- Introduction
- Slides
- Public Datasets
- Social Capital Atlas Report Data
- Link to original source
- Grads on the Go: Measuring College-Specific Labor Markets for Graduates Data
- Link to original source
- National Center for Educational Statistics High School District Data
- Link to original source
- National Center for Educational Statistics National ACT Data
- Link to original source
- Social Capital Atlas Report Data
- Code Files
- Social Capital Markdown File (SC.Rmd)
- Social Capital Render Script (SC.R)
- Grads on the Go Markdown File (GOTG.Rmd)
- Grads on the Go Render Script (GOTG.R)
- State Education Markdown File (StateEd.Rmd)
- State Education Render Script (StateEd.R)
- Output Files
When reporting or evaluating findings across different contexts, classes, or categories, you may need to create digestible reports for multiple groups of interest. Yet, manually creating such reports can be hauntingly monotonous and manually intensive. For the sake of your own time and sanity, it might be worthwhile to employ parameterized reporting. Clusters or strata within data allow for the subsetting of findings across individual variables or sets of variables (i.e., parameters). Parameterized reporting in R Markdown was developed as a method intended to foster computational reproducibility while facilitating scalability. As trends toward mandatory open access data and code sharing continue to emerge, adopting explicitly transparent processes in your research will help others reproduce your analyses and potentially attempt to replicate your conclusions. Whether you wish to compare standardized test scores among schools in a district, salesperson productivity across store locations, or quality of life measures between counties, parameterized reporting can be a powerfully convenient and ergonomic tool for this. This introductory presentation will conclude with a walkthrough of code examples from three publicly available, large-scale datasets.
Inspiration from Why R Is Magic from R For the Rest of Us.