1. Aprende
    2. /
  2. Cursos
    3. /
  3. Case Studies: Network Analysis in R
  • 1

    Exploring graphs through time

    Gratuito

    In this chapter you'll explore a subset of an Amazon purchase graph. You'll build on what you've already learned, finding important products and discovering what drives purchases. You'll also examine how graphs can change through time by looking at the graph during different time periods.

  • 2

    How do people talk about R on Twitter?

    In this lesson you'll explore some Twitter data about R by looking at conversations using '#rstats'. First you'll look at the raw data and think about how you want to build your graph. There's a number of ways to do this, and we'll cover two ways: retweets and mentions. You'll build those graphs and then compare them on a number of metrics.

  • 3

    Bike sharing in Chicago

    In this chapter you will analyze data from a Chicago bike sharing network. We will build on the concepts already covered in the introductory course, and add a few new ones to handle graphs with weighted edges. You will also start with data in a slightly more raw form and cover how to build your graph up from a data source you might find.

  • 4

    Other ways to visualize graph data

    So far everything we've done has been using plotting from igraph. It provides many powerful ways to plot your graph data. However many people prefer interacting with other plotting frameworks like ggplot2, or even interactive frameworks like d3.js. In this lesson you'll look at other plotting libraries that build on the ggplot2 framework. You'll also look at other non-"hairball" type methods like hive plots, as well as building interactive and animated plots.

Initializing

Ejercicio

Who is important in the conversation?

Different measures of centrality all try to get at the similar concept of "which vertices are most important." As we discussed earlier, these two metrics approach it slightly differently. Keep in mind that while each may give a similar distribution of centrality measures, how an individual vertex ranks according to both may be different. Now we're going to compare the top ranking vertices of Twitter users.

The vectors that store eigen and betweenness centrality are stored respectively as retweet_ec and retweet_btw.

Instrucciones

100 XP
  • Calculate the 0.99 quantile of the betweenness, retweet_btw.
  • Subset retweet_btw for values greater than this quantile to keep the top 1%.
  • Do the same for eigen-centrality, retweet_ec.
  • Run the code that puts these in a data frame and look at the results.