Shiny Package Exploration

##Introduction In this exercise, we will explore the shiny package, create a basic Shiny app, and extend its functionality. Outputs are explicitly included to ensure visibility during each step.

##Step 1: Package Discovery and Installation ###Installing and Loading the Package

# Install shiny (if not already installed)
if (!requireNamespace("shiny", quietly = TRUE)) {
  install.packages("shiny")
}
library(shiny)

# Confirm package loaded
print("Shiny package loaded successfully.")

## [1] "Shiny package loaded successfully."

##Step 2: Analyzing the Package Structure ###Listing Functions and Tools

# List all exported functions
functions <- ls("package:shiny")
print(head(functions, 10)) # Display first 10 functions for clarity

##  [1] "a"                "absolutePanel"    "actionButton"     "actionLink"      
##  [5] "addResourcePath"  "animationOptions" "appendTab"        "as.shiny.appobj" 
##  [9] "basicPage"        "bindCache"

# Explore vignettes
browseVignettes("shiny")

## No vignettes found by browseVignettes("shiny")

print("Browse the vignettes opened in your browser or RStudio Viewer.")

## [1] "Browse the vignettes opened in your browser or RStudio Viewer."

##Step 3: Solving a Practical Problem ###Building a Basic Shiny App Here, we’ll build an app step by step with outputs displayed.

Define the User Interface (UI)

ui <- fluidPage(
  titlePanel("MTCars Data Explorer"),
  sidebarLayout(
    sidebarPanel(
      sliderInput("mpgRange", "Select MPG Range:",
                  min = min(mtcars$mpg), max = max(mtcars$mpg),
                  value = c(min(mtcars$mpg), max(mtcars$mpg))),
      selectInput("cyl", "Select Cylinder:",
                  choices = unique(mtcars$cyl), selected = unique(mtcars$cyl)[1])
    ),
    mainPanel(
      plotOutput("mpgPlot")
    )
  )
)

print("UI defined successfully.")

## [1] "UI defined successfully."

Define the Server Logic

server <- function(input, output) {
  output$mpgPlot <- renderPlot({
    filtered_data <- subset(mtcars, mpg >= input$mpgRange[1] & mpg <= input$mpgRange[2] & cyl == input$cyl)
    plot(filtered_data$wt, filtered_data$mpg,
         main = "Weight vs. MPG",
         xlab = "Weight (1000 lbs)",
         ylab = "Miles per Gallon",
         pch = 19, col = "blue")
  })
}

print("Server logic defined successfully.")

## [1] "Server logic defined successfully."

Combine UI and Server

# Launch the app
print("Launching the Shiny app. Check your RStudio Viewer or browser.")

## [1] "Launching the Shiny app. Check your RStudio Viewer or browser."

shinyApp(ui = ui, server = server)

Shiny applications not supported in static R Markdown documents

##Step 4: Extending the Package ###Adding a Custom Utility Function We’ll add a function to standardize the appearance of plots and test it.

# Custom function to add titles and styling
customize_plot <- function(plot_obj, title, subtitle) {
  plot_obj +
    ggtitle(title, subtitle) +
    theme(
      plot.title = element_text(face = "bold", hjust = 0.5, size = 14),
      plot.subtitle = element_text(face = "italic", hjust = 0.5, size = 12)
    )
}

print("Custom function defined successfully.")

## [1] "Custom function defined successfully."

###Testing the Function

# Example usage
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.3

sample_plot <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
customized_plot <- customize_plot(sample_plot, "Weight vs. MPG", "Using a Custom Function")
print(customized_plot) # Display customized plot

##Conclusion In this exercise, we explored the shiny package step by step, ensuring visible outputs along the way. We:

1-Discovered and loaded the shiny package while reviewing its documentation and functions. 2-Built a simple interactive Shiny app for exploring the mtcars dataset. 3-Extended functionality by creating a custom utility function for plot customization and tested it using a ggplot visualization.