Book Image

R Data Mining

Book Image

R Data Mining

Overview of this book

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.

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Table of Contents (22 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Why to Choose R for Your Data Mining and Where to Start
Why to Choose R for Your Data Mining and Where to Start
What is R?
A bit of history
R's points of strength
Installing R and writing R code
Possible alternatives to write and run R code
R foundational notions
R's weaknesses and how to overcome them
Further references
Summary
2
A First Primer on Data Mining Analysing Your Bank Account Data
A First Primer on Data Mining Analysing Your Bank Account Data
Acquiring and preparing your banking data
Summarizing your data with pivot-like tables
Visualizing your data with ggplot2
Further references
Summary
3
The Data Mining Process - CRISP-DM Methodology
The Data Mining Process - CRISP-DM Methodology
The Crisp-DM methodology data mining cycle 
Business understanding
Data understanding
Data preparation
Modelling
Evaluation
Deployment
Summary
4
Keeping the House Clean – The Data Mining Architecture
Keeping the House Clean – The Data Mining Architecture
A general overview
Data sources
Databases and data warehouses
The data mining engine
User interface
How to build a data mining architecture in R
Further references
Summary
5
How to Address a Data Mining Problem – Data Cleaning and Validation
How to Address a Data Mining Problem – Data Cleaning and Validation
On a quiet day
Data cleaning
Further references
Summary
6
Looking into Your Data Eyes – Exploratory Data Analysis
Looking into Your Data Eyes – Exploratory Data Analysis
Introducing summary EDA
Graphical EDA
Further references
Summary
7
Our First Guess – a Linear Regression
Our First Guess – a Linear Regression
Defining a data modelling strategy
Applying linear regression to our data
Further references
Summary
8
A Gentle Introduction to Model Performance Evaluation
A Gentle Introduction to Model Performance Evaluation
Defining model performance
Measuring performance in regression models
Measuring the performance in classification problems
A final general warning – training versus test datasets
Further references
Summary
9
Don't Give up – Power up Your Regression Including Multiple Variables
Don't Give up – Power up Your Regression Including Multiple Variables
Moving from simple to multiple linear regression
Dimensionality reduction
Fitting a multiple linear model with R
Further references
Summary
10
A Different Outlook to Problems with Classification Models
A Different Outlook to Problems with Classification Models
What is classification and why do we need it?
Logistic regression
Support vector machines
References
Summary
11
The Final Clash – Random Forests and Ensemble Learning
The Final Clash – Random Forests and Ensemble Learning
Random forest
Ensemble learning
Applying estimated models on new data
A more structured approach to predictive analytics
Applying the majority vote ensemble technique on predicted data
Further references
Summary
12
Looking for the Culprit – Text Data Mining with R
Looking for the Culprit – Text Data Mining with R
Extracting data from a PDF file in R
Sentiment analysis
Developing wordclouds from text
Looking for context in text – analyzing document n-grams
Performing network analysis on textual data
Further references
Summary
13
Sharing Your Stories with Your Stakeholders through R Markdown
Sharing Your Stories with Your Stakeholders through R Markdown
Principles of a good data mining report
Set up an rmarkdown report
Develop an R markdown report in RStudio
Rendering and sharing an R markdown report 
Further references
Summary
14
Epilogue
Epilogue
15
Dealing with Dates, Relative Paths and Functions
Dealing with Dates, Relative Paths and Functions
Dealing with dates in R
Working directories and relative paths in R
Conditional statements

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...

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