Dasar-dasar ggnetwork

Pada pelajaran sebelumnya, Anda melihat bahwa paket ggnet2 menghasilkan plot mirip ggplot2 dengan sintaks yang cukup familier bagi pengguna igraph. Namun, paket ggnetwork bekerja sedikit berbeda. Paket ini mengonversi objek igraph menjadi data frame yang mudah diplot dengan ggplot2. Paket ini juga menambahkan beberapa geom baru yang dapat digunakan untuk membangun plot. Fungsi ggnetwork() mengonversi objek igraph menjadi data frame, dan beberapa parameter ditambahkan ke dalam data frame (dalam hal ini, parameter jarak panah/arrow gap) sehingga kemudian dapat diplot menggunakan ggplot. Dari situ, Anda membangun grafik dengan geom_edges() untuk sisi (edge) dan geom_nodes() untuk simpul (vertex). Dalam pelajaran ini, kita akan membuat dua plot dasar dari grafik retweet: satu dengan pengaturan bawaan ggplot, dan satu lagi dengan penataan tema dasar agar tampil sedikit lebih baik.

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Call ggplot
___(
  # Convert retweet_samp to a ggnetwork
  ___(retweet_samp), 
  # Specify x, y, xend, yend
  aes(x = ___, y = ___, xend = ___, yend = ___)) +
  # Add a node layer
  ___ +
  # Add an edge layer
  geom_edges()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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: Paket lain untuk memplot graf!Exercise 2: Dasar-dasar ggnet Exercise 3: Dasar-dasar ggnetwork

Latihan Saat Ini

Exercise 4: Lebih Banyak Opsi Plot ggnet Exercise 5: Lebih Banyak Opsi Plotting ggnetwork Exercise 6: Visualisasi interaktif Exercise 7: Plot interaktif dengan ggiraph Exercise 8: Plot javascript interaktif Exercise 9: Visualisasi alternatif Exercise 10: Cara alternatif memvisualisasikan graf: Hive plot Exercise 11: BioFabric sebagai widget HTML Exercise 12: Memplot grafik pada peta