Lab 1: Feedback and Discussion

Nicky Wakim

2026-01-21

Notes from me and the TAs

Get into groups of 2-4

  • No more than 4!
  • Introduce yourself if you do not know each other
  • Share you html documents with each other (email, airdrop, etc.)

Introduce Research Question

  • Share your research question with each other!
  • Share your other variables
  • Is everyone using the appropriate variables?
  • Did anyone see any potential issues with how many individuals are in each category of a variable?

Double check code and saved data

  • Did everyone in the group successfully save the dataset?
  • Can you open it right now?
  • Did everyone include all the race variables?
  • Any other issues with variables??

Compiled introductions

  • What major points did you include in your introduction?
  • Would you change anything after discussing with your group?

More discussion for 1/26

File Management

From Lesson 2: Folder organization

  • Make a folder for our class!

    • I suggest naming it something like BSTA_512_W25 to indicate the class and the term

  • Make these folders in your computer (or in OneDrive if you prefer)

    • Only make them in OneDrive if you have a desktop connection

  • For a project, I have the following folders

    • Background
    • Code
    • Data_Raw
    • Data_Processed
    • Dissemination
    • Reports
    • Meetings

  • For our class, I suggest making one folder for the course with the following folders in it:

    • Data
    • Homework
    • Project (with above subfolders)
    • Lessons
    • And other folders if you want

From Lesson 2: Creating project in RStudio

  • Way to designate a working directory: basically your home base when working in R

    • We have to tell R exactly where we are in our folders and where to find other things

    • A project makes it easier to tell R where we are

  • Basic steps to create a project

    • Go into RStudio

    • Create new project for this class (under File or top right corner)

      • I would chose “Existing Directory” since we have already set up our folders
      • Make the new project in the BSTA_512_W25 folder

  • Once we have projects, we can open one and R will automatically know that its location is the start of our working directory

  • Only make one project for now!!

From Lesson 2: Using here package

  • Within your console, type here() and enter

    • Try this with getwd() as well
library(here)
here()
[1] "/Users/wakim/Library/CloudStorage/OneDrive-OregonHealth&ScienceUniversity/Teaching/Classes/BSTA_512_26W/BSTA_512_26W_site"
getwd()
[1] "/Users/wakim/Library/CloudStorage/OneDrive-OregonHealth&ScienceUniversity/Teaching/Classes/BSTA_512_26W/BSTA_512_26W_site"

 

  • here can be used whenever we need to access a file path in R code
    • Importing data
    • Saving output
    • Accessing files

Multi-selection/multi-response variables

Multi-response/multi-selection variables like our race/ethnicity variables

4 Approaches to Multiple-Race Questions from We All Count

  • This method works for any multi-level variable

Final notes

  • For now, I suggest the binary approach!
    • This is the perfect level of pushing ourselves coding wise and thinking critically about these multi-response variables
    • This is what the dataset gives you!!

 

For our 8 variables on race/ethnicity

  • Make sure you understand what the indicators mean
    • Each variable is a 0/1 indicator for whether the individual selected that race/ethnicity
      • 0 indicates that they do not identify as that race/ethnicity
      • 1 indicates that they do identify at that race/ethnicity
      • When we go to interpret the coefficient for a specific indicator variable, we will interpret it as the mean difference in IAT score between those who identify as that race/ethnicity and those who do not
      • For example, a coefficient for re_black indicates the mean difference in IAT score between those who identify as Black or African American and those who do not identify as Black or African American
    • An individual can select more than one race/ethnicity
  • In Lab 1: Filtering out individuals with missing information on their race/ethnicity