Grafen plotten op een kaart

In deze les bouwen we een kaart waarop we de fietsgraph over de kaart heen leggen. Daarna kleuren we de randen op basis van abonnee/niet-abonnee en zetten we de lijndikte op het aantal ritten. Denk eraan: abonnees van het deelfietsnetwerk zijn vaste gebruikers en waarschijnlijk vaker lokaal, terwijl niet-abonnees vaker toeristen zijn. Ritten van niet-abonnees blijven meestal langs het meer, waar meer toeristische attracties zijn. Een ander patroon dat je zult zien: hoe verder west een station ligt, hoe dunner de lijn — er zijn dan minder ritten. Door de graph op de kaart te leggen, worden bepaalde netwerkpatronen veel duidelijker dan wanneer je alleen een gewone hairball-plot zou maken.

De fietsdata, bike_dat, en een kaart van Chicago, chicago, zijn beschikbaar.

Deze oefening maakt deel uit van de cursus

Casestudies: netwerkanalyse in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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
  ___(___ = ___)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Casestudies: netwerkanalyse 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

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.

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: Andere pakketten om grafen te plotten!Exercise 2: Basis van ggnet Exercise 3: Basis van ggnetwork Exercise 4: Meer ggnet-plotopties Exercise 5: Meer ggnetwork-plotopties Exercise 6: Interactieve visualisaties Exercise 7: Interactieve plots met ggiraph Exercise 8: Interactieve JavaScript-plots Exercise 9: Alternatieve visualisaties Exercise 10: Alternatieve manieren om een graaf te visualiseren: hive plots Exercise 11: BioFabric als een HTML-widget Exercise 12: Grafen plotten op een kaart

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