Tracciare grafi su una mappa

In questa lezione costruiremo una mappa sovrapponendo il grafo del bike sharing. Poi coloreremo gli spigoli in base a abbonato/non abbonato e imposteremo lo spessore in base al numero di viaggi. Ricorda: gli abbonati al servizio di bike sharing sono utenti abituali e con tutta probabilità più locali, mentre i non abbonati sono più spesso turisti. I viaggi dei non abbonati tendono a concentrarsi sulle zone del lungolago, dove ci sono più attrazioni turistiche. Vedremo anche un altro schema: più una stazione è a ovest, più sottile è la linea, cioè meno viaggi sono stati effettuati. Sovrapporre il grafo alla mappa aiuta a rendere molto più chiari certi pattern di rete rispetto a un normale hairball plot.

I dati delle bici, bike_dat, e una mappa di Chicago, chicago, sono disponibili.

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Casi di studio: analisi di reti in R

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weighted_trips_by_usertype <- bike_dat %>% 
  group_by(
    from_station_id, to_station_id, 
    from_latitude, from_longitude, 
    to_latitude, to_longitude, 
    usertype
  ) %>% 
  # Weight each journey by number of trips
  ___(___ = ___)

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Questo esercizio fa parte del corso

Casi di studio: analisi di reti in R

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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.

Exercise 1: Exploring your data set Exercise 2: Finding Dyads and Triads Exercise 3: Clustering and Reciprocity Exercise 4: Important Products Exercise 5: What Makes an Important Product?Exercise 6: Exploring temporal structure Exercise 7: Metrics through time Exercise 8: Plotting Metrics Over Time

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Exercise 1: Creating your retweet graph Exercise 2: Visualize the graph Exercise 3: Visualize nodes by degree Exercise 4: What's the distribution of centrality?Exercise 5: Who is important in the conversation?Exercise 6: Plotting important vertices Exercise 7: Building a mentions graph Exercise 8: Comparing mention and retweet graph Exercise 9: Assortativity and reciprocity Exercise 10: Finding who is talking to whom Exercise 11: Finding communities Exercise 12: Comparing community algorithms Exercise 13: Visualizing the communities

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.

Exercise 1: Creating our graph from raw data Exercise 2: Making Graphs of Different User Types Exercise 3: Compare Graphs of Different User Types Exercise 4: Compare graph distance vs. geographic distance Exercise 5: Compare Subscriber vs. Non-Subscriber Distances Exercise 6: Most Traveled To and From Stations Exercise 7: Most Traveled To and From Stations with Weights Exercise 8: Visualize central vertices Exercise 9: Weighted Measures of Centrality Exercise 10: Connectivity Exercise 11: Find the minimum cut 1 Exercise 12: Find the minimum cut 2 Exercise 13: Unweighted Clustering Randomizations Exercise 14: Weighted Clustering Randomizations

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.

Exercise 1: Altri pacchetti per visualizzare i grafi!Exercise 2: Nozioni di base su ggnet Exercise 3: Nozioni di base su ggnetwork Exercise 4: Altre opzioni di grafica con ggnet Exercise 5: Altre opzioni di grafici con ggnetwork Exercise 6: Visualizzazioni interattive Exercise 7: Grafici interattivi con ggiraph Exercise 8: Grafici javascript interattivi Exercise 9: Visualizzazioni alternative Exercise 10: Modi alternativi di visualizzare un grafo: hive plot Exercise 11: BioFabric come widget HTML Exercise 12: Tracciare grafi su una mappa

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