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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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Adding a simple moving average graph

Now we will create our first simple moving average (SMA) graph. This graph will be created with the package, and will show two lines. The black line will be the actual price data, and the blue line will be SMA.

Before we begin, and since ggplot2 graphs which make use of dates are better created with actual dates instead of timestamp strings, we add the time column to the ORIGINAL_DATA dataframe with the corresponding dates. This should be placed immediately after having loaded the data:

ORIGINAL_DATA$time <- timestamp_to_time.TimeStamp(ORIGINAL_DATA$timestamp)

Next we show how our sma_graph() function is implemented. As can be seen, it will receive two parameters, the data dataframe and the sma vector coming out of one of the SMA implementations mentioned before. The function is very simple, it creates a graph with time on the x axis and...

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R Programming By Example
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