This is the reproducible pitch presentation for the Development Data Products Week 4 peer graded assignment.

The shiny application that I deployed can be found below hyperlink.
shiny application

The codes for ui.R and server.R can be found here

My output predicts the Miles Per Gallon (MPG) per chosen variable. Shiny application has 2 tabs:

• Simple Linear Regression Model with a prediction line plotted with Intercept and Slope

• Linear Regression Model with a prediction line plotted with geom_smooth

“mtcars” dataset is used for the assignment.

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Following is the part of the ui.R code.

library(shiny)

shinyUI(fluidPage(
  titlePanel("MPG prediction with the Linear Regression Model"),
  h4("Miles Per Gallon (MPG) prediction per chosen variable"),
  sidebarLayout(
    # Compose the sidebar layout
    sidebarPanel(
      h3("Variables"),
      selectInput("select", "Choose a variable", c("cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
                                                   "gear", "carb")),
      submitButton("Submit")
    ),
    # Compose the main panel layout. Two tabs will be created; the first tab is with 
    # the linear regression model with a prediction line, 
    # the second tab is with the linear regression model with a geom_smooth.
    
    mainPanel(
      h3("Linear Regression Model"),
      tabsetPanel(type = "tabs",
                  tabPanel("Simple Linear Regression", br(), plotOutput("plot1"),
                           h3("Summary of Linear Model:"),
                           verbatimTextOutput("Regression_Model")),
                  tabPanel("Linear Regression with Geom Smooth", br(), plotOutput("plot2")),
      )
    )
  )
))

Following is the part of the server.R code

library(shiny)
library(ggplot2)

shinyServer(function(input, output) {

# Make a reactive expression for linear model to predict MPG vs. other variables.  
formula <-reactive({paste("mpg ~", input$select)})
lm_model <- reactive({summary(lm(formula(), data = mtcars))})

# This will plot a prediction line with the linear regression model. 
# The red dots are the collected data and the blue line is the predicted line by calculating 
# an intercept and a slope per each variable.
  output$plot1 <- renderPlot({
   if(input$select == "cyl") {
     ggplot(mtcars, aes(x = cyl, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                                 slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "disp") {
      ggplot(mtcars, aes(x = disp, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                                 slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "hp") {
      ggplot(mtcars, aes(x = hp, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                                 slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "drat") {
      ggplot(mtcars, aes(x = drat, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                                 slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "wt") {
      ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                                 slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "qsec") {
      ggplot(mtcars, aes(x = qsec, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                                slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "vs") {
      ggplot(mtcars, aes(x = vs, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                               slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "am") {
      ggplot(mtcars, aes(x = am, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                              slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "gear") {
      ggplot(mtcars, aes(x = gear, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                              slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    } else if(input$select == "carb") {
      ggplot(mtcars, aes(x = carb, y = mpg)) + geom_point(size = 5, col = "red", 
      alpha = 0.3) + geom_abline(intercept = lm_model()$coeff[1], 
                              slope = lm_model()$coeff[2], col = "blue", lwd = 1)
    }
    
  })

  # This is a lot with Geom_Smooth  
  output$plot2 <- renderPlot({
    # check for the input variable
    if (input$select == "cyl") {
      ggplot(mtcars, aes(x = cyl, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
      } else if(input$select == "disp") {
      ggplot(mtcars, aes(x = disp, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "hp") {
      ggplot(mtcars, aes(x = hp, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "drat") {
      ggplot(mtcars, aes(x = drat, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "wt") {
      ggplot(mtcars, aes(x = wt, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "qsec") {
      ggplot(mtcars, aes(x = qsec, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "vs") {
      ggplot(mtcars, aes(x = vs, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "am") {
      ggplot(mtcars, aes(x = am, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "gear") {
      ggplot(mtcars, aes(x = gear, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    } else if(input$select == "carb") {
      ggplot(mtcars, aes(x = carb, y = mpg)) + geom_smooth() + geom_point(
        size = 5, col = "red", alpha = 0.3)
    }
  })
  
  # This shows the summary of the linear regression model analysis. 
  # The summary is shown up on the "Simple Linear Regression" tab.
  output$Regression_Model <- renderPrint(lm_model())
})