R is a software package that provides a language and an environment for data manipulation and statistics calculation. The resulting statistics can be displayed graphically as well.
R has the following features:
A lean syntax to perform operations on your data
A set of tools to load and store data in a variety of formats, both local and over the Internet
Consistent syntax for operating on datasets in memory
A built-in and an open source collection of tools for data analysis
Methods to generate on-the-fly graphics and store graphical representations to disk
Chapter 1, Data Mining Patterns, covers data mining in R. In this instance, we will look for patterns in a dataset. This chapter will explore examples of using cluster analysis using several tools. It also covers anomaly detection, and the use of association rules.
Chapter 2, Data Mining Sequences, explores methods in R that allow you to discover sequences in your data. There are several R packages available that help you to determine sequences and portray them graphically for further analysis.
Chapter 3, Text Mining, describes several methods of mining text in R. We will look at tools that allow you to manipulate and analyze the text or words in a source. We will also look into XML processing capabilities.
Chapter 4, Data Analysis – Regression Analysis, explores different ways of using regression analysis on your data. This chapter has methods to run simple and multivariate regression, along with subsequent displays.
Chapter 5, Data Analysis – Correlation, explores several correlation packages. The chapter analyzes data using basic correlation and covariance as well as Pearson, polychor, tetrachoric, heterogeneous, and partial correlation.
Chapter 6, Data Analysis – Clustering, explores a variety of references for cluster analysis. The chapter covers k-means, PAM, and a number of other clustering techniques. All of these techniques are available to an R programmer.
Chapter 7, Data Visualization – R Graphics, discusses a variety of methods of visualizing your data. We will look at the gamut of data from typical class displays to interaction with third-party tools and the use of geographic maps.
Chapter 8, Data Visualization – Plotting, discusses different methods of plotting your data in R. The chapter has examples of simple plots with standardized displays as well as customized displays that can be applied to plotting data.
Chapter 9, Data Visualization – 3D, acts as a guide to creating 3D displays of your data directly from R. We will also look at using 3D displays for larger datasets.
Chapter 10, Machine Learning in Action, discusses how to use R for machine learning. The chapter covers separating datasets into training and test data, developing a model from your training data, and testing your model against test data.
Chapter 11, Predicting Events with Machine Learning, uses time series datasets. The chapter covers converting your data into an R time series and then separating out the seasonal, trend, and irregular components. The goal is to model or predict future events.
Chapter 12, Supervised and Unsupervised Learning, explains the use of supervised and unsupervised learning to build your model. It covers several methods in supervised and unsupervised learning.
For this book, you need R installed on your machine (or the machine you will be running scripts against). R is available for a number of platforms. This book is not constrained to particular versions of R at this time.
You need an interactive tool to develop R programs in order to use this book to its potential. The predominant tool is R Studio, a fully interactive, self-contained program available on several platforms, which allows you to enter R scripts, display data, and display graphical results. There is always the R command-line tool available with all installations of R.
This book is written for data analysts who have a firm grip over advanced data analysis techniques. Some basic knowledge of the R language and some data science topics is also required. This book assumes that you have access to an R environment and are comfortable with the statistics involved.
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, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the kmeans directive."
A block of code is set as follows:
kmeans(x,
centers,
iter.max = 10,
nstart = 1,
algorithm = c("Hartigan-Wong",
"Lloyd",
"Forgy",
"MacQueen"),
trace=FALSE)Any command-line input or output is written as follows:
seqdist(seqdata, method, refseq=NULL, norm=FALSE, indel=1, sm=NA, with.missing=FALSE, full.matrix=TRUE)
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: "You can see the key concepts: inflation, economic, conditions, employment, and the FOMC."
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