# Categorically Variable

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## flipsideR: Support for ASX Option Chain Data

I previously wrote about some ad hoc R code for downloading Option Chain data from Google Finance. I finally wrapped it up into a package called flipsideR, which is now available via GitHub. Since I last wrote on this topic I’ve also added support for downloading option data from the Australian Securities Exchange (ASX).

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## Durban Data Science Meetup

The next Durban Data Science Meetup will be taking place on 4 February. Check out the programme and come along if you’re in the area.

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## Kaggle: Santa's Stolen Sleigh

This morning I read Wendy Kan’s interesting post on Creating Santa’s Stolen Sleigh. I hadn’t really thought too much about the process of constructing an optimisation competition, but Wendy gave some interesting insights on the considerations involved in designing a competition which was both fun and challenging but still computationally feasible without military grade hardware.

This seems like an opportune time to jot down some of my personal notes and also take a look at the results. I know that this sort of discussion is normally the prerogative of the winners and I trust that my ramblings won’t be viewed as presumptuous.

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## Casting a Wide (and Sparse) Matrix in R

I routinely use melt() and cast() from the reshape2 package as part of my data munging workflow. Recently I’ve noticed that the data frames I’ve been casting are often extremely sparse. Stashing these in a dense data structure just feels wasteful. And the dismal drone of page thrashing is unpleasant.

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## Kaggle: Walmart Trip Type Classification

Walmart Trip Type Classification was my first real foray into the world of Kaggle and I’m hooked. I previously dabbled in What’s Cooking but that was as part of a team and the team didn’t work out particularly well. As a learning experience the competition was second to none. My final entry put me at position 155 out of 1061 entries which, although not a stellar performance by any means, is just inside the top 15% and I’m pretty happy with that. Below are a few notes on the competition.

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## MongoDB: Installing on Windows 7

It’s not my personal choice, but I have to spend a lot of my time working under Windows. Installing MongoDB under Ubuntu is a snap. Getting it going under Windows seems to require jumping through a few more hoops. Here are my notes. I hope that somebody will find them useful.

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## Review: Learning Shiny

I was asked to review Learning Shiny (Hernán G. Resnizky, Packt Publishing, 2015). I found the book to be useful, motivating and generally easy to read. I’d already spent some time dabbling with Shiny, but the book helped me graduate from paddling in the shallows to wading out into the Shiny sea.

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## Using Checksum to Guess Message Length: Not a Good Idea!

A question posed by one of my colleagues: can a checksum be used to guess message length? My immediate response was negative and, as it turns out, a simple simulation supported this knee-jerk reaction.

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## Goto

For a moment this morning I was regretting the fact that R doesn’t have a goto statement, but then…

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## Making Sense of Logarithmic Loss

Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in kaggle competitions. Since success in these competitions hinges on effectively minimising the Log Loss, it makes sense to have some understanding of how this metric is calculated and how it should be interpreted.

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## 2015 Data Science Salary Survey

The recently published 2015 Data Science Salary Survey conducted by O’Reilly takes a look at the salaries received, tools used and other interesting facts about Data Scientists around the World. It’s based on a survey of over 600 respondents from a variety of industries. The entire report is well worth a read, but I’ve picked out some highlights below.

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## Evolution of First Names: Fashionable and Popular Names

Last week I took a high level look at the trends in children’s names over the last century. Today I’ll dig a little deeper and examine the ebb and flow in popularity of some specific names.

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## Graph from Sparse Adjacency Matrix

I spent a decent chunk of my morning trying to figure out how to construct a sparse adjacency matrix for use with graph.adjacency(). I’d have thought that this would be rather straight forward, but I tripped over a few subtle issues with the Matrix package. My biggest problem (which in retrospect seems rather trivial) was that elements in my adjacency matrix were occupied by the pipe symbol.

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## Evolution of First Names: Changes over the Last Century

In light of recent developments, a bit of work that I did almost two years ago has become rather relevant.

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