T32 Training Sessions
1
Prerequisites
Session I
2
The Center for Data and Bioinformation Services
3
Best Practices for Research Data Management
3.1
Why Data Management?
3.1.1
Funding Agency Requirements
3.1.2
Publisher Requirements
3.1.3
Why Data Management? – What’s in it for me?
3.1.4
Don’t end up here!
Data Management Best Practices
3.2
Data Management Planning
3.2.1
Data Lifecycle
3.2.2
Planning
3.2.3
Data Management Plans (DMP’s)
3.2.4
Data Management Workflows
3.3
Data Collection
3.3.1
Variables - Best Practices
3.3.2
Data Documentation
3.4
File Organization
3.4.1
File Naming Issues
3.4.2
File Naming Conventions
3.4.3
File Naming Recap
3.5
Storage
3.5.1
Storage Solutions at UMB
3.5.2
Cloud Storage Options
3.5.3
Backup Considerations
3.5.4
Security Considerations
3.6
Preservation
3.6.1
Preservation Issues
3.6.2
Open Software Formats
3.6.3
Data Formats
3.7
Providing Access
3.7.1
Why Share Data?
3.7.2
Data Sharing Challenges
3.7.3
Providing Access to Data
3.7.4
Data Repositories
3.7.5
UMB Data Catalog
3.7.6
Data Publishing
3.8
Conclusion
3.9
Attributions
3.10
Photo References
Session II
4
Introduction to R and RStudio
4.1
Starting out in R
4.1.1
Downloading, Installing and Running R
4.2
RStudio
4.2.1
Installing and Loading Packages
4.2.2
Installing Packages - RStudio
4.2.3
Getting Help
4.3
Saving code in an R script
4.3.1
Setting your Working Directory
4.4
The Basics
4.4.1
R as a calculator
4.4.2
Creating Objects
4.4.3
Naming Objects
4.5
Vectors
4.5.1
Mixing types
4.6
Indexing vectors
4.6.1
Challenge: indexing
4.6.2
Sequences
4.7
Functions
4.7.1
Challenge: using functions
5
Welcome to the Tidyverse
5.1
Getting started
5.2
Install
5.3
Importing data
5.4
Working with columns
5.4.1
Select()
5.4.2
Working with dates
5.4.3
Renaming columns
5.5
Working with rows
5.5.1
filter()
5.5.2
Grouping and Summarizing data
5.6
Pipes
5.7
Creating new columns
5.8
Plotting with ggplot2
5.9
Joining datasets
5.9.1
Long vs Wide formats
Session III
6
Reproducible Project Management
6.1
RStudio Projects
6.1.1
What is Real?
6.1.2
Where does your analysis live?
6.1.3
Creating an RStudio project
6.2
Version Control and RStudio
6.2.1
Why Git?
6.2.2
What’s GitHub?
6.3
Setting up a remote repository on Github
6.4
Connecting Rstudio to Github
6.4.1
Introduce yourself to Git
6.5
Get a personal access token (PAT)
6.5.1
Create the PAT
6.5.2
Put your PAT into the Git credential store
6.6
Checking out a project from a version control remote repository
6.6.1
Clone the new GitHub repository to your computer via RStudio
6.7
Making some changes, save, commit.
6.8
Push your local changes online to GitHub
6.9
Confirm the local change propagated to the GitHub remote
6.10
Clean up
7
Reproducible Reports with R Markdown
7.1
Introduction
7.1.1
Communicating Results (Reproducibly)
7.2
What is R Markdown
7.3
How does R Markdown work
7.4
Getting help with R Markdown
8
Interactive Shiny Apps
Published with bookdown
T32 Working with Data Training
7.4
Getting help with R Markdown