Coursera Reproducible Pitch

• URL: https://github.com/joeljogy/DevelopingDataProducts
• Find here all the data that have been use for this presentation and also for the first part of the data Science Project: “First, you will create a Shiny application and deploy it on Rstudio's servers.Second, you will use Slidify or Rstudio Presenter to prepare a reproducible pitch presentation about your application.”

-“Since the beginning of the Data Science Specialization, we've noticed the unbelievable passion students have about our courses and the generosity they show toward each other on the course forums. A couple students have created quality content around the subjects we discuss, and many of these materials are so good we feel that they should be shared with all of our students.”

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

  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)
    })  })