rewrite example and minor corrections

vignettes/creating-custom-modules.Rmd

@@ -10,7 +10,8 @@ vignette: >
 
 ## Introduction
 
-The `teal` framework provides a large number of analysis modules to be incorporated into `teal` applications. However, it is also possible to create your own modules using the `module` function.
+The `teal` framework provides a large number of analysis modules to be incorporated into `teal` applications. 
+However, it is also possible to create your own modules using the `module` function.
 
 Here is an implementation of a simple module:
 
@@ -44,10 +45,11 @@ which can be added into `teal` apps using `example_module(label = "Label for tab
 
 ### UI function
 
-This function contains the UI required for the module. It should be a function with at least the arguments `id`. It can also contain the argument `data` for access to the application data. See the server section below for more details.
+This function contains the UI required for the module. It should be a function with at least the arguments `id`. 
+It can also contain the argument `data` for access to the application data. See the server section below for more details.
 
-The UI function can contain standard UI components alongside additional widgets provided by the `teal.widgets` package. In the example above we are using the `standard_layout` function of `teal.widgets` which generates a layout
-including an encoding panel on the left and main output covering the rest of the module's UI.
+The UI function can contain standard UI components alongside additional widgets provided by the `teal.widgets` package. 
+In the example above we are using the `standard_layout` function of `teal.widgets` which generates a layout including an encoding panel on the left and main output covering the rest of the module's UI.
 
 ### Server function
 
@@ -66,15 +68,13 @@ function(id,
 
 ```
 
-When used inside a `teal` application called with `init`, the `data` argument is a named list of reactive `data.frame`s containing the data after having been filtered through the filter panel.  It is of the `tdata` type and can be created using the `new_tdata` function.
+When used inside a `teal` application called with `init`, the `data` argument receives a `teal_data` object containing the data (`data.frame`s) after having been filtered through the filter panel.
 
 ## A More Complicated Example
 
 The `teal` framework also provides:
 
 - A way to create modules which then generate the R code needed to reproduce their outputs; these modules use the [`teal.code`](https://insightsengineering.github.io/teal.code/) package.
-- A way extract from and merge related datasets using the [`teal.transform`](https://insightsengineering.github.io/teal.transform/) package.
-- A way to allow app creators to customize your modules also using `teal.transform`.
 
 The annotated example below demonstrates these features within a simple histogram module, allowing app developers to choose the data and columns that their app users can select for display in a histogram.
 
@@ -84,18 +84,14 @@ See the package and function documentation for further details.
 library(teal)
 
 # ui function for the module
-# histogram_var is a teal.transform::data_extract_spec object
-# specifying which columns of which datasets users can choose
+# allows for selecting dataset and a numeric variable from it
 ui_histogram_example <- function(id, histogram_var) {
   ns <- NS(id)
   teal.widgets::standard_layout(
     output = plotOutput(ns("plot")),
     encoding = div(
-      teal.transform::data_extract_ui(
-        id = ns("histogram_var"),
-        label = "Variable",
-        data_extract_spec = histogram_var
-      )
+      uiOutput(ns("datasets")),
+      uiOutput(ns("numerics"))
     ),
     # we have a show R code button to show the code needed
     # to generate the histogram
@@ -104,37 +100,38 @@ ui_histogram_example <- function(id, histogram_var) {
 }
 
 # server function for the module
-# histogram_var is a teal.transform::data_extract_spec object
-# specifying which columns of which datasets users can choose
+# presents choices for selection of dataset and then numeric variable
+# plots histogram of selected variable
+# records code
 srv_histogram_example <- function(id, data, histogram_var) {
-  checkmate::assert_class(data, "tdata")
+  checkmate::assert_class(data, "reactive")
+  checkmate::assert_class(isolate(data()), "teal_data")
   moduleServer(id, function(input, output, session) {
-    # get the selected dataset and column from the UI
-    extracted <- teal.transform::data_extract_srv(
-      id = "histogram_var",
-      datasets = data,
-      data_extract_spec = histogram_var,
-      join_keys = teal.data::join_keys(data)
-    )
+    ns <- session$ns
 
-    dataname <- reactive(extracted()$dataname)
-    selected <- reactive(extracted()$select)
+    # present data sets columns for selection
+    output$datasets <- renderUI({
+      selectInput(ns("datasets"), "select dataset", choices = datanames(data()))
+    })
+    dataset <- reactive(input$datasets)
+
+    # present numeric columns for selection
+    output$numerics <- renderUI({
+      req(dataset())
+      dataset <- data()[[dataset()]]
+      nums <- vapply(dataset, is.numeric, logical(1L))
+      selectInput(ns("numerics"), "select numeric variable", choices = names(nums[nums]))
+    })
+    selected <- reactive(input$numerics)
 
     # the reactive which adds the code to plot the histogram into the qenv
     plot_code_q <- reactive({
-      validate(need(length(selected) == 1, "Please select a variable"))
+      validate(need(length(dataset()) == 1, "Please select a dataset"))
+      validate(need(length(selected()) == 1, "Please select a variable"))
+      req(selected() %in% names(data()[[dataset()]]))
 
       # take the filtered data from the data object and add it into the qenv environment
-      teal.code::new_qenv(tdata2env(data), code = get_code_tdata(data)) %>%
-        teal.code::eval_code(
-          substitute(
-            expr = p <- hist(dataname[, selected]),
-            env = list(
-              dataname = as.name(dataname()),
-              selected = selected()
-            )
-          )
-        )
+      within(data(), p <- hist(dataset[, selected], las = 1), dataset = as.name(dataset()), selected = selected())
     })
 
     # shiny component to view
@@ -152,16 +149,13 @@ srv_histogram_example <- function(id, data, histogram_var) {
 }
 
 # the function which creates the teal module for users
-tm_histogram_example <- function(label, histogram_var) {
+tm_histogram_example <- function(label) {
   checkmate::assert_character(label)
-  checkmate::assert_class(histogram_var, "data_extract_spec")
 
   module(
     label = label,
     server = srv_histogram_example,
     ui = ui_histogram_example,
-    ui_args = list(histogram_var = histogram_var),
-    server_args = list(histogram_var = histogram_var),
     datanames = "all"
   )
 }
@@ -176,19 +170,8 @@ An example `teal` application using this module is shown below:
 library(teal)
 
 app <- init(
-  data = teal_data(
-    IRIS = iris,
-    code = "IRIS <- iris"
-  ),
-  modules = tm_histogram_example(
-    label = "Simple Module",
-    histogram_var = data_extract_spec(
-      dataname = "IRIS",
-      select = select_spec(
-        choices = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
-      )
-    )
-  ),
+  data = teal_data(IRIS = iris, NPK = npk, code = expression(IRIS <- iris, NPK <- npk)),
+  modules = tm_histogram_example(label = "Simple Module"),
   header = "Simple app with custom histogram module"
 )
 
@@ -199,7 +182,10 @@ if (interactive()) {
 
 ## `shiny` input cycle
 
-When `teal` modules are run inside the `init` the initial shiny input cycle is empty for each of them. In practice, this means that some inputs might be initialized with `NULL` value, unnecessary triggering some observers. A developer has to be aware of this situation as often it will require `shiny::req` or `ignoreInit` argument in observers or `reactive` expressions. This side effect is caused by the `shiny::insertUI` function. We are aware of this inconvenience and have already started to look for a solution.
+When `teal` modules are run inside the `init` the initial shiny input cycle is empty for each of them.
+In practice, this means that some inputs might be initialized with `NULL` value, unnecessary triggering some observers.
+A developer has to be aware of this situation as often it will require `shiny::req` or `ignoreInit` argument in observers or `reactive` expressions.
+This side effect is caused by the `shiny::insertUI` function. We are aware of this inconvenience and have already started to look for a solution.
 
 ## Adding reporting to a module
 Refer to `vignette("adding_support_for_reporting")` to read about adding support for reporting in your `teal` module.