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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

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

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

Data cleaning

First of all, we need to actually import the data to our R environment (oh yeah, I was taking for granted that we are going to use R for this, hope you do not mind).

We can leverage our old friend the rio package, running it on all of the three files we were provided, once we have unzipped them. Take a minute to figure out if you can remember the function needed to perform the task.

Done? OK, find the solution as follows:

cash_flow_report <- import("cash_flow.csv")
customer_list    <- import("customer_list.txt")
stored_data      <- import("stored_data.rds")

Tidy data

Before actually looking at our data, we should define how we want it to be arranged in order to allow for future manipulation and analyses. Currently, one of the most adopted frameworks for data arrangement and handling is the so called tidy data framework. The concepts behind this framework were originally defined by Hadley Wickham, and nowadays come paired with a couple of R packages that help to apply it...