AjayKMehta / targets-tutorial

Short course on the targets R package

Home Page:https://rstudio.cloud/project/1699460

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Reproducible computation at scale in R with targets

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Data science can be slow. A single round of statistical computation can take several minutes, hours, or even days to complete. The targets R package keeps results up to date and reproducible while minimizing the number of expensive tasks that actually run. 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: https://blogs.rstudio.com/ai/posts/2018-01-11-keras-customer-churn. Participants gradually build up a targets-powered machine learning pipeline through guided hands-on R programming exercises.

Browser access

  1. Sign up for a free account at https://rstudio.cloud.
  2. Log into https://rstudio.cloud/project/1699460 to access a free instance of RStudio Server in the cloud.
  3. Proceed through the R notebooks in the syllabus in order.

Local access

  1. Install R from https://www.r-project.org.
  2. Install RStudio Desktop from https://rstudio.com/products/rstudio/download/#download.
  3. Download or clone the code at https://github.com/wlandau/targets-tutorial.
  4. Open the tutorial as an RStudio project in the RStudio Desktop.
  5. Run the setup script to install the required R and Python packages.

Help

Post an issue to https://github.com/wlandau/targets-tutorial to ask for help. Be sure to follow the code of conduct.

Syllabus

Topic Materials
Intro slides
Functions 1-functions.Rmd
Pipelines 2-pipelines.Rmd
Changes 3-changes.Rmd
Debugging 4-debugging.Rmd
Files 5-files.Rmd
Branching 6-branching.Rmd
Challenge 7-challenge.Rmd

Schedule

This schedule budgets time for a 4-hour iteration of the workshop (8 AM to noon).

Topic Format Breakout rooms Minutes Start End Materials
Intro presentation lecture no 20 8:00 8:20 slides
Q&A discussion no 10 8:20 8:30 slides
Functions for the case study exercises yes 15 8:30 8:45 1-functions.Rmd
Review functions lecture no 5 8:45 8:50 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
Review building up a pipeline lecture no 5 9:20 9:25 2-pipelines.Rmd
Iterate on changes exercises yes 20 9:25 9:45 3-changes.Rmd
Review iterating on changes lecture no 5 9:45 9:50 3-changes.Rmd
Break break no 10 9:50 10:00
Debugging pipelines exercises yes 20 10:25 10:45 4-debugging.Rmd
Review debugging pipelines lecture no 5 10:45 10:50 4-debugging.Rmd
Break break no 10 10:50 11:00
External files exercises yes 20 10:00 10:20 5-files.Rmd
Review external files lecture no 5 10:20 10:25 5-files.Rmd
Dynamic branching exercises yes 20 11:00 11:20 6-branching.Rmd
Review dynamic branching lecture no 5 11:20 11:25 6-branching.Rmd
Challenge exercise exercises yes 20 11:25 11:45 7-challenge.Rmd
Review challenge exercise lecture no 5 11:45 11:50 7-challenge.Rmd
Q&A discussion no 10 11:50 12:00

References

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Short course on the targets R package

https://rstudio.cloud/project/1699460

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