Topic list

• Install R and Rstudio
• All the Git and GitHub (and RStudio) things
  • Install Git
  • Install a Git client optional but recommended
  • Introduce yourself to Git
  • GitHub, can you hear me?
  • Test connection between RStudio and GitHub
  • GitHub credential caching
  • When RStudio can’t find Git
  • Take possession of your STAT 545 repo
  • Troubleshooting
  • The Shell
• Basic R and RStudio, workspace, working directory, RStudio Project
• Basic care and feeding of data in R
• R objects (beyond data.frames) and indexing
• Test drive R Markdown
• All the graph things, an annotated index
  • R graphics landscape slides
  • ggplot2 tutorial, heavy on code and examples
  • Do’s and don’ts of making effective graphs
  • R Graph Catalog, a visual, clickable index of 100+ figures + ggplot2 code to make them
  • Using colors in R
  • Taking control of qualitative colors in ggplot2
  • Secrets of a happy graphing life
  • Writing figures to file
  • Multiple plots on a page
• The dplyr package for data manipulation
  • Introduction to dplyr
  • dplyr functions for a single dataset
  • Cheatsheet for dplyr join functions
• Writing your own R functions
  • Part 1: get something that works, check arguments
  • Part 2: generalize, set default argument values
  • Part 3:NA handling, ..., unit test
  • Function-writing Practicum: fit a linear model and return estimated coefficients
• The plyr package for split-apply-combine
  • Data aggregation overview slides
  • Using plyr with data.frames
• Be the boss of your factors
• Writing and reading files
  • Indicative code from hands on activities
  • 2013 lesson 2014 update: do data manipulation with dplyr
• Why and how to tidy data
• Regular expressions and more about character data
• All the automation things
  • slides
  • Special considerations for Windows
  • Test drive Make from a shell and RStudio
  • Hands-on activity
  • Example pipelines
• All the package things
  • slides
  • System preparation for package development
  • Hands-on activity, part 1
  • Hands-on activity, part 2
• All the Shiny things
  • slides
  • Getting your system set up for Shiny
  • Hands-on activity-build a shiny app
  • Shiny and other interactive plotting links
• Get data from the web
  • make usual landing page and add bullet points
  • class meeting one and two

Course-specific material

This is just an index very course-specific stuff, like homework, or class landing pages that eventually lead to the same content that’s listed above.

• Class meeting 001 deals with “What is data science? What is data carpentry?” and includes links re: use of Twitter in academia.
• Class meeting 002 provides some Deep Thoughts about data analytic work, then shifts into a hands-on intro to R and RStudio.
• Class meeting 003 Git(Hub) and (R) Markdown crash course so first homework can be submitted. All hands-on.
• Class meeting 004 Data.frames are awesome. Let’s explore one (Gapminder).
• Class meeting 005 The R graphics landscape and intro to ggplot2
• Class meeting 006 R Markdown
• General homework rubric to apply in peer review
• Evaluation of peer review
• Homework 01 Edit README.md
• Homework 02 Explore Gapminder and use R markdown
• Homework 03 Manipulate and summarize the Gapminder data with dplyr; make companion figs with ggplot2
• Homework 04 Manipulate and summarize the Gapminder data with custom functions and plyr
• Homework 05 Prove you are in control of factors by writing and reading files
• Homework 06 Optional, unmarked activities for the STAT 545A / STAT 547M transition
• Homework 07 Data wrangling Grand Finale
• Homework 08 re: handling character data and data cleaning
• Homework 09 Automating Data-analysis Pipelines
• Homework 10 Write an R package
• Homework 11 Build a Shiny app
• Homework 12 Get data from the web

Short random things

• Lesson I contributed to Data Carpentry on tidying data
• Cheatsheet for dplyr join functions
• Store an API key as an environment variable
• Workflow for sending email with R and gmailr
• Make browsing your GitHub repos more rewarding