When learning R (or any other new task), it’s okay to start small: aim for improvement, not perfection

When I first thought about switching to R and doing reproducible data analysis, the idea was daunting. As a grad student, I couldn’t figure out how to even get my data into R. How would I figure out that plus mixed model analyses plus how to make figures in ggplot, with version control and a beautiful github rep for all of my work?! What I eventually accepted is: it’s okay to start small. Or, as a colleague of mine suggests: for any given project, aim to do one thing in R that you couldn’t before.

I’m not sure why I set the bar so high for initially learning R. When I was first learning how to knit (actually knit, with yarn and needles, not the R version of knit), I knit a square washcloth, not a sweater. So when learning R, why was I expecting I’d be able to start out with the coding version of knitting a sweater with multiple colors, a fancy pattern, and buttons?

File:Fair Isle knitwear geograph-3936603-by-Julian-Paren.jpg

Julian Paren / Fair Isle knitwear in the Shetland Museum / CC BY-SA 2.0 via wikimedia.org

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Guest post: Coding Club – trying to overcome the fear factor in teaching and learning quantitative skills

This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. In case you missed it, they wrote a wonderful guest post this summer on iPads and digital data collection in the field.

Ecology is a fast-paced science, with possibly hundreds of relevant papers published every week and new techniques and quantitative skills being developed all the time. It is easy to feel very behind and overwhelmed. The quantitative skills taught in undergraduate and graduate programs in ecology often lag behind those used in the literature. As ecologists at different stages of our academic careers, who have felt (and still do sometimes) pretty behind the eight ball in terms of quantitative skills, we wanted to do something about that for our students and peers. And that is how we came up with the idea of Coding Club.

How did it all begin?

Just about two years ago we had an idea. What if we set up an informal group and a website to teach key quantitative skills that could be useful to undergrads, grad students, postdocs, profs and ecologists working outside of academia? What if that website was built in a way that anyone could contribute tutorials or help to make the existing tutorials better? What if we taught people how to learn in their own working environment and how to develop their workflow using best practices in open science like version control from the very beginning? What if this content was aimed at people who felt afraid, anxious and behind in their own quantitative skills development. This was the beginning of Coding Club.

screen cap of the homepage for Coding Club; header says: Coding Club: A positive peer-learning community

The Coding Club website where we host all of our tutorials on data manipulation, data visualisation, modelling and more!

 

What is Coding Club?

Coding Club combines online and in-person resources to help teach quantitative skills to ecologists at all career stages. We have focused on trying to overcome “code fear” and “statistics anxiety”. Statistics anxiety – the worry about a lack of quantitative skills – and code fear – the fear of programming – can prevent people from learning. By building a sense of community around the development of skills, we hope to overcome the fear factor of ecology involving more code and math than people sometimes expect.

left panel shows six people posed, smiling at the camera; upper right panel shows a computer lab with people at work and someone at front; lower right shows three women talking and smiling

Part of the Coding Club team and snapshots of some of our workshops. Check out our team page for the full list of undergraduates, postgraduates and profs that have contributed to Coding Club! Photo credit for image on left: Sam Sills

 

Peer-to-peer teaching helps to reduce the fear factor

In Coding Club, we focus on peer teaching and interaction rather than having “trained experts” leading workshops as we feel people engage more when they are less intimidated. All of our teaching materials are developed by people who are actively learning data science skills at the same time as teaching them. We avoid hierarchy (though we love content on hierarchical modelling!) and encourage participation across different career stages from undergrad students through to PhD students, postdocs and staff. Moving away from the professor-student model and allowing everyone to engage as teachers and learners can be a pretty powerful way to break down barriers.

Coding Club covers a growing number of different quantitative skills

The Coding Club website contains a growing list of tutorials aimed at all levels of quantitative skills useful for ecologists and beyond. We cover topics from intro to advanced R tutorials, version control, data visualization to working with large datasets. We have a lot of R content but we don’t just do R! We are currently working on developing more tutorials using Python for process-based modelling and the Google Earth Engine for remote sensing analyses. We have been using the tutorials to teach in-person workshops at the British Ecological Society conference and at universities around the UK, but the tutorials are there online for everyone to use, provide feedback on or suggest revisions through GitHub. We are always looking for people to develop new content as well!

four badges, one for sharing quantitative skills, one for meta-analysis & bayesian statistics, one for spatial and population data, and one for pandas

A sample of the Coding Club tutorials, including a tutorial on how to make tutorials on GitHub. Data visualisation, mixed effects models, Stan models and more over here.

 

Quantitative learning should be active and not passive

We believe that the best way to teach coding and quantitative skills is through a problem-based approach that is question driven. We try to avoid approaches like ‘live coding’ as it encourages learners to be very passive with the subject matter and we believe this results in lower retention of the new material. To effectively learn a new skill, it is vitally important to know why you might want to learn that skill in the first place and to have a question that you want to answer to motivate you to learn. We also recognize that people learn in different ways and at different paces. In our in-person sessions, we encourage people to take as long or as little time as they wish to complete the tutorials. We believe this casual, non-compulsory and non-assessed nature of Coding Club also helps to reduce the fear and anxiety associated with quantitative skills.

 

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Coding our way towards finding out how population trends vary among different taxa, with cookies along the way. Not pictured: the standard error cookie. We forgot to make one, but of course we are all for reporting the uncertainty around effect sizes!

 

Quantitative skills are not hard – they just take some work to learn

We believe teaching quantitative skills is all about overcoming fear and building confidence. We try to avoid labeling skills as “hard” or “easy”, because we don’t want people labeling themselves as quantitative or not, or pre-judging the limits to their own capabilities. We aim to train people to be able to answer their own questions, resolve their own coding problems and seek out new skill sets independently. We are trying to teach people to train themselves beyond the timespan of a single workshop or course. Finally, we don’t think there is only one way to teach quantitative skills and promoting a diversity of approaches will reach the most people.

 

Coding Club has exceeded our expectations!

As of October 2018, the Coding Club website has received over 160,000 visits from over 73,000 unique IP addresses from over 180 countries. Our tutorials have been contributed by people from multiple universities (University of Edinburgh, University of Aberdeen, McGill University, Ghent University, Aarhus University) and used for quantitative training across several institutions so far (University of Edinburgh, University of Aberdeen, University of St Andrews, Queens University Belfast, Dartmouth College, Hebrew University, Calvin College, Centre for Ecology and Hydrology and more), and we are hoping to reach out further! If we can set up a network of people at universities and research institutes around the world who can work together to develop quantitative training from the ground up, then maybe we will all feel just a little less overwhelmed by our fast-paced discipline.

World map showing numbers of visitor, represented as blue dots. The dots are especially dark and big over the UK, but include visitors from around the world

The international audience of Coding Club – it’s been great to get feedback from people using our tutorials around the world!

 

The start of the new academic year feels like a fresh start. A chance to purchase some new office supplies, catch up on all the science missed over the summer, start a new work routine to enhance productivity and to set yourself some new challenges. Now that the term has started, maybe it is time for you to take the plunge and learn a new quantitative skill.

 

Are you a student or group of students wanting to increase your own quantitative skills? Are you someone who has a cool analytical technique that you want to share with your peers? Are you a prof. who wants to encourage your students and mentees academic development? Are you someone who feels like the quantitative training you got years ago is not enough for the ecological research today and want to brush up on your skills? Do you have thoughts on how we can improve quantitative training in ecology? If you answered yes to any of these questions, please comment below, check out the Coding Club website and get in touch if you are keen to join the team!

Guest Post: What not to say to a pregnant colleague

Today, we have a bit of a hybrid post. It starts with a guest post from someone who wishes to remain anonymous about things colleagues have said to her during her pregnancy. Her post definitely resonated with me – I thought of writing a similar post when I was pregnant with my third child, because I was so annoyed by some of the comments I received at work. After the guest post, I’ve added some thoughts of mine, as well as some questions that I’d love reader opinions on. My hope is that this post will encourage people to think more carefully about what they say to pregnant colleagues and create a space where people can talk about their preferences.

The guest post:

I am a postdoc who also happens to be pregnant. Around the sixth month of my pregnancy something happened. I must have become large enough that it was obvious to everyone in the department that I was indeed, pregnant. Suddenly, I began receiving comments about my body, my impending delivery, and what my life would look like after having a baby. (This is my second child; I have no delusions as to what postpartum life is like).

Here are a few of the comments I received over the span of two weeks:

My body:

“Wow, you’ve really let yourself go”.

“If a baby weighs 8 lbs then where do the other 25 lbs come from?”

Misconceptions about maternity leave:

“It will be so nice for you to have a break while you’re on maternity leave”.

“Think of all the writing you’ll get done while the baby is sleeping!”

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My strategies for mentoring undergraduate researchers

At this year’s ESA meeting, I was part of an Inspire session organized by Nate Emery on “Students As Ecologists: Collaborating with Undergraduates from Scientific Question to Publication”. It occurred to me that my talk would be good fodder for a blog post. So, here are (some of) my thoughts on some specific strategies for working with undergraduates in the lab. This post includes information both on types of projects that we’ve had undergraduates work on, as well as things that I think are important related to working with undergraduates in the lab.

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Put your take home message at the top of your slides!

Imagine you’re sitting in a talk. It’s Thursday morning at the ESA meeting and your brain is a little fried from sitting in lots of talks all week. You momentarily zone out, then try to turn your attention back to the talk. Which of these would be most useful to see on the slide as you tune back in?

Option 1:

Option 2:

Option 3:

You chose option 3, right? (If you are curious about the data, you can read a preprint here.)

Maybe you aren’t always giving a talk on Thursday morning during a jam-packed meeting, but there will always be people in your audience who are tired or get distracted. Make life easier for your audience by putting your take home message for each slide at the top!

Or, to quote Stanley Dodson*: “Make your top line your bottom line!”

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Guest Post: iPads and digital data collection in the field

From Meghan: This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. I loved their comment on my post on our new lab notebook backup system and asked them if they could turn it into a guest post. I was very happy that they agreed! Isla and Gergana are off to the Arctic this summer with the Team Shrub field crew for another year of hopefully successful digital data collection. To find out more about their research check out the Team Shrub website and blog (https://teamshrub.com/).

Guest post:

Two things have really changed my academic life over the past five years: the first is embracing GitHub for version control of code, data, manuscripts and my research group’s individual and combined science, and the other is switching over to digital data collection. For ecologists who haven’t made the switch from paper field books to iPads and digital data collection it is not as scary as you might think!!!

Caption: Collecting plant phenology data – the recorder sitting in the back with an iPad! (photo credit: Jeff Kerby)

The benefits of going digital

Digital data collection can be more rigorous with error checking as data are collected to prevent mistakes. Data can be better backed up. And finally, it forces us to put thought into the structure of data before we collect it (significant digits, continuous or categorical data, are the data unrestricted or constrained to a particular range or particular set of values, etc.), which helps down the road when it comes time for analysis. Digital data collection has saved days, if not months, of data entry each year for my team and has allowed us to go from ecological monitoring in the field to analysis of results within hours instead of days. Our work flows are streamlined and our iPads are waterproof, so data collection can occur under any conditions – and we work in the Arctic, so we experience it all from wet to dry, hot to cold, rain, snow, you name it.

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Rough drafts, getting words on the page, and the pain – and pleasure – of writing

Intro: this is the first of a series of posts exploring some common themes in three books I’ve read recently that relate to writing: Anne Lamott’s Bird by Bird, Helen Sword’s Air & Light & Time & Space: How Successful Academics Write, and Tad Hills’ Rocket Writes a Story. (And, yes, one of those is not like the other.) This post focuses on getting started with a new writing project, rough drafts, and the pleasures of writing.

The post:

Everyone I know flails around, kvetching and growing despondent, on the way to finding a plot and structure that work. You are welcome to join the club.

     – Anne Lamott, Bird by Bird

Unfortunately, a universal experience of writing is that getting started can be hard. Rocket knows this:

The next day, Rocket returned to his classroom. It was time to begin. He looked down at the blank page and the blank page looked up at him. But no story would come.

From Tad Hills’ Rocket Writes a Story

This is something that all writers struggle with, but that can be especially problematic for new writers. The task can seem so big and daunting – and there’s a decent chance that you are feeling like an imposter who is about to be exposed.

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My lab’s new lab notebook backup system. Part 2: The system

In yesterday’s post, I talked about my motivations for seeking a new system for backing up lab notebooks and data sheets. Here, I describe the system we’re now using for backing up lab notebooks, data sheets, etc. I think it’s working well. At the end, I ask for suggestions of systems that work for backing up files on lab members’ laptops, which I think we could do a better job of.

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My lab’s new lab notebook backup system. Part 1: The backstory

Back when I was an undergrad, the fire alarm went off while I was working in the lab. As people gathered outside the building, it became clear that it wasn’t a drill – I don’t recall specifics, but I think it was actually an issue in a neighboring building that caused someone in our building to pull the fire alarm. In the end, it wasn’t a big deal (even for the neighboring building!) But while people were standing around outside, the conversation turned to how much data would be lost if there was a full-on fire. It was clear that lots of people did not have complete backups of their lab notebooks and other data files.

When I was telling about this experience to a friend, Brooks Kuykendall, he told me of his father Bill’s related horror story. In the fall of 1963, Bill had completed his PhD research at Johns Hopkins in Archaeology, and started teaching at Erskine College in rural South Carolina. In the summer and early fall of 1964, after his first year of teaching, he had managed to finish writing his dissertation. In early October, he had assembled the six copies (and these were the days of carbon copies) ready to be submitted to his committee. They were stacked on the floor of his office, needing only to be packed off to go in the mail.

And then that night Bill heard the sirens. Rushing to his office, he found that the building was on fire. The firemen had cordoned it off, but somehow two students—of whom he forever after spoke with gratitude—managed to get in through the window of his ground-floor office, recovering only 1) a single rough draft for the whole text, and 2) a box that had the originals for all of the illustrations. The copies on the floor—and virtually everything else in the office—were ruined by the water.

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How my student has explored career interests outside academia

Last week, Terry McGlynn wrote a post with a list of things he wishes other people would write posts about. I read this minutes before heading to the airport, and this was like catnip given my #airportblogging habit. So, I sat in the airport thinking about this topic Terry suggested:

How PhD students and postdocs are getting professional development to do things other than become a tenure-track faculty member

This is something I’ve been discussing a lot on seminar trips, with prospective grad students, and with colleagues, but I hadn’t thought about writing a post on it before. So, with thanks to Terry for the prompt, here’s the story of how one of my students has explored career interests outside academia.

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