A paper about Versioned Data Delivery and the datastorr package

We think delivering versioned data to users in a simple and easy way will help science a lot. For more details check out:

Falster DS, FitzJohn RG, Pennell MW, Cornwell WK (2019) Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R. GigaScience 8: 1–8. doi: 10.1093/gigascience/giz035

and/or the preprint:

Falster DS, FitzJohn RG, Pennell MW, Cornwell WK (2017) Versioned data: why it is needed and how it can be achieved (easily and cheaply). doi 10.7287/peerj.preprints.3401v1

This repository contains code needed to reproduce the analysis in the above article.

Instructions

Install relevant software

All analyses were done in R. You need to download this repository, and then open an R session with working directory set to the root of the project.

To compile the paper, we use the remake package for R. You can install remake using the devtools package (run install.packages("devtools") to install devtools if needed):

devtools::install_github("richfitz/storr", dependencies=TRUE)
devtools::install_github("richfitz/remake", dependencies=TRUE)

Compiling the paper also requires a reasonably complete LaTeX installation (e.g. MacTeX for OSX or MikTex for windows). The LaTeX compilation will depend on a few packages from CTAN, make sure to allow automatic package installation by your LaTeX distribution.

Recreating the figures and paper

To generate all figures, analyses, and manuscript (PDF, using LaTeX), simply do:

remake::make()