Day 1 Tasks & Activities

Project organization, data import, file paths

1. Setup

Get the data and create a version-controlled R Project

  • Fork, add your name to end of the repo name, and clone this repo
  • Before you move on, read more about the data here
  • Add two subfolders to your R project: docs and figs
  • Create and save a new Quarto document as stl_lead_inequity.qmd in the docs folder

2. Data import and exploration

Read in & explore the data

In your .qmd:

  • Attach the tidyverse and janitor packages in a new code chunk

  • Read in the stl_blood_lead.csv data as stl_lead and use janitor::clean_names to convert all variable names to lower snake case

  • Do some basic exploration of the dataset (e.g. using summary, data visualizations and summary statistics).

  • In a new code chunk, from stl_blood_lead create a new data frame called stl_lead_prop that has one additional column called prop_white that returns the percent of each census tract identifying as white (variable white in the dataset divided by variable totalPop, times 100). You may need to do some Googling. Hint: dplyr::mutate(new_col = col_a / col_b) will create a new column new_col that contains the value of col_a / col_b

3. Visualize data

Create a scatterplot

  • In a new code chunk, create a scatterplot graph of the percentage of children in each census tract with elevated blood lead levels (pctElevated) versus the percent of each census tract identifying as white.
  • Customize by updating several aesthetics (e.g. size, opacity (see alpha =), color, etc.)
  • Store the scatterplot as stl_lead_plot
  • Have the scatterplot returned in the rendered html - customize the size that it appears when knitted
  • Also save a .png of the scatterplot to figs, with dimensions of (6” x 5”) (width x height)
  • In text above or below the scatterplot, write 1 - 2 sentences describing the overall trend that you observe from your graph

Create a histogram

  • Create a histogram of only the pctElevated column in the data frame (remember, this will only take one variable - the frequency is calculated for you by geom_histogram)
  • Customize the fill, color, and size aesthetics - test some stuff! Feel free to make it awful.
  • Once you’ve played around with customization, export the histogram as a .jpg to the figs folder
  • Make sure the histogram also shows up in your rendered html

4. Collaborate!

Render & push

  • Render your .qmd
  • Stage, commit, pull then push changes using the command line

Share & test

  • Share the link to your public GitHub repo with a neighbor
  • Fork the repo your neighbor shared, then clone to get set up locally in RStudio
  • Navigate to their .qmd
  • Render - does everything work? Cool, your neighbor made a reproducible project with file paths you can use too!

End Activity Session (Day 1)