# Variables and MPG

June 13, 2019

## Coursera Reproducible Pitch

### See the Regression Models Course Project

• URL: https://github.com/RechalC/Developing-Data-Products
• 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.”

## 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.

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

## mtcars Dataset - Format

A data frame with 32 observations on 11 variables.

Index Field Detail
[, 1] mpg Miles/(US) gallon
[, 2] cyl Number of cylinders
[, 3] disp Displacement (cu.in.)
[, 4] hp Gross horsepower
[, 5] drat Rear axle ratio
[, 6] wt Weight (lb/1000)
[, 7] qsec 1/4 mile time
[, 8] vs V/S
[, 9] am Transmission (0 = automatic, 1 = manual)
[,10] gear Number of forward gears
[,11] carb Number of carburetors

## 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)
})  })