R for Data Analysis & Visualization in Science
This is the repository for website used to teach the quarter-long graduate seminar course, R-DAVIS. Course content integrates and builds on Data Carpentry Ecology lessons, and is taught as part of the curriculum for students in the Graduate Groups of Ecology, Environmental Policy & Management, and Transportation Technology & Policy at the University of California, Davis, USA.
Because much of the varied content found in the lessons, there are a number of package dependencies to be aware of. This list may not be complete, but before building the site locally, these packages should be installed:
c("tidyverse", "ggplot2", "tidyr", "readr", "dplyr", "purrr", "emojifont","BrailleR","sonify","seewave", "viridis", "mapdata", "maps", "mapview", "measurements", "gapminder", "broom", "gutenbergr", "tidytext", "patchwork", "cowplot", "hexbin", "geosphere", "ggmap", "ggthemes", "magrittr", "leaflet", "plotly", "knitr", "rio")
To get an updated list of packages for the website, navigate to the repo on the command line, then try one of these commands:
grep -horE 'library\(.*?\)' . | sed 's/library//g; s/[()]//g; s/"//g' | sed "s/\'//g" | grep -Eo '^\w+' | sort | uniq
or
grep -horE 'library\(.*?\)' . | sed 's/library//g; s/[()]//g; s/"//g' | grep -Eo '^\w+' | sort | uniq
The course website can be rebuilt locally by using the "Build" tab in RStudio. Assuming the above packages are installed on your machine:
- Open the R-DAVIS project in RStudio. Click on the
Buildtab, and "More", select "Clean All". - Then select
Build Website. This process make take a few minutes.
You can also clean and build the website from the command line by navigating to the repo and running R -e 'rmarkdown::clean_site()' or R -e 'rmarkdown::render_site()'.
If you only need to rebuild one lesson or one page, you can run rmarkdown::render_site(input = "path_to_page_from_project_directory").
See the Releases tab in this repo to find the completed version for a given year. We expect further development of the website for future iterations as the course improves in the future.