Tag Archives: R package

R2ucare: An Interview with Olivier Gimenez

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At the International Statistical Ecology Conference in St Andrews this July (ISEC 2018) David Warton interviewed Olivier Gimenez about R2ucare. R2ucare is an R package for goodness-of-fit tests for capture-recapture models. The full Methods in Ecology and Evolution article on this package – R2ucare: An r package to perform goodness‐of‐fit tests for capture–recapture models – was published in the July 2018 issue of the journal.

David and Olivier also discuss some tips for creating R packages. They mention that if you’re new to writing R packages, there are some excellent resources online. Here’s one of them: A Quickstart Guide for Building Your First R Package

We’ll have more of David’s interviews from the ISEC coming out over the next few weeks. Keep an eye out for them here and on the Methods in Ecology and Evolution YouTube channel.

You can find David’s first ISEC interview (with Kate Jones) here.

The babette R Package: How to Sooth the Phylogenetic BEAST2

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Post provided by Richel Bilderbeek

 What is babette?

‘babette‘ is an R package that works with the popular phylogenetic tool BEAST2. BEAST2 uses one or more alignments and a model setup to create a Bayesian posterior of jointly estimated model parameters and phylogenies.

babette lets you call BEAST2 from an R script. This makes it easier to explore models and/or alignments than using the graphical user interface programs that BEAST2 provides. It will also help you to improve the reproducibility of your work with BEAST2.

babette Tutorial Videos

If you’re new to phylogentic analyses, the video ‘babette demo‘ demonstrates the package. It has all of the information that you need to be able to start using the package

Continue reading →

HistMapR: 12 Months from Coffee Break Musings to a Debut R Package

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Post provided by Alistair Auffret

I was really happy to hear that our paper, ‘HistMapR: Rapid digitization of historical land‐use maps in R’ was shortlisted for the 2017 Robert May Prize, and to be asked to write a blog to mark the occasion. The paper was already recommended in an earlier blog post by Sarah Goslee (the Associate Editor who took care of our submission), and described by me in an instructional video, so I thought that I would write the story of our first foray into making an R package, and submitting a paper to a journal that I never thought I would ever get published in.

Background: Changing Land-Use and Digitizing Maps

Land-use change in Europe is often typified by land-drainage to create arable fields.

Land-use change in Europe is often typified by land-drainage to create arable fields.

Land-use change is largely accepted to be one of the major threats to biodiversity worldwide at the moment. At the same time, a warming climate means that species’ ranges need to move poleward – something that can be hampered by changing land use. Quantifying how land use has changed in the past can help us to understand how species diversity and distributions respond to environmental change.

Unfortunately, quantifying this change by digitizing historical maps is a pretty tedious business. It involves a lot of clicking around various landscape features in a desktop GIS program. So, in many cases, historical land use is only analyzed in a relatively small number of selected landscapes for each particular study. In our group at Stockholm University, we thought that it would be useful to digitize maps over much larger areas, making it possible to assess change in all types of landscape and assess biodiversity responses to land-use change at macroecological scales. The question was, how could we do this? Continue reading →

Code-Based Methods and the Problem of Accessibility

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Post provided by Jamie M. Kass, Matthew E. Aiello-Lammens, Bruno Vilela, Robert Muscarella, Cory Merow and Robert P. Anderson

The namesake of our software and founder of the field of biogeography, Alfred Russel Wallace. Photo ©G. W. Beccaloni

The namesake of our software and founder of the field of biogeography, Alfred Russel Wallace. Photo ©G. W. Beccaloni

In ecology, new methods are increasingly being accompanied by code, and sometimes even full command-line software packages (usually in R). This is great, as it makes analyses more reproducible and transparent, which is essential for the development of open science. In an ideal world, code would have informative annotation, generalized functions for multipurpose use, and be written in a legible and consistent manner. After all, the code may be used by ecologists with a wide range of programming experience.

In reality, code is often poorly commented (or not commented at all!), hard to reuse for other projects, and difficult to interpret. To add to that, most code isn’t actively maintained, so users are on their own if they try to commandeer it for new purposes. Further, ecologists with little or no programming knowledge are unlikely to benefit from methods that exist only as poorly documented code. In a positive development, some new methods are accessible through software with graphic user interfaces (GUIs) developed by programmers spending significant time and effort. But too often these end up as tools with flashy controls and insufficient instruction manuals. Continue reading →

Issue 9.2

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Issue 9.2 is now online!

The February issue of Methods is now online!

This double-size issue contains six Applications articles (one of which is Open Access) and two Open Access research articles. These eight papers are freely available to everyone, no subscription required.

– Temperature Manipulation: Welshofer et al. present a modified International Tundra Experiment (ITEX) chamber design for year-round outdoor use in warming taller-stature plant communities up to 1.5 m tall.This design is a valuable tool for examining the effects of in situ warming on understudied taller-stature plant communities

– Zoon: The disjointed nature of the current species distribution modelling (SDM) research environment hinders evaluation of new methods, synthesis of current knowledge and the dissemination of new methods to SDM users. The zoon R package aims to overcome these problems by providing a modular framework for constructing reproducible SDM workflows.

– BEIN R Package: The Botanical Information and Ecology Network (BIEN) database comprises an unprecedented wealth of cleaned and standardised botanical data. The bien r package allows users to access the multiple types of data in the BIEN database. This represents a significant achievement in biological data integration, cleaning and standardisation.

Continue reading →

Policy on Publishing Code Virtual Issue

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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 →

Ending the Terror of R Errors

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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 →

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

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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 →

Making YOUR Code Reproducible: Tips and Tricks

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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 →

A Guide to Reproducible Code in Ecology and Evolution

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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 →