Apa yang Membuat Suatu Produk Penting?

Sekarang setelah kita memiliki definisi kerja tentang produk penting, mari kita lihat apakah produk tersebut memiliki sifat tertentu yang mungkin berkorelasi. Salah satu pasangan kandidat adalah salesrank.from dan salesrank.to. Kita dapat menanyakan apakah produk penting cenderung memiliki peringkat penjualan (sales rank) yang lebih tinggi daripada produk yang dibeli di hilir. Kita akan meninjaunya dengan terlebih dahulu membuat subset simpul penting, menggabungkannya kembali ke dataframe awal, lalu membuat dataframe baru menggunakan paket dplyr. Kita akan membuat graf baru, dan mewarnai sisi biru untuk peringkat tinggi (1, 2, 3) ke peringkat rendah (20, 21, 22) dan merah untuk kebalikannya. Jika peringkat berkorelasi dengan pembelian di hilir, maka kita akan melihat sebagian besar tautan berwarna biru, dan jika tidak ada hubungan, warnanya akan kurang lebih seimbang antara merah dan biru.

Himpunan data ip_df memuat informasi tentang produk penting.

Latihan ini adalah bagian dari kursus

Studi Kasus: Analisis Jaringan di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Select the from and to columns from ip_df
ip_df_from_to <- ip_df[c(___,___)]

# Create a directed graph from the data frame
ip_g <- graph_from_data_frame(___, directed = ___)

# Set the edge color. If salesrank.from is less than or 
# equal to salesrank.to then blue else red.
edge_color <- ifelse(
  ip_df$___ <= ip_df$___, 
  yes = ___, 
  no = ___
)

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: Menjelajahi himpunan data Anda Exercise 2: Menemukan Diad dan Triad Exercise 3: Klasterisasi dan Resiprositas Exercise 4: Produk Penting Exercise 5: Apa yang Membuat Suatu Produk Penting?

Latihan Saat Ini

Exercise 6: Menjelajahi struktur temporal Exercise 7: Metrik dari waktu ke waktu Exercise 8: Memplot Metrik dari Waktu ke Waktu

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: 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