Research compendium for a report on the effects of using alternate wetting and drying irrigation techniques and nitrogen rates on sheath blight disease in rice paddies.
Compendium DOI:
The files at the URL above will generate the results as found in the publication. The files hosted at https://github.com/adamhsparks/rice-awd-shb are the development versions and may have changed since the report was published.
Data DOI:
The raw data from this project are released and publicly available from Zenodo under a Creative Commons Attribution 4.0 International licence.
Authors of this repository:
Adam H. Sparks (adam.sparks@usq.edu.au)
Nancy P. Castilla (n.castilla@irri.org)
B. Ole Sander (b.sander@irri.org)
Michael Noel
Published in:
Sparks, A, xxxxx
Overview of contents
This repository is our research compendium for our analysis of the
effects of alternate wetting and drying irrigation technology on rice
sheath blight disease. The compendium contains all data, code, and text
associated with the publication. The Rmd files in the
analysis/paper/ directory contain details of how all the analyses
reported in the paper were conducted, as well as instructions on how to
rerun the analysis to reproduce the results. The data/ directory in
the analysis/ directory contains all the raw data. The data-raw
directory contains extra files to check weather for differences between
seasons, that are not included in the actual analysis.
The supplementary files
The data-raw directory contains:
- a IRRI_weather_data.R, a file used to check weather data from IRRI weather stations to ensure that there was no significant difference in weather between seasons
The analysis/ directory contains:
-
the manuscript as submitted (in MS Word format) and it’s Rmd source file
-
supplementary information source files (in R markdown format) and executed versions
-
all the figures that are included in the paper (in the
figures/directory)
The R package
This repository is organized as an R package. Mostly I used the R package structure to help manage dependencies, to take advantage of continuous integration for automated code testing, and so I didn’t have to think too much about how to organise the files.
To download the package source as you see it on GitHub, for offline browsing, use this line at the shell prompt (assuming you have Git installed on your computer):
git clone https://github.com/adamhsparks/rice-awd-shb.git
Once the download is complete, open the rice.awd.shb.Rproj in RStudio
to begin working with the package and compendium files.
The package has a number of dependencies on other R packages, and programs outside of R. These are listed at the bottom of this README. Installing these can be time-consuming and complicated, so we’ve used a Docker image. The Docker image includes all the necessary software, code and data to run our analysis. The Docker image may give a quicker entry point to the project, and is more self-contained, so might save some fiddling with installing things.
The Docker image
A Docker image is a lightweight GNU/Linux virtual computer that can be run as a piece of software on Windows and OSX (and other Linux systems). To capture the complete computational environment used for this project we have a Dockerfile that specifies how to make the Docker image that we developed this project in. The Docker image includes all of the software dependencies needed to run the code in this project, as well as the R package and other compendium files. To launch the Docker image for this project, first, install Docker on your computer. Then at a command line prompt, use the following commands to start the instance.
docker pull adamhsparks/rice-awd-shb
docker run -dp 8787:8787 adamhsparks/rice-awd-shb
This will start a server instance of RStudio. Then open your web browser
at localhost:8787 or or run docker-machine ip default in the shell to
find the correct IP address, and log in with rstudio/rstudio.
Once logged in, use the Files pane (bottom right) open the folder for
this project, and open the .Rproj file for this project. Once that’s
open, you’ll see the analysis/paper directory in the “Files” pane
where you can find the Rmarkdown document, and knit it to produce the
results in the paper. More information about using RStudio in Docker is
available at the Rocker
wiki
pages.
We developed and tested the package on this Docker container, so this is the only platform that we’re confident it works on, and so recommend to anyone wanting to use this package to generate the vignettes, etc.
Citation
Please cite this compendium as:
Authors, (2020). Reproducible Research Compendium for Analysing Effects of Water Management and Nitrogen on Rice Sheath Blight. Accessed 15 Oct 2020. Online at https://doi.org/xxx/xxx
Licenses
Manuscript: CC-BY-4.0
Code: MIT year: 2020, copyright holder: Adam Sparks
Data: CC-4.0 attribution requested in reuse
Dependencies
I used RStudio on MacOS. See
the colophon section of the docx file in analysis/paper for a full
list of the packages that this project depends on.
Contact
Adam Sparks, Associate Professor, Centre for Crop Health
University of Southern Queensland, West St.
Toowoomba, Qld 4350 Australia
(+61) 7.4631.1948
adam.sparks@usq.edu.au
https://adamhsparks.com/