In this Project I have developed a shiny web application which can be found here:

https://gaurabkundu1.shinyapps.io/ShinyApp_DDP/

The Github Repo contains the Code:

https://github.com/GaurabKundu1/Developing-Data-Products-Course-Project

Motor Trend Car Road Tests

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models). ### Source Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391-411.

##                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

A data frame with 32 observations on 11 variables.

library(shiny)

shinyUI(
  navbarPage("Shiny Application",
             tabPanel("Analysis",
                      fluidPage(
                        titlePanel("The relationship between variables and miles per gallon (MPG)"),
                        sidebarLayout(
                          sidebarPanel(
                            selectInput("variable", "Variable:",
                                        c("Number of cylinders" = "cyl",
                                          "Displacement (cu.in.)" = "disp",
                                          "Gross horsepower" = "hp",
                                          "Rear axle ratio" = "drat",
                                          "Weight (lb/1000)" = "wt",
                                          "1/4 mile time" = "qsec",
                                          "V/S" = "vs",
                                          "Transmission" = "am",
                                          "Number of forward gears" = "gear",
                                          "Number of carburetors" = "carb"
                                        )),
                            
                            checkboxInput("outliers", "Show BoxPlot's outliers", FALSE)
                          ),
                          
                          mainPanel(
                            h3(textOutput("caption")),
                            
                            tabsetPanel(type = "tabs", 
                                        tabPanel("BoxPlot", plotOutput("mpgBoxPlot")),
                                        tabPanel("Regression model", 
                                                 plotOutput("mpgPlot"),
                                                 verbatimTextOutput("fit")
                                        )
                            )
                          )
                        )
                      )
             ),
             tabPanel("About the Data Set",
                      
                      h3("Regression Models Course Project (from Coursera)"),
                      helpText("You work for Motor Trend, a magazine about the automobile industry Looking at a data set of a collection of cars, they are interested in exploring the relationship",
                               "between a set of variables and miles per gallon (MPG) (outcome). They are particularly interested in the following two questions: Is an automatic or manual transmission better for MPG. Quantify the MPG difference between automatic and manual transmissions"),
                      h3("Important"),
                      p("A data frame with 32 observations on 11 variables."),
                      
                      a("https://class.coursera.org/regmods-008")
             ),
             tabPanel("More Data Detail",
                      h2("Motor Trend Car Road Tests"),
                      hr(),
                      h3("Description"),
                      helpText("The data was extracted from the 1974 Motor Trend US magazine,",
                               " and comprises fuel consumption and 10 aspects of automobile design and performance",
                               " for 32 automobiles (1973-74 models)."),
                      h3("Format"),
                      p("A data frame with 32 observations on 11 variables."),
                      
                      p("  [, 1]   mpg         Miles/(US) gallon"),
                      p("  [, 2]     cyl     Number of cylinders"),
                      p("  [, 3]     disp    Displacement (cu.in.)"),
                      p("  [, 4]     hp  Gross horsepower"),
                      p("  [, 5]     drat    Rear axle ratio"),
                      p("  [, 6]     wt  Weight (lb/1000)"),
                      p("  [, 7]     qsec    1/4 mile time"),
                      p("  [, 8]     vs  V/S"),
                      p("  [, 9]     am  Transmission (0 = automatic, 1 = manual)"),
                      p("  [,10]     gear    Number of forward gears"),
                      p("  [,11]     carb    Number of carburetors"),
                      
                      h3("Source"),
                      
                      p("Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391-411.")
             ),
             tabPanel("Go back to my Github repository",
                      a("https://github.com/GaurabKundu1/Developing-Data-Products-Course-Project"),
                      hr(),
                      h4("I hope you like the Shiny App"),
                      h4("The name of the repository is Developing Data Products Course Project")
             )
  )
)

library(shiny)
library(datasets)

mpgData <- mtcars
mpgData$am <- factor(mpgData$am, labels = c("Automatic", "Manual"))

shinyServer(function(input, output) {
  
  formulaText <- reactive({
    paste("mpg ~", input$variable)
  })
  
  formulaTextPoint <- reactive({
    paste("mpg ~", "as.integer(", input$variable, ")")
  })
  
  fit <- reactive({
    lm(as.formula(formulaTextPoint()), data=mpgData)
  })
  
  output$caption <- renderText({
    formulaText()
  })
  
  output$mpgBoxPlot <- renderPlot({
    boxplot(as.formula(formulaText()), 
            data = mpgData,
            outline = input$outliers)
  })
  
  output$fit <- renderPrint({
    summary(fit())
  })
  
  output$mpgPlot <- renderPlot({
    with(mpgData, {
      plot(as.formula(formulaTextPoint()))
      abline(fit(), col=2)
    })
  })
  
})