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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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Graphing disaggregated data with boxplots

Creating bar graphs is useful when presenting results to people who are not familiar with statistics, but the fact that bar graphs aggregate information (just as we did in the bar graphs for top performers) means that, in reality, we lose information due to the reduction. If you're working with people who understand what quartiles are, then boxplots may be a useful visualization. They are an easy way to see individual distributions for different levels of a variable.

Each box represents the first quartile at the bottom, the third quartile at the top, and the median on the line in the middle. The lines that extend vertically reach up to any observation within 1.5 * IQR, where the interquartile range (IQR) is the distance between the first and third quartiles. Any observation beyond 1.5 * IQR is treated as an outlier and is shown individually...

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