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R Data Mining

You're reading from  R Data Mining

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781787124462
Pages 442 pages
Edition 1st Edition
Languages
Concepts

Table of Contents (22) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Why to Choose R for Your Data Mining and Where to Start A First Primer on Data Mining Analysing Your Bank Account Data The Data Mining Process - CRISP-DM Methodology Keeping the House Clean – The Data Mining Architecture How to Address a Data Mining Problem – Data Cleaning and Validation Looking into Your Data Eyes – Exploratory Data Analysis Our First Guess – a Linear Regression A Gentle Introduction to Model Performance Evaluation Don't Give up – Power up Your Regression Including Multiple Variables A Different Outlook to Problems with Classification Models The Final Clash – Random Forests and Ensemble Learning Looking for the Culprit – Text Data Mining with R Sharing Your Stories with Your Stakeholders through R Markdown Epilogue
Dealing with Dates, Relative Paths and Functions

Conditional statements


Conditional statements in R always follow the logical scheme reproduced as follows:

if (condition){then execute} else {execute}

This scheme basically means that the instruction enclosed within the first brackets will be executed only in the case of the condition being true, while in the opposite case only the second set of instructions will be executed.

Within the R language it is, moreover possible to concatenate more than one conditional statement leveraging else if, like in the example below:

if(1>2){print("what a strange world")}else if(1==2){print("still in a strange world")}else{print("we landed in the normal world")}

 

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