Data Products Project

Helping you select features for your new car

Felix E. Rivera-Mariani, PhD

INTRODUCTION

This brief presentation is part of the Coursera course Developing Data Products, which is part of the Johns Hopkins University 10-courses Data Science Specialization. This peer-assessed project is composed of two parts: 1) developing a Shiny application, and 2) preparing a pitch presentation related to the developed Shiny application. This presentation focuses on the second component of the project.

The developed application can be found at: https://friveramariani.shinyapps.io/Mtcars-Clustering/

The source code for the application can be found at:

https://github.com/friveramariani/data-products

Note: the ui and server files are within the app.R.

UTILITY OF THE APPLICATION

Using the mtcars dataset found in R, this application can aid you visualize how one feature of a car relates to the others. The features (i.e. variables) that you can evaluate re below:

  • mpg (miles per gallon)
  • cyl (# of cylinders)
  • disp (displacement)
  • hp (gross horsepower)
  • drat (rear axle ratio)
  • wt (weight [1000 pounds])
  • qsec (¼ mile time)
  • vs (V- or Straight-engine)
  • am (transmission type)
  • gear (# of forwards gears)
  • carb (# of carburetors)

MTCARS DATASET

The dataset use in this project is the Motor Trend Card Road tests (mtcars), which is found in R. Below are the dimensions and classess of the variabels. 

str(mtcars)

'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

EXAMPLE PLOT

Here is an example of the relationship between mpg (miles per gallon), disp (displacement), and wt (weight) variables. 

library(car)
scatterplot.matrix(~mpg+disp+wt, data=mtcars, cex.axis=1.5)

plot of chunk unnamed-chunk-2