Monthly Archives: April 2015

Oracle R, Hash Table Results, And VIM To The Rescue

April 27, 2015
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Oracle R, Hash Table Results, And VIM To The Rescue

Downloaded and installed Solaris 11.2 on my laptop, and WOW! That was a throwback to the late 90’s! Old version of GNOME, no truetype fonts so the whole visual experience was very pixelly. Firefox was installed but every website I visited yelled,...

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Exploration of Functional Diversity indices using Shiny

April 27, 2015
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Exploration of Functional Diversity indices using Shiny

Biological diversity (or biodiversity) is a complex concept with many different aspects in it, like species richness, evenness or functional redundancy. My field of research focus on understanding the effect of changing plant diversity on higher trophic levels communities but also ecosystem function. Even if the founding papers of this area of research already hypothesized

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EARL2015 Conference, London – Presenters Announced

April 27, 2015
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EARL2015 Conference, London – Presenters Announced

We are delighted to announce the impressive line up of speakers for September’s London EARL Conference.  The speakers represent industries including Energy, Leisure, Insurance, FCMG, Finance, Market Research, Healthcare and Sport and offer real world examples of the usage and application of … Continue reading →

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Randomly Sample Twitter Followers in R

April 27, 2015
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Randomly Sample Twitter Followers in R

So yesterday, I set up an #AmazonGiveaway for my new R book at https://giveaway.amazon.com/p/ea32d421d8d7672d — but I had my 10 year old input the number that will determine every nth person who gets the printed

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Comparing Tree-Based Classification Methods via the Kaggle Otto Competition

April 27, 2015
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Comparing Tree-Based Classification Methods via the Kaggle Otto Competition

In this post, I’m going to be looking at the progressive performance of different tree-based classification methods in R, using the Kaggle Otto Group Product Classification Challenge as an example. This competition challenges participants to correctly classify products into 1 of 9 classes based on data in 93 features. I’ll start with basic decision trees and … Continue reading Comparing...

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How to install rNOMADS with GRIB file support on Windows

April 26, 2015
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How to install rNOMADS with GRIB file support on Windows

Two years ago, I wrote a software package for R called “rNOMADS” that interfaces with online weather and sea ice model repositories to gather data in real time, for free.  The data are delivered in two ways: a simple, pure R, cross platform interface using GrADS-DODS, and binary files in GRIB format.  The one issue

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Keeping Track of an Evolving “Top N” Cutoff Threshold Value

April 26, 2015
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Keeping Track of an Evolving “Top N” Cutoff Threshold Value

In a previous post (Charts are for Reading), I noted how it was difficult to keep track of which times in an F1 qualifying session had made the cutoff time as a qualifying session evolved. The problem can be stated as follows: in the first session, with 20 drivers competing, the 15 drivers with the

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Doing quantitative archaeology with open source software

April 25, 2015
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(This is a guest post by Ben Marwick, originally published on ATOR blog) This short post is written for archaeologists who frequently perform common data analysis and visualization tasks in Excel, SPSS or similar commercial packages. It was motivated by my recent observations at the Society of American Archaeology meeting in San Francisco – the largest annual meeting of archaeologists in the...

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Unemployment of Europe in 2014 by NUTS 2 region

April 25, 2015
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Unemployment of Europe in 2014 by NUTS 2 region

During the Christmas break I worked on some code to show unemployment by NUTS 2 region. At that point no 2014 data was available. When I noticed the 214 was available I dug up the code and plotted again.Data and CodeAs written, the code was made beginn...

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Random Data Sets Quickly

April 24, 2015
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Random Data Sets Quickly

This post will discuss a recent GitHub package I’m working on, wakefield to generate random data sets. The post is broken into the following sections: Demo 1.1 Random Variable Functions 1.2 Random Data Frames 1.3 Missing Values 1.4 Default Data … Continue reading →

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