RSiteCatalyst Version 1.4.1 Release Notes

Changes

Version 1.4.1 of RSiteCatalyst is now available on CRAN. There were a handful of bug fixes and new features added, including:

  • Fixed bug in QueueRanked function where only 10 results were returned when requesting multiple element reports. Function now returns up to 50,000 per breakdown (API limit)
  • Created better error message to inform user to login with credentials instead of making function call without proper API credentials
  • Added support for using SAINT classifications in QueueRanked/QueueTrended functions
  • Added more error checking to make functions fail more elegantly
  • Added remaining GET methods from Reporting/Administration API

Additional GET methods

This version of RSiteCatalyst has roughly 20 new GET methods, mostly providing additional report suite information for those who might desire to generate their documentation programmatically rather than manually. New API methods include (but are not limited to):

  • GetMarketingChannelRules: Get a list of all criteria used to build the Marketing Channels report
  • GetReportDescription: For a given bookmark_id, get the report definition
  • GetListVariables: Get a list of the List Variables defined for a report suite
  • GetLogins: Get all logins for a given Company

If you were the type of person who enjoyed this blog post showing how to auto-generate Adobe Analytics documentation, I encourage you to take a look at these newly incorporated functions and use them to improve your documentation even further.

Feature Requests/Bugs

If you come across any bugs, or have any feature requests, please continue to use the RSiteCatalyst GitHub Issues page to make tickets. While I’ve responded to many of you via the maintainer email provided in the R package itself, it’s much more efficient (and you’re much more likely to get a response) if you use the GitHub Issues page. Don’t worry about cluttering up the page with tickets, please fill out a new issue for anything you encounter, unless you are SURE that it is the same problem someone else is facing.

And finally, like I end every blog post about RSiteCatalyst, please note that I’m not an Adobe employee. Please don’t send me your API credentials, expect immediate replies or ask to set up phone calls to troubleshoot your problems. This is open-source software…Willem Paling and I did the hard part writing it, you’re expected to support yourself as best as possible unless you believe you’re encountering a bug. Then use GitHub 🙂


Evaluating BreakoutDetection

A couple of weeks ago, Twitter open-sourced their BreakoutDetection package for R, a package designed to determine shifts in time-series data. The Twitter announcement does a great job of explaining the main technique for detection (E-Divisive with Medians), so I won’t rehash that material here. Rather, I wanted to see how this package works relative to the anomaly detection feature in the Adobe Analytics API, which I’ve written about previously.

Getting Time-Series Data Using RSiteCatalyst

To use a real-world dataset to evaluate this package, I’m going to use roughly ten months of daily pageviews generated from my blog. The hypothesis here is that if the BreakoutDetection package works well, it should be able to detect the boundaries around when I publish a blog post (of which the dates I know with certainty) and when articles of mine get shared on sites such as Reddit. From past experience, I get about a 3-day lift in pageviews post-publishing, as the article gets tweeted out, published on R-Bloggers or JuliaBloggers and shared accordingly.

Here’s the code to get daily pageviews using RSiteCatalyst (Adobe Analytics):

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#Installing BreakoutDetection package
install.packages("devtools")
devtools::install_github("twitter/BreakoutDetection")
library(BreakoutDetection)

library("RSiteCatalyst")
SCAuth("company", "secret")

#Get pageviews for each day in 2014
pageviews_2014 <- QueueOvertime('report-suite',
                               date.from = '2014-02-24',
                               date.to = '2014-11-05',
                               metric = 'pageviews',
                               date.granularity = 'day')

#v1.0.1 of package requires specific column names and dataframe format
formatted_df <- pageviews_2014[,c("datetime","pageviews")]
names(formatted_df) <- c("timestamp", "count")

One thing to notice here is that BreakoutDetection requires either a single R vector or a specifically formatted data frame. In this case, because I have a timestamp, I use lines 17-18 to get the data into the required format.

BreakoutDetection - Default Example

In the Twitter announcement, they provide an example, so let’s evaluate those defaults first:

breakoutdetection-defaults

In order to validate my hypothesis, the package would need to detect 12 ‘breakouts’ or so, as I’ve published 12 blog posts during the sample time period. Mentally drawing lines between the red boundaries, we can see three definitive upward mean shifts, but far fewer than the 12 I expected.

BreakoutDetection - Modifying The Parameters

Given that the chart above doesn’t fit how I think my data are generated, we can modify two main parameters: beta and min.size. From the documentation:

beta: A real numbered constant used to further control the amount of penalization. This is the default form of penalization, if neither (or both) beta or (and) percent are supplied this argument will be used. The default value is beta=0.008.

min.size:  The minimum number of observations between change points

The first parameter I’m going to experiment with is min.size, because it requires no in-depth knowledge of the EDM technique! The value used in the first example was 24 (days) between intervals, which seems extreme in my case. It’s reasonable that I might publish a blog post per week, so let’s back that number down to 5 and see how the result changes:

breakout-5

With 17 predicted intervals, we’ve somewhat overshot the number of blog posts mark. Not that the package is wrong per se; the boundaries are surrounding many of the spikes in the data, but perhaps having this many breakpoints isn’t useful from a monitoring standpoint. So setting the min.size parameter somewhere between 5 and 24 points would give us more than 3 breakouts, but less than 17. There is also the beta parameter that can be played with, but I’ll leave that as an exercise for another day.

Anomaly Detection - Adobe Analytics

From my prior post about Anomaly Detection with the Adobe Analytics API, Adobe has chosen to use Holt-Winters/Exponential Smoothing as their technique. Here’s what that looks like for the same time-period (code as GitHub Gist):

adobe_analytics

Even though the idea of both techniques are similar, it’s clear that the two methods don’t quite represent the same thing. In the case of the Adobe Analytics Anomaly Detection, it’s looking datapoint-by-datapoint, with a smoothing model built from the prior 35 points. If a point exceeds the upper- or lower-control limits, then it’s an anomaly, but not necessarily indicative of a true level shift like the BreakoutDetection package is measuring.

Conclusion

The BreakoutDetection package is definitely cool, but it is a bit raw, especially the default graphics. But the package definitely does work, as evidenced by how well it put boundaries around the traffic spikes when I set the min.size parameter equal to five.

Additionally, I tried to read more about the underlying methodology, but the only references that come up in Google seem to be references to the R package itself! I wish I had a better feeling for how the beta parameter influences the graph, but I guess that will come over time as I use the package more. But I’m definitely glad that Twitter open-sourced this package, as I’ve often wondered about how to detect level shifts in a more operational setting, and now I have a method to do so.


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():

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#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.


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