Syllabus
Overview
Epi/Biostats Summer Institute - Introduction to R for Public Health Researchers 140.604.73
This course will provide “hands-on” training for learning how to analyze data in the R statistical software package. We will cover data input/output, data management and manipulation, and how to make useful and informative graphics.
Objectives
By the end of the course, students should be comfortable:
- Reading data into R
- Recoding and manipulating data
- Using R add-on packages
- Making exploratory plots
- Performing basic statistical tests
- Understanding basic programming syntax
- Creating reproducible R documents
Logistics
When
- Dates: January 8-12 and 16-19, 2024 (In recognition of Martin Luther King Jr. Day, there will be no class on Monday January 15th, 2024.)
- Time: 1:30 p.m. - 4:50 p.m. ET
Where
- Class Website: http://jhudatascience.org/intro_to_r/
- CoursePlus: https://courseplus.jhu.edu/core/index.cfm/go/syl:syl.public.view/coid/20749/
- Course lectures and labs will be online through Zoom
- Zoom link and Slack link will be emailed to students
- Communication will mainly occur through Slack
Instruction Team
- Instructors: Carrie Wright (cwrigh60 at jhu.edu), Ava Hoffman (ava.hoffman at jhu.edu), Clifton McKee (cmckee7 at jhu.edu)
- Teaching Assistants: Padmashri Saravanan (psarava1 at jhu.edu), Alex Newman (anewma28 at jhu.edu)
Course Format
Each class will consist of 2 or 3 hour-long modules.
Each module features a lecture and an R programming lab, where students apply the skills taught in the modules to real data in breakout rooms.
Class sessions will be recorded and later posted.
If you have a question not covered during class, please post it on Slack. This allows everyone to see it. If another student does not answer your question (which we encourage!), we will try to answer it within 24 hours. If you feel uncomfortable posting a question publicly, let a TA or instructor know your question and we will post it for you anonymously.
To get the most out of this class, if possible, we suggest working virtually with a large monitor or two screens. This setup allows you to follow along on Zoom while also doing the hands-on coding.
Please click here for details about using Zoom.
Zoom + Working Virtually
- Please be aware that there is the option to use closed captioning:
closed captioning on zoom- During lectures you can indicate how you feel about the pace of the course like so:
reactions about pace- In breakout room sessions you can ask for help like so:
ask for help- For directions on how to change breakout rooms click here.
Course Evaluation
Course evaluations are very important for the school to determine what courses to continue to support. It also helps us to improve the course with your feedback.
- Use this link to access the evaluation: https://courseevaluations.jhsph.edu.
- Use your JHED ID or and password to log in. If you do not have a
JHED ID, sign in as a “Guest User” using the email provided to create
your JHU login during course registration. Please contact JHSPH.courseeval@jhu.edu for assistance.
- All non-auditing institute students (whether they are taking courses for credit or not) are eligible to evaluate their courses.
Assignments
Grading
All assignments are due January 24th at 11:59pm ET.
| Assignment | Percent of Grade |
|---|---|
| Attendance/Participation | 20% |
| Homework 1 | 15% |
| Homework 2 | 15% |
| Homework 3 | 15% |
| Final Project | 35% |
| Total | 100% |
Note: only people taking the course for credit must turn in the assignments. However, we will evaluate all submitted assignments in case others would like feedback on their work.
Homework
You should complete the following:
| Module | Details | Code |
|---|---|---|
| Homework 1 | Day 0 Instructions | Dataquest |
| Homework 2 | Rmd, HTML, Key, Key HTML | |
| Homework 3 | Rmd, HTML, Key, Key HTML |
Final Project
This project should entail:
- reading in a dataset of your choosing
- doing some light data cleaning
- performing some data summarization
- creating a couple of visualizations
- doing some very light statistical analysis, like regression or t-tests
You may use public datasets, or your own data for the project. If using your own dataset, be sure any sensitive information is protected.
Example projects can be found with the source code: Rmd, and the output html here.
See the guidelines/instructions for final projects, including dataset resources.
Submission
All assignments are due January 24th at 11:59pm ET.
Submit each assignment to the designated Drop Box on CoursePlus.
Assignment Extensions
We know that you will try your best to submit assignments on time. However, we understand that life happens. We can accept late assignments with a deduction of 2% of your assignment score per day late.
For example, if you scored 9/10 on an assignment (90%) but turned it in two days late, your score would be 8.6/10 (86%).
Code of Conduct
We would like to create an open, safe, welcoming, diverse, inclusive, intellectually stimulating, and hopefully fun class experience.
We strive to be a space in which individual differences are respected, so that each individual can reach their fullest potential.
Guidelines
- Demonstrating empathy and kindness toward other people
- Giving and gracefully accepting constructive feedback
- Avoiding making assumptions about others
- Being respectful of differing opinions, viewpoints, and
experiences
- Take the time to consider how your speech and actions may influence
others
- Actively seek to acknowledge and respect the boundaries of other
members
- Demonstrating academic integrity
- Students can work together, but assignments must show unique
contributions from the student who turns in the assignment
- Students can use online resources for help, but assignments must
show unique contributions from the student who turns in the assignment -
plagiarism is not allowed
- If students are struggling, reach out to instructors or TAs as early as possible
- Students can work together, but assignments must show unique
contributions from the student who turns in the assignment
This applies to emails, surveys, Slack, Zoom, office hours, meetings with other students, instructors, or TAs.
Please reach out to a TA or instructor if you witness or experience a violation of the class guidelines or other JHU codes of conduct.
Resources
- JHU Student Code of Conduct: https://studentaffairs.jhu.edu/policies-guidelines/student-code/
- Hopkins School of Medicine: https://www.hopkinsmedicine.org/research/resources/offices-policies/OPC/Research_Integrity/som_code_of_conduct_04302020.pdf
- JHSPH Academic Ethics Code: https://www.jhsph.edu/offices-and-services/student-affairs/resources/student-policies/_documents/academic-ethics-code.pdf
The University has developed avenues for reporting and for seeking help including:
- JHU Sexual Assault Helpline, 410-516-7333 (confidential)
- Campus Safety and Security, 410-516-7777
- University Sexual Assault Response and Prevention website
- Johns Hopkins Compliance Hotline, 844-SPEAK2US (844-733-2528)
- JHU Office of Institutional Equity 410-516-8075
(nonconfidential)
- Johns Hopkins Student Assistance Program (JHSAP), 443-287-7000
- University Health Services - Mental Health (UHS-MS),
410-955-1892
- The Faculty and Staff Assistance Program (FASAP), 443-997-7000