PDC 102: Statistics in R for OEHS Professionals
University of Michigan School of Public Health
Ann Arbor, MI
United States of America
Sat, 5/30: 8:00 AM - 5:00 PM CDT
P102
Professional Development Courses (PDCs)
P102
Professional Development Courses (PDCs)
Ernest N. Morial New Orleans Convention Center
Room: 391
CM Credit Hours: 7
Description
*Please note: PDCs are at an additional cost, and preregistration is required*
To register, please visit: https://aihaconnect.org/register
R is a free and widely used programming language for statistical analysis across a variety of fields. While many professionals shy away from statistical software they perceive as intimidating—often preferring Excel or other tools—this approach has limitations, including unreproducible analyses, data size constraints and slower data processing.
The good news: even those with limited statistical experience can learn to use R effectively. This professional development course introduces participants to the fundamentals of R and its application in industrial hygiene (IH) data analysis. Topics include:
• An introduction to R and its interface
• R packages useful for IH data analysis
• Data tidying techniques to eliminate erroneous data points
• Data visualization using the ggplot2 package
• Basic statistical analysis in R
• Creating new variables to support analysis and modeling
• An introduction to Bayesian analysis in R
Occupational noise exposure data will be used as a sample dataset throughout the course. Each section includes a brief lecture on key concepts and demonstrations of how R code can be assembled to build a complete analysis. Participants will also have the opportunity to write and run their own R code to analyze IH data.
*Registration includes boxed luncheon. **Power strips will not be provided at each attendee's seat; however, laptop charging stations will be provided in each course room.
To register, please visit: https://aihaconnect.org/register
R is a free and widely used programming language for statistical analysis across a variety of fields. While many professionals shy away from statistical software they perceive as intimidating—often preferring Excel or other tools—this approach has limitations, including unreproducible analyses, data size constraints and slower data processing.
The good news: even those with limited statistical experience can learn to use R effectively. This professional development course introduces participants to the fundamentals of R and its application in industrial hygiene (IH) data analysis. Topics include:
• An introduction to R and its interface
• R packages useful for IH data analysis
• Data tidying techniques to eliminate erroneous data points
• Data visualization using the ggplot2 package
• Basic statistical analysis in R
• Creating new variables to support analysis and modeling
• An introduction to Bayesian analysis in R
Occupational noise exposure data will be used as a sample dataset throughout the course. Each section includes a brief lecture on key concepts and demonstrations of how R code can be assembled to build a complete analysis. Participants will also have the opportunity to write and run their own R code to analyze IH data.
*Registration includes boxed luncheon. **Power strips will not be provided at each attendee's seat; however, laptop charging stations will be provided in each course room.
Course Outline
• Introduction to R
o Learn the basics of R, including its interface and core functionality.
• Data Cleaning and Processing
o Explore techniques for tidying and preparing data for analysis.
• Graphical Analysis
o Use R to create visualizations that communicate data insights effectively.
• Basic Statistics
o Perform foundational statistical analyses using R.
• Creating New Columns and Calculating New Values
o Learn how to generate new variables to support deeper analysis.
• Basic Modeling (Linear, Logistic)
o Apply linear and logistic regression models to interpret data relationships.
• Introduction to Bayesian Analysis
o Gain an overview of Bayesian methods and how they can be applied in R.
• Vibe Coding (Using AI)
o Experiment with AI-assisted coding techniques to streamline analysis.
• Flex/Overflow Time
o Use this time for additional practice, questions or deeper exploration of course topics.
o Learn the basics of R, including its interface and core functionality.
• Data Cleaning and Processing
o Explore techniques for tidying and preparing data for analysis.
• Graphical Analysis
o Use R to create visualizations that communicate data insights effectively.
• Basic Statistics
o Perform foundational statistical analyses using R.
• Creating New Columns and Calculating New Values
o Learn how to generate new variables to support deeper analysis.
• Basic Modeling (Linear, Logistic)
o Apply linear and logistic regression models to interpret data relationships.
• Introduction to Bayesian Analysis
o Gain an overview of Bayesian methods and how they can be applied in R.
• Vibe Coding (Using AI)
o Experiment with AI-assisted coding techniques to streamline analysis.
• Flex/Overflow Time
o Use this time for additional practice, questions or deeper exploration of course topics.
Learning Outcomes
Upon completion, the participant will be able to:
• Use R to analyze occupational noise exposure data.
• Manage raw data so that it can be used for a statistical analysis.
• Create graphics that can be used to communicate the data distribution.
• Conduct statistical tests based on the underlying data distribution.
• Calculate variables that can aid in the formation of similarly exposed groups (SEGs).
• Utilize industrial hygiene statistics to aid in decision making.
• Demonstrate linear and logistic regression models to control for confounding factors.
• Compare a Bayesian analysis to a Frequentist analysis.
• Use R to analyze occupational noise exposure data.
• Manage raw data so that it can be used for a statistical analysis.
• Create graphics that can be used to communicate the data distribution.
• Conduct statistical tests based on the underlying data distribution.
• Calculate variables that can aid in the formation of similarly exposed groups (SEGs).
• Utilize industrial hygiene statistics to aid in decision making.
• Demonstrate linear and logistic regression models to control for confounding factors.
• Compare a Bayesian analysis to a Frequentist analysis.
Prerequisites
Participants should bring a laptop with R and RStudio pre-installed (both are free). Basic computing skills are required, along with at least one prior course in statistics, biostatistics or epidemiology.
Value Added
All software and code used in this course are freely available and can be customized for use in an individual's occupational and environmental health and safety practice.
Business Case/IH Value Statement
Using statistics with the free R programming language can support more efficient decision-making by helping ensure overexposures are quickly identified, controlled and reassessed to confirm that controls are working as intended.
Learning Aids
Laptop
Content Level
Intermediate
Core Competencies
Biostatistics and Epidemiology
Exposure Assessment
IH/OH Program Management
Keywords
Consulting
Education and training
Exposure Assessment
OHS Management systems, auditing, and compliance
Session Availability
In-person
Targeted Audience
Practitioner
Transfer of Knowledge
Group activities
Hands-on demonstrations and practicum
Practical Application
Problem Solving /Practice Exercises
Small Group Discussion