1
Syllabus
1.1
Is this course for you?
1.2
General schedule
2
Pre-course tasks
2.1
Installing programs
2.2
Getting familiar with RStudio
2.3
Installing R packages
2.4
Setting up Git and GitHub
2.5
Course introduction
3
Code of Conduct
3.1
Expected Behavior
3.2
Unacceptable Behavior
3.3
Consequences of Unacceptable Behavior
4
Group project assignment
4.1
Specific tasks
4.2
Quick “checklist” for a “good” project
4.3
Expectations for the project
5
Lecture slides
Introduction to the course
Finding and obtaining open datasets
Collaboration and teamwork
Era of reproducible and open science
6
Management of R projects
6.1
What is a project and why use it?
6.1.1
RStudio and R Projects
6.1.2
Exercise: Reading the READMEs
6.1.3
Exercise: Better file naming
6.1.4
Next steps after creating the project
6.2
RStudio layout and usage
6.3
Basics of using R
6.4
Using auto-completion in RStudio
6.5
R object naming practices
6.6
Exercise: Make code more readable
6.7
Automatic styling in RStudio
6.8
Packages, data, and file paths
6.9
Encountering problems and finding help
6.10
Summary of session
6.11
Final exercise: Group work
7
Data management and wrangling
7.1
“Messy” vs. “tidy” data
7.2
Managing and working with data in R
7.3
Load the packages and dataset
7.4
Exercise: Become familiar with the dataset
7.5
Select specific columns in a dataset
7.6
Rename specific columns
7.7
Chaining functions with the pipe
7.8
Exercise: Practice what we’ve learned
7.9
Filter the data by row
7.10
(Re)Arranging the rows of your data by column
7.11
Transform or add columns
7.12
Exercise: Piping, filtering, and mutating
7.13
Split-apply-combine: Summarizing data
7.14
Converting between wide and long data
7.14.1
Pivot from wide to long
7.14.2
Pivot from long to wide
7.15
Pivot, then split-apply-combine
7.16
Saving datasets as files
7.17
Final exercise: Group work
8
Version control with Git
8.1
What is version control?
8.2
What is Git?
8.3
Basics of Git
8.4
Using Git in RStudio
8.5
Exercise: Committing to history
8.6
“Remotes”: Storing your repository online
8.7
Exercise: Clone GitHub repository from RStudio
8.8
Synchronizing with GitHub
8.9
Exercise: Push and pull
8.10
Dealing with file conflicts between the local and remote
8.11
Exercise: Dealing with merge conflicts
8.12
Collaborating using Git and GitHub
8.13
Summary of session
8.14
Final exercise: Group work
9
Data visualization
9.1
Basic principles for creating graphs
9.2
Basic structure of using ggplot2
9.3
Graph individual variables
9.4
Plotting two variables
9.4.1
Two discrete variables
9.4.2
Discrete and continuous variables
9.5
Exercise: Create plots with one or two variables
9.6
Visualizing three or more variables
9.7
Colours: Make your graphs more accessible
9.8
Titles, axis labels, and themes
9.9
Saving the plot
9.10
Final exercise: Group work
10
Analytically reproducible documents
10.1
Why try to be reproducible?
10.2
Creating an R Markdown file
10.3
Exercise: Create another R Markdown document.
10.4
Inserting R code into your document
10.5
Exercise: Creating a table using R code
10.6
Formatting text with Markdown syntax
10.6.1
Headers
10.6.2
General text formatting
10.6.3
Lists
10.6.4
Block quotes
10.6.5
Adding footnotes
10.6.6
Adding links to websites
10.6.7
Inserting (simple) tables
10.6.8
Inline R code
10.7
Exercise: Practice using Markdown for writing text
10.8
Inserting figures, as files or from R code
10.9
Other R Markdown features
10.9.1
Making your report prettier
10.9.2
Collaborating on R Markdown documents
10.10
Final exercise: Group work
Appendix
A
Resources for learning
B
Acknowledgements
C
For Instructors
C.1
Workshop details
C.1.1
Instructor and helper number
C.1.2
Setting up teams
C.1.3
Before your session
C.1.4
First day
C.1.5
Throughout the sessions
C.1.6
Making use of the stickies
C.2
Lesson material details
C.3
Version control
C.3.1
About the slides
D
License
References
Published with bookdown
Check out the material on GitLab
Reproducible Research in R
5
Lecture slides
Introduction to the course
Finding and obtaining open datasets
Collaboration and teamwork
Era of reproducible and open science