Blog Archives

Visualizing Website Pathing With Sankey Charts

September 10, 2014
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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 Related posts:

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Visualizing Website Pathing With Network Graphs

September 8, 2014
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Last week, version 1.4 of RSiteCatalyst was released, and now it’s possible to get site pathing information directly within R. Now, it’s easy to create impressive looking network graphs from your Adobe Analytics data using RSiteCatalyst and d3Network. In this blog post, I will cover simple and force-directed network graphs, which show the pairwise representation between pages. Related posts:

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RSiteCatalyst Version 1.4 Release Notes

September 1, 2014
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RSiteCatalyst Version 1.4 Release Notes

It felt like it would never happen, but RSiteCatalyst v1.4 is now available on CRAN! There are numerous changes in this version of the package, so unlike previous posts, there won’t be any code examples. THIS VERSION IS ONE BIG BREAKING CHANGE While not the most important improvement, it can’t be stressed enough that migrating Related posts:

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Maybe I Don’t Really Know R After All

June 26, 2014
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Maybe I Don’t Really Know R After All

Lately, I’ve been feeling that I’m spreading myself too thin in terms of programming languages. At work, I spend most of my time in Hive/SQL, with the occasional Python for my smaller data. I really prefer Julia, but I’m alone at work on that one. And since I maintain a package on CRAN (RSiteCatalyst), I frequently spend Related posts:

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Using Julia As A ‘Glue’ Language

June 24, 2014
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While much of the focus in the Julia community has been on the performance aspects of Julia relative to other scientific computing languages, Julia is also perfectly suited to ‘glue’ together multiple data sources/languages. In this blog post, I will cover how to create an interactive plot using Gadfly.jl, by first preparing the data using Related posts:

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Five Hard-Won Lessons Using Hive

June 12, 2014
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I’ve been spending a ton of time lately on the data engineering side of ‘data science’, so I’ve been writing a lot of Hive queries. Hive is a great tool for querying large amounts of data, without having to know very much about the underpinnings of Hadoop. Unfortunately, there are a lot of things about Five Hard-Won...

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Building JSON in R: Three Methods

May 13, 2014
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When I set out to build RSiteCatalyst, I had a few major goals: learn R, build a CRAN-worthy package and learn the Adobe Analytics API. As I reflect back on how the package has evolved over the past two years and what I’ve learned, I think my greatest learning was around how to deal with JSON Building JSON...

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Real-time Reporting with the Adobe Analytics API

March 10, 2014
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Starting with version 1.3.1 of RSiteCatalyst, you can now access the real-time reporting capabilities of the Adobe Analytics API through a familiar R interface. Here’s how to get started… GetRealTimeConfiguration Before using the real-time reporting capabilities of Adobe Analytics, you first need to indicate which metrics and elements you are interested in seeing in real-time. To Real-time Reporting...

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RSiteCatalyst Version 1.3 Release Notes

February 4, 2014
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RSiteCatalyst Version 1.3 Release Notes

Version 1.3 of the RSiteCatalyst package to access the Adobe Analytics API is now available on CRAN! Changes include: Search via regex functionality in QueueRanked/QueueTrended functions Support for Realtime API reports: Overtime and one-element Ranked report Allow for variable API request timing in Queue functions Fixed validate flag in JSON request to work correctly Deprecated RSiteCatalyst Version...

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Quickly Create Dummy Variables in a Data Frame

January 2, 2014
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Quickly Create Dummy Variables in a Data Frame

On Quora, a question was asked about how to fix the error of the randomForest package in R not being able to handle more than 32 levels in a categorical variable. Seeing as how I’ve seen this question asked on Kaggle forums, StackOverflow and elsewhere, here’s the answer: code your own dummy variables instead of Quickly Create...

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