Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/wlandau/learndrake
An interactive free online short course on the drake R package
https://github.com/wlandau/learndrake
Last synced: 8 days ago
JSON representation
An interactive free online short course on the drake R package
- Host: GitHub
- URL: https://github.com/wlandau/learndrake
- Owner: wlandau
- License: gpl-3.0
- Created: 2019-05-20T18:50:04.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-02-06T22:31:22.000Z (almost 4 years ago)
- Last Synced: 2024-10-11T18:25:05.672Z (about 1 month ago)
- Language: HTML
- Homepage: https://rstudio.cloud/project/627076
- Size: 33 MB
- Stars: 146
- Watchers: 6
- Forks: 18
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
output: github_document
---
[](https://rstudio.cloud/project/627076)
[](https://mybinder.org/v2/gh/wlandau/learndrake/binder?urlpath=rstudio)
## Consider `targets`
`drake` is [superseded](https://www.tidyverse.org/lifecycle/#superseded). The [`targets`](https://docs.ropensci.org/targets/) R package is the long-term successor of `drake`, and it is more robust and easier to use. Please visit for full context and advice on transitioning.
# The drake short course
`learndrake` is a free online short course on the [`drake` R package](https://github.com/ropensci/drake). With guided web-based hands-on exercises, you will gradually build up a [`drake`](https://github.com/ropensci/drake)-powered [machine learning project](https://blogs.rstudio.com/tensorflow/posts/2018-01-11-keras-customer-churn/) and practice the fundamentals of [`drake`](https://github.com/ropensci/drake). If you run into trouble or have questions along the way, please post an issue at to ask for help.
## How to take the course
1. Read the intro slides at and watch the video recording at .
2. Sign up for a free RStudio Cloud account at .
3. Log into the public RStudio Cloud workspace at . You are now running an instance of RStudio Server with everything you need.
4. In the "Files" pane of the IDE, locate the numbered folders with `*.Rmd` notebooks inside. Work through the notebooks in order.
Topic | Notebook
---|---
Custom functions | `1-functions/1-functions.Rmd`
`drake` plans | `2-plans/2-plans.Rmd`
Changing workflows | `3-changes/3-changes.Rmd`
Static branching | `4-static/4-static.Rmd`
Dynamic branching | `5-dynamic/5-dynamic.Rmd`
Files and R Markdown | `6-files/6-files.Rmd`
Notebooks `3-changes/3-changes.Rmd` and `4-static/4-static.Rmd` rely on the following Shiny apps.
App | Notebook |
---|---
| `3-changes/3-changes.Rmd`
| `4-static/4-static.Rmd`
| `4-static/4-static.Rmd`
# The `learndrake` package
The [`learndrake`](https://github.com/wlandau/learndrake) R package contains the materials of the short course. It helps students take the workshop locally if they so choose, and it helps educators and administrators host the workshop from alternative platforms.
## Installation
You can install [`learndrake`](https://github.com/wlandau/learndrake) from GitHub.
```{r, eval = FALSE}
install.packages("remotes")
remotes::install_github("wlandau/learndrake")
```
To run the exercises locally, you will also need [TensorFlow](https://www.tensorflow.org) and [Keras](https://keras.io/).
```{r, eval = FALSE}
keras::install_keras()
# Check if the installation succeeded.
tensorflow::tf_config()
```
If you are using RStudio version 1.2.5003 and encounter [this fatal error](https://github.com/rstudio/rstudio/issues/6049), consider downgrading TensorFlow to version 1.13.1. Note: `install_keras()` silently tries to upgrade TensorFlow to version >= 2, so you will need to run it with `tensorflow = "1.13.1`.
## Usage
The functions in `learndrake` deploy and serve the course materials.
Function | Purpose
---|---
`save_notebooks()` | Save the `*.Rmd` notebooks to your computer.
`view_slides()` | Open the intro slides in a web browser.
`save_slides()` | Save the intro slides to your computer.
`launch_app()` | Launch a supporting Shiny app.
`save_app()` | Save the code files of a supporting Shiny app.
## Accompanying resources
This short course focuses mostly on `drake`'s mental model and core concepts.
* How to break up a project into functions.
* How to think about and create `drake` plans.
* How `drake` decides what is up to date and what is outdated.
The following resources cover important topics that did not make it into the syllabus.
### In practice
- [Miles McBain](https://github.com/MilesMcBain)'s [excellent blog post](https://milesmcbain.xyz/the-drake-post/) explains the motivating factors and practical issues {drake} addresses for most projects, how to set up a project as quickly and painlessly as possible, and how to overcome common obstacles.
- Miles' [`dflow`](https://github.com/MilesMcBain/dflow) package generates the file structure for a boilerplate `drake` project. It is a more thorough alternative to `drake::use_drake()`.
- `drake` is heavily function-oriented by design, and Miles' [`fnmate`](https://github.com/MilesMcBain/fnmate) package automatically generates boilerplate code and docstrings for functions you mention in `drake` plans.
### High-performance computing
- The [official manual](https://books.ropensci.org/drake) has a [chapter on `drake`'s high-performance computing](https://books.ropensci.org/drake/hpc.html) capabilities, which range from multicore computing on a local machine to large-scale deployment to a cluster.
# Thanks
Thanks to | For
---|---
[Edgar Ruiz](https://github.com/edgararuiz) | Uniting `drake` and `keras` at and providing valuable advice on the construction of the course.
[Matt Dancho](https://github.com/mdancho84) | Publishing the original [blog post](https://blogs.rstudio.com/tensorflow/posts/2018-01-11-keras-customer-churn/) with the course's underlying case study.
[Eric Nantz](https://github.com/rpodcast) | Reviewing the materials and providing feedback.