Big data, the Internet of Things, and artificial intelligence have become the hottest technology buzzwords in recent years. Although there are many different terms used to define these technologies, the common concept is that they're all driven by data. Simply having data is not enough; being able to unlock its value is essential. Therefore, data scientists have begun to focus on how to gain insights from raw data.
Data science has become one of the most popular subjects among academic and industry groups. However, as data science is a very broad discipline, learning how to master it can be challenging. A beginner must learn how to prepare, process, aggregate, and visualize data. More advanced techniques involve machine learning, mining various data formats (text, image, and video), and, most importantly, using data to generate business value. The role of a data scientist is challenging and requires a great deal of effort. A successful data scientist requires a useful tool to help solve day-to-day problems.
In this field, the most widely used tool by data scientists is the R language, which is open source and free. Being a machine language, it provides many data processes, learning packages, and visualization functions, allowing users to analyze data on the fly. R helps users quickly perform analysis and execute machine learning algorithms on their dataset without knowing every detail of the sophisticated mathematical models.
R for Data Science Cookbook takes a practical approach to teaching you how to put data science into practice with R. The book has 12 chapters, each of which is introduced by breaking down the topic into several simple recipes. Through the step-by-step instructions in each recipe, you can apply what you have learned from the book by using a variety of packages in R.
The first section of this book deals with how to create R functions to avoid unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL operations for heterogeneous data sources with R packages. An example of data manipulation is provided that illustrates how to use the dplyr and data.table packages to process larger data structures efficiently, while there is a section focusing on ggplot2 that covers how to create advanced figures for data exploration. Also, you will learn how to build an interactive report using the ggvis package.
This book also explains how to use data mining to discover items that are frequently purchased together. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, and dimension reduction.
With R for Data Science Cookbook in hand, I can assure you that you will find data science has never been easier.
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.
To follow the book's examples, you will need a computer with access to the Internet and the ability to install the R environment. You can download R from http://www.cran.r-project.org/. Detailed installation instructions are available in the first chapter.
The examples provided in this book were coded and tested with R version 3.2.4 on Microsoft Windows. The examples are likely to work with any recent version of R installed on either Mac OS X or a Unix-like OS.
R for Data Science Cookbook is intended for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.
In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).
To give clear instructions on how to complete a recipe, we use these sections as follows:
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Package and function names are shown as follows: "You can then install and load the package RCurl."
A block of code is set as follows:
> install.packages("RCurl") > library(RCurl)
Any URL is written as follows:
http://data.worldbank.org/topic/economy-and-growth
Variable name, argument name, new terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "In R, a missing value is noted with the symbol NA (not available), and an impossible value is NaN (not a number)."
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