Research project code for seedling survival analyses using Stan.
The required data files for this project are available for download at ScienceDB (https://doi.org/10.57760/sciencedb.02276).
The workflow and code (in R and Stan) for this analysis are managed with the targets R package, which can be found here: ropensci/targets.
Analyses can be conducted either locally or within an container (Docker and Apptainer).
For instructions on using the containerized environment, refer to the Reproducible R Project Template.
Create an Apptainer container with the following command, which includes R, Quarto, and cmdstan:
sudo apptainer build radian.sif radian.defBefore starting the analysis, run ./make_renviron.sh to create a .Renviron file.
This helps to manage package caching efficiently.
To initiate the analysis, use the ./run.sh script:
# For the first run, to install R packages
# Rscript -e "renv::restore()"
> ./run.sh
1) tar_make() on local (or inside Docker)
2) tar_make() on Apptainer
3) Enter the Apptainer container
4) Enter the Singularity container on HPC
Enter number:Use tar_make() for single-threaded execution and tar_make_clustermq() for multi-threaded, parallel processing.
Please update the following:
Computational Intensity Warning:
Due to the computationally intensive nature of some analyses, you may need to execute only a subset of the targets pipeline, especially when system memory (RAM) is a limiting factor.
Some slurm scripts for High-Performance Computing (HPC) are also available under slurm.
Docker Usage for Less Intensive Analysis: For parts of the analysis that are less computationally demanding, Docker can be utilized, as explained in the Reproducible R Project Template.
- Apptainer (or Singularity)
- Docker
- A Linux-based OS