Plot javascript interaktif

Salah satu kerangka kerja yang banyak digunakan untuk membuat plot interaktif adalah D3.js. D3.js memiliki standar khusus untuk membuat plot jaringan yang dapat kita hasilkan secara otomatis di R. Di satu sisi, ini sangat praktis karena dengan hanya beberapa baris kode, Anda dapat membuat plot D3.js yang sepenuhnya interaktif. Kekurangannya, kustomisasi yang sesungguhnya baru dapat dilakukan ketika Anda langsung mengedit kode sumber javascript keluaran dari R (yang berada di luar cakupan kursus ini). Meski begitu, membuat plot jaringan D3.js yang bagus di R menggunakan pustaka d3network tetap cepat dan mudah. Dalam pelajaran ini, kita akan memuat himpunan data Twitter #rstats dan menambahkan keanggotaan komunitas. Lalu kita akan membuat subgraf dari beberapa komunitas saja dan merender grafik jaringan D3.js.

Objek grafik tweet, retweet_samp, tersedia.

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Run this to see the static version of the plot
ggplot(ggnetwork(retweet_samp, arrow.gap = 0.01), 
       aes(x = x, y = y, xend = xend, yend = yend)) + 
  	   geom_edges(color = "black") + 
	   geom_nodes(aes(color = as.factor(comm))) + 
       theme_blank()   

# Convert retweet_samp to a networkD3 object
nd3 <-igraph_to_networkD3(___, V(___)$___)

# View the data structure
str(nd3)

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 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

Latihan Saat Ini

Exercise 9: Visualisasi alternatif Exercise 10: Cara alternatif memvisualisasikan graf: Hive plot Exercise 11: BioFabric sebagai widget HTML Exercise 12: Memplot grafik pada peta