Monthly Archives: May 2017

A tribute to Lucy D’Agostino McGowan’s git commit emoji game

May 2, 2017
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Do you know Lucy? She is a very talented biostatistics PhD candidate that I had the chance to e-meet thanks to R-Ladies. One maybe superficial reason to admire her, on top of her other achievements, is her emoji game in git commits. Looking at Lucy’s...

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Monthly seasonality

May 2, 2017
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I regularly get asked why I don’t consider monthly seasonality in my models for daily or sub-daily time series. For example, this recent comment on my post on seasonal periods, or this comment on my post on daily data. The fact is, I’ve never seen a time series with monthly seasonality, although that does not mean it does not...

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A tribute to Lucy D’Agostino McGowan’s git commit emoji game

May 2, 2017
By

Do you know Lucy? She is a very talented biostatistics PhD candidate that I had the chance to e-meet thanks to R-Ladies. One maybe superficial reason to admire her, on top of her other achievements, is her emoji game in git commits. Looking at Lucy’s...

Read more »

Shiny in Medicine

May 2, 2017
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Shiny in Medicine

Shiny Apps are becoming ubiquitous as a way for data scientists to present the results of an analysis, and also to engage with information consumers who may not be coders. The trend I see is that the greater the variety of skills and interests of the information consumers for any particular project, the more valued are interactive visualizations that...

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Monthly seasonality

May 2, 2017
By

I regularly get asked why I don’t consider monthly seasonality in my models for daily or sub-daily time series. For example, this recent comment on my post on seasonal periods, or this comment on my post on daily data. The fact is, I’ve never seen a time series with monthly seasonality, although that does not mean it does not exist....

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Le Monde puzzle [#1006]

May 2, 2017
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Le Monde puzzle [#1006]

Once the pseudo-story removed, a linear programming Le Monde mathematical puzzle: For the integer linear programming problem max 2x¹+2x²+x³+…+x¹⁰ under the constraints x¹__x²+x³, x²__x³+x⁴, …, x⁹__x¹⁰+x¹, x¹⁰__x¹+x² find a solution with the maximal number of positive entries. Expressed this way, it becomes quite straightforward to solve with the help of a linear programming R

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Why to use wrapr::let()

May 2, 2017
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Why to use wrapr::let()

I have written about referential transparency before. In this article I would like to discuss “leaky abstractions” and why wrapr::let() supplies a useful (but leaky) abstraction for R programmers. Abstractions A common definition of an abstraction is (from the OSX dictionary): the process of considering something independently of its associations, attributes, or concrete accompaniments. In … Continue reading Why...

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Forecasting for small business Exercises (Part-4)

May 2, 2017
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Forecasting for small business Exercises (Part-4)

Uncertainty is the biggest enemy of a profitable business. That is especially true of small business who don’t have enough resources to survive an unexpected diminution of revenue or to capitalize on a sudden increase of demand. In this context, it is especially important to be able to predict accurately the change in the markets Related exercise sets:Forecasting: Linear...

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The Datasaurus Dozen

May 2, 2017
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The Datasaurus Dozen

There's a reason why data scientists spend so much time exploring data using graphics. Relying only on data summaries like means, variances, and correlations can be dangerous, because wildly different data sets can give similar results. This is a principle that has been demonstrated in statistics classes for decades with Anscombe's Quartet: four scatterplots which despite being qualitatively different...

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twidlr: data.frame-based API for model and predict functons

May 2, 2017
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twidlr: data.frame-based API for model and predict functons

@drsimonj here to introduce my latest tidy-modelling package for R, “twidlr”. twidlr wraps model and predict functions you already know and love with a consistent data.frame-based API! All models wrapped by twidlr can be fit to data and used to make predictions as follows: library(twidlr) fit

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