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The SQL Ecology Lessons

This post originally appeared on the Data Carpentry website

I am fond of saying that ecologists should not be afraid of big data – instead, we have to deal with small, complex, and poorly connected data. Understanding how we can stay on top of things, data-wise, is becoming more and more important. And some of the practices used to collect a small amount of data do not scale well at all when the amount of data increases, even if slightly.

Knowing ecology is important. Understanding the natural history of your model, the conditions of your field site, the big theories that we used to make sense of results, are obviously things that matter, and they receive a lot of emphasis during the training of ecologists. But moving from observation to insight requires to make sense of the observations, and this is turn require good data management practices.

I couldn’t have been happier to help develop the Data Carpentry SQL ecology lesson. Up to this point, it was an important resource in the lab, and in the community at large. It served as a basis for some material in a semester long class I am currently giving. And the question I keep coming back to, whenever we discuss adding, tweaking, or removing material from the lesson, is: “does it lead the learner to better practices?”.

I do not think this lesson teaches “best practices”. I don’t think I am even remotely qualified to discuss best practices of SQL. But emphasizing good practices is something I feel comfortable doing. So what are good practices?

Good practices are something that you could, realistically, apply tomorrow. This means covering the basics, and hinting at the very cool things that can be done with more advanced features of the language. In-class, I will motivate this by inviting guest speakers that added some of this methodology in their toolkit, and discuss how it helped them do research. In a workshop, it can be short motivational story that will be familiar to former workshop attendees.

More concretely, good practices are that which minimizes the chance that something goes wrong (there has been much PDF ink spilled on the fact that Excel does, in fact, sometimes changes values to other values), and give you a productivity boost. Learning a new anything can be intimidating, so good practices must be, to some extent, re-insuring.

The things we focus most on the lesson are organizing data to avoid duplication (why have the field site info at each row, when you can just use another table and link them with IDs?), and data retrieval. Data retrieval, in SQL, can encompass a lot of operations: merging, aggregating, conditionals, counting, calculating averages, and so forth. I like the “ah ah!” moment where learners realize that operations that would take a few lines of R, or all your willpower and sanity in a spreadsheet, can be done in a single SQL line.

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