Book Image

R Programming By Example

By : Omar Trejo Navarro
Book Image

R Programming By Example

By: Omar Trejo Navarro

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.
Table of Contents (12 chapters)

Simulating the sales data

Enough concepts; let's start programming. To get a clear idea of where we're heading, we start by initializing the sales data frame we will be using, with zero observations for now. We do so by defining the available categories for each factor variable, and defining empty values with the data type we need for each variable. As you can see, it has the identifiers SALE_ID and CLIENT_ID, which will allow us to link this data with the one from clients and client_messages. To understand this, let's have a look at the following code:

status_levels <- c("PENDING", "DELIVERED", "RETURNED", "CANCELLED")
protein_source_levels <- c("BEEF", "FISH", "CHICKEN", "VEGETARIAN")
continent_levels <- c("AMERICA", "EUROPE", "ASIA")
delivery_levels...