Visualisasikan simpul pusat

Seperti yang kita lihat pada pelajaran sebelumnya, stasiun 275 memiliki rasio derajat keluar/masuk terendah. Kita dapat memvisualisasikannya menggunakan make_ego_graph() untuk melihat semua jalur keluar dari stasiun ini. Akan lebih informatif jika diplot pada tata letak koordinat geografis, bukan tata letak bawaan igraph. Secara default, igraph menggunakan fungsi layout_nicely() untuk menampilkan graf Anda, dengan menebak secara algoritmik tata letak yang paling sesuai. Namun, dalam kasus ini kita ingin menentukan koordinat tiap stasiun, karena ketika sebuah simpul berada di atas simpul lain artinya posisinya memang di utara.

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Make an ego graph of the least traveled graph
g275 <- make_ego_graph(___, 1, nodes = "___", mode= "out")[[1]]

# Plot ego graph
plot(
  g275, 
  # Weight the edges by weight attribute 
  edge.width = E(g275)$weight
)

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: Membuat graf dari data mentah Exercise 2: Membuat Graf untuk Berbagai Jenis Pengguna Exercise 3: Bandingkan Graf dari Jenis Pengguna yang Berbeda Exercise 4: Bandingkan jarak graf vs. jarak geografis Exercise 5: Bandingkan Jarak Pelanggan Berlangganan vs. Non-Berlangganan Exercise 6: Stasiun dengan Arus Masuk dan Keluar Terbanyak Exercise 7: Stasiun Paling Sering Dikunjungi Masuk dan Keluar dengan Bobot Exercise 8: Visualisasikan simpul pusat

Latihan Saat Ini

Exercise 9: Ukuran Sentralitas Berbobot Exercise 10: Konektivitas Exercise 11: Cari minimum cut 1 Exercise 12: Temukan minimum cut 2 Exercise 13: Pengacakan Klaster Tanpa Bobot Exercise 14: Randomisasi Klaster Berbobot

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: Other packages for plotting graphs!Exercise 2: ggnet Basics Exercise 3: ggnetwork Basics Exercise 4: More ggnet Plotting Options Exercise 5: More ggnetwork Plotting Options Exercise 6: Interactive visualizations Exercise 7: Interactive plots with ggiraph Exercise 8: Interactive javascript plots Exercise 9: Alternative visualizations Exercise 10: Alternative ways to visualize a graph: Hive plots Exercise 11: BioFabric as an HTML widget Exercise 12: Plotting graphs on a map