Book Image

Learning Data Analysis with R [Video]

By : Fabio Veronesi
Book Image

Learning Data Analysis with R [Video]

By: Fabio Veronesi

Overview of this book

<p>R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.</p> <p>This video delivers viewers the ability to conduct data analysis in practical contexts with R, using core language packages and tools. The end goal is to provide analysts and data scientists a comprehensive learning course on how to manipulate and analyse small and large sets of data with R. It will introduce how CRAN works and will demonstrate why viewers should use them.</p> <p>You will start with the most basic importing techniques, to downloading compressed data from the web and learn of more advanced ways to handle even the most difficult datasets to import. Next, you will move on to create static plots, while the second will show how to plot spatial data on interactive web platforms such as Google Maps and Open Street maps. Finally, you will learn to implement your learning with real-world examples of data analysis.</p> <p>This video will lay the foundations for deeper applications of data analysis, and pave the way for advanced data science.</p> <h1>Style and Approach</h1> <p>In this practical video, you will learn to perform a range of data analysis tasks along with real world data sets. This video delivers viewers the ability to conduct data analysis in practical contexts with R, using core language packages and tools.</p> <p>The overall vision of this video is structured as a series of practical tutorials which cover single tasks, with their appropriate R packages, in detail.</p>
Table of Contents (15 chapters)
Chapter 13
Time Series Analysis of Wind Speed Data
Content Locked
Section 3
Subsetting and Temporal Functions
Dealing with time-series sometimes means extracting data according to their location along the time line. This can be done in R but require some explanation to do it correctly. - Subsetting ts and xts objects - Quantify temporal changes - Temporal functions