June 2015

A comparison of high-performance computing techniques in R

June 1, 2015 | David Smith

When it comes to speeding up "embarassingly parallel" computations (like for loops with many iterations), the R language offers a number of options: An R looping operator, like mapply (which runs in a single thread) A parallelized version of a looping operator, like mcmapply (which can use multiple cores) Explicit ... [Read more...]

Cluster analysis on earthquake data from USGS

June 1, 2015 | Fabio Veronesi

Theoretical BackgroundIn some cases we would like to classify the events we have in our dataset based on their spatial location or on some other data. As an example we can return to the epidemiological scenario in which we want to determine if the spread of a certain disease is ... [Read more...]

Analysing the Twitter Mentions Network

June 1, 2015 | Mango Solutions

By Douglas Ashton, Consultant One of the big successes of data analytics is the cultural change in how business decisions are being made. There is now wide spread acceptance of the role that data science has to play in decision … Continue reading → [Read more...]

Simple Data Science To Maximize Return On Lottery Investment

June 1, 2015 | aschinchon

Every finite game has an equilibrium point (John Nash, Non-Cooperative Games, 1950) I read recently this amazing book, where I discovered that we (humans) are not capable of generating random sequences of numbers by ourselves when we play lottery. John Haigh demonstrates this fact analyzing a sample of 282 raffles of 6/49 UK ...
[Read more...]

“Mail merge” with RMarkdown

June 1, 2015 | mine

The term “mail merge” might not be familiar to those who have not worked in an office setting, but here is the Wikipedia definition: Mail merge is a software operation describing the production of multiple (and potentially large numbers of) documents from … Continue reading → [Read more...]
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