Developing Data Products - Week 4 Course Project

James C

26 February 2018

Introduction

Here is some insight into Diamonds and which conditions determine the price. - Color: best(D) to worst(J) - Clarity: measures how clear the diamond is from best to worst (IF, VVS2, VVS1, VS2, VS1, SI2, SI1, I1) - Cut: quality of the diamond from best to worst (Ideal, Premium, Very Good, Good, Fair)

This shiny app will allow you to manipulate these factors to predict the diamond price. Happy shopping!

Documentation

To add a bit of context her is a summary of the “diamonds” dataset found in the ggplot2 R package. Factors were measured in over 50,000 diamonds and therefore I think that it can be considered fairly accurate.

## Classes 'tbl_df', 'tbl' and 'data.frame':    53940 obs. of  5 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...

Summary plot of the diamond data.