Topic list

• Install R and Rstudio
• All the Git and GitHub (and RStudio) things
  • Install Git
  • Install a Git client optional but recommended
  • Tell RStudio where to find Git
  • Introduce yourself to Git
  • Test connection between RStudio and GitHub
  • GitHub credential caching
  • STAT545 RStudio and GitHub workflow
• 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
  • add sub-bullet points
• Get data from the web
  • make usual landing page and add bullet points
  • class meeting one and two

Course-specific material

• 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 Started basic flavors of R objects; 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 Continue exploring data in a data.frame, maybe using ggplot2 and in an R Markdown document
• Class meeting 006 Open Q and A on all content and course mechanics so far, complete introduction to ggplot2
• Class meeting 007 Introduction to dplyr
• Class meeting 008 Writing your own R functions, beginning
• Class meeting 009 Writing your own R functions, conclusion; data aggregation with plyr
• Class meeting 010 Be the boss of your factors
• Class meeting 011 Writing to file, reading from file
• Class meeting 012 Effective graphs
• Class meeting 101 Practical graphing
• Class meeting 102 Tidy data
• Class meeting 103 Regular expressions
• Class meeting 104 Data cleaning
• Class meeting 105 Automating Data-analysis Pipelines
• Class meeting 106 More automating Data-analysis Pipelines
• Class meeting 107 Writing an R package
• Class meeting 108 More writing an R package
• Class meeting 109 Build a Shiny app
• Class meeting 110 More build a Shiny app
• Class meeting 111 Get data from the web via an API
• Class meeting 112 Get data from the web via an API and web scraping
• General homework guidelines
• 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

What’s coming

Topics traditionally covered in STAT 545A, with light updating:

• Introduction to R and the RStudio IDE done
• R scripts and workspaces, RStudio Projects; how to get your work done done
• Creating reports from R scripts and R Markdown, using knitr done
• Deep thoughts about data analytic work done
• Care and feeding of data in R; data frames done
• R objects – beyond data frames done
• Indexing, subsetting done
• Data aggregation; “apply” functions, plyr, dplyr done
• Making figures with ggplot2 (was lattice in past) done
• How to help yourself, how to ask questions to get useful answers done
• How to get data in and out of R, staying as “open” as possible done
• How to get figures out of R done
• Be the boss of your factors, i.e. categorical variables done
• Use of color in R done
• Single quantitative variable: visualizations and descriptive statistics
• Two quantitative variables: visualizations and descriptive statistics
• Categorical variables: visualizations and descriptive statistics
• Multivariate visualizations
• Visualizing and summarizing data when “grouped” done
• Coding style and project organization

New topics for STAT 545A and/or STAT 547M will be selected from here:

• Bash shell / unix basics, personal system administration.
• Version control with Git, collaboration via GitHub done
• The tabular data mentality, “tidy” data, data reshaping done
• Regular expressions, programmatic transformation and searching of character data done
• Writing R functions done
• Creating interactive pages, apps, and graphics via Shiny done and (maybe) ggvis
• Unit testing, at least as a mentality. Maybe will cover formal unit testing, e.g. testthat done
• Stats particularly useful in exploration (and often neglected in standard intro stats courses)
  • robust summary statistics
  • robust regression
  • smoothing
  • density estimation
  • cluster analysis, PCA, SVD, MDS
• Embrace the web: done
  • getting data from the web, e.g. using an API or via scraping
  • exposing your hard work on the web (data, code, results)
• Distributing data and code to the world via an R package done
• Automating an analytical pipeline, e.g. via Make. done