Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787127524
Length 394 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (4):
Arrow left icon
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
Karthik Ganapathy Karthik Ganapathy
Author Profile Icon Karthik Ganapathy
Karthik Ganapathy
Tushar Sharma Tushar Sharma
Author Profile Icon Tushar Sharma
Tushar Sharma
Arrow right icon
View More author details
Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Social Media Analytics 2. Twitter – What's Happening with 140 Characters FREE CHAPTER 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

Data Science and StackExchange


Data science is not just an industry buzzword but an actual field of study which encompasses a whole lot of academic research and industry level application of these concepts. The https://datascience.stackexchange.com/ is one of those sites where users from different backgrounds and levels of expertise ask questions and discuss a whole lot of interesting concepts and things related to the field of data science, machine learning, advanced analytics, and so on.

As part of this use case, we will be making use of the Posts.xml file primarily from the said site for the analysis and uncovering of insights. Introduced in the previous section, we will utilize the same utility to load the XML and perform a couple of pre-processing steps, such as date-time cleanup to get our dataset in useable form. The following snippet performs the cleanup as well as brings the Tags attribute into useable form:

PostsDF <- loadXMLToDataFrame(paste0(path,"Posts.xml"))

# change data...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at AU $24.99/month. Cancel anytime