Coursera Reproducible Pitch

See the Regression Models Course Project

• URL: https://github.com/prateeksarangi/DataScienceCoursera/tree/master/RPackages

• 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.”

Find all details here

URL: https://www.coursera.org/learn/data-products/peer/tMYrn/course-project-shiny-application-and-reproducible-pitch

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.

##                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

mtcars Dataset - Format

A data frame with 32 observations on 11 variables.

——- | —– | —— |

Analysis - Main Code

  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)
    })  })