Decision Mechanics

Insight. Applied.

And if thine data offend Excel, change it

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genetic code

I’ve written numerous articles on issues with spreadsheets and why they shouldn’t play any part in a data analytics pipeline. However, trying to persuade people to stop defaulting to Excel is a thankless task.

So, it was no surprise (but very depressing) to read that geneticists changed their naming conventions to prevent Excel mangling their data.

My dismay was alleviated, partially, by a great quote in the article that originally alerted me to this travesty.

[W]e won’t be putting anything into production that relies on data supplied to us as spreadsheets.

The anti-spreadsheet manifesto in a single sentence.

A passion for decision analysis

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When discussing the value we offer to clients, the focus is often on technical aspects—experience, understanding the psychology, skill with tools, processes, etc.

However, it occurs to me that often much of the value we bring to the table is energy and a fresh set of eyes. In many circumstances decision-makers have decision fatigue. It might be due to the number of decisions they have to make, or how long they have spent mulling over this particular decision.

When you are exhausted you make poor decisions.

Engaging the support of a decision scientist gets you someone who isn’t drained. In fact, it gets you someone who is excited about looking at your decision in detail, and from all angles. This work isn’t a burden to the consultant. It’s their job.

No-one likes to admit they are just plain exhausted/exasperated. But you can often see the weight of the world lifting off the shoulders of decision-makers when we come in to help.

Photo by Ian Schneider

JavaScript for data science?

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Arquero is a data manipulation library written in JavaScript. It’s modelled on the excellent R dyplr library.

JavaScript isn’t known as a data science language. However, it’s probably the best language for data visualisation—and Google’s relentless optimisation of the V8 engine means it has better performance that Python or R.

The JavaScript ecosystem lacks extensive data science libraries, but Arquero coupled with, say, D3.js makes for a capable data visualisation platform.

Photo by Anton Darius

R now has native pipes

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The latest release of R introduces an F#-style pipe operator.

Pipes have been available in R for a while, via the magrittr package. However, with the release of R 4.1.0, pipes are supported in base R.

The syntax has changed from that used in magrittr. The magrittr pipe operator is %>%. The new base R pipe operator is |>.

So, instead of

df %>% head()

we have

df |> head()

R 4.1.0 also introduces a lightweight, lambda-style function syntax, which can be usefully combined with the new pipe operator when piping to an argument other than the first one.

iris |> {\(x) lm(Sepal.Width ~ Sepal.Length, data = x)}()