Can coding become addictive?

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A student of ecology knocks on a supervisor’s office door,

-” Excuse me…are you busy right now? …wanted to show you something”

 The supervisor (always -and never- busy) replies:

-“Sure thing…do come in’

 

The student walks in, takes a seat and pulls-open a laptop. The screen shows R code and different consoles. For the next many minutes, the excited student shows code and graphs and explains in careful detail how all was achieved, what packages were downloaded and how this changes the last analysis done so far, of the data collected last summer.  The supervisor nods in agreement while entertains a shy smile. This exact situation occurred a week ago, and last month and… who knows how many times.

 

This scenario has probably been experienced in recent times by students and supervisors throughout academia. The adoption of R-coding to analyze ecological data has been a game changer. Accessibility, almost unlimited capabilities and power are attractive assets, but importantly also is the fact that it is constantly evolving through the work of peers. After dancing with a variety of modeling and statistical tools for years, several quite costly, at last it seems scientists have reached the ultimate tool. And students are encouraged from their undergraduate days to embrace it. And don’t they do!

 

Now the virtues of coding and rigorous data analysis can rapidly turn wry for ecology students. Exploring, analyzing and plotting data can come become an endless task, exposed to constant improvement as learning new capabilities proceeds. And if this is not a problem, it can give way to an emerging concern: losing perspective of the ecological questions for which the data was collected. 

 

What was the working hypothesis driving the work? What is the conceptual framework that guides it? The imaginary student wants to show progress and seeks approval from a supervisor but forgets to remind them what the analysis seeks to test. Is data being tortured to ‘talk’?

 

Ecology is about looking for patterns that help us grasp how the natural world works, how its components interact and ultimately it could be argued, how can us-humans- relate to it in the best possible ways.  Ecology needs ecologists to get out there,  to help improve our understanding of nature. The first step is necessarily asking the right, concept-based questions- the natural word is too massive and we too few, to acquire knowledge in a piecemeal fashion- and collect the information that matters to it. This can take many forms, but all seek to interpret or represent nature. Mathematical tools have proven to be an immense help in this route, 

 

The risk of getting addicted to coding is just as that of any other tech hype that we are exposed too.  There is always something else that can be done. Getting hooked on the stats and its nitty-gritty details can cloud a vision which needs re focusing. Let us gladly embrace what R has to offer and be ready to let go. The data, from a well-designed experiment or sampling protocol (with which emerges an idea of what analysis is needed once collected) will arbitrate between ideas or hypothesis. We should not forget that screen time is only a part of this and how much to invest in it, should be part of current student mentoring.

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