Simulating text messages that actually make sense is very hard, and we won't attempt it here. Instead, what we'll do is leverage a dataset that was published about food reviews on Amazon. The dataset was published as part of the paper published by McAuley and Leskovec, From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews, 2013. You can find the dataset in Kaggle (https://www.kaggle.com/snap/amazon-fine-food-reviews). We won't show the code that prepared the data for this example, but basically, what it does is rename the variables we want STARS, SUMMARY, and MESSAGE, delete the rest, and save the data frame into the reviews.csv file. For the interested reader, the code that accomplishes this task, as well as the original and processed data, is inside the code repository for this book ...
R Programming By Example
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R Programming By Example
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Overview of this book
R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R.
We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization.
By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.
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Current Title:
R Programming By Example
Table of Contents (12 chapters)
Preface
Free Chapter
Introduction to R
Understanding Votes with Descriptive Statistics
Predicting Votes with Linear Models
Simulating Sales Data and Working with Databases
Communicating Sales with Visualizations
Understanding Reviews with Text Analysis
Developing Automatic Presentations
Object-Oriented System to Track Cryptocurrencies
Implementing an Efficient Simple Moving Average
Adding Interactivity with Dashboards
Customer Reviews