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)

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' ,
+...