Tag: R package

Virtual Issues

Policy on Publishing Code Virtual Issue

In January 2018, Methods in Ecology and Evolution launched a Policy on Publishing Code. The main objective of this policy is to make sure that high quality code is readily available to our readers. set out four key principles to help achieve this, as well as explaining what code outputs we publish, giving some examples of things that make it easier to review code, and giving some advice on how to store code once it’s been published.

To help people to understand how to meet the guidelines and principles of the new policy, a group of our Applications Associate Editors (Nick Golding, Sarah Goslee, Tim Poisot and Samantha Price) have put together a Virtual Issue of Applications articles published over the past couple of years that have followed at least one aspect of the guidelines particularly well. Continue reading “Policy on Publishing Code Virtual Issue”

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General, Meetings

Ending the Terror of R Errors

Post provided by Paul Mensink

Last year, I introduced R to petrified first-year biology students in a set of tutorials. I quickly realised that students were getting bogged down in error messages (even on very simple tasks), so most of my time was spent jumping between students like a wayward Markov chain. I would often find a desperate face at the end of a raised hand looking hopelessly towards their R console muttering some version of “What the $%# does this mean?”. I instantly morphed from teacher to translator and our class progress was slower than a for-loop caught in the second Circle.

Error messages are often not very helpful
Error messages are often not very helpful

Fast forward to Ecology Across Borders last December in Ghent, where rOpenSci and special interest groups from the BES, GfÖ and NecoV  and Methods in Ecology and Evolution  co-hosted a pre-conference R hackathon. I was elated to see that one of the challenges was focused on translating R error messages into “Plain English” (thanks to @DanMcGlinn for the original suggestion!). Continue reading “Ending the Terror of R Errors”

Unknown's avatarIndia Stephenson2 Comments
Uncategorized

The BES Quantitative Ecology SIG: Who We Are, What We Do and What to Look Out for at #EAB2017

Post provided by Susan Jarvis and Laura Graham

Ecologists are increasingly in need of quantitative skills and the British Ecological Society Quantitative Ecology Special Interest Group (QE SIG) aims to support skills development, sharing of good practice and highlighting novel methods development within quantitative ecology. We run events throughout the year, as well as contributing to the Annual Meeting and providing a mailing list to share events, jobs and quantitative news.

Ecology Hackathon

The run up to the Ecology Across Borders joint Annual Meeting in Ghent this month is an exciting time for the SIG as we look forward to catching up with existing members as well as hopefully meeting some new recruits! Several of our SIG committee members will be in attendance and if you’ve been lucky enough to get a place at the Hackathon on the Monday you’ll meet most of us there. The Hackathon has been jointly developed by us and two of our allied groups; the GfÖ Computational Ecology Working Group and the NecoV Ecological Informatics SIG and is being sponsored by Methods in Ecology and Evolution. We’ll be challenging participants to work together to produce R packages suggested by the ecological community. You can see the list of package suggestions here. If you weren’t able to book a place at the Hackathon, but are interested in writing your own packages, you may be interested in the new Guide to Reproducible Code from the BES. Continue reading “The BES Quantitative Ecology SIG: Who We Are, What We Do and What to Look Out for at #EAB2017”

Unknown's avatarIndia Stephenson1 Comment
Publishing Tips & Tricks, Society

Making YOUR Code Reproducible: Tips and Tricks

When we were putting together the British Ecological Society’s Guide to Reproducible Code we asked the community to send us their advice on how to make code reproducible. We got a lot of excellent responses and we tried to fit as many as we could into the Guide. Unfortunately, we ran out of space and there were a few that we couldn’t include.

Luckily, we have a blog where we can post all of those tips and tricks so that you don’t miss out. A massive thanks to everyone who contributed their tips and tricks for making code reproducible – we really appreciate it. Without further ado, here’s the advice that we were sent about making code reproducible that we couldn’t squeeze into the Guide:

Organising Code

©Leejiah Dorward

“Don’t overwrite data files. If data files change, create a new file. At the top of an analysis file define paths to all data files (even if they are not read in until later in the script).” – Tim Lucas, University of Oxford

“Keep one copy of all code files, and keep this copy under revision management.” – April Wright, Iowa State University

“Learn how to write simple functions – they save your ctrl c & v keys from getting worn out.” – Bob O’Hara, NTNU

“For complex figures, it can make sense to pre-compute the items to be plotted as its own intermediate output data structure. The code to do the calculation then only needs to be adjusted if an analysis changes, while the things to be plotted can be reused any number of times while you tweak how the figure looks.” – Hao Ye, UC San Diego Continue reading “Making YOUR Code Reproducible: Tips and Tricks”

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General, Society

A Guide to Reproducible Code in Ecology and Evolution

Post provided by Natalie Cooper and Pen-Yuan Hsing

Cover image by David J. Bird

The way we do science is changing — data are getting bigger, analyses are getting more complex, and governments, funding agencies and the scientific method itself demand more transparency and accountability in research. One way to deal with these changes is to make our research more reproducible, especially our code.

Although most of us now write code to perform our analyses, it’s often not very reproducible. We’ve all come back to a piece of work we haven’t looked at for a while and had no idea what our code was doing or which of the many “final_analysis” scripts truly was the final analysis! Unfortunately, the number of tools for reproducibility and all the jargon can leave new users feeling overwhelmed, with no idea how to start making their code more reproducible. So, we’ve put together the Guide to Reproducible Code in Ecology and Evolution to help. Continue reading “A Guide to Reproducible Code in Ecology and Evolution”

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General, Meetings

Solving YOUR Ecology Challenges with R: Ecology Hackathon in Ghent

Scientific software is an increasingly important part of scientific research, and ecologists have been at the forefront of developing open source tools for ecological research. Much of this software is distributed via R packages – there are over 200 R packages for ecology and evolution on CRAN alone. Methods regularly publishes Application articles introducing R packages (and other software) that enable ecological research, and we’re … Continue reading Solving YOUR Ecology Challenges with R: Ecology Hackathon in Ghent

Unknown's avatarIndia Stephenson1 Comment
Editor Recommendations, Methods papers

Editor Recommendation – HistMapR: Rapid Digitization of Historical Land-Use Maps in R

Post provided by Sarah Goslee For an ecologist interested in long-term dynamics, one of the most thrilling experiences is discovering a legacy dataset stashed away somewhere. For an ecologist interested in long-term dynamics, one of the most daunting experiences is figuring how to turn that box full of paper into usable data. The new tool HistMapR, described in ’HistMapR: Rapid digitization of historical land-use maps in … Continue reading Editor Recommendation – HistMapR: Rapid Digitization of Historical Land-Use Maps in R

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Lab Methods, Methods papers, Photo Post

Building Universal PCR Primers for Aquatic Ecosystem Assessments

Post provided by Vasco Elbrecht Many things can negatively affect stream ecosystems – water abstraction, eutrophication and fine sediment influx are just a few. However, only intact freshwater ecosystems can sustainably deliver the ecosystem services – such as particle filtration, food biomass production and the supply of drinking water – that we rely on. Because of this, stream management and restoration has often been in the … Continue reading Building Universal PCR Primers for Aquatic Ecosystem Assessments

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Conservation and management, Methods papers, Online extras

Digitizing Historical Land-use Maps with HistMapR

Habitat destruction and degradation represent serious threats to biodiversity, and quantification of land-use change over time is important for understanding the consequences of these changes to organisms and ecosystem service provision. Historical land-use maps are important for documenting how habitat cover has changed over time, but digitizing these maps is a time consuming process. HistMapR is an R package designed to speed up the digitization … Continue reading Digitizing Historical Land-use Maps with HistMapR

Unknown's avatarIndia Stephenson2 Comments
Methods papers, Robert May Prize, Statistical methods

piecewiseSEM: Exploring Nature’s Complexity through Statistics

Post provided by Jonathan S. Lefcheck

Nature is complicated. As a scientist, you might say, “Well, duh,” but as students of nature, this complexity is probably the single greatest challenge we must face in trying to dissect the hows and whys of the natural world.

History is a Set of Lies Agreed Upon: Moving beyond ANOVA

For a long time, we tried to strip this complexity away by conducting very controlled experiments adhering to rigid designs. The ‘two-way fully-crossed analysis of variance’ will be familiar to anyone who has taken even the most basic stats class, because, for many decades, it was the gold standard for any experiment.

It might be tough to manipulate this whole reef.

The problem is: the real world doesn’t adhere to an ANOVA design. By this, I mean that by their very nature, manipulative experiments are artificial. It’s hard—if not impossible—to manipulate an entire forest or a coral reef, and as such, we retreat to more tractable, smaller investigations. There is certainly a lot of value in determining whether the phenomenon can occur, but these tightly regulated designs say nothing about whether they are likely to occur, particularly at the scales most relevant to humanity.

To get at the latter point, we must leave the safety of the greenhouse. However, our trusty ANOVA toolbox isn’t very useful anymore, because real-world data often violate the most basic statistical assumptions, not to mention the presence of numerous additional influences that may drive spurious relationships. Continue reading “piecewiseSEM: Exploring Nature’s Complexity through Statistics”

Unknown's avatarIndia Stephenson2 Comments