Topic list

This material gets updated during Sept - Nov each year on a rolling basis.

• Non-STAT545-specific advice for getting help.
• Get your R act together
  • Install R and Rstudio (or update! get current!)
  • Basic R and RStudio, workspace, working directory, RStudio Project
  • Test drive R Markdown
• Git, GitHub, and RStudio
  • Content now here: http://happygitwithr.com. Install Git, install a Git client or GUI, establish pull/push connection from computer to GitHub in the shell, establish GitHub connection via RStudio, credential caching, troubleshooting.
  • Take possession of your STAT 545 repo
• Data analysis 1: One data frame (or “tibble”), the Tidyverse
  • Basic care and feeding of data in R: data frames (and tibbles) are awesome.
  • Introduction to dplyr: filter, select, the pipe.
  • dplyr functions for a single dataset: All the single table verbs.
  • Tidy data using Lord of the Rings: tidy data, tidyr.
  • Writing and reading files.
• Data analysis 2: Doing remedial work on certain vector types
  • Be the boss of your factors, using forcats
  • Character vectors: basic manipulation and regular expressions.
  • Character encoding
  • Mixed encoding case study
  • placeholder for date-times; TLDR; consider the lubridate package
• Data analysis 3: Bring info from a secondary source into your main tibble
  • When one tibble is not enough: Overview of binds, joins, lookups.
  • Join (a.k.a. merge) two tables: dplyr join cheatsheet with comic characters and publishers
  • Table look up
• R as a programming language
  • R objects (beyond data.frames) and indexing
  • Writing your own R functions Part 1: get something that works, check arguments
  • Writing your own R functions Part 2: generalize, set default argument values
  • Writing your own R functions Part 3:NA handling, ..., unit test
  • Function-writing Practicum: fit a linear model and return estimated coefficients
• All the graph things
  • 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
• All the package things
  • slides
  • System preparation for package development
  • Write your own R package
• All the automation things
  • slides
  • Special considerations for Windows
  • Test drive Make from a shell and RStudio
  • Hands-on activity
  • Example pipelines
• Get data from the web
  • Slides
  • Lesson 1: Use API-wrapping packages
  • Lesson 2: DIY web data
• All the Shiny things
  • Slides on Google Docs
  • Shiny tutorial with hands-on activity to build app with BC Liquor Store dataset
• Deprecated material that I no longer use. But last I checked, it’s not actually wrong.
  • Overview of split-apply-combine: slides on speakerdeck
  • Split-apply-combine with dplyr::do()
  • Example: a wrapper around lm to use with dplyr::do()
  • Split-apply-combine with plyr
  • Why and how to tidy data
  • Be the boss of your factors: 2015 version, pre-forcats
  • 2014 Write an R package, hands-on activity, part 1
  • 2014 Write an R package, hands-on activity, part 2

Short random things

• Draw the rest of the owl, a pep talk for building off simple examples
• Using dplyr + purrr + tidyr to analyze data about GitHub repos via the gh package
• Using dplyr + purrr + tidyr + xml2 to tame the annoying XML from Google Sheets
• Make browsing your GitHub repos more rewarding
• sheet for dplyr join functions
• Workflow for sending email with R and gmailr
• Store an API key as an environment variable
• Lesson I contributed to Data Carpentry on tidying data