The Posit.Cloud workspace is available here.

Data Management, R Markdown & Shiny

This training is delivered in two-parts: Data Management & R Markdown, and an introduction to Shiny apps.

Data Management & R Markdown

Data Management & R Markdown covers an introduction to data management in RStudio, common data objects in R, the basics of Markdown and knitr, table outputs (tibble, kable and gtsummary), graphics (ggplot2). The materials also cover importing and caching data, and rendering M Markdown files into various outputs.

Shiny covers an introduction to UI layouts, reactivity (inputs, expressions, and outputs), and deployment. Additional example applications are available on the Posit.Cloud project.

Importing Data

  • Using the IDE

  • Importing SAS files

  • Importing multiple files

  • Using parameters

Common Data Objects

  • Atomic vectors

  • S3 vectors

  • Lists

  • data.frames and tibbles

R Markdown Anatomy

  • Why use R Markdown?

  • Metadata (YAML)

  • Text (human readable)

  • Code (machine readable)

R Markdown Tables

  • Standard static outputs (tibble & kable)

  • Interactive tables (paged)

  • Table packages (gtsummary)

R Markdown Graphs

  • Introduction to ggplot2

  • Layers (data, aesthetics, and geoms)

  • Global vs. local mapping

  • Setting vs. mapping aesthetics

  • Facets

Shiny Apps

Shiny app anatomy

  • ui

  • server

  • run

User Interface (UI)

  • Layout

  • Reactivity

  • UI inputs & outputs

Server

  • Accessing inputs & outputs

  • render*_() functions

  • Reactive expressions