Social network analysis is one the trending topics in the world of data science. As we have seen throughout the chapter, these platforms not only provide us with ways to connect but they also present a unique opportunity to study human dynamics at a global scale. Through this chapter, we have learned some interesting techniques. We started off by understanding data mining in the social network context followed by the importance of visualizations. We focused on Twitter and understood different objects and APIs to manipulate them. We used various packages from R, such as TwitteR and TM, to connect, collect, and manipulate data for our analysis. We used data from Twitter to learn about frequency throughout. Finally, we presented some of the challenges posed by social networks words and associations, popular devices used by tweeple, hierarchical clustering and even touched upon topic modeling. We used ggplot2 and wordcloud to visualize our results to the data mining process in general. While concluding this chapter, we are sure that by now you can appreciate the amazing dynamics behind these platforms and R's ability to analyze it all. We aren't done with @Twitter yet, hold on to your #sentiments!