Originally developed for a workshop at CSEE 2023 by:
- Alec L. Robitaille (Memorial University of Newfoundland and Labrador)
- Isabella C. Richmond (Concordia University)
Projects
- Directories
- READMEs
- RStudio Projects
Short break
Functions
- Introduction
- Recommended approach
- Checks
- Options
Lunch break
{targets}
- Introduction
- Writing workflows
- Visualizing
- Running workflows
- Extensions
Short break
{renv} + {conflicted}
- Saving package versions
- Checking conflicts
Overall
- Approach analyses in a more holistic way (whole project vs script by script)
- Share data across projects and software versions with minimal stress
- Use workflows that reduce analysis errors and mental load
Section 1: Projects
- Construct a RStudio project that is thoroughly documented using file structure and data management best practices
- Use RStudio projects to effectively share their own work, and use other people's
Section 2: Functions
- Read and understand structure of functions in R
- Refactor code into functions that do one thing
- Add tests and checks to ensure functions work and error when expected
- Recognize the value of functions as chunks of code that are reusable and easier to debug
Section 3: {targets}
- (For a given project) map out relationships between inputs, outputs and analysis steps
- Identify discrete chunks/steps and write corresponding (or use available) functions
- Execute a workflow in {targets} that reads in data, performs a function, and saves an output
- Recognize the value of workflows for reducing mental load and improving efficiency
Section 4: {renv} + {conflicted}
- Use {renv} to preserve current package versions to ensure the environment is reproducible, portable and isolated
- Use {conflicted} to detect conflicting function names
This workshop is aimed at improving our ability to use and create reproducible workflows. All the materials should be accessible from the side bar (slides, exercises, resources for further reading, and the link to the GitHub repository can be accessed by clicking on the GitHub icon).
We don't have any strict dependencies on specific versions of R or R packages, but it would be good to have at least R version 4.0 and a recent version of RStudio.
We are using Quarto to build the workshop's website and exercises, so it could be helpful for you to install it too. If you don't have time to, you can always complete exercises in an R script - so no pressure.
Install first the Quarto CLI from the here then the package with the command at the bottom.
Please install the following packages (after updating R):
pkgs <- c(
'targets',
'igraph',
'data.table',
'dplyr',
'ggplot2',
'testthat',
'janitor',
'renv',
'rlang',
'conflicted',
'palmerpenguins',
'visNetwork',
'quarto',
'xml2',
'downlit',
'usethis'
)
install.packages(pkgs)To download the workshop materials for a participant, use this command:
library(usethis)
# (Set your own destination directory)
use_course(
'https://github.com/robitalec/reproducible-workflows-workshop/archive/refs/heads/participant.zip',
destdir = '~/Documents')Or by downloading and unziping the ZIP file at this link: https://github.com/robitalec/reproducible-workflows-workshop/archive/refs/heads/participant.zip.
Then open up the RStudio project.
Example data for this workshop is borrowed from the Palmer Long-Term Ecological Research (LTER). Here is the study description from the Palmer LTER site:
The Palmer Long-Term Ecological Research (LTER) study area is located to the west of the Antarctic Peninsula extending South and North of the Palmer Basin from onshore to several hundred kilometers off shore. Palmer Station is one of the three United States research stations located in Antarctica. It is on Anvers Island midway down the Antarctic Peninsula at latitude 64.7 South, longitude 64.0 West.
The Palmer LTER studies a polar marine biome with research focused on the Antarctic pelagic marine ecosystem, including sea ice habitats, regional oceanography and terrestrial nesting sites of seabird predators. The Palmer LTER is one of more than 26 LTER research sites located throughout the United States, Puerto Rico and Tahiti; each focused on a specific ecosystem, that together constitute the LTER Network.
We gratefully acknowledge the Palmer LTER for releasing data freely and openly for diverse uses - in our case for training analytical skills of researchers in ecology.
The first dataset is already available in R through the palmerpenguins R package. There is a raw version and a cleaned version. They contain data for 344 penguins, with the following variables (cleaned version):
- species
- island
- bill_length_mm
- bill_depth_mm
- flipper_length_mm
- body_mass_g
- sex
- year
The following datasets are available directly from the Palmer LTER Data Catalog. To download the data to the raw-data/ directory, run the function download_example_data() (R/download_example_data.R).
This second dataset contains monthly averaged weather timeseries from Palmer Station, Antarctica, with the following variables:
- Date
- Year
- Month
- Average Temperature
- Temperature Count
- Average Pressure
- Pressure Count
- Average Melted Precipitation
- Precipitation Count
Link to data (CSV):
The third dataset contains monthly sea ice area from the region around the Palmer Station, Antarctica, with the following variables:
- Year
- Month
- Area
Note: this data is formatted with months as columns, years as rows, and cells filled with the corresponding area.
Link to data (TXT):
The fourth dataset contains Adelie penguin adult and chick counts
- studyName
- Date GMT
- Time GMT
- Island
- Colony
- Adults
- Chicks
Link to data (CSV):
This project is released under the GNU General Public License v3.0. Read it here.