In this post, the authors share their inspiration behind the ggtree package for R and present new resources of ggtree and a series of other related packages.
The team publishing the ggtree paper is working in the field of emerging infectious diseases. Particularly the corresponding author Tommy Lam (TL) has been advocating the integration of different biological and epidemiological information in the studies of fast-evolving viral pathogens. The lead author Guangchuang Yu (GY) joined The University of Hong Kong to pursue his doctorate degree under the supervision of TL and Yi Guan (co-author in the paper), as he was very curious about the application of genomics and phylogenetics in the study of emerging infectious diseases.
Reproducible research is important for three main reasons. Firstly, it makes it much easier to revisit a project a few months down the line, for example when making revisions to a paper which has been through peer review.
Secondly, it allows the reader of a published article to scrutinise your results more easily – meaning it is easier to show their validity. For this reason, some journals and reviewers are starting to ask authors to provide their code.
Thirdly, having clean and reproducible code available can encourage greater uptake of new methods. It’s much easier for users to replicate, apply and improve on methods if the code is reproducible and widely available
Throughout my PhD and Postdoctoral research, I have aimed to ensure that I use a reproducible workflow and this generally saves me time and helps to avoid errors. Along the way I’ve learned a lot through the advice of others, and trial and error. In this post I have set out a guide to creating a reproducible workflow and provided some useful tips. Continue reading →