Welcome to STAT 545
I Get your R act together
1 Install R and RStudio
2 R basics and workflows
II Version control and R Markdown
Overview
3 Git, GitHub, and RStudio
4 R Markdown
III Data analysis 1
5 Basic care and feeding of data in R
6 Introduction to dplyr
7 Single table dplyr functions
8 Tidy data
9 Writing and reading files
IV Data analysis 2
10 Be the boss of your factors
11 Character vectors
12 Character encoding
13 Dates and times
V Data analysis 3
14 When one tibble is not enough
15 Join two tables
16 Table lookup
VI R as a programming language
17 R objects and indexing
18 Write your own R functions, part 1
19 Write your own R functions, part 2
20 Write your own R functions, part 3
21 Function-writing practicum
VII All the graph things
Overview
22 R graphics landscape
23 ggplot2 tutorial
24 Do’s and don’ts of making effective graphics
25 Using colors in R
26 Taking control of qualitative colors in ggplot
27 Secrets of a happy graphing life
28 Writing figures to file
29 Multiple plots on a page
VIII All the package things
Overview
- Resources
30 Write your first R package
31 System preparation for package development
32 Write your own R package
IX All the automation things
Overview
- Resources
33 Why and how we automate data analyses + examples
34 make: special considerations for Windows
35 Automation: test drive make
36 Automating data-analysis pipelines
37 More example pipelines
X Get data from the web
38 Slides
39 Use API-wrapping packages
40 DIY web data
XI All the Shiny things
Overview
- Awesome add-on packages to Shiny
- Resources
41 Slides
42 Building Shiny apps
Appendix
A Deprecated
B Short random things
References
License: CC-BY-SA

STAT 545

Chapter 33 Why and how we automate data analyses + examples

See “Automating data analysis pipelines” slides by Shaun Jackman and Jenny Bryan.