Type:	Package
Title:	Data Model for 'teal' Applications
Version:	0.7.0
Date:	2025-01-27
Description:	Provides a 'teal_data' class as a unified data model for 'teal' applications focusing on reproducibility and relational data.
License:	Apache License 2.0
URL:	https://insightsengineering.github.io/teal.data/, https://github.com/insightsengineering/teal.data/
BugReports:	https://github.com/insightsengineering/teal.data/issues
Depends:	R (≥ 4.0), teal.code (≥ 0.6.0)
Imports:	checkmate (≥ 2.1.0), lifecycle (≥ 0.2.0), methods, rlang (≥ 1.1.0), stats, utils
Suggests:	knitr (≥ 1.42), rmarkdown (≥ 2.23), testthat (≥ 3.2.2), withr (≥ 2.0.0)
VignetteBuilder:	knitr, rmarkdown
RdMacros:	lifecycle
Config/Needs/verdepcheck:	insightsengineering/teal.code, mllg/checkmate, r-lib/lifecycle, r-lib/rlang, yihui/knitr, rstudio/rmarkdown, r-lib/testthat, r-lib/withr
Config/Needs/website:	insightsengineering/nesttemplate
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.3.2
Collate:	'cdisc_data.R' 'data.R' 'formatters_var_labels.R' 'deprecated.R' 'dummy_function.R' 'join_key.R' 'join_keys-c.R' 'join_keys-extract.R' 'join_keys-names.R' 'join_keys-parents.R' 'join_keys-print.R' 'join_keys-utils.R' 'join_keys.R' 'teal.data.R' 'teal_data-class.R' 'teal_data-constructor.R' 'teal_data-extract.R' 'teal_data-get_code.R' 'teal_data-names.R' 'teal_data-show.R' 'testhat-helpers.R' 'topological_sort.R' 'verify.R' 'zzz.R'
NeedsCompilation:	no
Packaged:	2025-01-28 19:20:02 UTC; unardid
Author:	Dawid Kaledkowski [aut, cre], Aleksander Chlebowski [aut], Marcin Kosinski [aut], Andre Verissimo [aut], Pawel Rucki [aut], Mahmoud Hallal [aut], Nikolas Burkoff [aut], Maciej Nasinski [aut], Konrad Pagacz [aut], Junlue Zhao [aut], Chendi Liao [rev], Dony Unardi [rev], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer:	Dawid Kaledkowski <dawid.kaledkowski@roche.com>
Repository:	CRAN
Date/Publication:	2025-01-28 20:20:02 UTC

teal.data: Reproducible data model for teal applications

Description

This package extends teal applications by module which stores the data with they relationships (keys) and reproducible code. Package offers also to load data from files, databases and it's easily extendable by another data sources.

Author(s)

Maintainer: Dawid Kaledkowski dawid.kaledkowski@roche.com (ORCID)

Authors:

• Aleksander Chlebowski aleksander.chlebowski@contractors.roche.com (ORCID)

• Marcin Kosinski marcin.kosinski.mk1@roche.com

• Andre Verissimo andre.verissimo@roche.com (ORCID)

• Pawel Rucki pawel.rucki@roche.com

• Mahmoud Hallal mahmoud.hallal@roche.com

• Nikolas Burkoff

• Maciej Nasinski

• Konrad Pagacz

• Junlue Zhao

Other contributors:

• Chendi Liao chendi.liao@roche.com [reviewer]

• Dony Unardi unardid@gene.com [reviewer]

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/teal.data/

• https://github.com/insightsengineering/teal.data/

• Report bugs at https://github.com/insightsengineering/teal.data/issues

Test if two objects are (nearly) equal

Description

all.equal(target, current) is a utility to compare join_keys objects target and current testing ⁠near equality⁠.

Usage

## S3 method for class 'equal.join_keys'
all(target, current, ...)

Arguments

Details

If they are different, comparison is still made to some extent, and a report of the differences is returned. Do not use all.equal directly in if expressions—either use isTRUE(all.equal(....)) or identical if appropriate.

The parents attribute comparison tolerates NULL and empty lists and will find no difference.

The list containing all the relationships is treated like a map and ignores entries with NULL if they exist.

See Also

base::all.equal()

Check Compatibility of keys

Description

Helper function to assert if two key sets contain incompatible keys.

Usage

assert_compatible_keys(join_key_1, join_key_2)

Value

Returns TRUE if successful, otherwise raises error.

Verify key set compatibility

Description

Helper function to ensuring compatibility between two sets of keys

Usage

assert_compatible_keys2(x, y)

Value

Returns TRUE if successful, otherwise raises error.

Validate parent-child key

Description

Helper function checks the parent-child relations are valid.

Usage

assert_parent_child(x)

Arguments

Value

join_keys invisibly

Data input for teal app

Description

Function is a wrapper around teal_data() and guesses join_keys for given datasets whose names match ADAM datasets names.

Usage

cdisc_data(
  ...,
  join_keys = teal.data::default_cdisc_join_keys[names(rlang::list2(...))],
  code = character(0)
)

Arguments

Details

This function checks if there were keys added to all data sets.

Value

A teal_data object.

Examples

data <- cdisc_data(
  join_keys = join_keys(
    join_key("ADSL", "ADTTE", c("STUDYID" = "STUDYID", "USUBJID" = "USUBJID"))
  )
)

data <- within(data, {
  ADSL <- example_cdisc_data("ADSL")
  ADTTE <- example_cdisc_data("ADTTE")
})

Variable labels

Description

Get or set variable labels in a data.frame.

Usage

col_labels(x, fill = FALSE)

col_labels(x) <- value

col_relabel(x, ...)

Arguments

Details

Variable labels can be stored as a label attribute set on individual variables. These functions get or set this attribute, either on all (col_labels) or some variables (col_relabel).

Value

For col_labels, named character vector of variable labels, the names being the corresponding variable names. If the label attribute is missing, the vector elements will be the variable names themselves if fill = TRUE and NA if fill = FALSE.

For ⁠col_labels<-⁠ and col_relabel, copy of x with variable labels modified.

Source

These functions were taken from formatters package, to reduce the complexity of the dependency tree and rewritten.

Examples

x <- iris
col_labels(x)
col_labels(x) <- paste("label for", names(iris))
col_labels(x)
y <- col_relabel(x, Sepal.Length = "Sepal Length of iris flower")
col_labels(y)

Names of data sets in teal_data object

Description

Use names() instead of datanames().

datanames() is deprecated. If object should be hidden, then use a . (dot) prefix for the object's name.

Usage

datanames(x)

datanames(x) <- value

## S3 replacement method for class 'teal_data'
names(x) <- value

Arguments

Value

The contents of ⁠@datanames⁠ or teal_data object with updated ⁠@datanames⁠.

List containing default joining keys for CDISC datasets

Description

This data object is created at loading time from cdisc_datasets/cdisc_datasets.yaml.

Usage

default_cdisc_join_keys

Format

An object of class join_keys (inherits from list) of length 19.

Source

internal

Generate sample CDISC datasets

Description

Retrieves example CDISC datasets for use in examples and testing.

Usage

example_cdisc_data(
  dataname = c("ADSL", "ADAE", "ADLB", "ADCM", "ADEX", "ADRS", "ADTR", "ADTTE", "ADVS")
)

Arguments

Details

This function returns a dummy dataset and should only be used within teal.data. Note that the datasets are not created and maintained in teal.data, they are retrieved its dependencies.

Value

A CDISC dataset as a data.frame.

Get code from teal_data object

Description

Retrieve code from teal_data object.

Usage

## S4 method for signature 'teal_data'
get_code(
  object,
  deparse = TRUE,
  names = NULL,
  datanames = lifecycle::deprecated(),
  ...
)

Arguments

Details

Retrieve code stored in ⁠@code⁠, which (in principle) can be used to recreate all objects found in the environment (⁠@.xData⁠). Use names to limit the code to one or more of the datasets enumerated in the environment.

Value

Either a character string or an expression. If names is used to request a specific dataset, only code that creates that dataset (not code that uses it) is returned. Otherwise, all contents of ⁠@code⁠.

Extracting dataset-specific code

When names is specified, the code returned will be limited to the lines needed to create the requested datasets. The code stored in the ⁠@code⁠ slot is analyzed statically to determine which lines the datasets of interest depend upon. The analysis works well when objects are created with standard infix assignment operators (see ?assignOps) but it can fail in some situations.

Consider the following examples:

Case 1: Usual assignments.

data <- teal_data() |>
  within({
    foo <- function(x) {
      x + 1
    }
    x <- 0
    y <- foo(x)
  })
get_code(data, names = "y")

x has no dependencies, so get_code(data, names = "x") will return only the second call.
y depends on x and foo, so get_code(data, names = "y") will contain all three calls.

Case 2: Some objects are created by a function's side effects.

data <- teal_data() |>
  within({
    foo <- function() {
      x <<- x + 1
    }
    x <- 0
    foo()
    y <- x
  })
get_code(data, names = "y")

Here, y depends on x but x is modified by foo as a side effect (not by reassignment) and so get_code(data, names = "y") will not return the foo() call.
To overcome this limitation, code dependencies can be specified manually. Lines where side effects occur can be flagged by adding "⁠# @linksto <object name>⁠" at the end.
Note that within evaluates code passed to expr as is and comments are ignored. In order to include comments in code one must use the eval_code function instead.

data <- teal_data() |>
  eval_code("
    foo <- function() {
      x <<- x + 1
    }
    x <- 0
    foo() # @linksto x
    y <- x
  ")
get_code(data, names = "y")

Now the foo() call will be properly included in the code required to recreate y.

Note that two functions that create objects as side effects, assign and data, are handled automatically.

Here are known cases where manual tagging is necessary:

• non-standard assignment operators, e.g. ⁠%<>%⁠

• objects used as conditions in if statements: ⁠if (<condition>)⁠

• objects used to iterate over in for loops: ⁠for(i in <sequence>)⁠

• creating and evaluating language objects, e.g. ⁠eval(<call>)⁠

Examples

tdata1 <- teal_data()
tdata1 <- within(tdata1, {
  a <- 1
  b <- a^5
  c <- list(x = 2)
})
get_code(tdata1)
get_code(tdata1, names = "a")
get_code(tdata1, names = "b")

tdata2 <- teal_data(x1 = iris, code = "x1 <- iris")
get_code(tdata2)
get_code(verify(tdata2))

Checks whether a graph is a ⁠Directed Acyclic Graph (DAG)⁠

Description

Checks whether a graph is a ⁠Directed Acyclic Graph (DAG)⁠

Usage

is_dag(graph)

Arguments

Value

logical(1) TRUE if the graph is a DAG; FALSE otherwise

Create a relationship between a pair of datasets

Description

Create a relationship between two datasets, dataset_1 and dataset_2. By default, this function establishes a directed relationship with dataset_1 as the parent. If dataset_2 is not specified, the function creates a primary key for dataset_1.

Usage

join_key(dataset_1, dataset_2 = dataset_1, keys, directed = TRUE)

Arguments

Value

object of class join_key_set to be passed into join_keys function.

See Also

join_keys(), parents()

Examples

join_key("d1", "d2", c("A"))
join_key("d1", "d2", c("A" = "B"))
join_key("d1", "d2", c("A" = "B", "C"))

Manage relationships between datasets using join_keys

Description

Facilitates the creation and retrieval of relationships between datasets. join_keys class extends list and contains keys connecting pairs of datasets. Each element of the list contains keys for specific dataset. Each dataset can have a relationship with itself (primary key) and with other datasets.

Note that join_keys list is symmetrical and assumes a default direction, that is: when keys are set between ds1 and ds2, it defines ds1 as the parent in a parent-child relationship and the mapping is automatically mirrored between ds2 and ds1.

Usage

## Constructor, getter and setter
join_keys(...)

## Default S3 method:
join_keys(...)

## S3 method for class 'join_keys'
join_keys(...)

## S3 method for class 'teal_data'
join_keys(...)

## S3 method for class 'join_keys'
x[i, j]

## S3 replacement method for class 'join_keys'
x[i, j, directed = TRUE] <- value

## S3 method for class 'join_keys'
c(...)

## S3 method for class 'join_key_set'
c(...)

join_keys(x) <- value

## S3 replacement method for class 'join_keys'
join_keys(x) <- value

## S3 replacement method for class 'teal_data'
join_keys(x) <- value

## S3 method for class 'join_keys'
format(x, ...)

## S3 method for class 'join_keys'
print(x, ...)

Arguments

Value

join_keys object.

Methods (by class)

• join_keys(): Returns an empty join_keys object when called without arguments.

• join_keys(join_keys): Returns itself.

• join_keys(teal_data): Returns the join_keys object contained in teal_data object.

• join_keys(...): Creates a new object with one or more join_key_set parameters.

Functions

• x[names]: Returns a subset of the join_keys object for given names, including parent names and symmetric mirror keys between names in the result.

• x[i, j]: Returns join keys between datasets i and j, including implicit keys inferred from their relationship with a parent.

• x[i, j] <- value: Assignment of a key to pair ⁠(i, j)⁠.

• x[i] <- value: This (without j parameter) is not a supported operation for join_keys.

• join_keys(x)[i, j] <- value: Assignment to join_keys object stored in x, such as a teal_data object or join_keys object itself.

• join_keys(x) <- value: Assignment of the join_keys in object with value. value needs to be an object of class join_keys or join_key_set.

See Also

join_key() for creating join_keys_set, parents() for parent operations, teal_data() for teal_data constructor and default_cdisc_join_keys for default CDISC keys.

Examples

# Creating a new join keys ----

jk <- join_keys(
  join_key("ds1", "ds1", "pk1"),
  join_key("ds2", "ds2", "pk2"),
  join_key("ds3", "ds3", "pk3"),
  join_key("ds1", "ds2", c(pk1 = "pk2")),
  join_key("ds1", "ds3", c(pk1 = "pk3"))
)

jk

# Getter for join_keys ---

jk["ds1", "ds2"]

# Subsetting join_keys ----

jk["ds1"]
jk[1:2]
jk[c("ds1", "ds2")]

# Setting a new primary key ---

jk["ds4", "ds4"] <- "pk4"
jk["ds5", "ds5"] <- "pk5"

# Setting a single relationship pair ---

jk["ds1", "ds4"] <- c("pk1" = "pk4")

# Removing a key ---

jk["ds5", "ds5"] <- NULL
# Merging multiple `join_keys` objects ---

jk_merged <- c(
  jk,
  join_keys(
    join_key("ds4", keys = c("pk4", "pk4_2")),
    join_key("ds3", "ds4", c(pk3 = "pk4_2"))
  )
)
# note: merge can be performed with both join_keys and join_key_set

jk_merged <- c(
  jk_merged,
  join_key("ds5", keys = "pk5"),
  join_key("ds1", "ds5", c(pk1 = "pk5"))
)
# Assigning keys via join_keys(x)[i, j] <- value ----

obj <- join_keys()
# or
obj <- teal_data()

join_keys(obj)["ds1", "ds1"] <- "pk1"
join_keys(obj)["ds2", "ds2"] <- "pk2"
join_keys(obj)["ds3", "ds3"] <- "pk3"
join_keys(obj)["ds1", "ds2"] <- c(pk1 = "pk2")
join_keys(obj)["ds1", "ds3"] <- c(pk1 = "pk3")

identical(jk, join_keys(obj))
# Setter for join_keys within teal_data ----

td <- teal_data()
join_keys(td) <- jk

join_keys(td)["ds1", "ds2"] <- "new_key"
join_keys(td) <- c(join_keys(td), join_keys(join_key("ds3", "ds2", "key3")))
join_keys(td)

The names of a join_keys object

Description

The names of a join_keys object

Usage

## S3 replacement method for class 'join_keys'
names(x) <- value

Arguments

Names of data sets in teal_data object

Description

Functions to get the names of a teal_data object. The names are obtained from the objects listed in the qenv environment.

Usage

## S3 method for class 'teal_data'
names(x)

Arguments

Details

Objects named with a . (dot) prefix will be ignored and not returned. To get the names of all objects, use ls(x, all.names = TRUE), however, it will not group the names by the join_keys topological structure.

In order to rename objects in the teal_data object, use base R functions (see examples).

Value

A character vector of names.

Examples

td <- teal_data(iris = iris)
td <- within(td, mtcars <- mtcars)
names(td)

# hidden objects with dot-prefix
td <- within(td, .CO2 <- CO2)
names(td) # '.CO2' will not be returned

# rename objects
td <- teal_data(iris = iris)
td <- within(td, {
  new_iris <- iris
  rm(iris)
})
names(td) # only 'new_iris' will be returned

Internal constructor

Description

Internal constructor

Usage

new_join_keys()

Value

an empty join_keys list

Get and set parents in join_keys object

Description

parents() facilitates the creation of dependencies between datasets by assigning a parent-child relationship.

Usage

parents(x)

## S3 method for class 'join_keys'
parents(x)

## S3 method for class 'teal_data'
parents(x)

parents(x) <- value

## S3 replacement method for class 'join_keys'
parents(x) <- value

## S3 replacement method for class 'teal_data'
parents(x) <- value

parent(x, dataset_name)

Arguments

Details

Each element is defined by a list element, where list("child" = "parent").

Value

a list of character representing the parents.

For parent(x, dataset_name) returns NULL if parent does not exist.

Methods (by class)

• parents(join_keys): Retrieves parents of join_keys object.

• parents(teal_data): Retrieves parents of join_keys inside teal_data object.

Functions

• parents(x) <- value: Assignment of parents in join_keys object.

• parents(join_keys) <- value: Assignment of parents of join_keys object.

• parents(teal_data) <- value: Assignment of parents of join_keys inside teal_data object.

• parent(): Getter for individual parent.

See Also

join_keys()

Examples

# Get parents of join_keys ---

jk <- default_cdisc_join_keys["ADEX"]
parents(jk)
# Get parents of join_keys inside teal_data object ---

td <- teal_data(
  ADSL = rADSL,
  ADTTE = rADTTE,
  ADRS = rADRS,
  join_keys = default_cdisc_join_keys[c("ADSL", "ADTTE", "ADRS")]
)
parents(td)
# Assignment of parents ---

jk <- join_keys(
  join_key("ds1", "ds2", "id"),
  join_key("ds5", "ds6", "id"),
  join_key("ds7", "ds6", "id")
)

parents(jk) <- list(ds2 = "ds1")

# Setting individual parent-child relationship

parents(jk)["ds6"] <- "ds5"
parents(jk)["ds7"] <- "ds6"
# Assignment of parents of join_keys inside teal_data object ---

parents(td) <- list("ADTTE" = "ADSL") # replace existing
parents(td)["ADRS"] <- "ADSL" # add new parent
# Get individual parent ---

parent(jk, "ds2")
parent(td, "ADTTE")

Random adverse events

Description

Random adverse events

Usage

rADAE

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1934 rows and 92 columns.

Source

internal

Random concomitant medications

Description

Random concomitant medications

Usage

rADCM

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3685 rows and 83 columns.

Source

internal

Random response

Description

Random exposure.

Usage

rADEX

Format

An object of class tbl_df (inherits from tbl, data.frame) with 6400 rows and 79 columns.

Source

internal

Random lab analysis

Description

Random lab analysis

Usage

rADLB

Format

An object of class tbl_df (inherits from tbl, data.frame) with 8400 rows and 102 columns.

Source

internal

Random response

Description

Random response

Usage

rADRS

Format

An object of class tbl_df (inherits from tbl, data.frame) with 3200 rows and 65 columns.

Source

internal

Random patient listing

Description

Random patient listing

Usage

rADSL

Format

An object of class tbl_df (inherits from tbl, data.frame) with 400 rows and 55 columns.

Source

internal

Random data rADTR

Description

Random data rADTR

Usage

rADTR

Format

An object of class data.frame with 2800 rows and 76 columns.

Source

internal

Random time to event analysis dataset

Description

Random time to event analysis dataset

Usage

rADTTE

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2000 rows and 67 columns.

Source

internal

Random data rADVS

Description

Random data rADVS

Usage

rADVS

Format

An object of class tbl_df (inherits from tbl, data.frame) with 16800 rows and 87 columns.

Source

internal

Show teal_data object

Description

Prints teal_data object.

Usage

## S4 method for signature 'teal_data'
show(object)

Arguments

Value

Input teal_data object.

Examples

teal_data()
teal_data(x = iris, code = "x = iris")
verify(teal_data(x = iris, code = "x = iris"))

Comprehensive data integration function for teal applications

Description

Initializes a data for teal application.

Usage

teal_data(..., join_keys = teal.data::join_keys(), code = character(0))

## S3 method for class 'teal_data'
x[names]

Arguments

Details

A teal_data is meant to be used for reproducibility purposes. The class inherits from teal.code::qenv and we encourage to get familiar with teal.code first. teal_data has following characteristics:

• It inherits from the environment and methods such as $, get(), ls(), as.list(), parent.env() work out of the box.

• teal_data is a locked environment, and data modification is only possible through the teal.code::eval_code() and within.qenv() functions.

• It stores metadata about the code used to create the data (see get_code()).

• It supports slicing (see teal.code::subset-qenv)

• Is immutable which means that each code evaluation does not modify the original teal_data environment directly.

• It maintains information about relationships between datasets (see join_keys()).

Value

A teal_data object.

Subsetting

x[names] subsets objects in teal_data environment and limit the code to the necessary needed to build limited objects.

See Also

teal.code::eval_code, get_code(), join_keys(), names.teal_data()

Examples

teal_data(x1 = iris, x2 = mtcars)


# Subsetting
data <- teal_data()
data <- eval_code(data, "a <- 1;b<-2")
data["a"]
data[c("a", "b")]

join_keys(data) <- join_keys(join_key("a", "b", "x"))
join_keys(data["a"]) # should show empty keys
join_keys(data["b"])
join_keys(data)["a"] # should show empty keys
join_keys(data)["b"]

Reproducible data

Description

Reproducible data container class. Inherits code tracking behavior from teal.code::qenv.

Details

This class provides an isolated environment in which to store and process data with all code being recorded. The environment, code, data set names, and data joining keys are stored in their respective slots. These slots should never be accessed directly, use the provided get/set functions.

As code is evaluated in teal_data, messages and warnings are stored in their respective slots. If errors are raised, a qenv.error object is returned.

Slots

.xData

(environment) environment containing data sets and possibly auxiliary variables. Access variables with get(), $, teal.code::get_var() or [[[]. No setter provided. Evaluate code to add variables into ⁠@.xData⁠.

code

(list of character) representing code necessary to reproduce the contents of qenv. Access with teal.code::get_code(). No setter provided. Evaluate code to append code to the slot.

join_keys

(join_keys) object specifying joining keys for data sets in ⁠@.xData⁠. Access or modify with join_keys().

verified

(logical(1)) flag signifying that code in ⁠@code⁠ has been proven to yield contents of ⁠@.xData⁠. Used internally. See verify() for more details.

Code

Each code element is a character representing one call. Each element is named with the random identifier to make sure uniqueness when joining. Each element has possible attributes:

• warnings (character) the warnings output when evaluating the code element.

• messages (character) the messages output when evaluating the code element.

• dependency (character) names of objects that appear in this call and gets affected by this call, separated by ⁠<-⁠ (objects on LHS of ⁠<-⁠ are affected by this line, and objects on RHS are affecting this line).

Topological graph sort

Description

Graph is a list which for each node contains a vector of child nodes in the returned list, parents appear before their children.

Usage

topological_sort(graph)

Arguments

Details

Implementation of Kahn algorithm with a modification to maintain the order of input elements.

Updates the keys of the datasets based on the parents

Description

Updates the keys of the datasets based on the parents

Usage

update_keys_given_parents(x)

Arguments

Value

(self) invisibly for chaining

Verify code reproducibility

Description

Checks whether code in teal_data object reproduces the stored objects.

Usage

verify(x)

Arguments

Details

If objects created by code in the ⁠@code⁠ slot of x are all_equal to the contents of the environment (⁠@.xData⁠ slot), the function updates the ⁠@verified⁠ slot to TRUE in the returned teal_data object. Once verified, the slot will always be set to TRUE. If the ⁠@code⁠ fails to recreate objects in teal_data's environment, an error is raised.

Value

Input teal_data object or error.

Examples

tdata1 <- teal_data()
tdata1 <- within(tdata1, {
  a <- 1
  b <- a^5
  c <- list(x = 2)
})
verify(tdata1)

tdata2 <- teal_data(x1 = iris, code = "x1 <- iris")
verify(tdata2)
verify(tdata2)@verified
tdata2@verified

tdata3 <- teal_data()
tdata3 <- within(tdata3, {
  stop("error")
})
try(verify(tdata3)) # fails


a <- 1
b <- a + 2
c <- list(x = 2)
d <- 5
tdata4 <- teal_data(
  a = a, b = b, c = c, d = d,
  code = "a <- 1
          b <- a
          c <- list(x = 2)
          e <- 1"
)
tdata4
## Not run: 
verify(tdata4) # fails

## End(Not run)