modules. The top panel shows the number of records which remain after filtering.

In the example below:

  • For the IRIS dataset, only rows satisfying the conditions Petal.Length >= 3.4 and Species %in% c("setosa", "virginica") are included thereby keeping 50 rows.
  • For the MTCARS dataset, only rows satisfying the condition cyl %in% c(4, 6) are included, thereby keeping 18 rows.

Example filter panel

Encoding panel

The left hand side of the application is (usually) dedicated to module specific controls. For modules which include reproducibility functionality, it often contains a “Show R Code” button which when clicked will show the code required to re-generate the output (including any filters added on the filter panel and library calls to load required packages).

Creating your own applications

init is the key function to use to create your teal application and it requires two key arguments: data and modules.

Application Data

The data argument to the init function specifies the data used by your application. This can contain data currently in your R session, as in the example above, but also can contain connectors which describe where to “pull” data from when the application is run. This can be used to allow data to be pulled into teal applications from external sources which require your application users to enter credentials.

In the example above we call teal_data to convert the raw datasets into teal specific datasets and to bind them in one R object. This function can also be used to specify relationships between different datasets. In order to use CDISC clinical trial data in a teal application the cdisc_data function is used instead.

For further details we recommend exploring the teal.data package documentation.

Modules

The modules argument to init consists of a list of teal modules (which can be wrapped together using the function modules). We recommend creating applications using predefined teal modules. See the references below for links to these modules.

Defining filters

The init function has an additional argument filters which allows you to initialize the application with certain filters preselected. See the documentation for init for further details.

Reporting

If any of the modules in your teal application support reporting (see teal.reporter for more details) then users of your application can add these outputs to a report. This report can be downloaded and a special “Report Previewer” module will be added to your application as an additional tab so your users can view and configure their reports before downloading them.

Where to go next

To learn more about the teal framework we recommend first exploring some of the available analysis modules.

For example see:

The teal framework relies on a set of supporting packages whose documentation provides more in-depth information. The packages which are of most interest when defining tealapplications are:

  • teal.data: defining data for teal application.
  • teal.slice: defining data filtering before passing into teal modules.
  • teal.transform: defining the way arguments are passed into teal modules.