Creating new projects

EPI 590R final project

Your goal will be to create an analysis that

• I can reproduce on my own computer
• Is easy to rerun if I tell you, for example, to remove the 12th row of your dataset

We’ll start this in class!

New projects

We cloned our first project from GitHub; now we are going to start a new project from scratch

1. File > New Project > New Directory > New Project

• If you ever want to convert an existing folder that holds an analysis into an R project, you can choose “Existing Directory”
• You’ll also see other options besides “New Project” – an R package, a Shiny app, etc.
  • These will get you set up with some initial files for these types of projects
  • You can also make a template of your own!

New projects

2. Choose a name for your new project and where to store it on your computer

• Check “Create a git repository”
  • This gets you all set to connect to GitHub and creates a .gitignore file
• You can leave “Use renv with this project” unchecked (we’ll be introducing the {renv} package later!)

Initial Git commit

3. Stage and commit the files

• I usually use “initial commit” as my first commit message since I haven’t don’t anything yet!
• We can’t yet push because we haven’t connected to a remote repository

Creating a new repo on GitHub

4. Open up your web browser to GitHub and make a new repository

   • From github.com
   • From github.com/username

Repository options

5. Choose a name (preferably one that matches the name you gave your R project).

• You can choose to make it private, if you wish
  • Private repos have some fewer features unless you have GitHub Pro (which you can get for free as a student with the GitHub student developer pack!)
• You don’t need to click anything else

Connect the local to the remote

• You created your local repo with RStudio in a directory you chose
• Now you need to connect it to the remote repo on GitHub

6. Copy the code from the second section: “push an existing repository from the command line” in the terminal within RStudio.

Connect the local to the remote

7. Run the three lines of code one at a time, then refresh your GitHub page!

.gitignore

You likely don’t want to push everything to GitHub, even if you have a private repository

• Be especially careful about data and passwords
• You also can’t push very large files (>100 mb)

A .gitignore is a special text file that tells Git not to track certain files

• RStudio starts you off with a few entries, including .Rhistory since no one needs to see everything you’ve run in R!

.gitignore exercises

8. Create a new file called secrets.txt within this new repo

• Write down your deepest, darkest secrets and save

9. Open .gitignore via the RStudio filepane

• Add “secrets.txt” below the files that RStudio helpfully ignored for you
• Save

Keep your eye on the Git pane!

Starting the final project

10. Set up your folders how you’d like in your repo (you can always change this)

• Find some data, download it, and store it in your repo
• Commit and push to GitHub!

For your final project, your data must be something that can be stored online and accessed by me.

Some fun options for data are:

• https://data.fivethirtyeight.com/
• https://github.com/rfordatascience/tidytuesday#datasets
• https://github.com/higgi13425/medicaldata/tree/master/data (descriptions: https://higgi13425.github.io/medicaldata/#list-of-datasets)

Exercises

Get started making a new project and GitHub repo for your final project and finding some fun data

You can always change anything you want later, and even delete the whole thing and start fresh!