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R Programming By Example

R Programming By Example

By : Trejo Navarro, Omar Trejo Navarro
3 (4)
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R Programming By Example

R Programming By Example

3 (4)
By: Trejo Navarro, 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)
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Calculating simple moving averages inefficiently

The algorithm we will work with for the rest of the chapter is called simple moving average (SMA). It's a very well-known tool for doing technical analysis of time-series, specially for financial markets and trading. The idea behind SMA is that you will compute an average at each point in time by looking back at a predefined number periods. For example, let's say you're looking at a minute-by-minute time-series, and you will compute an SMA(30). This means that at each observation in your time-series, you will take the observations that correspond to the previous 30 minutes from starting at a specific observation (30 observations back), and will save the average for those 30 observations as the SMA(30) value for that point in time.

In the later diagram, you can visualize the idea behind SMAs. The diagram shows a monotone...

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