• T32 Training Sessions
  • 1 Introduction
    • 1.1 Acknowledgements
    • 1.2 License
  • Session I
  • 2 The Center for Data and Bioinformation Services
    • 2.1 Visit Our New Web Portal
    • 2.2 Data Management Planning
    • 2.3 Data Collection
    • 2.4 Finding Data
    • 2.5 Data Sharing and the UMB Data Catalog
    • 2.6 Workshops
    • 2.7 Data Analysis Support
    • 2.8 Working with Molecular Data
    • 2.9 Research Computing
    • 2.10 Expert Consultations
    • 2.11 Project Contribution
  • 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 Learning Objectives
    • 4.2 Why learn R?
    • 4.3 Starting out in R
      • 4.3.1 Downloading, Installing and Running R
      • 4.3.2 RStudio
    • 4.4 Working in the Console
    • 4.5 Objects
      • 4.5.1 Creating Objects
    • 4.6 Saving code in an R script
      • 4.6.1 Setting your Working Directory
    • 4.7 Functions and their arguments
      • 4.7.1 Challenge: using functions
      • 4.7.2 Getting Help
    • 4.8 Packages
    • 4.9 Vectors
      • 4.9.1 Missing Data
      • 4.9.2 Mixing types
      • 4.9.3 Indexing and Subsetting vectors
      • 4.9.4 Data frames and tibbles
  • 5 Welcome to the Tidyverse
    • 5.1 Install
    • 5.2 The Data
    • 5.3 Importing data
    • 5.4 Working with columns
      • 5.4.1 Select()
      • 5.4.2 Renaming columns
      • 5.4.3 Creating new columns with mutate()
    • 5.5 Working with rows
      • 5.5.1 filter()
      • 5.5.2 Grouping and Summarizing data
    • 5.6 Plotting with ggplot2
  • 6 Joining Datasets
    • 6.1 Long vs Wide formats
  • Session III
  • 7 Reproducible Project Management
    • 7.1 RStudio Projects
      • 7.1.1 What is Real?
      • 7.1.2 Where does your analysis live?
      • 7.1.3 Creating an RStudio project
    • 7.2 Version Control and RStudio
      • 7.2.1 Why Git?
      • 7.2.2 What’s GitHub?
    • 7.3 Setting up a remote repository on Github
    • 7.4 Connecting Rstudio to Github
      • 7.4.1 Introduce yourself to Git
    • 7.5 Get a personal access token (PAT)
      • 7.5.1 Create the PAT
      • 7.5.2 Put your PAT into the Git credential store
    • 7.6 Checking out a project from a version control remote repository
      • 7.6.1 Clone the new GitHub repository to your computer via RStudio
    • 7.7 Making some changes, save, commit.
    • 7.8 Push your local changes online to GitHub
    • 7.9 Confirm the local change propagated to the GitHub remote
    • 7.10 Clean up
  • 8 Reproducible Reports with R Markdown
    • 8.1 What is R Markdown
    • 8.2 R Markdown Related Packages
    • 8.3 How does R Markdown work
      • 8.3.1 Creating an R Markdown file
      • 8.3.2 R Markdown Basic Components
      • 8.3.3 Markdown
      • 8.3.4 R code chunks
    • 8.4 Resources for R Markdown
  • 9 Shiny Apps
    • 9.1 Shiny app basics
    • 9.2 Create an empty Shiny app
      • 9.2.1 Alternate way to create a Shiny app: separate UI and server files
      • 9.2.2 Let RStudio fill out a Shiny app template for you
    • 9.3 An example Shiny App
    • 9.4 Host your Shiny App
    • 9.5 Resources
  • Appendix
  • A Further Resources
    • A.1 Installing R and RStudio
    • A.2 The Tidyverse
    • A.3 Graphing in R
    • A.4 R Based Technologies
  • B Selected Glossary of R Terminology
  • C Packages and Functions Used
  • Published with bookdown

T32 Working with Data Training

9.5 Resources

Shiny is a very popular package and has lots of resources on the web. Here’s a compiled list of a few recommended resources which are all fairly easy to read and understand.

  • Shiny official website
  • Shiny official tutorial
  • Shiny cheatsheet
  • Lots of short useful articles about different topics in Shiny - highly recommended
  • Shiny in R Markdown
  • Get help from the Shiny Google group or StackOverflow
  • Publish your apps for free with shinyapps.io
  • Host your app on your own Shiny server
  • Learn about how reactivity works
  • Learn about useful debugging techniques