docs: reorder the example code

vignettes/data-extract-merge.Rmd

@@ -32,15 +32,37 @@ Note that primary key columns are maintained when selecting columns from dataset
 
 Let's see how to achieve this dynamic `select`, `filter`, and `merge` operations in a `shiny` app using `teal.transform`.
 
-#### Step 1/5 - Creating data extract specifications
-
-In the following code block, we create a `data_extract_spec` object for each dataset, as illustrated above.
+#### Step 1/5 - Preparing the Data
 
 ```{r}
 library(teal.transform)
 library(teal.data)
 library(shiny)
 
+# Define data.frame objects
+ADSL <- teal.transform::rADSL
+ADTTE <- teal.transform::rADTTE
+
+# create a list of reactive data.frame objects
+datasets <- list(
+  ADSL = reactive(ADSL),
+  ADTTE = reactive(ADTTE)
+)
+
+# create join_keys
+join_keys <- join_keys(
+  join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")),
+  join_key("ADSL", "ADTTE", c("STUDYID", "USUBJID")),
+  join_key("ADTTE", "ADTTE", c("STUDYID", "USUBJID", "PARAMCD"))
+)
+```
+
+
+#### Step 2/5 - Creating data extract specifications
+
+In the following code block, we create a `data_extract_spec` object for each dataset, as illustrated above.
+
+```{r}
 adsl_extract <- data_extract_spec(
   dataname = "ADSL",
   select = select_spec(
@@ -70,7 +92,7 @@ adtte_extract <- data_extract_spec(
 data_extracts <- list(adsl_extract = adsl_extract, adtte_extract = adtte_extract)
 ```
 
-#### Step 2/5 - Creating the UI
+#### Step 3/5 - Creating the UI
 
 Here, we define the `merge_ui` function, which will be used to create the UI components for the `shiny` app.
 
@@ -104,7 +126,7 @@ merge_ui <- function(id, data_extracts) {
 }
 ```
 
-#### Step 3/5 - Creating the Server Logic
+#### Step 4/5 - Creating the Server Logic
 
 Here, we define the `merge_srv` function, which will be used to create the server logic for the `shiny` app.
 
@@ -133,27 +155,6 @@ merge_srv <- function(id, datasets, data_extracts, join_keys) {
 }
 ```
 
-#### Step 4/5 - Preparing the Data
-
-```{r}
-# Define data.frame objects
-ADSL <- teal.transform::rADSL
-ADTTE <- teal.transform::rADTTE
-
-# create a list of reactive data.frame objects
-datasets <- list(
-  ADSL = reactive(ADSL),
-  ADTTE = reactive(ADTTE)
-)
-
-# create join_keys
-join_keys <- join_keys(
-  join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")),
-  join_key("ADSL", "ADTTE", c("STUDYID", "USUBJID")),
-  join_key("ADTTE", "ADTTE", c("STUDYID", "USUBJID", "PARAMCD"))
-)
-```
-
 #### Step 5/5 - Creating the `shiny` App
 
 Finally, we include `merge_ui` and `merge_srv` in the UI and server components of the `shinyApp`, respectively,

vignettes/data-extract.Rmd

@@ -30,49 +30,13 @@ The role of `data_extract_spec` is twofold: to create a UI component in a `shiny
 from the UI to the module itself. 
 Let's delve into how it fulfills both of these responsibilities.
 
-#### Step 1/4 - Creating the `shiny` UI and Server Modules
-
-To demonstrate different initialization options of `data_extract_spec`, let's first define a `shiny` module that
-utilizes `data_extract_ui` and `data_extract_srv` to handle `data_extract_spec` objects.
-This module creates a UI component for a single `data_extract_spec` and prints a list of values returned from the `data_extract_srv` module. 
-For more information about `data_extract_ui` and `data_extract_srv`, please refer to the package documentation.
+#### Step 1/4 - Preparing the Data
 
 ```{r}
 library(teal.transform)
 library(teal.data)
 library(shiny)
 
-extract_ui <- function(id, data_extract) {
-  ns <- NS(id)
-  sidebarLayout(
-    sidebarPanel(
-      h3("Encoding"),
-      data_extract_ui(ns("data_extract"), label = "variable", data_extract)
-    ),
-    mainPanel(
-      h3("Output"),
-      verbatimTextOutput(ns("output"))
-    )
-  )
-}
-
-extract_srv <- function(id, datasets, data_extract, join_keys) {
-  moduleServer(id, function(input, output, session) {
-    reactive_extract_input <- data_extract_srv("data_extract", datasets, data_extract, join_keys)
-    s <- reactive({
-      format_data_extract(reactive_extract_input())
-    })
-    output$output <- renderPrint({
-      cat(s())
-    })
-  })
-}
-```
-
-
-#### Step 2/4 - Preparing the Data
-
-```{r}
 # Define data.frame objects
 ADSL <- teal.transform::rADSL
 ADTTE <- teal.transform::rADTTE
@@ -90,7 +54,7 @@ join_keys <- join_keys(
 )
 ```
 
-#### Step 3/4 - Creating a `data_extract_spec` Object
+#### Step 2/4 - Creating a `data_extract_spec` Object
 
 Consider the following example, where we create two UI elements, one to filter on a specific level from `SEX` variable,
 and a second one to select a variable from `c("BMRKR1", "AGE")`.
@@ -104,6 +68,41 @@ simple_des <- data_extract_spec(
 )
 ```
 
+#### Step 3/4 - Creating the `shiny` UI and Server Modules
+
+To demonstrate different initialization options of `data_extract_spec`, let's first define a `shiny` module that
+utilizes `data_extract_ui` and `data_extract_srv` to handle `data_extract_spec` objects.
+This module creates a UI component for a single `data_extract_spec` and prints a list of values returned from the `data_extract_srv` module. 
+For more information about `data_extract_ui` and `data_extract_srv`, please refer to the package documentation.
+
+```{r}
+extract_ui <- function(id, data_extract) {
+  ns <- NS(id)
+  sidebarLayout(
+    sidebarPanel(
+      h3("Encoding"),
+      data_extract_ui(ns("data_extract"), label = "variable", data_extract)
+    ),
+    mainPanel(
+      h3("Output"),
+      verbatimTextOutput(ns("output"))
+    )
+  )
+}
+
+extract_srv <- function(id, datasets, data_extract, join_keys) {
+  moduleServer(id, function(input, output, session) {
+    reactive_extract_input <- data_extract_srv("data_extract", datasets, data_extract, join_keys)
+    s <- reactive({
+      format_data_extract(reactive_extract_input())
+    })
+    output$output <- renderPrint({
+      cat(s())
+    })
+  })
+}
+```
+
 #### Step 4/4 - Creating the `shiny` App
 
 Finally, we include `extract_ui` in the UI of the `shinyApp`, and utilize `extract_srv` in the server function of the `shinyApp`:

vignettes/data-merge.Rmd

@@ -19,12 +19,13 @@ knitr::opts_chunk$set(
 
 Combining datasets is an essential step when working with relational datasets.
 Within the context of `teal`, we use the term "merge" to refer to the process of combining datasets.
-To support this, two functions are provided:
 
-1. `merge_expression_module` can be used when there is no need to process the `data_extract` list.
+To support this, two functions are provided depending on how to process the `data_extract_spec` object:
+
+1. `merge_expression_module` can be used when there is no need to process the list of `data_extract_spec`.
 This function reads the data and the list of `data_extract_spec` objects and applies the merging.
 Essentially, it serves as a wrapper that combines `data_extract_multiple_srv()` and `merge_expression_srv()`.
-2. `merge_expression_srv` and `data_extract_multiple_srv` can be used in scenarios where additional processing of the `data_extract` list is necessary or `data_extract_srv()` to customize the `selector_list` input.
+2. `merge_expression_srv` and `data_extract_multiple_srv` can be used in scenarios where additional processing of the list of `data_extract_spec` is necessary or `data_extract_srv()` to customize the `selector_list` input.
 
 The following sections provide examples for both scenarios.
 
@@ -33,13 +34,35 @@ The following sections provide examples for both scenarios.
 Using `merge_expression_module` alone requires a list of `data_extract_spec` objects for the `data_extract` argument,
 a list of reactive or non-reactive `data.frame` objects, and a list of join keys corresponding to each `data.frame` object.
 
-#### Step 1/5 - Creating the Data Extracts
+#### Step 1/5 - Preparing the Data
 
 ```{r}
 library(teal.transform)
 library(teal.data)
 library(shiny)
 
+# Define data.frame objects
+ADSL <- teal.transform::rADSL
+ADTTE <- teal.transform::rADTTE
+
+# create a list of reactive data.frame objects
+datasets <- list(
+  ADSL = reactive(ADSL),
+  ADTTE = reactive(ADTTE)
+)
+
+# create  join_keys
+join_keys <- join_keys(
+  join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")),
+  join_key("ADSL", "ADTTE", c("STUDYID", "USUBJID")),
+  join_key("ADTTE", "ADTTE", c("STUDYID", "USUBJID", "PARAMCD"))
+)
+```
+
+
+#### Step 2/5 - Creating the Data Extracts
+
+```{r}
 adsl_extract <- data_extract_spec(
   dataname = "ADSL",
   select = select_spec(
@@ -64,7 +87,7 @@ adtte_extract <- data_extract_spec(
 data_extracts <- list(adsl_extract = adsl_extract, adtte_extract = adtte_extract)
 ```
 
-#### Step 2/5 - Creating the UI
+#### Step 3/5 - Creating the UI
 
 ```{r}
 merge_ui <- function(id, data_extracts) {
@@ -94,7 +117,7 @@ merge_ui <- function(id, data_extracts) {
 }
 ```
 
-#### Step 3/5 - Creating the Server Logic
+#### Step 4/5 - Creating the Server Logic
 
 ```{r}
 merge_srv <- function(id, datasets, data_extracts, join_keys) {
@@ -116,27 +139,6 @@ merge_srv <- function(id, datasets, data_extracts, join_keys) {
 }
 ```
 
-#### Step 4/5 - Preparing the Data
-
-```{r}
-# Define data.frame objects
-ADSL <- teal.transform::rADSL
-ADTTE <- teal.transform::rADTTE
-
-# create a list of reactive data.frame objects
-datasets <- list(
-  ADSL = reactive(ADSL),
-  ADTTE = reactive(ADTTE)
-)
-
-# create  join_keys
-join_keys <- join_keys(
-  join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")),
-  join_key("ADSL", "ADTTE", c("STUDYID", "USUBJID")),
-  join_key("ADTTE", "ADTTE", c("STUDYID", "USUBJID", "PARAMCD"))
-)
-```
-
 #### Step 5/5 - Creating the `shiny` App
 
 ```{r eval=FALSE}