dsci-310-group-7-data-dudes

Predicting Age from Drug Use Patterns: A Statistical Analysis

Contributors: Jade Chen, Jessica Luo, Heidi Lantz, Nazia Edroos

Table of Contents

Summary:

Our dataset, obtained from FiveThirtyEight, contains the results of a nationwide survey that collected information about drug use and health.

We use this data to predict an individual’s age group as Youth (Under 21) or Adult (21 & Over) based on their history of reported substance use.

We created and ran three different models using a train/test split on our data. All three models were unable to completely correctly classify youth vs. adult due to some limitations with our dataset, mostly a limited sample size and imbalance in how data was categorized.

How to Run the Analysis:

Use the following steps to reproduce the analysis in a containerized environment:

Option 1 - Quick Start with Pre-Built Image

  1. Pull the latest pre-built Docker image from Docker Hub:

     docker pull jadeeechen/dsci-310-group-7-data-dudes:latest
    • This will pull the container onto your local computer
  2. Use the provided docker-compose.yaml file in this repository to start the container.
    • Clone this repository by running git clone https://github.com/DSCI-310-2025/dsci-310-group-7-data-dudes.git in your terminal
    • In the directory containing the downloaded/pulled compose file, run docker-compose up
    • Navigate to http://localhost:8787/ in your browser which will open an rstudio window in your browser, no login needed!
  3. Once you are in the rstudio window, run make all
    • This will run all the scripts to clean the data, create EDA figures and tables, and create the model and visualizations
    • It will also create an index.html file in the docs folder to allow you to easily view the full report.

Option 2 - Manual Docker Build & Local Environment Setup

  1. In your terminal, use the following bash command to clone this repository from GitHub to your local machine.

     git clone https://github.com/DSCI-310-2025/dsci-310-group-7-data-dudes.git

  2. Set this newly cloned repository as your local working directory.

     cd dsci-310-group-7-data-dudes/

  3. Open the Docker application.

    1. Download and install Docker:
    2. Open the Docker app by clicking on its icon (from Finder, Applications or Launchpad).
    3. To test if Docker is working, type the following in the terminal: 
      docker run hello-world

      You should see something like this if you were successful:

       Unable to find image 'hello-world:latest' locally
 latest: Pulling from library/hello-world
 1b930d010525: Pull complete
 Digest: sha256:451ce787d12369c5df2a32c85e5a03d52cbcef6eb3586dd03075f3034f10adcd
 Status: Downloaded newer image for hello-world:latest

 Hello from Docker!
 This message shows that your installation appears to be working correctly.

 To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
     (amd64)
 3. The Docker daemon created a new container from that image which runs the
     executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it
     to your terminal.

 To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

 Share images, automate workflows, and more with a free Docker ID:
 https://hub.docker.com/

 For more examples and ideas, visit:
 https://docs.docker.com/get-started/

  4. Within your local computer, set up the environment using Docker image:

    • Windows users, run the following:

        docker build -t data-dudes-analysis .

        docker run -it --rm -p 8787:8787 -e PASSWORD="sushi" data-dudes-analysis

    • Mac users, run the following:

        docker build --platform=linux/amd64 -t data-dudes-analysis .

        docker run --platform=linux/amd64 -it --rm -p 8787:8787 -e PASSWORD="sushi" data-dudes-analysis

  5. Access the analysis

    • Open a browser and go to http://localhost:8787.
      • Username: rstudio
      • Password: sushi
    • Navigate to the file project folder in the bottom right panel to view all files.

  6. Run the analysis script

    • In the RStudio terminal, type cd project to set the correct working directory
    • In the RStudio terminal, type make all, which:
      • Cleans any older renders of our analyss file, cleans data/ and output/ directories
      • Runs the analysis scripts
      • Renders the Quarto document

  7. View the results

    • In the Files panel on the bottom right, click the newly created index.html file
    • Select View in Web Browser to view the results

Attribution: DSCI310 Group 02 instructions

Dependencies

System Dependencies

R Dependencies

Licenses

These are the licences contained in this repository:

GitHub license

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