Book Image

R Data Analysis Projects

By : Gopi Subramanian
Book Image

R Data Analysis Projects

By: Gopi Subramanian

Overview of this book

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this book, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.
Table of Contents (9 chapters)

Time series data


Let us quickly look at some examples of time series data. We will use some data from Rob J Hyndman, from https://robjhyndman.com/TSDL/.

We will use the age of death of successive kings of England dataset.

Let us store the time series data as a ts object:

> kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)
Read 42 items
> king.ts <- ts(kings)
> king.ts
Time Series:
Start = 1 
End = 42 
Frequency = 1 
 [1] 60 43 67 50 56 42 50 65 68 43 65 34 47 34 49 41 13 35 53 56 16 43 69 59 48 59 86 55 68 51 33 49 67 77
[35] 81 67 71 81 68 70 77 56
>

Using the scan function, we get the data from the URL. Following that, we create a time series object using ts.

Let us plot the time series:

plot(king.ts)

The standard R plot function knows how to plot time series data. The time series plot is as shown in the following diagram:

Time series can be either non-seasonal or seasonal.

Non-seasonal time series

Non-seasonal time series tend to have a trend component and...