summarize in three sentences what this project is about and what central features it has.
renv brings project-local R dependency management to our project.
renv uses a lockfile (renv.lock) to capture the state of your
library at some point in time. Based on renv.lock, RStudio should
automatically recognize that it’s being needed, thereby downloading and
installing the appropriate version of renv into the project library.
After this has completed, you can then use renv::restore() to restore
the project library locally on your machine. When new packages are used,
install.packages() does not install packages globally, it does in an
environment only used for our project. You can find this library in
renv/library (but it should not be necessary to look at it). If renv
fails, you will be presented something in the like of when you first
start R after cloning the repo:
renv::restore()
This project has not yet been activated. Activating this project will ensure the project library is used during restore. Please see ?renv::activate for more details. Would you like to activate this project before restore? [Y/n]:
Follow along with Y and renv::restore() will do its work downloading
and installing all dependencies. renv uses a local .Rprofile and
renv/activate.R script to handle our project dependencies.
If you need to add a new package, you can install it as usual
(install.packages etc.). Then, to add your package to the renv.lock:
renv::snapshot()
and commit and push your renv.lock.
Other team members can then run renv::restore() to install the added
package(s) on their laptop.
You need the following data files in order to run this project:
system2("tree", c("data/raw")) # works on mac and potentially linux. [the following can also be moved to the wiki if you decide to have one]
Default Definition of Done can be found here. Adapt if needed.
[the following can also be moved to the wiki if you decide to have one]
explain how the output(s) of this project can be handled/operated, for example:
- how to knit the report(s)
- where to create/find the data visualizations
- how to update data
- what would need to be updated if someone wanted to re-run your analysis with different data
be honest about the limitations of your project, e.g.:
- methodological: maybe another model would be more suitable?
- reproducibility: what are limits of reproducibility? is there something hard-coded/specific to the data that you used?
- best practices: maybe some code is particularly messy and people working on it in the future should know about it in advance?