Chapter 1,Why to Choose R for Your Data Mining and Where to Start, gives you some relevant facts about R's history, its main strengths and weaknesses, and how to install the language on your computer and write basic code.

Chapter 2, A First Primer on Data Mining -Analyzing Your Bank Account Data, applies R to our data.

Chapter 3, The Data Mining Process - the CRISP-DM Methodology, teaches you to organize and conduct a data mining project through the CRISP-DM methodology.

Chapter 4, Keeping the Home Clean – The Data Mining Architecture, defines the static part of our data mining projects, the data mining architecture.

Chapter 5, How to Address a Data Mining Problem – Data Cleaning and Validation, covers data quality and data validation, where you will find out which metrics define the level of quality of our data and discover a set of checks that can be employed to assess this quality.

Chapter 6, Looking into Your Data Eyes – Exploratory Data Analysis, teaches you about the concept of exploratory data analysis and how it can be included within the data analysis process.

Chapter 7, Our First Guess – A Linear Regression, lets us estimate a simple linear regression model and check whether its assumptions have been satisfied.

Chapter 8, A Gentle Introduction to Model Performance Evaluation, covers the tools used to define and measure the performance of data mining models.

Chapter 9, Don't Give Up – Power Up Your Regression Including Multiple Variables, predicts the output of our response variable when more than one exploratory variable is involved.

Chapter 10, A Different Outlook to Problems with Classification Models, looks into classification models, the need of them and they are uses.

Chapter 11, The Final Clash – Random Forest and Ensemble Learning, in this chapter we will learn how to apply ensemble learning to estimated classification models.

Chapter 12, Looking for the Culprit – Text Data Mining with R, shows how to prepare the data frame for text mining activities, removing irrelevant words and transforming it from a list of sentences to a list of words. You also learn to perform sentiment analyses, wordcloud development, and n-gram analyses on it.

Chapter 13, Sharing Your Stories with Your Stakeholders through R Markdown, employs R markdown and shiny, two powerful instruments made available within the RStudio ecosystem.

Chapter 14, Epilogue, is the unique background story made to learn the topics in a very engaging manner.

Appendix, Dealing with Dates, Relative Paths, and Functions, includes additional information to get things running in R.

You will easily be able to sail through the chapters by employing R and UNIX or Windows. The version used is R 3.4.0.

If you are a budding data scientist or a data analyst with basic knowledge of R, and you want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, path names, dummy URLs, user input, and Twitter handles are shown as follows: "Finally, ggplot2 gives you the ability to highly customize your plot, adding every kind of graphical or textual annotation to it."

A block of code is set as follows:

install.packages("ggplot2")
library(ggplot2)

New terms and important words are shown in bold.

Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."

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