Before You Begin

If you already are an expert in “R”, “RStudio”, and “GitHub”, you are likely not in the place I was when I began the DACSS program, and bless you! For me, it took a lot of remedial catch-up work to become even remotely fluent in these new languages.

A few different entry points to understand a little about data are in this list, though not exhaustive and definitely not the final words on the matter!

Background

Articles

• What is “tidy data”? Hadley Wickham

• Sidenote: who is Hadley Wickham? Someone you’ll learn a lot about

• What’s the big deal about reproducable data? Roger Peng

Books

• R for Data Science Open Source

Videos

• Hadley Wickham on R for Data Science

Understand the Elements of Your New Workflow

• Complete these installation instructions.

• Test your connection between GitHub and RStudio following these steps.

• NOTE: Experts strongly recommend that if you are not already a fluent GitHub user you choose HTTPS over SSH.

• Read through as much as possible on Happy Git With R to understand how Git, GitHub, R, and RStudio and how they all work together.

Installations

After installing Git/GitHub/R/RStudio, there are some packages you will use from the beginning, which you can install by connecting to the internet, opening RStudio, and running at the command line:

```r
> install.packages(c("usethis", "remotes", "distill", 
                   "postcards", "here", "tidyverse"))
```

Get Down with Markdown

• It’s easy to complete this 10-minute interactive tutorial on Markdown.

• This is your resource for RMarkdown

Configure Your GitHub Profile

• Personalize your profile

• Pin projects to profile

Customize Your Profile README

You can share information about yourself with the community on GitHub by creating a profile README. GitHub shows your profile README at the top of your profile page.