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teal is ashiny-based interactive exploration framework for analyzing data.teal applications require app developers to specify:

• Data, which can be:
  • CDISC data, commonly used for clinical trial reporting
  • Independent datasets, for example from adata.frame
  • Related datasets, for example a set ofdata.frames with key columns to enable data joins
  • MultiAssayExperiment objects which areR data structures for representing and analyzing multi-omics experiments
• teal modules:
  • teal modules areshiny modules built within theteal framework that specify analysis to be performed. For example, it can be a module for exploring outliers in the data, or a module for visualizing the data in line plots. Although these can be created from scratch, manyteal modules have been released and we recommend starting with modules found in the following packages:
    • teal.modules.general: general modules for exploring relational/independent/CDISC data
    • teal.modules.clinical: modules specific to CDISC data and clinical trial reporting
    • teal.modules.hermes: modules for analyzingMultiAssayExperiment objects

A lot of the functionality of theteal framework derives from the following packages:

• teal.logger: standardizes logging withinteal framework.
• teal.code: handles reproducibility of outputs.
• teal.data: creating and loading the data needed forteal applications.
• teal.widgets:shiny components used withinteal.
• teal.slice: provides a filtering panel to allow filtering of data.
• teal.reporter: allowsteal applications to generate reports.
• teal.transform: allows the creation of reproducible transform and merge module for teal applications.

Dive deeper intoteal with our comprehensive video guide.Please click the image below to start learning:

install.packages("teal")

Alternatively, you might also use the development version.

# install.packages("pak")pak::pak("insightsengineering/teal")

library(teal)app<- init(data= teal_data(iris=iris),modules=list(    module(label="iris histogram",server=function(input,output,session,data) {        updateSelectInput(session=session,inputId="var",choices= names(data()[["iris"]])[1:4])output$hist<- renderPlot({          req(input$var)          hist(x= data()[["iris"]][[input$var]])        })      },ui=function(id) {ns<- NS(id)list(          selectInput(inputId= ns("var"),label="Column name",choices=NULL),          plotOutput(outputId= ns("hist"))        )      }    )  ))shinyApp(app$ui,app$server)

Please seeteal.gallery andTLG Catalog to see examples ofteal apps.

Please start with the"Technical Blueprint" article,"Getting Started" article, and then otherpackage vignettes for more detailed guide.

If you encounter a bug or have a feature request, please file an issue. For questions, discussions, and updates, use theteal channel in thepharmaverse slack workspace.

This package is a result of a joint efforts by many developers and stakeholders. We would like to thank everyone who contributed so far!