Case Studies in Reproducible Research
1
Course Overview
1.1
Meeting Times, Location, Requirements
1.2
The origin of this seminar
1.3
Course Organizers
1.4
Course Goals
1.5
Weekly Syllabus
1.5.1
Week 1 — Introduction and Getting Your Workspace Set Up
1.5.2
Week 2 — RStudio project organization; using git and GitHub; Quick RMarkdown
1.5.3
Week 3 — Tibbles. Reading data in. Data rectangling
1.5.4
Week 4 —
2
Week One Meeting
2.1
Software Installation
2.2
Get an account on GitHub
2.2.1
Private repositories
2.3
Open an RStudio Project from GitHub
2.4
Assignment for next week: Create an RStudio Project with Your Own Data
2.5
Reading for next week
3
Week Two Meeting
3.1
Workflow and Project Recap
3.1.1
Workflow: basics
3.1.2
Workflow: projects
3.1.3
Workflow: scripts
3.2
Let’s talk about the pipe
%>%
3.3
Tibbles and “rectangular” data
3.3.1
Tibble excercises
3.4
Data import
3.4.1
RStudio’s GUI importer
4
Week Three Meeting
4.1
Git Basics
4.1.1
An overview of Version Control Systems (VCS)
4.1.2
Git versus GitHub
4.1.3
Using git through RStudio
4.1.4
Go for it everyone!
4.1.5
How does git store and keep track of things
4.2
Pushing and Pulling With GitHub
4.2.1
Creating a Repository on GitHub and the initial push
4.2.2
Subsequent pushes
4.2.3
Assign collaborators
4.3
Next Week’s Assignment
5
Week Four Meeting
5.1
Knit your README.Rmd files
5.2
Changing to factors
5.3
The
group_by()
function
5.4
How do I learn about all the vectorized functions I can use in
mutate()
and
summarize()
?
5.5
Next Week’s Assignment
6
Week Five Meeting
6.1
Some things regarding people’s repositories
6.1.1
Data Compression
6.2
A quick aside about missing data
6.3
Brief Highlights of the Joins Chapter
6.3.1
A few thoughts on keys
6.4
An example of using some joins
6.4.1
When would I use
right_join()
?
6.4.2
What about that
inner_join()
6.4.3
An anti_join example
6.4.4
Just for fun, let’s see a full_join()
6.5
Working with R Notebooks
6.5.1
Open your own R Notebook
6.5.2
Working with R Notebooks
6.5.3
Some caveats about notebooks
6.5.4
Opening a .nb.html file in Rstudio
6.6
For Next Week:
7
Week 6 meeting: using ggplot2
7.0.1
Your Mission
7.1
Some lecture notes from a few years ago
7.1.1
Goals for this hour:
7.2
About ggplot2
7.2.1
Basics
7.2.2
What is this grammar of graphics?
7.2.3
ggplot2
7.2.4
Components of the grammar of graphics
7.2.5
In a nutshell
7.3
An example, please
7.3.1
A pole vaulting example
7.3.2
A first ggplot
7.3.3
ggplot’s system of defaults
7.3.4
How many geoms are there?
7.3.5
Getting even sillier
7.3.6
More! Add some text to it!
8
Week 7: Making Simple Maps with R
8.1
Intro
8.1.1
Today’s Goals
8.1.2
Prerequisites
8.1.3
Load up a few of the libraries we will use
8.2
Plotting maps-package maps with ggplot
8.2.1
The main players:
8.2.2
Maps in the maps package
8.2.3
Makin’ data frames from map outlines
8.2.4
The structure of those data frames
8.2.5
Plot the USA map
8.2.6
State maps
8.2.7
zoom in?
8.2.8
True zoom.
8.3
ggmap
8.3.1
Three examples
8.3.2
How ggmap works
8.3.3
Sisquoctober
8.3.4
Big Creek in Big Sur
8.3.5
How about a bike ride?
8.3.6
Fish sampling locations
8.4
In-class review and short assignment
8.4.1
Short assignment of plotting with
maps
8.4.2
Short assignment of plotting with ggmap()
9
Plotting “Spatial” Data with ggplot
9.1
Point and Vector Data Types
9.1.1
Some example vector data
9.1.2
Reading in Vector Spatial Data
9.1.3
Reading Shapefiles Take Two
9.1.4
Quick Visualization of our Vector Data
9.1.5
The Structure of Spatial*DataFrames
9.1.6
Subsetting Spatial*DataFrames
9.2
Coordinate Reference Systems
9.3
Plotting things with ggspatial
9.3.1
Want a background?
9.4
Simple Intersection of Spatial Objects
9.5
Spatial Raster Data
9.6
Wrapping Up
10
A Tidy Approach to Spatial Data: Simple Features
10.1
Prelims
10.2
Reading spatial data into sf objects
10.3
Plotting sf objects
10.4
Geometric operations
10.5
Interactive plotting with leaflet via the
mapview
package
10.5.1
Mapping attributes
10.5.2
Adding layers
10.5.3
Saving maps
10.6
Adding map tiles from other sources
10.7
More info
References
Published with bookdown
Case Studies in Reproducible Research: a spring seminar at UCSC
References