Coursera Pitch Presentation

See the Developing Data Products Week 4 Course Project

• URL: https://github.com/agnivsaikia/Developing-Data-Products-Course-Project
• Find here the Shiny application UI and Server, according to the specifications of the first part of this Course Project.

mtcars Dataset

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. The first 5 observations are as follows

library(datasets)
head(mtcars, 5)

##                    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

mtcars Dataset - Format

A data frame with 32 observations on 11 variables.

Analysis - Main Code

fText <- reactive({
paste("mpg ~", input$variable)})

ftPoint <- reactive({
paste("mpg ~", "as.integer(", input$variable, ")")})

fit <- reactive({
lm(as.formula(ftPoint()), data=mpg)
})

output$caption <- renderText({
fText()})

output$mpgBoxPlot <- renderPlot({
boxplot(as.formula(fText()), 
        data = mpg,
        outline = input$outliers)})

output$fit <- renderPrint({
summary(fit())})

output$mpgPlot <- renderPlot({
with(mpg, {
  plot(as.formula(ftPoint()))
  abline(fit(), col=2)
})  })