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You're reading from R Data Mining Implement data mining techniques through practical use cases and real-world datasets

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Length 442 pages

Edition 1st Edition

Languages

Tools

ggplot

Concepts

Data Mining

Author (1):

Andrea Cirillo

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Table of Contents (16) Chapters

Preface

1. Why to Choose R for Your Data Mining and Where to Start FREE CHAPTER

2. A First Primer on Data Mining Analysing Your Bank Account Data

3. The Data Mining Process - CRISP-DM Methodology

4. Keeping the House Clean – The Data Mining Architecture

5. How to Address a Data Mining Problem – Data Cleaning and Validation

6. Looking into Your Data Eyes – Exploratory Data Analysis

7. Our First Guess – a Linear Regression

8. A Gentle Introduction to Model Performance Evaluation

9. Don't Give up – Power up Your Regression Including Multiple Variables

10. A Different Outlook to Problems with Classification Models

11. The Final Clash – Random Forests and Ensemble Learning

12. Looking for the Culprit – Text Data Mining with R

13. Sharing Your Stories with Your Stakeholders through R Markdown

14. Epilogue

15. Dealing with Dates, Relative Paths and Functions

Graphical EDA

You can include as Graphical Exploratory Data Analysis (Graphical EDA) any kind of technique that implies visualizing your data following some kind of conventional system. This can involve representing it on a Cartesian plot or following polar coordinates; the main point here is that you do not rely on any kind of summary, but just on your eyes to read the story your data has to tell.

Visualizing a variable distribution

One of the first things you can do when performing graphical EDA is to look at the distribution of your data, one variable at a time. To do that, two main types of plot come in to help:

Histogram
Boxplot

Histogram

A histogram is a special kind of bar plot, having on the x axis the unique values shown from your variable, or some kind of clusterization of these values, and on the y axis the frequency of these values. Let's plot a histogram for each of our three variables within the cash_flow_report. We are going to use ggplot, which somebody told me you should already...