Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/wlandau/targets-tutorial
Short course on the targets R package
https://github.com/wlandau/targets-tutorial
data-science make pipeline r r-package reproducibility reproducible-research rstats targets workflow
Last synced: about 1 month ago
JSON representation
Short course on the targets R package
- Host: GitHub
- URL: https://github.com/wlandau/targets-tutorial
- Owner: wlandau
- License: other
- Created: 2020-07-30T19:39:09.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-11-29T13:40:05.000Z (10 months ago)
- Last Synced: 2024-06-05T01:43:24.612Z (4 months ago)
- Topics: data-science, make, pipeline, r, r-package, reproducibility, reproducible-research, rstats, targets, workflow
- Language: R
- Homepage: https://rstudio.cloud/project/1699460
- Size: 3.98 MB
- Stars: 94
- Watchers: 5
- Forks: 23
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - wlandau/targets-tutorial - Short course on the targets R package (R)
README
# Reproducible computation at scale in R with [`targets`](https://github.com/ropensci/targets)
[](https://rstudio.cloud/project/1699460)
Data science can be slow. A single round of statistical computation can take several minutes, hours, or even days to complete. The [`targets`](https://github.com/ropensci/targets) R package keeps results up to date and reproducible while minimizing the number of expensive tasks that actually run. [`targets`](https://github.com/ropensci/targets) arranges the steps of your pipeline, skips costly runtime for steps that are already up to date, runs the rest with optional implicit parallel computing, abstracts files as R objects, and shows tangible evidence that the output matches the underlying code and data. In other words, the package saves time while increasing your ability to trust the results. This hands-on workshop teaches targets using a realistic case study from , to an 2018 RStudio AI Blog post by Matt Dancho: . Participants gradually build up a [`targets`](https://github.com/ropensci/targets)-powered machine learning pipeline through guided hands-on R programming exercises.
# Browser access
1. Sign up for a free account at .
1. Log into to access a free instance of RStudio Server in the cloud.
1. Proceed through the R notebooks in the syllabus in order.
# Local access
1. Install R from .
1. Install RStudio Desktop from .
1. [Download or clone](https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) the code at .
1. Open the tutorial as an [RStudio project](https://github.com/wlandau/targets-tutorial/blob/main/targets-tutorial.Rproj) in the [RStudio Desktop](https://rstudio.com/products/rstudio/download/#download).
1. Run the [setup script](https://github.com/wlandau/targets-tutorial/blob/main/setup/local.R) to install the required R and Python packages.
# Help
Post an issue to to ask for help. Be sure to follow the [code of conduct](https://github.com/wlandau/targets-tutorial/blob/main/CODE_OF_CONDUCT.md).
# Syllabus
Topic | Materials
---|---
Intro | [slides](https://wlandau.github.io/targets-tutorial)
Functions | [`1-functions.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/1-functions.Rmd)
Pipelines | [`2-pipelines.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/2-pipelines.Rmd)
Changes | [`3-changes.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/3-changes.Rmd)
Debugging | [`4-debugging.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/4-debugging.Rmd)
Files | [`5-files.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/5-files.Rmd)
Branching | [`6-branching.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/6-branching.Rmd)
Challenge | [`7-challenge.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/7-challenge.Rmd)
# Schedule
This schedule budgets time for a 4-hour iteration of the workshop (8 AM to noon).
| Topic | Format | [Breakout rooms](https://blog.zoom.us/using-zoom-breakout-rooms/) | Minutes | Start | End | Materials |
|------------------------------|------------|----------------|---------|-------|-------|-------------------------------------------------------------------------|
| Intro presentation | lecture | no | 20 | 8:00 | 8:20 | [slides](https://wlandau.github.io/targets-tutorial) |
| Q&A | discussion | no | 10 | 8:20 | 8:30 | [slides](https://wlandau.github.io/targets-tutorial) |
| Functions for the case study | exercises | yes | 15 | 8:30 | 8:45 | [`1-functions.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/1-functions.Rmd) |
| Review functions | lecture | no | 5 | 8:45 | 8:50 | [`1-functions.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/1-functions.Rmd) |
| Break | break | no | 10 | 8:50 | 9:00 | |
| Build up a pipeline | exercises | yes | 20 | 9:00 | 9:20 | [`2-pipelines.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/2-pipelines.Rmd) |
| Review building up a pipeline | lecture | no | 5 | 9:20 | 9:25 | [`2-pipelines.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/2-pipelines.Rmd) |
| Iterate on changes | exercises | yes | 20 | 9:25 | 9:45 | [`3-changes.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/3-changes.Rmd) |
| Review iterating on changes | lecture | no | 5 | 9:45 | 9:50 | [`3-changes.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/3-changes.Rmd) |
| Break | break | no | 10 | 9:50 | 10:00 | |
| Debugging pipelines | exercises | yes | 20 | 10:25 | 10:45 | [`4-debugging.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/4-debugging.Rmd) |
| Review debugging pipelines | lecture | no | 5 | 10:45 | 10:50 | [`4-debugging.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/4-debugging.Rmd) |
| Break | break | no | 10 | 10:50 | 11:00 | |
| External files | exercises | yes | 20 | 10:00 | 10:20 | [`5-files.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/5-files.Rmd) |
| Review external files | lecture | no | 5 | 10:20 | 10:25 | [`5-files.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/5-files.Rmd) |
| Dynamic branching | exercises | yes | 20 | 11:00 | 11:20 | [`6-branching.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/6-branching.Rmd) |
| Review dynamic branching | lecture | no | 5 | 11:20 | 11:25 | [`6-branching.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/6-branching.Rmd) |
| Challenge exercise | exercises | yes | 20 | 11:25 | 11:45 | [`7-challenge.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/7-challenge.Rmd) |
| Review challenge exercise | lecture | no | 5 | 11:45 | 11:50 | [`7-challenge.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/7-challenge.Rmd) |
| Q&A | discussion | no | 10 | 11:50 | 12:00 | |
# References
* Dancho (2018, Jan. 11). RStudio AI Blog: Deep Learning With Keras To Predict Customer Churn. Retrieved from .