Merge pull request #7 from posit-conf-2023/module-exercise1

Add module code-along and exercise

_freeze/materials/d1-02-structure/codealong-1/execute-results/html.json

@@ -0,0 +1,16 @@
+{
+ "hash": "3bc604251443b99b15f919abb6ad30ca",
+ "result": {
+ "markdown": "---\ntitle: Modules for LEGO attribute selections\nformat:\n html:\n code-line-numbers: false\n---\n\n\nAs you make the transition to building production Shiny applications, Shiny modules are a very important tool to improve code organization and overall flow of your application. We will work together to build Shiny modules that let the user filter the underlying LEGO metadata based on a key variables in the overall data.\n\n## Requirements\n\nCreste three inputs for the user to subset the LEGO metadata by the following key variables:\n\n* Theme(s) associated with sets. Default should be all sets, but let the user customize selections with a search box.\n* Year range for when set was created. Give the user a visual cue of how many sets are present in each year.\n* Pre-defined ranges for how many parts are present in each set. Ranges are the following:\n + Small (1-50 parts)\n + Medium (51-200 parts)\n + Large (201 or more parts)\n + All sizes\n\n## Data\n\nThe following data sets included in the application contain the variables needed for the requirements (note that these snippets are a reduced sample of each source data set).\n\n### `sets`\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndplyr::glimpse(sets)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 368\nColumns: 6\n$ set_num <chr> \"001-1\", \"002-1\", \"1030-1\", \"1038-1\", \"1039-1\", \"1237-1\", \"1…\n$ name <chr> \"Gears\", \"4.5V Samsonite Gears Motor Set\", \"TECHNIC I: Simpl…\n$ year <dbl> 1965, 1965, 1985, 1985, 1986, 2001, 1999, 1999, 1999, 1999, …\n$ theme_id <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …\n$ num_parts <dbl> 43, 3, 210, 120, 39, 56, 30, 29, 28, 26, 28, 103, 98, 64, 28…\n$ img_url <chr> \"https://cdn.rebrickable.com/media/sets/001-1.jpg\", \"https:/…\n```\n\n\n:::\n:::\n\n\n### `themes`\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndplyr::glimpse(themes)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\nRows: 30\nColumns: 3\n$ theme_id <dbl> 191, 708, 693, 398, 650, 234, 56, 604, 136, 736, 692, 676, 2…\n$ name <chr> \"Dark Forest\", \"The LEGO Batman Movie\", \"Monkie Kid\", \"FIRST…\n$ parent_id <dbl> 186, 697, NA, NA, 632, 233, 52, NA, 126, 501, 535, NA, 227, …\n```\n\n\n:::\n:::\n\n\n## Plan\n\n1. Create a new module R script (and possible a supporting R script) by using `golem::add_module()` in the `dev/02_dev.R` script.\n1. Pick a Shiny input type that matches our requirements.\n1. Ensure the server-side portion of the module returns the result of the selection, with additional post-processing if necessary.\n1. Plug in the UI and server-side portions of the module in the main `app_ui.R` and `app_server.R` files, respectively.\n\n## Solution (Theme Picker)\n\nCreate the module file with the following snippet:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ngolem::add_module(name = \"theme_picker\", with_test = FALSE)\n```\n:::\n\n\nWhile we could choose a `selectInput` that displays the name of the theme as the choices and allow multiple to be selected, we want to incorporate a search box with snappy performance. The [`virtualSelectInput`](https://dreamrs.github.io/shinyWidgets/reference/virtualSelectInput.html) from the [`{shinyWidgets}`](https://dreamrs.github.io/shinyWidgets/index.html) package is an excellent solution.\n\nSince we have the `themes` data set available, we can also leverage the supporting function `shinyWidgets::prepare_choices()` to get the possible choices ready for the input. The module will simply return the selection as a reactive variable.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# UI portion\nmod_theme_picker_ui <- function(id, label = NULL) {\n ns <- NS(id)\n\n themes <- dplyr::arrange(themes, name)\n\n choices_list <- shinyWidgets::prepare_choices(\n themes,\n label = name,\n value = theme_id\n )\n\n tagList(\n virtualSelectInput(\n ns(\"virt_theme_id\"),\n label = label,\n choices = choices_list,\n selected = themes$theme_id,\n multiple = TRUE,\n search = TRUE\n )\n )\n}\n\n# server portion\nmod_theme_picker_server <- function(id){\n moduleServer( id, function(input, output, session){\n ns <- session$ns\n\n return(reactive(input$virt_theme_id))\n })\n}\n```\n:::\n\n\nAt the bottom of the module script, `{golem}` automatically includes commented code for referencing the module UI and server blocks in your main Shiny application. We can copy them into the `app_ui.R` and `app_server.R` files.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# app_ui.R\nmod_theme_picker_ui(\"theme_picker_1\")\n\n# app_server.R\ninput_theme_ids <- mod_theme_picker_server(\"theme_picker_1\")\n```\n:::\n",
+ "supporting": [
+ "codealong-1_files"
+ ],
+ "filters": [
+ "rmarkdown/pagebreak.lua"
+ ],
+ "includes": {},
+ "engineDependencies": {},
+ "preserve": {},
+ "postProcess": true
+ }
+}

materials/d1-02-structure/codealong-1.qmd

@@ -0,0 +1,126 @@
+---
+title: Modules for LEGO attribute selections
+format:
+ html:
+ code-line-numbers: false
+---
+
+As you make the transition to building production Shiny applications, Shiny modules are a very important tool to improve code organization and overall flow of your application. We will work together to build Shiny modules that let the user filter the underlying LEGO metadata based on a key variables in the overall data.
+
+## Requirements
+
+Creste three inputs for the user to subset the LEGO metadata by the following key variables:
+
+* Theme(s) associated with sets. Default should be all sets, but let the user customize selections with a search box.
+* Year range for when set was created. Give the user a visual cue of how many sets are present in each year.
+* Pre-defined ranges for how many parts are present in each set. Ranges are the following:
+ + Small (1-50 parts)
+ + Medium (51-200 parts)
+ + Large (201 or more parts)
+ + All sizes
+
+## Data
+
+The following data sets included in the application contain the variables needed for the requirements (note that these snippets are a reduced sample of each source data set).
+
+### `sets`
+
+```{r}
+#| echo: true
+#| eval: false
+dplyr::glimpse(sets)
+```
+
+```{r}
+#| echo: false
+#| eval: true
+sets <- readRDS("../../data_examples/df_sets.rds")
+dplyr::glimpse(sets)
+```
+
+### `themes`
+
+```{r}
+#| echo: true
+#| eval: false
+dplyr::glimpse(themes)
+```
+
+```{r}
+#| echo: false
+#| eval: true
+themes <- readRDS("../../data_examples/df_themes.rds")
+dplyr::glimpse(themes)
+```
+
+## Plan
+
+1. Create a new module R script (and possible a supporting R script) by using `golem::add_module()` in the `dev/02_dev.R` script.
+1. Pick a Shiny input type that matches our requirements.
+1. Ensure the server-side portion of the module returns the result of the selection, with additional post-processing if necessary.
+1. Plug in the UI and server-side portions of the module in the main `app_ui.R` and `app_server.R` files, respectively.
+
+## Solution (Theme Picker)
+
+Create the module file with the following snippet:
+
+```{r}
+#| echo: true
+#| eval: false
+golem::add_module(name = "theme_picker", with_test = FALSE)
+```
+
+While we could choose a `selectInput` that displays the name of the theme as the choices and allow multiple to be selected, we want to incorporate a search box with snappy performance. The [`virtualSelectInput`](https://dreamrs.github.io/shinyWidgets/reference/virtualSelectInput.html) from the [`{shinyWidgets}`](https://dreamrs.github.io/shinyWidgets/index.html) package is an excellent solution.
+
+Since we have the `themes` data set available, we can also leverage the supporting function `shinyWidgets::prepare_choices()` to get the possible choices ready for the input. The module will simply return the selection as a reactive variable.
+
+```{r}
+#| echo: true
+#| eval: false
+
+# UI portion
+mod_theme_picker_ui <- function(id, label = NULL) {
+ ns <- NS(id)
+
+ themes <- dplyr::arrange(themes, name)
+
+ choices_list <- shinyWidgets::prepare_choices(
+ themes,
+ label = name,
+ value = theme_id
+ )
+
+ tagList(
+ virtualSelectInput(
+ ns("virt_theme_id"),
+ label = label,
+ choices = choices_list,
+ selected = themes$theme_id,
+ multiple = TRUE,
+ search = TRUE
+ )
+ )
+}
+
+# server portion
+mod_theme_picker_server <- function(id){
+ moduleServer( id, function(input, output, session){
+ ns <- session$ns
+
+ return(reactive(input$virt_theme_id))
+ })
+}
+```
+
+At the bottom of the module script, `{golem}` automatically includes commented code for referencing the module UI and server blocks in your main Shiny application. We can copy them into the `app_ui.R` and `app_server.R` files.
+
+```{r}
+#| echo: true
+#| eval: false
+
+# app_ui.R
+mod_theme_picker_ui("theme_picker_1")
+
+# app_server.R
+input_theme_ids <- mod_theme_picker_server("theme_picker_1")
+```

materials/d1-02-structure/ex-1.qmd

@@ -0,0 +1,50 @@
+---
+title: "Value Metrics Module"
+---
+
+## Access Instructions
+
+The project used for this particular exercise is hosted on [Posit Cloud](https://posit.cloud) in this [space](https://posit.cloud/spaces/400774/join?access_code=DDgV_peF5WCCCpB5JHjQtMN2aHByWoNF0k5p8Wp7). The project for this exercise is called **modules-exercise1**.
+
+## Exercise Instructions
+
+Your task is to create a new Shiny module that displays three important metrics to the user: 
+
+* Total number of sets
+* Total number of parts among the sets
+* Total number of mini-figures
+
+Note that these quantities are dependent on the user selections from the inputs contained in the left sidebar (theme, year, and parts range). 
+
+Keeping with the overall user interface style, you are recommended to use the [`value_box`](https://rstudio.github.io/bslib/reference/value_box.html) function from the [`{bslib}`](https://rstudio.github.io/bslib/) package. The metrics can be derived using the function below. In the application code, you will find reactive data frames called `sets_rv` and `part_meta_rv` which can be used in the parameters of the function below.
+
+```r
+#' Derive key LEGO data set metrics
+#' 
+#' @param sets_rv data frame containing sets information
+#' @param part_meta_rv data frame containing parts metadata information
+#' 
+#' @import dplyr
+derive_widget_metrics <- function(sets_rv, part_meta_rv) {
+ # number of sets
+ n_sets <- length(unique(sets_rv$set_num))
+
+ # number of parts
+ n_parts <- sum(sets_rv$num_parts, na.rm = TRUE)
+
+ # number of minifigs
+ n_minifigs <- part_meta_rv |>
+ summarize(total_minifigs = sum(minifig_ind)) |>
+ collect() |>
+ pull(total_minifigs)
+
+ return(
+ list(
+ n_sets = n_sets,
+ n_parts = n_parts,
+ n_minifigs = n_minifigs
+ )
+ )
+}
+```
+

units/d1-02-structure.qmd

@@ -15,6 +15,17 @@ listing:
  sort-ui: false
  filter-ui: false
  image-placeholder: assets/img/placeholder.png
+ - id: code-along
+ contents:
+ - ../materials/d1-02-structure/codealong-*.qmd
+ type: table
+ fields: [subtitle, title]
+ field-display-names: 
+ subtitle: "Code-Along"
+ sort: [filename]
+ sort-ui: false
+ filter-ui: false
+ image-placeholder: assets/img/placeholder.png
 tbl-colwidths: [5,20,75]
 ---
 
@@ -29,4 +40,13 @@ These slides are under construction and will be finalized prior to the workshop
 ```{=html}
 <iframe class="slide-deck" src="../materials/d1-02-structure/" height="420" width="747" style="border: 1px solid #2e3846;"></iframe>
 ```
+## Code-Along Examples
+
+::: {#code-along}
+:::
+
+## Exercises
+
+::: {#exercises}
+:::