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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining 2. Fuzzy Logic Induced Content-Based Recommendation FREE CHAPTER 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Graphs in R


We will use the R package, igraph, for our graph analysis needs. We will leverage the arules package to manipulate our data. If you don't have them installed, proceed to install them as follows:

>  install.packages("arules")
>  install.packages("igraph")

You can use the sessionInfo function from the utils package to look at the packages available for you in the current session.

Let's get started; create a simple graph, and plot it:

> library(igraph, quietly = TRUE)
> simple.graph <- graph_from_literal(A-B, B-C, C-D, E-F, A-E, E-C)
> plot.igraph(simple.graph)

This produces the following graph plot:

After including the igraph library, we used the graph_from_literal function to create a simple undirected graph with six nodes. The igraph package provides the plot.igraph function to visualize the graphs. There are several ways in which we can create a graph. For a complete list of the different methods available to create graphs, refer to http://igraph.org/r/#docs.

Alternatively...

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