This module contains the final practical part for the Certificate of Advanced Studies in Advanced Statistical Data Science (CAS ASDS) at the University of Berne for the class of 2024.
The practical module has been packaged as an R package, so everything (data, scripts, reports, …) should be contained in a single bundle, and analyses should be reproducible.
This setup follows suggestions from (Marwick, Boettiger, and Mullen 2018b, 2018a), (Flight 2014), and (Wickham and Bryan 2023) (which provided more or less the instructions and toolchain recommendations based on which this package has been created).
There are other opinions and tools (e.g. (Flight 2021) and (Landau 2024, 2021)) for a lighter-weight reproducible research approach, which I might explore in the future.
For more inspiration and available tools, see (Blischak et al. 2024).
You can install the development version of asds2024.nils.practical
from GitHub with:
# install.packages("devtools") # <- for `install_github` to be available uncomment this and run it (unless you've already installed it)
# Notes:
# - you probably want to install the suggested dependencies as well, since this package only uses suggested dependencies
# - when `install_github`-ing, you need to explicitly specify that you want the vignettes built as well
devtools::install_github(
"nils-s/cas-asds-practical",
dependencies = c("Depends", "Imports", "LinkingTo", "Suggests"),
build_vignettes = TRUE)Since not all documents are provided as vignettes, you probably want to clone the package sources into a local directory as well:
git clone https://github.com/nils-s/cas-asds-practical.gitFrom there, you can more directly explore the raw data, and read documents that are not packaged as vignettes.
Assuming the devtools package is installed (and install_github is
available), this package by itself should not cause problems (simply
because it contains very little stuff that could cause problems).
However, it depends on a bunch of dependencies, which will be installed
when installing this package’s suggested dependencies as shown in the
code snippet above.
The main suspect in this regard is the sf package, which has a few
dependencies of its own (not all of which are R packages). The first
thing to try (after studying the error messages, of course) is to make
sure all prerequisites for sf are fulfilled (e.g. the
GEOS, GDAL, and
PROJ libraries).
On a Fedora machine, the following should get you started:
sudo dnf install gdal gdal-devel udunits2-devel proj proj-devel geos geos-develSee the sf documentation for more
information.
library(asds2024.nils.practical)
vignette("get-started", package = "asds2024.nils.practical")Blischak, John, Alison Hill, Ben Marwick, Daniel Sjoberg, and Will Landau. 2024. “CRAN Task View: Reproducible Research.” February 20, 2024. https://cran.r-project.org/view=ReproducibleResearch.
Flight, Robert M. 2014. “Analyses as Packages.” July 28, 2014. https://rmflight.github.io/posts/2014-07-28-analyses-as-packages.
———. 2021. “Packages Don’t Work Well for Analyses in Practice.” March 2, 2021. https://rmflight.github.io/posts/2021-03-02-packages-dont-work-well-for-analyses-in-practice.
Landau, William Michael. 2021. “The targets R Package: A Dynamic Make-Like Function-Oriented Pipeline Toolkit for Reproducibility and High-Performance Computing.” Journal of Open Source Software 6 (57): 2959. https://doi.org/10.21105/joss.02959.
———. 2024. targets: Dynamic Function-Oriented Make-Like Declarative Pipelines. https://docs.ropensci.org/targets/.
Marwick, Ben, Carl Boettiger, and Lincoln Mullen. 2018a. “Packaging Data Analytical Work Reproducibly Using R (and Friends).” The American Statistician 72 (1): 80–88. https://doi.org/10.1080/00031305.2017.1375986.
———. 2018b. “Packaging Data Analytical Work Reproducibly Using R (and Friends).” PeerJ Preprints 6 (March): e3192v2. https://doi.org/10.7287/peerj.preprints.3192v2.
Wickham, Hadley, and Jennifer Bryan. 2023. R Packages. 2. ed. O’Reilly. https://r-pkgs.org.