Publications by Randy Zwitch
A Beginner’s Look at Julia
Over the past month or so, I’ve been playing with a new scientific programming language called ‘Julia‘, which aims to be a high-level language with performance approaching that of C. With that goal in mind, Julia could be a replacement for the ‘multi-language’ problem of needing to move between R, Python, MATLAB, C, Fortran, Scala, etc....
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Tabular Data I/O in Julia
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 readdlm function, which will create an array:...
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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 app...
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RSiteCatalyst Version 1.1 Release Notes
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 ...
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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 have also given...
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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 made it much more diffi...
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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...
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Adobe Analytics Implementation Documentation in 60 seconds
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 t...
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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 relying on Factors! Code snipp...
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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 re...
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