Monthly Archives: April 2019

Standard Evaluation Versus Non-Standard Evaluation in R

April 2, 2019
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Standard Evaluation Versus Non-Standard Evaluation in R

There is a lot of unnecessary worry over “Non Standard Evaluation” (NSE) in R versus “Standard Evaluation” (SE, or standard “variables names refer to values” evaluation). This very author is guilty of over-discussing the issue. But let’s give this yet another try. The entire difference between NSE and regular evaluation can be summed up in … Continue reading Standard...

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Data: a cultural transformation and not a quick fix

April 2, 2019
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Data: a cultural transformation and not a quick fix

Amid stronger business competition than ever before, companies need to do more than simply embrace buzzwords or trends. It’s something we see all the time when out in the field talking to customers, or speaking at events. When it comes to the role of data, the emphasis should instead be on instilling transformation into the very DNA of an...

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Randomisation tests comparing dependent correlations

April 2, 2019
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This is about some academic work I did that never got published. But, I think it should be out there … More

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Random sampling of files

April 2, 2019
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A great part of my job as a bat ecologist is to classify bat species from their echolocation calls. I regularly use automatic recording devices that generate thousands of recordings per location. Dealing with this huge amount of information is not an easy task as you can imagine. In the old days each recording was … Continue reading Random...

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Symbolic Regression, Genetic Programming… or if Kepler had R

April 2, 2019
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Symbolic Regression, Genetic Programming… or if Kepler had R

A few weeks ago we published a post about using the power of the evolutionary method for optimization (see Evolution works!). In this post we will go a step further, so read on… A problem researchers often face is that they have an amount of data and need to find some functional form, e.g. some … Continue reading "Symbolic...

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So you want to deploy multiple containers running different R models?

April 2, 2019
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So you want to deploy multiple containers running different R models?

This tutorial is the second part of a series on professional R deployment. Please find the previous part here (How to make a dockerized plumber API secure with SSL and Basic Authentication). If you followed the first part in this tutorial series, you have achieved the following things: running your R code with a plumber... Der Beitrag So you want...

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tint 0.1.1: New Styles

April 1, 2019
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tint 0.1.1: New Styles

With almost year passed since the previous 0.1.0 release, a nice new release of the tint package arrived on CRAN today. Its name expands from tint is not tufte as the package offers a fresher take on the Tufte-style for html and pdf presentations. Th...

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Data Science Software Used in Journals: Stat Packages Declining (including R), AI/ML Software Growing

April 1, 2019
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Data Science Software Used in Journals: Stat Packages Declining (including R), AI/ML Software Growing

In my neverending quest to track The Popularity of Data Science Software, it’s time to update the section on Scholarly Articles. The rapid growth of R could not go on forever and, as you’ll see below, its use actually declined … Continue reading →

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Gravity Falls and Tidy Data Principles (Part 2)

April 1, 2019
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Gravity Falls and Tidy Data Principles (Part 2)

Motivation The first part left an open door to analyze Gravity Falls contents using tf-idf, bag-of-words or some other NLP techniques. Here I’m also taking a lot of ideas from Julia Silge’s blog. Note: If some images appear too small on your screen you can open them in a new tab to show them in their original size. Term Frequency The most basic...

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A unified syntax for accessing models’ information

April 1, 2019
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The richness and variety of packages for building and fitting statistical models in R is absolutely astonishing and contributes to the language’s popularity. However, this diversity makes it hard for developpers that want to create tools that work with different types of models. Indeed, the way to access models’ internal information (such as parameters names, formulae, data, etc.) is...

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