{"id":14067003,"url":"https://github.com/wlandau/targets-tutorial","last_synced_at":"2025-03-16T13:31:18.352Z","repository":{"id":45055089,"uuid":"283862261","full_name":"wlandau/targets-tutorial","owner":"wlandau","description":"Short course on the targets R package","archived":false,"fork":false,"pushed_at":"2023-11-29T13:40:05.000Z","size":4176,"stargazers_count":97,"open_issues_count":0,"forks_count":23,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-03-16T02:47:17.949Z","etag":null,"topics":["data-science","make","pipeline","r","r-package","reproducibility","reproducible-research","rstats","targets","workflow"],"latest_commit_sha":null,"homepage":"https://rstudio.cloud/project/1699460","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wlandau.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-07-30T19:39:09.000Z","updated_at":"2025-03-05T02:35:15.000Z","dependencies_parsed_at":"2024-08-13T07:11:53.172Z","dependency_job_id":"99af1a57-b71f-453e-ac99-1320c0e57770","html_url":"https://github.com/wlandau/targets-tutorial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wlandau%2Ftargets-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wlandau%2Ftargets-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wlandau%2Ftargets-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wlandau%2Ftargets-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wlandau","download_url":"https://codeload.github.com/wlandau/targets-tutorial/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243875122,"owners_count":20361951,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","make","pipeline","r","r-package","reproducibility","reproducible-research","rstats","targets","workflow"],"created_at":"2024-08-13T07:05:22.621Z","updated_at":"2025-03-16T13:31:17.356Z","avatar_url":"https://github.com/wlandau.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"# Reproducible computation at scale in R with [`targets`](https://github.com/ropensci/targets)\n\n[![Launch RStudio Cloud](https://img.shields.io/badge/RStudio-Cloud-blue)](https://rstudio.cloud/project/1699460)\n\nData 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: \u003chttps://blogs.rstudio.com/ai/posts/2018-01-11-keras-customer-churn\u003e. Participants gradually build up a [`targets`](https://github.com/ropensci/targets)-powered machine learning pipeline through guided hands-on R programming exercises.\n\n# Browser access\n\n1. Sign up for a free account at \u003chttps://rstudio.cloud\u003e.\n1. Log into \u003chttps://rstudio.cloud/project/1699460\u003e to access a free instance of RStudio Server in the cloud.\n1. Proceed through the R notebooks in the syllabus in order.\n\n# Local access\n\n1. Install R from \u003chttps://www.r-project.org\u003e.\n1. Install RStudio Desktop from \u003chttps://rstudio.com/products/rstudio/download/#download\u003e.\n1. [Download or clone](https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) the code at \u003chttps://github.com/wlandau/targets-tutorial\u003e.\n1. 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).\n1. Run the [setup script](https://github.com/wlandau/targets-tutorial/blob/main/setup/local.R) to install the required R and Python packages.\n\n# Help\n\nPost an issue to \u003chttps://github.com/wlandau/targets-tutorial\u003e to ask for help. Be sure to follow the [code of conduct](https://github.com/wlandau/targets-tutorial/blob/main/CODE_OF_CONDUCT.md).\n\n# Syllabus\n\nTopic | Materials\n---|---\nIntro | [slides](https://wlandau.github.io/targets-tutorial)\nFunctions | [`1-functions.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/1-functions.Rmd)\nPipelines | [`2-pipelines.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/2-pipelines.Rmd)\nChanges | [`3-changes.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/3-changes.Rmd)\nDebugging | [`4-debugging.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/4-debugging.Rmd)\nFiles | [`5-files.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/5-files.Rmd)\nBranching | [`6-branching.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/6-branching.Rmd)\nChallenge | [`7-challenge.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/7-challenge.Rmd)\n\n# Schedule\n\nThis schedule budgets time for a 4-hour iteration of the workshop (8 AM to noon).\n\n| Topic                        | Format     | [Breakout rooms](https://blog.zoom.us/using-zoom-breakout-rooms/) | Minutes | Start | End   | Materials                                                               |\n|------------------------------|------------|----------------|---------|-------|-------|-------------------------------------------------------------------------|\n| Intro presentation           | lecture    | no             | 20      | 8:00  | 8:20  | [slides](https://wlandau.github.io/targets-tutorial)                             |\n| Q\u0026A                          | discussion | no             | 10      | 8:20  | 8:30  | [slides](https://wlandau.github.io/targets-tutorial)                             |\n| 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) |\n| Review functions             | lecture    | no             | 5       | 8:45  | 8:50  | [`1-functions.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/1-functions.Rmd) |\n| Break                        | break      | no             | 10      | 8:50  | 9:00  |                                                                         |\n| 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) |\n| 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) |\n| Iterate on changes           | exercises  | yes            | 20      | 9:25  | 9:45  | [`3-changes.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/3-changes.Rmd)   |\n| 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)   |\n| Break                        | break      | no             | 10      | 9:50  | 10:00 |                                                                         |\n| Debugging pipelines  | exercises  | yes            | 20      | 10:25 | 10:45 | [`4-debugging.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/4-debugging.Rmd) |\n| Review debugging pipelines    | lecture    | no             | 5       | 10:45 | 10:50 | [`4-debugging.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/4-debugging.Rmd) |\n| Break                        | break      | no             | 10      | 10:50 | 11:00 |                                                                         |\n| External files               | exercises  | yes            | 20      | 10:00 | 10:20 | [`5-files.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/5-files.Rmd)     |\n| Review external files                 | lecture    | no             | 5       | 10:20 | 10:25 | [`5-files.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/5-files.Rmd)     |\n| Dynamic branching        | exercises  | yes            | 20      | 11:00 | 11:20 | [`6-branching.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/6-branching.Rmd) |\n| Review dynamic branching           | lecture    | no             | 5       | 11:20 | 11:25 | [`6-branching.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/6-branching.Rmd) |\n| Challenge exercise           | exercises  | yes            | 20      | 11:25 | 11:45 | [`7-challenge.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/7-challenge.Rmd) |\n| Review challenge exercise            | lecture    | no             | 5       | 11:45 | 11:50 | [`7-challenge.Rmd`](https://github.com/wlandau/targets-tutorial/blob/main/7-challenge.Rmd) |\n| Q\u0026A                          | discussion | no             | 10      | 11:50 | 12:00 |                                                                         |\n\n# References\n\n* Dancho (2018, Jan. 11). RStudio AI Blog: Deep Learning With Keras To Predict Customer Churn. Retrieved from \u003chttps://blogs.rstudio.com/tensorflow/posts/2018-01-11-keras-customer-churn/\u003e.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwlandau%2Ftargets-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwlandau%2Ftargets-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwlandau%2Ftargets-tutorial/lists"}