This is a deliverable for the course Developing Data Products as part of the Coursera Data Science Specialization.

The application is running on (https://pratikshekhar.shinyapps.io/shinyApplicationRepPitch/)

ui.R, and Server.R Code in my github repository (https://github.com/Pratik-Shekhar/DevelopingDataProducts)

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.

library(datasets)
head(mtcars, 3)

##                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.

• mpg - Miles/(US) gallon
• cyl - Number of cylinders
• disp - Displacement (cu.in.)
• hp - Gross horsepower
• drat - Rear axle ratio
• wt - Weight (lb/1000)
• qsec - 1/4 mile time
• vs - V/S
• am - Transmission (0 = automatic, 1 = manual)
• gear - Number of forward gears
• carb - Number of carburetors

  formulaTextPoint <- reactive({
    paste("mpg ~", "as.integer(", input$variable, ")")  })
  
  fit <- reactive({
    lm(as.formula(formulaTextPoint()), data=mpgData)  })
  ...
  output$fit <- renderPrint({
    summary(fit()) })
  
  output$mpgPlot <- renderPlot({
    with(mpgData, {
      plot(as.formula(formulaTextPoint()))
      abline(fit(), col=2)
    })  })