Add edited slides.

app_demo/app.R

@@ -17,7 +17,7 @@ ui <- navbarPage(
     fluidRow(
       column(width = 3,
              textInput("patientName", "Patient Name:"),
-             numericInput("age", "Age:", value = NULL, min = 1, max = 150),
+             numericInput("age", "Age:", value = NULL, min = 1, max = 100),
              selectInput("sex", "Sex:", choices = c("Male", "Female")),
              numericInput("height", "Height (cm):", value = NULL, min = 50, max = 250, step = 0.1),
              numericInput("mass", "Mass (kg):", value = NULL, min = 1, max = 300, step = 0.1),
@@ -101,6 +101,7 @@ server <- function(input, output, session) {
                                           input$systolic,
                                           input$smoker)
 
+
       # Render the results
       output$t1 <- renderText("Body Mass Index (BMI) Status")
       output$t2 <- renderText("Blood Pressure Status")

index.html

@@ -715,7 +715,7 @@ <h2>Hands On</h2>
 <p><strong>Goal</strong></p>
 </div>
 <div class="callout-content">
-<p>Create a Shiny app to do data exploration (visualization) and classification (several classification algorithm)</p>
+<p>Create a Shiny app to do health screening (BMI, hypertension, diabetes, and 10-year cardiovascular risk)</p>
 </div>
 </div>
 </div>
@@ -728,8 +728,7 @@ <h2>Hands On</h2>
 <p><strong>Data</strong></p>
 </div>
 <div class="callout-content">
-<p>In this example, we will use data on obesity level (UCI Machine Learning Repository, 2019)</p>
-<p>Download <a href="https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition">here</a></p>
+<p>The data is from the patient based on several screening/ lab test.</p>
 </div>
 </div>
 </div>

index.qmd

@@ -292,15 +292,13 @@ server <- function(input, output) {
 ::: callout-note
 ## Goal
 
-Create a Shiny app to do data exploration (visualization) and classification (several classification algorithm)
+Create a Shiny app to do health screening (BMI, hypertension, diabetes, and 10-year cardiovascular risk)
 :::
 
 ::: callout-note
 ## Data
 
-In this example, we will use data on obesity level (UCI Machine Learning Repository, 2019)
-
-Download [here](https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition)
+The data is from the patient based on several screening/ lab test.
 :::
 
 ::: callout-note

tutorial.qmd

@@ -9,7 +9,9 @@ editor: visual
 
 1.  Having R and R studio installed. If you haven't already, you can see the installation guide [here](https://posit.co/download/rstudio-desktop/).
 
-2.  Having these packages installed: `tidyverse`, `shiny`, `shinythemes`, `rmarkdown`, and `officer`. You can install these packages by running this code:
+2.  Download the helper function [here](https://github.com/Dewi-Amaliah/its_shiny_slide/blob/main/app_demo/helper.R). It is a function to calculate 10-year cardiovascular risk based on Framingham Risk Score.
+
+3.  Having these packages installed: `tidyverse`, `shiny`, `shinythemes`, `rmarkdown`, and `officer`. You can install these packages by running this code:
 
 ```{r, eval = FALSE, echo = TRUE}
     install.packages(c("tidyverse", "shiny", "shinythemes",
@@ -26,4 +28,6 @@ Based on the lesson, think of the UI, server, and layout component of the app!
 
 ## Steps
 
-1.  Create project:
+1.  Create project: Open R Studio -\> File -\> New Directory -\> Shiny Application
+2.  Start write the code for UI and Server
+3.  The code for the app is provided here,