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)

Looking at geographical data with static maps

Maps can be very useful tools to get an intuition behind geographical data. In this section, we will produce a map with the ggplot2 package. The objective is to show the location of our client's messages, the PRICE associated to their purchases, and the corresponding PROFIT_RATIO. This example will show us how to join data from the sales and client_messages data frames.

Our graph_client_messages_static() function receives as parameters the client_messages and sales data frames, and that's all it needs as we are showing unfiltered (full) datasets. First, we need to merge our two data frames using the identifier they share, which is SALE_ID. To do so we use the merge() function, and we specify that we want to keep all observation on the x data frame, which is the first one (client_messages), and we don't want to keep observations...