R

Explore X (formerly Twitter) data clusters to uncover user behaviors (e.g., repost and reply patterns) within online communities. This guide focuses on a politically charged data set to illustrate the process of visualizing and analyzing social data.

Python and R empower data scientists to solve problems using elegant syntactic sugar, simplifying coding and solution exploration. Each language brings its unique capabilities and approach to bear.

Microsoft’s Power BI is one of the most popular software solutions used to perform social network analysis. Here’s how to create custom Power BI visuals in R for compelling and flexible results.

Centrality and text analysis allow users to get more out of their social network data. Here’s how you can leverage them using R and Gephi.

Thanks to rapid advances in technology, large amounts of data generated on social networks can be analyzed with relative ease, especially for those who use the R programming language and Gephi.

IMDb ratings have genre bias: For example, dramas tend to score higher. Removing common feature bias and keeping unique characteristics, it’s possible to create a new, refined score based on IMDb information.

As a language, R is strongly tied to data and is thus used mostly by statisticians and data scientists. Many who already use R for machine learning, though, are not aware that data munging can be done faster in R, meaning another tool is not required for that task.

In this article, Freelance Software Engineer Jan Gorecki explores tabular data transformations and introduces us to one of the fastest open-source data wrangling tools available.