I chose to build a Shiny App to explore the HSB2 dataset from the openintro package.

I have provided some descriptions and screenshots below, along with the code.

With the appropriate packages installed, the code can be pasted into R for an interactive experience.

Tab1 Plots

Boxplot - There are dropdown menus to select the x-axis, y-axis, and color by options. The descriptive text below the plot updates with your selection. It is a good way to view the scores by different categorical variables.

Scatterplot - I made this plot interactive using the plotly package. If you hover over the datapoints it will tell you their values. There is also a checkbox, “add best fit line to Scatterplot,” which is selected by default. This adds the linear regression lines for your “color by” selection. The descriptive text below the plot updates with your selection. It is a good way to view the differences in scores by a categorical variable. The size of dots and alpha can be selected from the slidebars.

Tab2 Histograms

Interactive histograms (hover over for values) for X and Y selections from dropdown menu. Number of bins can be selected from the slidebar.

Tab3 Regression

Some summary statistics (correlation and averages) and a simple linear regression and plots for the X and Y selections (scores only). The regression plot is also interactive and size and alpha can also be selected. The text also updates with selections.

Tab4 Tukey

Tukey test for differences in score selection by categorical value selection.

Tab5 Data Table

Interactive data table. You can sort, search, and select the number of entries to view.

Code for HSB2 Shiny App:

library(shiny)
library(ggplot2)
library(dplyr)
library(shinythemes)
library(openintro)
library(plotly)
library(DT)
data(hsb2)

#Shiny Project Paul Jozefek 

#Add CSS styling to descriptions
my_css <- "

#descriptionreg {
color: darkred; font-size: 18px; font-style: bold;}

#descriptionbox {
color: darkred; font-size: 18px; font-style: bold;}

#description {
color: darkred; font-size: 18px; font-style: bold;}

#descriptiontukey {
color: darkred; font-size: 18px; font-style: bold;}
"

# Define UI for application 
ui <- fluidPage(theme = shinytheme("sandstone"),
                
# Add the CSS style to the Shiny app
tags$style(my_css),
                
  titlePanel("Analysis of the HSB2 Dataset, by Paul Jozefek"),
  
  # Sidebar layout with a input and output definitions 
  sidebarLayout(
    
    # Inputs
    sidebarPanel(
    
    h3("Plotting"),      # Third level header: Plotting
      
      # Select variable for y-axis
      selectInput(inputId = "y", 
                  label = "Y-axis:",
                  choices = c("Reading" = "read", "Writing" ="write", "Math" = "math", "Science" = "science", "Social Studies" = "socst"), 
                  selected = "math"),
    
      # Select variable for x-axis
      selectInput(inputId = "x", 
                  label = "Scatterplot X-axis:",
                  choices = c("Reading" = "read", "Writing" ="write", "Math" = "math", "Science" = "science", "Social Studies" = "socst"), 
                  selected = "write"),
     
     # Select variable for w-axis Boxplot
      selectInput(inputId = "w", 
                  label = "Boxplot X-axis:",
                  choices = c("Gender" = "gender", "Race" = "race", "Socioeconomic Status" = "ses", "School Type" = "schtyp", "Program" = "prog"), 
                  selected = "race"),
    
    # Select variable for color
      selectInput(inputId = "z", 
                  label = "Color by:",
                  choices = c("Gender" = "gender", "Race" = "race", "Socioeconomic Status" = "ses", "School Type" = "schtyp", "Program" = "prog"),
                  selected = "gender"),
     
     # Add checkbox for best fit line
      checkboxInput("fit", "Add best fit line to Scatterplot", TRUE),
      
    # Set alpha level
      sliderInput(inputId = "alpha", 
                  label = "Alpha:", 
                  min = 0, max = 1, 
                  value = 1),
      
      # Set point size
      sliderInput(inputId = "size", 
                  label = "Size:", 
                  min = 0, max = 5, 
                  value = 1),
    
    # Bin width for Histogram
    sliderInput(inputId = "binsin",
                label = "Number of bins for histogram:",
                min = 1,
                max = 50,
                value = 20),
    
    # Built with Shiny by RStudio for Baruch
    br(), br(),
    h5("Built with",
       img(src = "https://www.rstudio.com/wp-content/uploads/2014/04/shiny.png", height = "30px"),
       "by",
       img(src = "https://www.rstudio.com/wp-content/uploads/2014/07/RStudio-Logo-Blue-Gray.png", height = "30px"),
       " for ",
       img(src = "https://upload.wikimedia.org/wikipedia/commons/thumb/9/9b/Baruch_logo.svg/2000px-Baruch_logo.svg.png", height = "30px"))

    ),
    
    # Outputs
    mainPanel(
      tabsetPanel(type="tabs",
                  
      #Tab 1 Plots
      tabPanel(title="Plots",
      h3("Boxplot"),    # Third level header: Boxplot
      plotOutput(outputId = "boxplot"),
      textOutput(outputId = "descriptionbox"),
      br(),                 # Single line break for a little bit of visual separation
      h3("Scatterplot"),    # Third level header: Scatterplot
      plotlyOutput(outputId = "scatterplot"),
      textOutput(outputId = "description"),
      br(),                 # Single line break for a little bit of visual separation
      br(),                 # Single line break for a little bit of visual separation
      br()),                 # Single line break for a little bit of visual separation),
      
      #Tab 2 Histograms
      tabPanel(title="Histograms",
      h3("Histogram for selected X value"),    # Third level header: Densityplot
      plotlyOutput(outputId = "histogramplot"),
      br(),                 # Single line break for a little bit of visual separation
      h3("Histogram for selected Y value"),    # Third level header: Scatterplot
      plotlyOutput(outputId = "histogramplottwo")),
      
      #Tab 3 Regression
      tabPanel(title="Regression",
      h3("Summary Statistics and Simple Linear Regression"),    # Third level header: Regression
      h4("Using Scatterplot Y and X axis."),    # Fourth level header: Regression
      textOutput(outputId = "correlation"),
      htmlOutput(outputId = "avgs"),
      textOutput(outputId = "descriptionreg"),
      verbatimTextOutput(outputId = "lmoutput"),
      h3("Regression Plots"),    # Third level header: Regression Plots
      plotlyOutput(outputId = "regline"),
      plotOutput(outputId = "regplots")),
      
      #Tab 4 Tukey Test
      tabPanel(title="Tukey",
      h3("Tukey's HSD Test"), # Third level header: Tukey
      verbatimTextOutput(outputId = "tukeyoutput"),
      textOutput(outputId = "descriptiontukey")),
          
      #Tab 5 Data
      tabPanel(title="Data Table",
      h3("Data table"),     # Third level header: Data table
      h5("Click the button Show (on left) to see the data by your specified number of rows"),     # Fifth level header
      DT::dataTableOutput("datatable"))
    
      )
    )
  )
)

# Define server function required to create the scatterplot
server <- function(input, output) {
  
  # Create boxplot object the plotOutput function is expecting
  output$boxplot <- renderPlot({
    ggplot(data = hsb2, aes_string(x = input$w, y = input$y, color=input$z)) +
      geom_boxplot(fill="peachpuff")+theme_minimal()
  })
  
  # Create descriptive text
  output$descriptionbox <- renderText({
    paste0("The plot above visualizes the relationship between ", 
           input$w, " and ", input$y, ", conditional on ", input$z, ".")
  })
    
  # Create scatterplot object the plotlyOutput function is expecting
  output$scatterplot <- renderPlotly({
    ggplotly({
    p <- ggplot(data = hsb2, aes_string(x = input$x, y = input$y, color=input$z)) +
      geom_point(alpha = input$alpha, size = input$size)+theme_minimal()
    
    #if check box selected plot best fit line
    if (input$fit) {
      p <- p + geom_smooth(method = "lm")
    }
    p
    
    })
  })
  
  # Create descriptive text
  output$description <- renderText({
    paste0("The plot above visualizes the relationship between ", 
           input$x, " and ", input$y, ", conditional on ", input$z, ".")
  })
  
  # Create histogram
  output$histogramplot <- renderPlotly({
    ggplotly({
    ggplot(data = hsb2, aes_string(x = input$x)) +
      geom_histogram(bins=input$binsin, color="black", fill="seagreen1")+theme_minimal()
    })
  })
  
  # Create histogram2
  output$histogramplottwo <- renderPlotly({
    ggplotly({
    ggplot(data = hsb2, aes_string(x = input$y)) +
      geom_histogram(bins=input$binsin, color="black", fill="plum1")+theme_minimal()
    }) 
  })
  
  # Create text output stating the correlation between the two ploted 
  output$correlation <- renderText({
    r <- round(cor(hsb2[, input$x], hsb2[, input$y], use = "pairwise"), 3)
    paste0("Correlation between ",input$x, " and ", input$y, " = ", r, ".")
  })
  
  # Calculate averages
  output$avgs <- renderUI({
    avg_x <- hsb2 %>% pull(input$x) %>% mean() %>% round(2)
    avg_y <- hsb2 %>% pull(input$y) %>% mean() %>% round(2)
    HTML(
      paste("Average", input$x, "=", avg_x),
      "<br/>",
      paste("Average", input$y, "=", avg_y)
    )
  })

  # Create descriptive text
  output$descriptionreg <- renderText({
    paste0("The regression below is between the dependent variable ", 
           input$y, " and the independent variable ", input$x, "")
  })  
  
  # Create regression output
  output$lmoutput <- renderPrint({
    x <- hsb2 %>% pull(input$x)
    y <- hsb2 %>% pull(input$y)
    print(summary(lm(y ~ x, data = hsb2)), digits = 3, signif.stars = FALSE)
  })
  
  # Create regression line
  output$regline <- renderPlotly({
    ggplotly({
    ggplot(data = hsb2, aes_string(x = input$x, y = input$y)) +
      geom_point(alpha = input$alpha, size = input$size, color="red")+
      geom_smooth(method='lm')+theme_minimal()
    })
  })
  
  # Create regression plots
  output$regplots <- renderPlot({
    x <- hsb2 %>% pull(input$x)
    y <- hsb2 %>% pull(input$y)
    par(mfrow=c(2,2))
    plot(lm(y ~ x, data = hsb2))
  
  })
  
  # Create Tukey output
  output$tukeyoutput <- renderPrint({
    y <- hsb2 %>% pull(input$y)
    w <- hsb2 %>% pull(input$w)
    print(TukeyHSD(aov(y ~ w, data = hsb2)))
  })
  
  # Create descriptive text
  output$descriptiontukey <- renderText({
    paste0("The Tukey test helps determine if there's a statistically significant
           difference in ", input$y, " scores among the category ", input$w, "")
  })

  # Output data table
  output$datatable <- DT::renderDataTable({
    hsb2
  
  })
  
}

# Create a Shiny app object
shinyApp(ui = ui, server = server)