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JUMPING RIVERS BLOG

Edinbr: Text Mining with R

Edinbr: Text Mining with R

During a very quick tour of Edinburgh (and in particular some distilleries), Dave Robinson (Tidytext fame), was able to drop by the Edinburgh R meet-up group to give a very neat talk on tidy text. The first part of the talk set the scene: What does does text mean? Why make text tidy? What sort of problems can you solve?

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Free ticket to eRum

Free ticket to eRum

So… big news. Jumping Rivers is sponsoring eRum 2018 and in light of this news we are giving away a free place at the conference! (Not to mention our very own lead consultant, Colin Gillespie, is one of the invited speakers.) The Main Competition: Here at Jumping Rivers, we maintain the site meetingsR.

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Our Logo In R

Our Logo In R

Hi all, so given our logo here at Jumping Rivers is a set of lines designed to look like a Gaussian Process, we thought it would be a neat idea to recreate this image in R. To do so we’re going to need a couple packages. We do the usual install.packages() dance (remember this step can be performed in parallel).

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Styling Base R Graphics

Styling Base R Graphics

Base R graphics get a bad press (although to be fair, they could have chosen their default values better). In general, they are viewed as a throw back to the dawn of the R era. I think that most people would agree that, in general, there are better graphics techniques in R (e.g. {ggplot2}).

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StanCon 2018 Highlights

StanCon 2018 Highlights

This year we had the privilege of sponsoring StanCon. Unfortunately, we weren’t able to actually attend the conference. Rather than let our ticket go to waste, we ran a small competition, which Ignacio Martinez won with his very cool (but in alpha stage) R package.

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Our consultants have experience in programming, business and analytics. We provide bespoke solutions to complex problems. Jumping Rivers has strong links with academia and is kept up-to-date with the latest techniques.

  • Statistics - saved 2 months of work
  • Increased response times by 80%
  • Sorting 90% of data into understandable structure
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