Next Steps

Now that you have learned the fundamentals of building a data science workflow in R, here are some resources to help you continue developing your skills.

Reproducibility and Scaling

One of the key ideas from this workshop is that your workflow should be reproducible and scalable.

A good workflow allows you to: - rerun your entire analysis from start to finish
- save outputs in a consistent and organized way
- apply the same process to larger datasets or more powerful computing environments

For example, you can save model outputs:

write_csv(eval_df, "outputs/lm_test_predictions.csv")

You can also run your entire workflow as a script:

Rscript workshop_workflow.R

This becomes especially useful when: - working with larger datasets
- automating analyses
- running jobs on high-performance computing (HPC) systems

The same workflow you used in this workshop can scale from your laptop to much larger systems.

Workshops

Please visit LibCal to register for additional R workshops. Topics include:

  • Effective Data Visualization in R

Online Resources