Synopsis: So you've spent hours on a cool data analysis project - how do you share it with your colleagues, your stakeholders, or the public? And how do you ensure other analysts can reproduce what you've done?

In the first part of this class, we'll discuss how to organize and document your data projects so others can understand and recreate what you’ve done. We'll address good file management, data pipelines, and documentation practices. In the second part, we'll discuss ways to turn your analyses into reports, web pages, and dashboards using open-source R tools such as Rmarkdown, shinydashboard, and flexdashboard.

Themes:

• reproducibility
• documentation for future users and analysts
• idea of well-documented and -organized data pipelines

Topics to cover:

• Example of non-reproducible, undocumented data analysis in Excel
• Paradigm of report + embedded code (show example)
  • Contrast with older paradigm of code with embedded comments
• Documenting an exploratory data analysis as you go (example)
• File naming, file management
• Data pipelines and documentation
• Intro to Rmarkdown, embedding code, working with .rmd files in RStudio, knitting, (maybe an interactive example here for students to see how it works?)
• Creating shareable reports with Rmarkdown (example)
• Creating websites with Rmarkdown (Robert's website example)
• Creating pseudo-interactive dashboards with Rmarkdown and plotly (Lauren's shinydashboard example)