Including Data in teal Applications

NEST CoreDev

Data in teal Applications

The teal framework readily accepts general, non-relational data. Modules defined in the teal.modules.general package are designed to work well with that kind of data. Relational data is handled just as well and the mechanism of passing data to applications is virtually the same. This includes clinical data that conforms to the ADaM standard. We are working on making the framework extendable so that support for other data structures can be added with relative ease. Currently some support is offered for the MultiAssayExperiment class.

All applications use the teal_data class as a data container. teal_data objects are passed to init to build the application, where they are modified by the filter panel (if applicable) and passed on to modules. Thus, the first step of building a teal app is creating a teal_data object.

General data

A teal_data object is created by calling the teal_data function and passing data objects as name:value pairs.


# create teal_data
data <- teal_data(iris = iris, cars = mtcars)

Note that iris and cars have been added to the datanames property of data (see datanames property).

This is sufficient to run a teal app.

# build app
app <- init(
  data = data,
  modules = example_module()

# run app
shinyApp(app$ui, app$server)

Reproducible data

A teal_data object stores data in a separate environment. Therefore, modifying the stored datasets requires that processing code be evaluated in that environment. Following that logic, one can create an empty teal_data object and populate it by evaluating code. This can be done using the eval_code function or, more conveniently, using the within function.

# create empty object
data_empty <- teal_data()

# run code in the object
data_populated_1 <- eval_code(data_empty, code = "iris <- iris
                                                  cars <- mtcars")
# alternative
data_populated_2 <- within(data_empty, {
  iris <- iris
  cars <- mtcars

The key difference between eval_code and within is that the former accepts code as character vector or language objects (calls and expressions), while within accepts only inline code. See ?qenv for more details.

Note that in the first example data was created by passing data objects, so the code that was used to create the data objects is unknown and therefore the process cannot be reproduced. Inspecting code the in the app created above reveals a note that the preprocessing code is absent.

The necessary code can be supplied to the code argument of the teal_data function.

data_with_code <- teal_data(
  iris = iris, cars = mtcars,
  code = "iris <- iris
          cars <- mtcars"

Keep in mind this code is not executed in the teal_data’s environment, so it may not reproduce the environment. Such an object is considered unverified (see verified property).

If reproducibility is required, we recommend creating an empty teal_data object and then evaluating code.

code from file

The ability to pass code as a character vector to eval_code opens the door to using code stored in a file.

# not run
data_from_file <- teal_data()
data_from_file <- eval_code(data, readLines("<path_to_file>"))

Creating data in-app

The one departure from passing a teal_data object to init is when the data does not exist in the environment where the app is run, e.g. when it has to be pulled from a remote source. In those cases a teal_data_module must be used. See this vignette for a detailed description.

Clinical data

Currently teal supports two specialized data formats.

ADaM data

The ADaM data model, defined in CDISC standards, specifies relationships between the subject-level parent dataset and observation-level child datasets. The cdisc_data function takes advantage of that fact to automatically set default joining keys (see join_keys property). In the example below, two standard ADaM datasets (ADSL and ADTTE) are passed to cdisc_data.

# create cdisc_data
data_cdisc <- cdisc_data(ADSL =, ADTTE =

#> [1] "ADSL"  "ADTTE"
#> A join_keys object containing foreign keys between 2 datasets:
app <- init(
  data = data_cdisc,
  modules = example_module()
shinyApp(app$ui, app$server)

MultiAssayExperiment data

The MultiAssayExperiment package offers a data structure for representing and analyzing multi-omics experiments that involve multi-modal, high-dimensionality data, such as DNA mutations, protein or RNA abundance, chromatin occupancy, etc., in the same biological specimens.

The MultiAssayExperiment class is described in detail here.

MultiAssayExperiment objects (MAEs) are placed in teal_data just like normal objects.


data_mae <- teal_data(MAE = miniACC)

app <- init(
  data = data_mae,
  modules = example_module()
shinyApp(app$ui, app$server)

Due to the unique structure of a MAE, teal requires special considerations when building teal modules. Therefore, we cannot guarantee that all modules will work properly with MAEs. The package has been developed specifically with MAE in mind and will be more reliable.

The filter panel supports MAEs out of the box.

teal_data properties


The datanames property lists the objects stored in the teal_data environment that constitute datasets of interest. Objects passed to teal_data become automatically listed in the datanames property of the resulting object. Objects created in teal_data by evaluating code need not be data objects of interest and as such they are not automatically added to datanames. For convenience, an empty datanames property is considered to mean “all objects in the container”. datanames can be read or modified with the datanames function.

data_with_objects <- teal_data(iris = iris, cars = mtcars)
data_with_code <- teal_data() %>%
    iris <- iris
    cars <- mtcars
    not_a_dataset <- "data source credits"
#> [1] "iris" "cars"
#> character(0)
datanames(data_with_code) <- c("iris", "cars")
#> [1] "iris" "cars"

The datanames property serves as a communication bridge between the data container and modules in a teal application. In teal all modules are called with a datanames argument that determines which of the variables in the teal_data object they are to access. Only variables enumerated in the datanames property are eligible for use in modules.

Note that specifying datanames in teal_data is optional; if the property is empty, all objects are considered eligible. Likewise, the datanames argument in the module call defaults to "all", which means that module will attempt to access all eligible variables in the teal_data object.

For a detailed explanation of datanames, see this vignette.

(back to General Data)


Using relational data requires specifying joining keys for each pair of datasets. Primary keys are unique row identifiers in individual datasets and thus should be specified for each dataset. Foreign keys describe mapping of variables between datasets. Joining keys are stored in the join_keys property, which can be set when creating a teal_data object, using the join_keys argument, or using the join_keys function.

ds1 <- data.frame(
  id = seq(1, 10),
  group = rep(c("A", "B"), each = 5)
ds2 <- data.frame(
  group = c("A", "B"),
  condition = c("condition1", "condition2")
keys <- join_keys(
  join_key("DS1", keys = "id"),
  join_key("DS2", keys = "group"),
  join_key("DS1", "DS2", keys = c("group" = "group"))
data_relational1 <- teal_data(DS1 = ds1, DS2 = ds2, join_keys = keys)
data_relational2 <- teal_data(DS1 = ds1, DS2 = ds2)
join_keys(data_relational2) <- keys

For a detailed explanation of join keys, see this vignette.

(back to ADaM Data)


teal_data allows for tracking code from data creation through data filtering through data analysis so that the whole process can be reproduced. The verified property designates whether or not reproducibility has been confirmed. teal_data objects that are created empty and only modified by evaluating code within them are considered verified by default. Those created with data objects alone or with data objects and code are not verified by default, but can become verified by running the verify function.

#> ✅︎ verified teal_data object
#> <environment: 0x7fc96c428208> [L]
#> Parent: <environment: package:teal.reporter>
#> Bindings:
#> • not_a_dataset: <chr> [L]
#> • cars: <df[,11]> [L]
#> • iris: <df[,5]> [L]

data_with_objects_and_code <- teal_data(iris = iris, cars = mtcars, code = expression(iris <- iris, cars <- mtcars))
#> ✖ unverified teal_data object
#> <environment: 0x7fc96c3f69b0> [L]
#> Parent: <environment: package:teal.reporter>
#> Bindings:
#> • cars: <df[,11]> [L]
#> • iris: <df[,5]> [L]

data_with_objects_and_code_ver <- verify(data_with_objects_and_code)
#> ✅︎ verified teal_data object
#> <environment: 0x7fc96c3f69b0> [L]
#> Parent: <environment: package:teal.reporter>
#> Bindings:
#> • cars: <df[,11]> [L]
#> • iris: <df[,5]> [L]

For a detailed explanation of verification, see this vignette.

(back to Reproducible Data)

Further reading

For a complete guide to the teal_data class, please refer to the package.