What this book covers

Chapter 1, Functions in R, describes how to create R functions. This chapter covers the basic composition, environment, and argument matching of an R function. Furthermore, we will look at advanced topics such as closure, functional programming, and how to properly handle errors.

Chapter 2, Data Extracting, Transforming, and Loading, teaches you how to read structured and unstructured data with R. The chapter begins by collecting data from text files. Subsequently, we will look at how to connect R to a database. Lastly, you will learn how to write a web scraper to crawl through unstructured data from a web page or social media site.

Chapter 3, Data Preprocessing and Preparation, introduces you to preparing data ready for analysis. In this chapter, we will cover the data preprocess steps, such as type conversion, adding, filtering, dropping, merging, reshaping, and missing-value imputation, with some basic R functions.

Chapter 4, Data Manipulation, demonstrates how to manipulate data in an efficient and effective manner with the advanced R packages data.table and dplyr. The data.table package exposes you to the possibility of quickly loading and aggregating large amounts of data. The dplyr package provides the ability to manipulate data in SQL-like syntax.

Chapter 5, Visualizing Data with ggplot2, explores using ggplot2 to visualize data. This chapter begins by introducing the basic building blocks of ggplot2. Next, we will cover advanced topics on how to create a more sophisticated graph with ggplot2 functions. Lastly, we will describe how to build a map with ggmap.

Chapter 6, Making Interactive Reports, reveals how to create a professional report with R. In the beginning, the chapter discusses how to write R markdown syntax and embed R code chunks. We will also explore how to add interactive charts to the report with ggvis. Finally, we will look at how to create and publish an R Shiny report.

Chapter 7, Simulation from Probability Distributions, begins with an emphasis on sampling data from different probability distributions. As a concrete example, we will look at how to simulate a stochastic trading process with a probability function.

Chapter 8, Statistical Inference in R, begins with a discussion on point estimation and confidence intervals. Subsequently, you will be introduced to parametric and non-parametric testing methods. Lastly, we will look at how one can use ANOVA to analyze whether the salary basis of an engineer differs based on his job title and location.

Chapter 9, Rule and Pattern Mining with R, exposes you to the common methods used to discover associated items and underlying frequency patterns from transaction data. In this chapter, we use a real-world blog as example data so that you can learn how to perform rule and pattern mining on real-world data.

Chapter 10, Time Series Mining with R, begins by introducing you to creating and manipulating time series from a finance dataset. Subsequently, we will learn how to forecast time series with HoltWinters and ARIMA. For a more concrete example, this chapter reveals how to predict stock prices with ARIMA.

Chapter 11, Supervised Machine Learning, teaches you how to build a model that makes predictions based on labeled training data. You will learn how to use regression models to make sense of numeric relationships and apply a fitted model to data for continuous value prediction. For classification, you will learn how to fit data into a tree-based classifier.

Chapter 12, Unsupervised Machine Learning, introduces you to revealing the hidden structure of unlabeled data. Firstly, we will look at how to group similarly located hotels together with the clustering method. Subsequently, we will learn how to select and extract features on the economy freedom dataset with PCA.