A repository of R code regarding ways to analyze data from the Sarah Gets a Diamond case (UVA-QA-0702) presented in the First Year Decision Analysis course at Darden. The basis for the data is a case regarding a hopeless romantic MBA student choosing the right diamond for his bride-to-be, Sarah.
The best way to learn R is to try it out for yourself. After downloading the R and RStudio software, open up RStudio and start running the code to see what it does. For this session:
- Open up RStudio, click File -> New File -> R Script
- Go to
script.Rand copy/paste all the code into your new, blank script - Start running the code from the script to see what it does by clicking the "Run" button in top right of script
If you need instructions on how to setup your computer go here:
https://github.com/DardenDSC/intro-to-r-programming#installing-r-and-rstudio
R uses "packages" of functions that people develop to perform very specific routines in R, like fitting a decision tree. These packages must be installed before referencing them, so in order to run code in this repository you must first open up RStudio, and copy-paste the following code into your Console. Don't be alarmed if red text pops up and text starts wizzing by on the screen. This means that the package installations are happening. Once they're done, your computer is setup to do some advanced machine learning!
# data manipulation packages
install.packages("tidyverse")
# modeling packages
install.packages("rpart")
install.packages("rpart.plot")
The data contains 9,142 records on individual diamonds and their attributes. The attributes cover many common characteristics to consider when valuing and choosing diamonds, such as the 4 C's (Carat, Cut, Color, Clarity) along with price. The data are split into 6,000 records labeled as "Train" meant to be used when building models and the remaining 3,142 records are labeled as "Test" and meant to be used in assessing model accuracy
| Variable | Data Type | Data Definition |
|---|---|---|
| ID | integer | Uniquely identifies each observation (diamond) |
| Carat Weight | numeric | The weight of the diamond in metric carats. One carat is equal to 0.2 grams, roughly the same weight as a paperclip |
| Cut | character | One of five values indicating the cut of the diamond in the following order of desireability (Signature-Ideal, Ideal, Very Good, Good, Fair) |
| Color | character | One of six values indicating the diamond's color in the following order of desireability (D, E, F - Colorless, G, H, I - Near colorless) |
| Clarity | character | One of seven values indicating the diamond's clarity in the following order of desireability (F - Flawless, IF - Internally Flawless, VVS1 or VVS2 - Very, Very Slightly Included, or VS1 or VS2 - Very Slightly Included, SI1 - Slightly Included) |
| Polish | character | One of four values indicating the diamond's polish (ID - Ideal, EX - Excellent, VG - Very Good, G - Good) |
| Symmetry | character | One of four values indicating the diamond's symmetry (ID - Ideal, EX - Excellent, VG - Very Good, G - Good) |
| Report | character | One of of two values "AGSL" or "GIA" indicating which grading agency reported the qualities of the diamond qualities |
| Price | numeric | The amount in USD that the diamond is valued |
| Dataset | character | One of two values "Train" or "Test" indicating whether the observation should be used to train the model or in a test of its accuracy |
This case was prepared by Greg Mills (MBA ’07) under the supervision of Phillip E. Pfeifer, Alumni Research Professor of Business Administration. It was written as a basis for class discussion rather than to illustrate effective or ineffective handling of a romantic situation. Copyright (c) 2007 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved. To order copies, send an e-mail to [email protected]
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