Visualizing Website Pathing With Sankey Charts

In my prior post on visualizing website structure using network graphs, I referenced that network graphs showed the pairwise relationships between two pages (in a bi-directional manner). However, if you want to analyze how your visitors are pathing through your site, you can visualize your data using a Sankey chart.

Visualizing Single Page-to-Next Page Pathing

Most digital analytics tools allow you to visualize the path between pages. In the case of Adobe Analytics, the Next Page Flow diagram is limited to 10 second-level branches in the visualization. However, the Adobe Analytics API has no such limitation, and as such we can use RSiteCatalyst to create the following visualization (GitHub Gist containing R code):

The data processing for this visualization is near identical to the network diagrams. We can use QueuePathing() from RSiteCatalyst to download our pathing data, except in this case, I specified an exact page name as the first level of the pathing pattern instead of using the ::anything:: operator. In all Sankey charts created by d3Network, you can hover over the right-hand side nodes to see the values (you can also drag around the nodes on either side if you desire!). It’s pretty clear from this diagram that I need to do a better job retaining my visitors, as the most common path from this page is to leave. 🙁

Many-to-Many Page Pathing

The example above picks a single page related to Hadoop, then shows how my visitors continue through my site; sometimes, they go to other Hadoop pages, some view Data Science related content or any number of other paths. If we want, however, we can visualize how all visitors path through all pages. Like the force-directed graph, we can get this information by using the ("::anything::", "::anything::") path pattern with QueuePathing():

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
#Multi-page pathing
library("d3Network")
library("RSiteCatalyst")

#### Authentication
SCAuth("name", "secret")

#### Get All Possible Paths with ("::anything::", "::anything::")
pathpattern <- c("::anything::", "::anything::")
next_page <- QueuePathing("zwitchdev",
                          "2014-01-01",
                          "2014-08-31",
                          metric="pageviews",
                          element="page",
                          pathpattern,
                          top = 50000)

#Optional step: Cleaning my pagename URLs to remove to domain for clarity
next_page$step.1 <- sub("http://randyzwitch.com/","",
                        next_page$step.1, ignore.case = TRUE)
next_page$step.2 <- sub("http://randyzwitch.com/","",
                        next_page$step.2, ignore.case = TRUE)

#Filter out Entered Site and duplicate rows, >120 for chart legibility
links <- subset(next_page, count >= 120 & step.1 != "Entered Site")

#Get unique values of page name to create nodes df
#Create an index value, starting at 0
nodes <- as.data.frame(unique(c(links$step.1, links$step.2)))
names(nodes) <- "name"
nodes$nodevalue <- as.numeric(row.names(nodes)) - 1

#Convert string to numeric nodeid
links <- merge(links, nodes, by.x="step.1", by.y="name")
names(links) <- c("step.1", "step.2", "value", "segment.id", "segment.name", "source")

links <- merge(links, nodes, by.x="step.2", by.y="name")
names(links) <- c("step.2", "step.1", "value", "segment.id", "segment.name","source", "target")

#Create next page Sankey chart
d3output = "~/Desktop/sankey_all.html"
d3Sankey(Links = links, Nodes = nodes, Source = "source",
         Target = "target", Value = "value", NodeID = "name",
         fontsize = 12, nodeWidth = 50, file = d3output, width = 750, height = 700)

Running the code above provides the following visualization:

For legibility purposes, I’m only plotting paths that occur more than 120 times. But given a large enough display, it would be possible to visualize all valid combinations of paths.

One thing to keep in mind is that with the d3.js library, there is a weird hiccup where if your dataset contains “duplicate” paths such that both Source -> Target & Target -> Source exists, d3.js will go into an infinite loop/not show any visualization. My R code doesn’t provide a solution to this issue, but it should be trivial to remove these “duplicates” should they arise in your dataset.

Interpretation

Unlike the network graphs, Sankey Charts are fairly easy to understand. The “worst” path on my site in terms of keeping visitors on site is where I praised Apple for fixing my MacBook Pro screen out-of-warranty. The easy explanation for this poor performance is that this article attracts people who aren’t really my target audience in data science, but looking for information about getting THEIR screens fixed. If I wanted to engage these readers more, I guess I would need to write more Apple-related content.

To the extent there are multi-stage paths, these tend to be Hadoop and Julia-related content. This makes sense as both technologies are fairly new, I have a lot more content in these areas, and especially in the case of Julia, I’m one of the few people writing practical content. So I’m glad to see I’m achieving some level of success in these areas.

Hopefully this blog post and my previous post on visualizing your website visitors using network graphs have given a feel for the new functionality available in RSiteCatalyst v1.4, as well providing a new way of thinking about data visualization beyond just the default graphs provided by the Adobe Analytics interface.

  • RSiteCatalyst Version 1.4.12 (and 1.4.11) Release Notes
  • Self-Service Adobe Analytics Data Feeds!
  • RSiteCatalyst Version 1.4.10 Release Notes
  • WordPress to Jekyll: A 30x Speedup
  • Bulk Downloading Adobe Analytics Data
  • Adobe Analytics Clickstream Data Feed: Calculations and Outlier Analysis
  • Adobe: Give Credit. You DID NOT Write RSiteCatalyst.
  • RSiteCatalyst Version 1.4.8 Release Notes
  • Adobe Analytics Clickstream Data Feed: Loading To Relational Database
  • Calling RSiteCatalyst From Python
  • RSiteCatalyst Version 1.4.7 (and 1.4.6.) Release Notes
  • RSiteCatalyst Version 1.4.5 Release Notes
  • Getting Started: Adobe Analytics Clickstream Data Feed
  • RSiteCatalyst Version 1.4.4 Release Notes
  • RSiteCatalyst Version 1.4.3 Release Notes
  • RSiteCatalyst Version 1.4.2 Release Notes
  • Destroy Your Data Using Excel With This One Weird Trick!
  • RSiteCatalyst Version 1.4.1 Release Notes
  • Visualizing Website Pathing With Sankey Charts
  • Visualizing Website Structure With Network Graphs
  • RSiteCatalyst Version 1.4 Release Notes
  • Maybe I Don't Really Know R After All
  • Building JSON in R: Three Methods
  • Real-time Reporting with the Adobe Analytics API
  • RSiteCatalyst Version 1.3 Release Notes
  • Adobe Analytics Implementation Documentation in 60 Seconds
  • RSiteCatalyst Version 1.2 Release Notes
  • Clustering Search Keywords Using K-Means Clustering
  • RSiteCatalyst Version 1.1 Release Notes
  • Anomaly Detection Using The Adobe Analytics API
  • (not provided): Using R and the Google Analytics API
  • My Top 20 Least Useful Omniture Reports
  • For Maximum User Understanding, Customize the SiteCatalyst Menu
  • Effect Of Modified Bounce Rate In Google Analytics
  • Adobe Discover 3: First Impressions
  • Using Omniture SiteCatalyst Target Report To Calculate YOY growth
  • Google Analytics Individual Qualification (IQ) - Passed!
  • Google Analytics SEO reports: Not Ready For Primetime?
  • An Afternoon With Edward Tufte
  • Google Analytics Custom Variables: A Page-Level Example
  • Xchange 2011: Think Tank and Harbor Cruise
  • Google Analytics for WordPress: Two Methods
  • WordPress Stats or Google Analytics? Yes!
  • Building a Data Science Workstation (2017)
  • JuliaCon 2015: Everyday Analytics and Visualization (video)
  • Vega.jl, Rebooted
  • Sessionizing Log Data Using data.table [Follow-up #2]
  • Sessionizing Log Data Using dplyr [Follow-up]
  • Sessionizing Log Data Using SQL
  • Review: Data Science at the Command Line
  • Introducing Twitter.jl
  • Code Refactoring Using Metaprogramming
  • Evaluating BreakoutDetection
  • Creating A Stacked Bar Chart in Seaborn
  • Visualizing Analytics Languages With VennEuler.jl
  • String Interpolation for Fun and Profit
  • Using Julia As A "Glue" Language
  • Five Hard-Won Lessons Using Hive
  • Using SQL Workbench with Apache Hive
  • Getting Started With Hadoop, Final: Analysis Using Hive & Pig
  • Quickly Create Dummy Variables in a Data Frame
  • Using Amazon EC2 with IPython Notebook
  • Adding Line Numbers in IPython/Jupyter Notebooks
  • Fun With Just-In-Time Compiling: Julia, Python, R and pqR
  • Getting Started Using Hadoop, Part 4: Creating Tables With Hive
  • Tabular Data I/O in Julia
  • Hadoop Streaming with Amazon Elastic MapReduce, Python and mrjob
  • A Beginner's Look at Julia
  • Getting Started Using Hadoop, Part 3: Loading Data
  • Innovation Will Never Be At The Push Of A Button
  • Getting Started Using Hadoop, Part 2: Building a Cluster
  • Getting Started Using Hadoop, Part 1: Intro
  • Instructions for Installing & Using R on Amazon EC2
  • Video: SQL Queries in R using sqldf
  • Video: Overlay Histogram in R (Normal, Density, Another Series)
  • Video: R, RStudio, Rcmdr & rattle
  • Getting Started Using R, Part 2: Rcmdr
  • Getting Started Using R, Part 1: RStudio
  • Learning R Has Really Made Me Appreciate SAS