Developing an App: Relationship between variables and MPG based on mtcars dataset.

Shiny Application:

URL: https://tonuta.shinyapps.io/Developing-Data-Products—Part-1/

A shiny-App shows the relationship between variables and miles per gallon (MPG).

Get all SourceCode @Github: https://github.com/tonuta/Developing-Data-Products—Part-1

mtcars dataset - Description (from Motor Trend Car Road Tests)

Source of data (Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391-411.)

The data comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models).

require(plotly)
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 - A data frame with 32 observations on 11 variables.

• [, 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.

plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter",
        color = ~factor(cyl), size = ~hp)

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) }) })