Drop-in / Q&A

We asked learners to vote on the following topics for a mini-lesson for the last session before the show-and-tell session:

working with dates and times in R
working with strings and factors in R
packaging complicated workflows (analyses that include multiple scripts, more than just R code, etc.)
I would like the entire time for a question/answer session

They overwhelmingly chose #3

Targets

Show slides from part 1 of previous targets workshop
Demo repo (targets version on a branch): https://github.com/cct-datascience/targets-demo/tree/with-targets
- Explore files
  - R/
  - _targets.R
- Demonstrate tar_visnetwork(), tar_make(), tar_read()
  - Investigate _targets/
- Demonstrating invalidating a target by adding species to the model fit by lm() in one of the functions in fit_models.R.
  - re-run tar_visnetwork() and tar_make()
Explore a medium complexity targets project on GitHub: https://github.com/ecohealthalliance/mpx-diagnosis
- maybe try to reproduce it
Mention that targets can be configured to work on UA HPC to run individual targets as SLURM jobs or multiple cores with Open on Demand.
- Open on Demand example: https://github.com/cct-datascience/targets-uahpc
Direct people to targets manual and discussions

Executing R code in the shell / shell code in R

Example of running R and Python scripts from bash:

Rscript 01-save_penguins.R
bash 02-rename_penguins.sh
python 03-print_penguins.py

Example of multi-lingual quarto doc

Citation

BibTeX citation:

@online{scott2023,
  author = {Scott, Eric and Diaz, Renata and Guo, Jessica and Riemer,
    Kristina},
  title = {Drop-in / {Q\&A}},
  date = {2023},
  url = {https://cct-datascience.github.io/repro-data-sci//lessons/12-drop-in/notes.html},
  doi = {10.5281/zenodo.8411612},
  langid = {en}
}

For attribution, please cite this work as:

Scott, Eric, Renata Diaz, Jessica Guo, and Kristina Riemer. 2023. “Drop-in / Q&A.” Reproducibility & Data Science in R. 2023. https://doi.org/10.5281/zenodo.8411612.