Lab 2 - Data Workflow

Lab 2 — Your First Quarto Document

Goal: create, edit, and render a Quarto document — the format you’ll use for every lab and both projects this semester. Again, no real statistics here. You’re learning the container your work will live in, while it’s low-stakes.

Prerequisite: Lab 1 done (R and RStudio installed and working).

What to submit: your rendered document (details in the last step).

Why this matters (30 seconds of context)

In this course, your writing and your analysis live in the same document. You write a sentence, then a chunk of R code, then the code’s output appears right there when you “render.” This is the heart of reproducible science: anyone can see exactly how you got your result, and re-run it. Getting comfortable with this now means you never have to think about it again.

Step 1 — Create a new Quarto document

  1. In RStudio: File → New File → Quarto Document…
  2. Give it a title like “Lab 2” and put your name in the author field.
  3. Leave the other defaults. Click Create.
  4. RStudio opens a new document with some example text already in it. You’ll see two kinds of content: plain text, and gray code chunks that start with ```{r}.

Step 2 — Render it as-is

Before changing anything, click the Render button (near the top of the document — it has a blue arrow icon). RStudio may ask you to install additional packages, follow those instructions. It may also ask you to save the file first; save it somewhere you’ll find it.

The first render may take a moment. When it finishes, a formatted document appears (in a viewer pane or your browser). That’s rendering: your .qmd source became a shareable output document. Look at how the example text and the code output map back to the source. This is the whole idea.

Step 3 — Visual vs. Source

In the top left you will see two “buttons” (Source, Visual). Click back and forth between them to examine the differences. These are Quarto documents which operate like a Markdown. These allow you to put regular text and your analyses all in one document. An example of this would be writing your results section and having all the numerical values populate accordingly. It’s like magic.

Source

This is the raw Markdown text. Beginner information about formatting around Markdown specifically can be found here (R Markdown: The Definitive Guide), but that isn’t necessarily a focus of the course.

Visual

This is what you are used to seeing when working with a document. The Visual editor takes the Source material and formats it appropriately (bold, italics, links, etc.)

You can choose either to work with. I may go back and forth between them. Sometimes debugging is a little bit easier in the Source.

Step 4 – Editing

Now edit the source. Delete the example text below the title block (everything under the second ---) and replace it with your own:

  1. Add a header by starting a line with ##:

    ## PSYC 640

  2. Add a short list (each item starts with a dash):

    - My favorite movie is ...
- Something I'm hoping to learn this semester is ...

  3. Now add a code chunk. You can click the green “+C” insert-chunk button, or type it manually:

    ```{r}
mean(c(10, 20, 30))
```

    Code chunks can be run by clicking the “Play” button on the top right of the chunk. You can also put the cursor on the line you want to run and use the keyboard shortcut Ctrl + Enter (for PC) Command + Enter (for Mac).

  4. Inside the chunk you can put any R. Here we’re just averaging three numbers again.

  5. Add one more chunk that does simple arithmetic of your choosing — anything like 100 / 4 or 2 ^ 5. The point is just to see your code produce your output.

Step 5 – Your First Analysis

  1. Create another Code Chunk

  2. Load the tidyverse library (library(tidyverse))

  3. Create the object datawars and assign dataset called starwars to it (hint: datawars <- starwars)

  4. Use View(), head() and glimpse() to look at datawars.

  5. In the text below, answer this question:

    What do each of these do?

  6. Create another code chunk and use summary() to get descriptives of all variables in the dataset.

    1. Look at the output of your summary() command. For the mass and height variables, you’ll see a value for NA's. In your own words, what do you think NA means in this context?

Visualize your data

Now we want to investigate the relationship between mass and height in this dataset.

  • Create a scatterplot using ggplot().

  • The plot should show height on the x-axis and mass on the y-axis.

  • Add some labels to make your plot clear and professional.

    • Hint: Use the code below as a template and fill in the blanks.
ggplot(data = __, aes(x = __, y = __)) +
  geom_point() +
  labs(title = "__",
       x = "__",
       y = "__")
  • Look at your plot. Do you notice any characters that seem unusually heavy for their height? Briefly describe one.

Step 6 — Render again and check

Click Render again. Your new text, header, list, and the output of your code chunks should all appear in the formatted document. If a number you computed shows up in the output, your code ran successfully.

If it won’t render: the most common cause is a code chunk with a typo (a missing backtick or parenthesis). Check that each chunk opens with ```{r} and closes with ``` on its own line. If you’re still stuck, submit the .qmd file with a note about what happened and we’ll look next week.

Step 7 — Submit

Render one final time to produce the output document (it’ll be an .html file in the same folder as your .qmd, unless you chose otherwise). Submit both the .qmd source and the rendered output if you can; if only one, send the rendered one.

Include one to two sentences about Lab 2: how did that feel — smooth, fiddly, confusing? Honest reactions help me pace Week 2.

The takeaway

You just did, in miniature, what every assignment this semester will ask: write in a .qmd, run code in chunks, render to a shareable document. The statistics will get more involved; this container won’t change. You’re set.