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R Data Analysis Projects
R Data Analysis Projects

R Data Analysis Projects: Build end to end analytics systems to get deeper insights from your data

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Profile Icon Gopi Subramanian
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Paperback Nov 2017 366 pages 1st Edition
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Arrow left icon
Profile Icon Gopi Subramanian
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Free Trial
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
Paperback Nov 2017 366 pages 1st Edition
eBook
Mex$179.99 Mex$902.99
Paperback
Mex$1128.99
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eBook
Mex$179.99 Mex$902.99
Paperback
Mex$1128.99
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R Data Analysis Projects

Fuzzy Logic Induced Content-Based Recommendation

When a friend comes to you for a movie recommendation, you don't arbitrarily start shooting movie names. You try to suggest movies while keeping in mind your friend's tastes. Content-based recommendation systems try to mimic the exact same process. Consider a scenario in which a user is browsing through a list of products. Given a set of products and the associated product properties, when a person views a particular product, content-based recommendation systems can generate a subset of products with similar properties to the one currently being viewed by the user. Typical content-based recommendation systems tend to also include the user profile. In this chapter, however, we will not be including the user profiles. We will be working solely with the item/product profiles. Content-based recommendation systems are also...

Introducing content-based recommendation

To understand the inner workings of a content-based recommendation system, let's look at a simple example. We will use the wine dataset from https://archive.ics.uci.edu/ml/datasets/wine

This dataset is the result of the chemical analysis of wine grown in the same region in Italy. We have data from three different cultivars (From an assemblage of plants selected for desirable characters, Wikipedia: https://en.wikipedia.org/wiki/Cultivar).

Let's extract the data from UCI machine learning repository:

> library(data.table)
> wine.data <- fread('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0...

News aggregator use case and data

We have 1,000 news articles from different publishers. Each article belongs to a different category: technical, entertainment, and others. Our case is to alleviate the cold start problem faced by our customers. Simply put, what do we recommend to a customer when we don't have any information about his preferences? We are either looking at the customer for the first time or we don't have any mechanism set up yet to capture customer interaction with our products/items.

This data is a subset of the news aggregator dataset from https://archive.ics.uci.edu/ml/datasets/News+Aggregator.

A subset of the data is stored in a csv file.

Let's quickly look at the data provided:

> library(tidyverse)
> library(tidytext)
> library(tm)
> library(slam)
>
>
> cnames <- c('ID' , 'TITLE' , 'URL' ,
+...

Designing the content-based recommendation engine

To rewrite our customer requirements in plain English: When a customer browses a particular article, what other articles should we suggest to him?

Let's quickly recap how a content-based recommendation engine works. When a user is browsing a product or item, we need to provide recommendations to the user in the form of other products or items from our catalog. We can use the properties of the items to come up with the recommendations. Let's translate this to our use case.

Items in our case, are news articles.

The properties of a news article are as follows:

  • Its content, stored in a text column
  • The publisher--who published the article
  • The category to which the article belongs

So when a user is browsing a particular news article, we need to give him other news articles as recommendations, based on:

  • The text content...

Complete R Code

The complete R code is shown as follows:

library(data.table)

wine.data <- fread('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')
head(wine.data)

table(wine.data$V1)

wine.type <- wine.data[,1]
wine.features <- wine.data[,-1]

wine.features.scaled <- data.frame(scale(wine.features))
wine.mat <- data.matrix(wine.features.scaled)

rownames(wine.mat) <- seq(1:dim(wine.features.scaled)[1])
wine.mat[1:2,]

wine.mat <- t(wine.mat)
cor.matrix <- cor(wine.mat, use = "pairwise.complete.obs", method = "Pearson")
dim(cor.matrix)
cor.matrix[1:5,1:5]

user.view <- wine.features.scaled[3,]
user.view

sim.items <- cor.matrix[3,]
sim.items
sim.items.sorted <- sort(sim.items, decreasing = TRUE)
sim.items.sorted[1:5]

rbind(wine.data[3,]
,wine.data[52,]
,wine.data[51,]
,wine.data[85,]
,wine.data[15,]
)








library(tidyverse)
library...

Summary

We started the chapter by introducing content-based filtering. We discussed how content based filtering methods can help with cold-start problems in recommendation systems. We then explained the new aggregator use case. We explored the data provided by the customer--various news articles from different publishers belonging to different categories. Based on the data, we came up with a design for our content-based recommendation system.

We implemented a similarity dictionary; given a news article, this dictionary would be able to provide the top N matching articles. The similarity was calculated based on the words present in the article. We leveraged the vector space model for text and ultimately used the cosine distance to find the similarities between articles.

We implemented a simple search based on the similarity dictionary to get a list of matching news articles. We...

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Key benefits

  • • A handy guide to take your understanding of data analysis with R to the next level
  • • Real-world projects that focus on problems in finance, network analysis, social media, and more
  • • From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end-to-end data analysis pipelines using R

Description

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.

Who is this book for?

If you are looking for a book that takes you all the way through the practical application of advanced and effective analytics methodologies in R, then this is the book for you. A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this book.

What you will learn

  • • Build end-to-end predictive analytics systems in R
  • • Build an experimental design to gather your own data and conduct analysis
  • • Build a recommender system from scratch using different approaches
  • • Use and leverage RShiny to build reactive programming applications
  • • Build systems for varied domains including market research, network analysis, social media analysis, and more
  • • Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively
  • • Communicate modeling results using Shiny Dashboards
  • • Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling

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Publication date : Nov 17, 2017
Length: 366 pages
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Language : English
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Length: 366 pages
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ISBN-13 : 9781788621878
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Languages :
Concepts :
Tools :

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Table of Contents

8 Chapters
Association Rule Mining Chevron down icon Chevron up icon
Fuzzy Logic Induced Content-Based Recommendation Chevron down icon Chevron up icon
Collaborative Filtering Chevron down icon Chevron up icon
Taming Time Series Data Using Deep Neural Networks Chevron down icon Chevron up icon
Twitter Text Sentiment Classification Using Kernel Density Estimates Chevron down icon Chevron up icon
Record Linkage - Stochastic and Machine Learning Approaches Chevron down icon Chevron up icon
Streaming Data Clustering Analysis in R Chevron down icon Chevron up icon
Analyze and Understand Networks Using R Chevron down icon Chevron up icon

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Anand Nov 12, 2018
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Good book covering projects with background and complete codes in shiny. However for the background stuff about algorithms you need to refer other sources, which-acceptably- cannot be included in a single book. Variety of tasks allow you to pick a chapter individually but a sequential reading is also useful since some topics are inter related. Good for those whio have understanding of R and Shiny.
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