Author: NEST CoreDev
Date: 2026-05-14

# Using teal.picks in a teal application

#### NEST CoreDev

#### 2026-05-14

## What this package is for

`teal.picks` helps you expose analysis choices in a `teal` Shiny app in a way that mimicks how people think about data: first which dataset, then which columns, and optionally which values to filter to. You set all those steps at once with `picks()`; the app renders the right controls and keeps selections consistent when upstream choices change.

Typical workflow:

1. Prepare a `teal.data::teal_data()` object (when you relate multiple tables use `join_keys` so teal knows how they link).
2. Define one or more `picks()` specs: `datasets()`, optionally `variables()`, optionally `values()` for filters or ranges.
3. In your teal module, wire UI and server so those specs drive your tables, plots, or merged analysis data (see Writing your own module).

The examples below show the usual building blocks—not every app needs every level.

    library(teal)
    library(teal.data)
    library(teal.picks)

    data <- teal_data()
    data <- within(data, {
      ADSL <- teal.data::rADSL
      ADLB <- teal.data::rADLB
    })

    join_keys(data) <- teal.data::default_cdisc_join_keys[c("ADSL", "ADLB")]

### Choose a dataset

When the analysis can draw from more than one table, let the user pick the active source (here, demographics vs labs).

    picks_datasets <- list(
      source = picks(
        datasets(
          choices = c("ADSL", "ADLB"),
          selected = "ADLB"
        )
      )
    )

### Choose dataset and columns

Add `variables()` so, after a dataset is chosen, the user picks one or more columns. You can list names explicitly or use tidyselect-style expressions; see `?picks` for details.

    picks_datasets_variables <- list(
      adsl_cols = picks(
        datasets(choices = "ADSL", selected = "ADSL"),
        variables(
          choices = c("USUBJID", "AGE", "SEX"),
          selected = "AGE",
          multiple = FALSE
        )
      )
    )

### Add a value filter (levels or ranges)

`values()` sits after `variables()`. It adapts to the column type—for example category levels for a `PARAM`-style variable, or a numeric or date range for continuous data.

    picks_datasets_variables_values <- list(
      labs = picks(
        datasets(choices = "ADLB", selected = "ADLB"),
        variables(choices = "PARAM", selected = "PARAM", multiple = FALSE),
        values(
          choices = c("ALT", "AST", "CRP", "GLU"),
          selected = c("ALT", "AST"),
          multiple = TRUE
        )
      )
    )

### Define choices and selections

Each `picks()` element has `choices` and `selected`. You can define them in four ways:

Approach | Works with
--- | ---
Default behavior - no arguments needed for generic usage | `datasets()`, `variables()`, `values()`
Static — character vector, integer index, or numeric range | `datasets()`, `variables()`, `values()`
`tidyselect` helpers — `everything()`, `starts_with()`, `where()`, … | `datasets()`, `variables()` only
Function — an R function applied at runtime to the data | `datasets()`, `variables()`, `values()`

The sections below walk through each approach with runnable examples.

#### Defaults

Defaults depend on the slot type.

- For `datasets()`, the default `choices` are all available datasets. A `picks()` call needs a `datasets()` as first positional argument, except when `check_no_dataset = FALSE`.
- For `variables()`, the default `choices` is all variables in the selected dataset.
- For `values()`, the default `choices` is all values of the selected variable.
- For `selected`, the default is the first available choice, or all choices when `multiple = TRUE`.

    picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables()
    )

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: tidyselect::everything()
         selected: 1L
         [3mmultiple=FALSE, ordered=FALSE, fixed=FALSE, allow-clear=FALSE[0m

    picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables(choices = "SEX", selected = "SEX", multiple = FALSE),
      values()
    )

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: SEX
         selected: SEX
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE, allow-clear=FALSE[0m
       [1m<values>[0m:
         choices: <fn>
         selected: <fn>
         [3mmultiple=TRUE, ordered=FALSE, fixed=FALSE[0m

#### Static choices

Pass a character vector to `choices` to enumerate options exactly. Use `selected` to set the default. Integer indices work too (for example `selected = 1L` means the first element of `choices`).

    # Datasets — user may switch between ADSL and ADLB; ADSL is the default
    p_datasets <- picks(
      datasets(
        choices  = c("ADSL", "ADLB"),
        selected = "ADSL"
      )
    )

    # Variables — only a named subset is offered; first column pre-selected
    p_variables <- picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables(
        choices  = c("AGE", "SEX", "ARM"),
        selected = "AGE",
        multiple = FALSE
      )
    )

    # Values — categorical filter; two levels pre-selected
    p_values <- picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables(choices = "SEX", selected = "SEX", multiple = FALSE),
      values(
        choices  = c("M", "F"),
        selected = "F"
      )
    )

    p_datasets

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL, ADLB
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=FALSE[0m

    p_variables

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: AGE, SEX, ARM
         selected: AGE
         [3mmultiple=FALSE, ordered=FALSE, fixed=FALSE, allow-clear=FALSE[0m

    p_values

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: SEX
         selected: SEX
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE, allow-clear=FALSE[0m
       [1m<values>[0m:
         choices: M, F
         selected: F
         [3mmultiple=TRUE, ordered=FALSE, fixed=FALSE[0m

#### `tidyselect` helpers

`tidyselect` predicates let `choices` and `selected` adapt to the actual data at runtime instead of being hard-coded. This is supported for `datasets()` and `variables()`.

Commonly used helpers:

- `tidyselect::everything()` — all items
- `tidyselect::starts_with()` / `tidyselect::ends_with()` / `tidyselect::contains()` — pattern matching
- `tidyselect::matches()` — regex matching
- `tidyselect::where(predicate)` — columns satisfying a predicate function
- `tidyselect::all_of()` / `tidyselect::any_of()` — vector-based selection (error-safe or silent)
- Integer indices such as `1L`, `1L:3L` — select by position

> Note: `tidyselect` is not supported by `values()`. Use explicit vectors or a function there instead (see Functions).

    # Datasets — offer any data.frame in the teal_data object
    p_any_dataset <- picks(
      datasets(
        choices  = tidyselect::where(is.data.frame),
        selected = 1L # first dataset by default
      )
    )

    # Variables — all numeric columns; first one pre-selected
    p_numeric_vars <- picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables(
        choices  = tidyselect::where(is.numeric),
        selected = 1L,
        multiple = FALSE
      )
    )

    # Variables — columns whose names start with "A"; first two pre-selected
    p_a_prefix <- picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables(
        choices  = tidyselect::starts_with("A"),
        selected = 1L:2L,
        multiple = TRUE
      )
    )

    p_any_dataset

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: <fn>
         selected: 1L
         [3mmultiple=FALSE, ordered=FALSE, fixed=FALSE[0m

    p_numeric_vars

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: <fn>
         selected: 1L
         [3mmultiple=FALSE, ordered=FALSE, fixed=FALSE, allow-clear=FALSE[0m

    p_a_prefix

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: tidyselect::starts_with("A")
         selected: <int>
         [3mmultiple=TRUE, ordered=FALSE, fixed=FALSE, allow-clear=FALSE[0m

#### Functions

You can pass a plain R function as `choices` or `selected`. The function receives the relevant context object (the current dataset for `datasets()` and `variables()`, the current column vector for `values()`) and must return the subset to use. This is the only runtime-dynamic approach supported by `values()`.

    # Variables — use the package helper is_categorical() as a column predicate.
    # Without "des-delayed", the resolver calls it via vapply(data, fn, logical(1)),
    # so it must accept one column and return a single logical value — which is_categorical() does.
    picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables(
        choices  = is_categorical(),
        selected = 1L,
        multiple = TRUE
      )
    )

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: <fn>
         selected: 1L
         [3mmultiple=TRUE, ordered=FALSE, fixed=FALSE, allow-clear=FALSE[0m

    # Values — select only even ages from the AGE column.
    # Functions passed to values() must carry the "des-delayed" class so the resolver
    # calls them with the column vector rather than treating them as a column predicate.
    even_vals <- function(x) sort(unique(x[x %% 2 == 0]))
    class(even_vals) <- append(class(even_vals), "des-delayed")

    p_even_ages <- picks(
      datasets(choices = "ADSL", selected = "ADSL"),
      variables(choices = "AGE", selected = "AGE", multiple = FALSE),
      values(
        choices  = even_vals,
        selected = even_vals
      )
    )

    p_even_ages

     [1m<picks>[0m
       [1m<datasets>[0m:
         choices: ADSL
         selected: ADSL
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE[0m
       [1m<variables>[0m:
         choices: AGE
         selected: AGE
         [3mmultiple=FALSE, ordered=FALSE, fixed=TRUE, allow-clear=FALSE[0m
       [1m<values>[0m:
         choices: <function>
         selected: <function>
         [3mmultiple=TRUE, ordered=FALSE, fixed=FALSE[0m

#### Use of multiple to select more than one columns at once

Use `multiple = TRUE` when analysts should pass more than one variable into the next step (for example stratifiers or outcomes together).

    picks_multiple_variables <- list(
      demo = picks(
        datasets(choices = "ADSL", selected = "ADSL"),
        variables(
          choices = c("USUBJID", "AGE", "SEX"),
          selected = c("AGE", "SEX"),
          multiple = TRUE,
          ordered = TRUE
        )
      )
    )

See `?picks` for other arguments that affect how choices/selections are presented to users regardless of using static, tidyselect or functions to define them.

#### Choices and Selection Summary

| Static | `tidyselect` | Function
--- | --- | --- | ---
`datasets()` | yes | yes | yes
`variables()` | yes | yes | yes
`values()` | yes | no | yes

Use static choices when the set of options is fixed and known at app-development time. Use `tidyselect` when you want the choices to adapt to the shape of the data without writing a custom function. Use functions when the logic is more involved, or when you need runtime behavior for `values()`.

## Trying these patterns inside teal

You can paste the `picks` objects above into any module that consumes them. To quickly explore picks object(s) in teal apps, the package includes `tm_merge()`: a small example teal module that connects `picks` to a merged preview table. That is not required for normal use—it is simply a convenient host while you learn the `picks()` API.

Run the next block to explore a teal app with different picks patterns

    library(shiny)

    app <- init(
      data = data,
      modules = modules(
        modules(
          label = "teal.picks patterns",
          tm_merge(
            label = "1. Dataset choice",
            picks = picks_datasets
          ),
          tm_merge(
            label = "2. Dataset & variables",
            picks = picks_datasets_variables
          ),
          tm_merge(
            label = "3. Dataset, variables & values",
            picks = picks_datasets_variables_values
          ),
          tm_merge(
            label = "4. Multiple variables",
            picks = picks_multiple_variables
          )
        )
      )
    )

    if (interactive()) {
      shinyApp(app$ui, app$server)
    }

## Writing your own module

In real apps you usually integrate `picks` with your own `teal::module()`. Two functions do the wiring:

- `picks_ui()` — place the selector controls in your picks object in your module UI
- `picks_srv()` — in the module server, pass `data` as a reactive `teal_data` object; it returns the resolved picks (what the user chose, after any dynamic choices are applied).

    tm_picks_preview <- function(label = "Custom picks module", picks) {
      teal::module(
        label = label,
        ui = function(id, picks) {
          ns <- shiny::NS(id)
          shiny::tagList(
            teal.picks::picks_ui(ns("sel"), picks = picks),
            shiny::tags$h5("Preview (first rows)"),
            shiny::tableOutput(ns("preview")),
            shiny::tags$h5("Resolved picks"),
            shiny::verbatimTextOutput(ns("resolved"))
          )
        },
        server = function(id, data, picks) {
          shiny::moduleServer(id, function(input, output, session) {
            resolved <- teal.picks::picks_srv("sel", picks = picks, data = data)
            preview_tbl <- shiny::reactive({
              shiny::req(data(), resolved())
              ds <- resolved()$datasets$selected
              vars <- resolved()$variables$selected
              shiny::req(length(ds) == 1L, length(vars) >= 1L)
              data()[[ds]][, vars, drop = FALSE]
            })
            output$preview <- shiny::renderTable({
              utils::head(preview_tbl(), 8L)
            })
            output$resolved <- shiny::renderPrint({
              shiny::req(resolved())
              str(resolved(), max.level = 2L, give.attr = FALSE)
            })
          })
        },
        ui_args = list(picks = picks),
        server_args = list(picks = picks),
        datanames = "ADSL"
      )
    }

    app <- init(
      data = data,
      modules = modules(
        tm_picks_preview(
          label = "Custom picks module",
          picks = picks_datasets_variables$adsl_cols
        )
      )
    )

    if (interactive()) {
      shinyApp(app$ui, app$server)
    }

See `?picks_ui` and `?picks_srv` for container options and multiple named selectors.

## See also

- `?picks` — full `datasets()` / `variables()` / `values()` reference.
- `vignette("teal-picks-standalone-shiny", package = "teal.picks")` — `picks_ui()` / `picks_srv()` in plain Shiny (no `init()`).
- `teal` documentation for `init()`, `modules()`, and `teal::module()`.