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Learning Social Media Analytics with R

You're reading from Learning Social Media Analytics with R Transform data from social media platforms into actionable business insights

Product type Paperback

Published in May 2017

Publisher Packt

ISBN-13 9781787127524

Length 394 pages

Edition 1st Edition

Languages

R

Tools

GitHub

Concepts

Data Analysis

Authors (4):

Sarkar

Karthik Ganapathy

Raghav Bali

Sharma

View More author details

Table of Contents (16) Chapters

Learning Social Media Analytics with R

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with R and Social Media Analytics FREE CHAPTER

2. Twitter – What's Happening with 140 Characters

3. Analyzing Social Networks and Brand Engagements with Facebook

4. Foursquare – Are You Checked in Yet?

5. Analyzing Software Collaboration Trends I – Social Coding with GitHub

6. Analyzing Software Collaboration Trends II - Answering Your Questions with StackExchange

7. Believe What You See – Flickr Data Analysis

8. News – The Collective Social Media!

Index

Analyzing repository trends

In this section, we will focus on various ways to analyzing different aspects of repositories, which include visualizing repository creation and updates over time, looking at various repository metrics and the various relationships among them. To start with, you can load up the necessary dependencies and the trending_repos.RData dataset which we created in the previous section in case you haven't done so already:

source('load_packages.R')
load('trending_repos.RData')

Now we are ready to analyze various trends with regard to our trending repositories and each of the following sub-sections will focus on a particular aspect of repository trends.

Analyzing trending repositories created over time

We will look at the trends with regard to our trending repositories based on their creation date over time. Basically we want to view the repository creation frequency over time and observe if there are any peaks or dips from the graph which we will be plotting. Our dataset has...

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Authors (4)

Sarkar

Sarkar

Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.

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Karthik Ganapathy

Karthik Ganapathy

Contacted for Reviewing

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Raghav Bali

Raghav Bali

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

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Sharma

Sharma

Tushar Sharma has a master's degree specializing in data science from the International Institute of Information Technology, Bangalore. He works as a data scientist with Intel. In his previous job he used to work as a research engineer for a financial consultancy firm. His work involves handling big data at scale generated by the massive infrastructure at Intel. He engineers and delivers end to end solutions on this data using the latest machine learning tools and frameworks. He is proficient in R, Python, Spark, and mathematical aspects of machine learning among other things. Tushar has a keen interest in everything related to technology. He likes to read a wide array of books ranging from history to philosophy and beyond. He is a running enthusiast and likes to play badminton and tennis.

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