Blog Archives

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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Adobe Analytics Implementation Documentation in 60 seconds

December 9, 2013
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When I was working as a digital analytics consultant, no question quite had the ability to cause belly laughs AND angst as, “Can you send me an updated copy of your implementation documentation?” I saw companies that were spending six-or-seven-figures annually on their analytics infrastructure, multi-millions in salary for employees and yet the only way Adobe Analytics Implementation...

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RSiteCatalyst Version 1.2 Release Notes

November 5, 2013
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RSiteCatalyst Version 1.2 Release Notes

Version 1.2 of the RSiteCatalyst package to access the Adobe Analytics API is now available on CRAN! Changes include: Removed RCurl package dependency Changed argument order for GetAdminConsoleLog to avoid error when date not passed Return proper numeric type for metric columns Fixed bug in GetEVars function Added validate:true flag to API to improve error reporting Removed remaining RSiteCatalyst Version 1.2...

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Clustering Search Keywords Using K-Means Clustering

September 17, 2013
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Clustering Search Keywords Using K-Means Clustering

One of the key tenets to doing impactful digital analysis is understanding what your visitors are trying to accomplish. One of the easiest methods to do this is by analyzing the words your visitors use to arrive on site (search keywords) and what words they are using while on the site (on-site search). Although Google has Clustering Search Keywords...

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Fun With Just-In-Time Compiling: Julia, Python, R and pqR

September 2, 2013
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Fun With Just-In-Time Compiling: Julia, Python, R and pqR

Recently I’ve been spending a lot of time trying to learn Julia by doing the problems at Project Euler. What’s great about these problems is that it gets me out of my normal design patterns, since I don’t generally think about prime numbers, factorials and other number theory problems during my normal workday. These problems Fun With Just-In-Time...

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RSiteCatalyst Version 1.1 Release Notes

August 25, 2013
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RSiteCatalyst version 1.1 is now available on CRAN. Changes from version 1 include: Support for Correlations/Subrelations in the QueueRanked function Support for Current Data in all ‘Queue‘ functions Support Anomaly Detection for QueueOvertime and QueueTrended functions (example usage with ggplot2 graph) Decrease in wait time for API calls (from 5 seconds to 2 seconds) and extending RSiteCatalyst Version 1.1...

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Anomaly Detection Using The Adobe Analytics API

August 15, 2013
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Anomaly Detection Using The Adobe Analytics API

As digital marketers & analysts, we’re often asked to quantify when a metric goes beyond just random variation and becomes an actual “unexpected” result. In cases such as A/B..N testing, it’s easy to calculate a t-test to quantify the difference between two testing populations, but for time-series metrics, using a t-test is likely not appropriate. Anomaly Detection Using...

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Tabular Data I/O in Julia

August 6, 2013
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Importing tabular data into Julia can be done in (at least) three ways: reading a delimited file into an array, reading a delimited file into a DataFrame and accessing databases using ODBC. Reading a file into an array using readdlm The most basic way to read data into Julia is through the use of the Tabular Data I/O...

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