start add shinylive demo for remaining examples

vignettes/blockr_examples.Rmd

@@ -57,26 +57,7 @@ ui_input.slider_field <- function(x, id, name) {
 #' @method validate_field slider_field
 #' @export
 validate_field.slider_field <- function(x) {
-  val <- value(x)
-  min <- value(x, "min")
-  max <- value(x, "max")
-  step <- value(x, "step")
-
-  validate_number(val)
-
-  if (length(min)) {
-    validate_number(min, "min")
-  }
-
-  if (length(max)) {
-    validate_number(max, "max")
-  }
-
-  if (length(step)) {
-    validate_number(step, "step")
-  }
-
-  NextMethod()
+  x
 }
 
 #' @method ui_update slider_field
@@ -145,13 +126,14 @@ blockr::register_block(
 
 In this example, we'll create a pipeline that fetches recent stock data using the [quantmod package](https://github.com/joshuaulrich/quantmod), performs time series analysis, and forecasts future stock prices using the [Prophet model](https://github.com/facebook/prophet). We first design the `stock_data_block`, containing a field to select the stock items and generate the data. The `prophet_forecast_block` does all the modeling part.
 
-```{r stock_example}
+```{r stock_example, eval=FALSE}
+# Does not run on shinylive as quantmod/prophet not available
 library(blockr)
 library(quantmod)
 library(prophet)
 
 # Custom block to fetch stock data
-stock_data_block <- function(...) {
+new_stock_data_block <- function(...) {
   # stocks to pick (top 10)
   pick_stock <- \()  c("NVDA", "TSLA", "AAPL", "MSFT", "AVGO", "AMZN", "AMD", "PLTR", "TSM", "META")
 
@@ -171,7 +153,7 @@ stock_data_block <- function(...) {
 }
 
 # Custom block for Prophet forecasting
-prophet_forecast_block <- function(columns = character(), ...) {
+new_prophet_forecast_block <- function(columns = character(), ...) {
   all_cols <- function(data) colnames(data)[2:length(colnames(data))]
 
 
@@ -198,15 +180,15 @@ prophet_forecast_block <- function(columns = character(), ...) {
 
 # Register custom blocks
 register_block(
-  stock_data_block,
+  new_stock_data_block,
   name = "Stock Data",
   description = "Fetch stock data",
   classes = c("stock_data_block", "data_block"),
   input = NA_character_,
   output = "data.frame"
 )
 register_block(
-  prophet_forecast_block,
+  new_prophet_forecast_block,
   name = "Prophet Forecast",
   description = "Forecast using Prophet",
   classes = c("prophet_forecast_block", "plot_block"),
@@ -216,24 +198,53 @@ register_block(
 
 # Create the stack
 stock_forecast_stack <- new_stack(
-  stock_data_block(),
-  prophet_forecast_block()
+  new_stock_data_block(),
+  new_prophet_forecast_block()
 )
 
 serve_stack(stock_forecast_stack)
 ```
-
+
+```{r blockr-stock, echo=FALSE, fig.cap='Stock model demo', fig.align = 'center', out.width='100%'}
+knitr::include_graphics("vignettes/figures/blockr-stock-demo.png")
+```
 
 ### 2. Pharmaceutical: Clinical Trial Analysis
 
 #### 2.1 AE Forest Plot
 
 This forest plot visualizes the relative risk of adverse events between two treatment arms in a clinical trial. In this case, it compares "Xanomeline High Dose" to "Xanomeline Low Dose" starting from the [pharmaverseadam](https://github.com/pharmaverse/pharmaverseadam) `adae` dataset.
 
-```{r ae_forest}
+```{r, eval=TRUE, echo=FALSE}
+card(
+  create_app_link(
+    "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-kLMhl8sVysdhPYGAI4ITPr4sronAcTnIfHrdAVBuVqVTwabgpbbY76Jq3ZcfdIA7gfCnmontfZuv1hubsTnZU7i57K7XG51eqIY63cNrGZ4BCc8YJjlJ2NyRfzdBqMiId5y3lPVzRkNgyQTLRCXKVNjR7OVQOUGdnXdVNbTqbhQI3XgWUAvgNGHUdDVVcpDVSS00NrW0GhYPs0VIPhKLddCZGsBgiCcVAWToPYEw49h2Bw7g8PeDcyPHVJXRY6jgJgCpKzqPhCzUcVklkOwOK4tpAjABoONQFN1kfGR02fV98VJABBABRKl5AARycCgCBYb5CQgBo6QMqwMQ4ScOVleTdmbVCZNtW12M47jeI3JklCEngRPwjRNy1XcbzvbgKOY1igoUuj12NU0yB041aPo9E-QDYAiMbTUw3UmRNOinShH0ohDLAXLZKw94eTlIg2GUlkEKQ2ReNUGD1XHaTWJgAdLL4VAfIYPypuKikwMMIsH1tREAGU4Cw9EsU6mQADkZBgJQc3eRznMpWzlH4hLCV4aaa1tbFuPZZChXZUb8wUnY5tkqKuJ4vj4sE4TRKvPdb3IqSqIiuSCqUr8eBNLadKCkaSvNJqWu0qb2oMlIwbRn6WXFTazXYWQsZEwmLSpiLUFffgkvhjL90k9JU0RABxOAc1RFgnM-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-jwyBN0U4kJrjaERONAEDOvjs42LDJfVIvC0a2i4qgGowjRHiMkdI2R9iZAKJUeozR2ignOMMRPah4SPGsyiVYzhtiHEJOCS41pxj3FCDMeabxGSs49ICUE20zTQltJSR4gh3TYkyHiQUgp8zkkmNGRMkgmTpmF02duCBgsTIxl8GUUkzpqTlxpDmekqYuScBZM6GcgMe7s1tNApal94x-m4FNYZ9D+kLNPllMMsghAyE0PkzZLTkGLN2ZEzpKzHgxOyTw3ShgAgLJBcs1h0TrFcKhbpGQlRlDSCCfi9pey0WsMmX4rF0KwC4tYbSpZez0kHKmbE1laQwC5NYntNMplmSkheY812zyvg-FdjotGALjrkC9jmdodA2ANEVQUqVby1YABJlUqkvuwFabBSBiSELPDA5tdY2rteYB17xBWyD1faA18C3Vyp5B6gRJTXLeoefqrgBqikBpYOAiKL9ZLwCgCpe53IQ1QE9VgeF-CiCCKmu6w1-qhGBtYuG3uagc2hqLcI9N70WDWATR0MA3BphgCkN1St7E0BaG3g0RwU0JaMWUeg1l8kUiVsTF7Ka6ClGVs2BAOAWg4DVp0D2iADg+AAA5B0wGHaxOgRAvbsBYAAL1JGoCdABmAArCOzU2bUCZlELUfYu6yZNqIDsHgIhnIBGnce3STQGDiA-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-76IhsteBxFUHNkFs2XTAAeSW3gIJq3RSI-R7mbgqOkdfaga+bH93kSXeu7L76DhFfc5kPbGo99KfvZpyc2XYyyp9YgPmGohn2dA6aQOoJMAIJqDMxUMdGmB3lnIIh8zt31fE7ABdX8-5AJa-OjXMsC26IXCgDsD7tORWVrEStKaTk-jrmtpspk9RWSAz1fSPbGkhwIQPsTY25F88QtL81P+DNTkRQjhjgg8ZBLGc0ESKEHJCaFc3gcRt9JitmBkxFKrYdaxZfOYiLj9haG9w-OoGU2J77d4gPISfPo+8SB1bl-Lwbe8HHhTt59YWIukmiyHJEPqZBxj+LDu03wmQnfWmB9MErCSJtpHyIPreSRkhi8QGAtGqz+yBxk45jyzX4N6xB1A6R1qf51CeJkCd48jd6sIMJFZIG2ZsCczqq-pqwYD3TXzX6cR6ADjPpPI04PgZjP4z6cAJidxojTqGbYxBwsDYzUESDNib6sHiAb59YwjqgNas7kBeRgYwhAYtpCyejeglJsBr5cG2ab456oEghMTpAPh1AMCxycEET0H1wiRMEsFcASDJBgARwhhAA", # nolint
+    "app",
+    mode = "editor"
+  ),
+  full_screen = TRUE
+)
+```
+
+<p class="text-center">
+<a 
+class="btn btn-primary" 
+data-bs-toggle="collapse" 
+href="#forest-block" 
+role="button" 
+aria-expanded="false" 
+aria-controls="forest-block">
+Toggle code
+</a>
+</p>
+
+<div class="collapse" id="forest-block">
+
+```r
 library(dplyr)
 library(tidyr)
 library(forestplot)
+library(blockr.pharmaverseadam)
 
 # Function to create adverse event forest plot
 create_ae_forest_plot <- function(data, usubjid_col, arm_col, aedecod_col, n_events) {
@@ -332,7 +343,7 @@ create_ae_forest_plot <- function(data, usubjid_col, arm_col, aedecod_col, n_eve
   )
 }
 
-forest_plot_block <- function(...) {
+new_forest_plot_block <- function(...) {
   new_block(
     fields = list(
       usubjid_col = new_select_field(
@@ -369,7 +380,7 @@ forest_plot_block <- function(...) {
 
 # Register the custom block
 register_block(
-  forest_plot_block,
+  new_forest_plot_block,
   name = "Adverse Event Forest Plot",
   description = "Create a forest plot of adverse events comparing two treatment arms",
   classes = c("adverse_event_plot_block", "plot_block"),
@@ -379,16 +390,15 @@ register_block(
 
 # Create the stack
 clinical_trial_stack <- new_stack(
-  new_dataset_block(
-    selected = "adae",
-    package = "pharmaverseadam"
-  ),
+  new_adam_block(selected = "adae"),
   # filter_in_block(),
-  forest_plot_block()
+  new_forest_plot_block()
 )
 
 serve_stack(clinical_trial_stack)
 ```
+</div>
+
 #### 2.2 Demographics Table
 
 This demographics table is taken from the `cardinal` package of [FDA Safety Tables and Figures](https://github.com/pharmaverse/cardinal) and demonstrates `gt` and `rtables` outputs starting from the [pharmaverseadam](https://github.com/pharmaverse/pharmaverseadam) `adsl` dataset.