Chapter 3 Using DaSEH Materials 
3.1 Learning Objectives
This chapter will provide guidance on how to use DaSEH resources for instruction including how to:
- Determine prerequisite knowledge and skills
- Identify what material is appropriate for beginner, intermediate, or advanced learners.
- Use the full set of DaSEH resources, some of the modules, or just the data for different kinds of instruction.
- Extend the materials to serve as a template for homework assignments or independent student exploration.
The examples presented in this chapter are merely suggestions - modifications to the material to fit student needs are expected and encouraged! If you come up with a different way to use our resources, please let us know what you come up with so that other educators may be inspired by your creativity.
3.1.1 Prerequisites
3.1.1.1 Environmental Health Subject Matter
The materials in DaSEH use data related to environmental health. There is no requirement for any prior knowledge on environmental health. The resources are also applicable for those interested in data science for other uses.
3.1.1.2 Statistics
The DaSEH materials do expect some familiarity with statistics and focuses mostly on the application of R for analysis, rather than the theory of statistics. We recommend additional resources for statistics if you are teaching a statistics course.
3.1.1.3 Coding/Data Science
All materials for DaSEH use the R statistical programming language for data analysis. No familiarity with R basics is expected for learners.
3.1.1.4 Software
All case studies use the R statistical programming language for data analysis. While there is no specific R version requirement for the case studies, the OCSdata package, which can be used to get and load the data, does require R 3.5. Furthermore, R packages used to run specific analyses in each case study may have their own R version requirements. R version requirements may be checked in the sessionInfo() section in each case study.
3.1.2 Experience Level Descriptions
The table below explains how we define the beginner, intermediate, and advanced experience levels.
| Experience Level | Description |
|---|---|
| Beginner | Little to no previous experience with coding and/or statistical analysis. |
| Intermediate | Familiar with at least one programming language and has experience working with data and statistics. |
| Advanced | Fluent in at least one programming language and likely familiar with more. Has a depth of experience working with data science projects. |
Typically, most middle/high school and first year undergraduate students will fit in the beginner category. Upperclassmen undergraduates and some graduate students are often at the intermediate level. Most advanced level students will be at the graduate level. However, this is a generalization, and a student may be considered beginner, intermediate, or advanced at any academic level depending on their independent studies and experiences.
3.2 DaSEH in the Classroom
The DaSEH materials are structured in a modular manner to support both partial and full use of our materials. Educators are also free to use the DaSEH data by itself.
3.2.1 Teaching the full set of materials
The DaSEH materials are written to provide a comprehensive introduction to environmental health data science. Our materials provide students with experience in all the standard aspects of a data science workflow as well as best practices regarding reproducibility. The following list provides a few examples of how educators could use the materials:
- Use our full set of slides as lecture and materials as we have used them
- Do a flipped classroom approach and assigned students to read the slides as homework and do labs in the class together
- Assign students to extend analysis beyond what is shown in our lecture or lab materials to dive deeper or provide more homework
3.2.2 Teaching Part of the DaSEH Materials
Some educators may find that only certain modules are relevant to their course learning objectives. Each provides information about how to access the appropriate data. Note that you may have to add some introduction to explain any functions that were explained in a previous module.
For a data visualization course, the following modules could be useful:
- Basic R (only if students don’t have familiarity with R)
- RStudio (only if students don’t have familiarity with R)
- Manipulating Data in R (to convert data from wide to long format to facilitate data visualization)
- Intro to Data Visualization
- Data Visualization
- Factors
For a data wrangling course, the following modules could be useful:
- Basic R
- RStudio (only if students don’t have familiarity with R)
- Subsetting Data in R
- Data Classes
- Data Cleaning
- Manipulating Data in R
- Factors
For a reproducibility course the following modules could be useful:
- Reproducibility
- Data Input
- Data Output
- Functions
For a data ethics course the following materials could be useful:
- Reproducibility
- Version Control (from the codeathon materials)
- Data ethics (from the codeathon materials)
3.2.3 Teaching With DaSEH Data Only
Educators can use the data available with the DaSEH without using the DaSEH materials as a whole. The data is available on GitHub in the data directory. See the data section of the infrastructure chapter for more information about how to access the data.
The data can also be accessed in R by updating the path of daseh.org/data/ with the name of the data file in the URL like the following examples:
library(readr)
er <-
read_csv("https://daseh.org/data/CO_ER_heat_visits.csv")
er_visits_age <- read_csv("https://daseh.org/data/CO_ER_heat_visits_by_age.csv")
Denver_heat_Data.csv <- ("https://daseh.org/data/Denver_heat_data.csv")Be careful to make sure that the name of the file matches exactly include the case of the letters:
A table of which data is used in which materials is available here: https://daseh.org/data.html
3.3 DaSEH Level Recommendations
Overall the DaSEH materials are intended for anyone with zero to minimal familiarity with R, although we have had learners with more intermediate levels of experience who have reported getting a lot out of the material.
Across the program, the modules also involve different levels of skill, as some would be fine on their own for beginners, while others may require some basic familiarity. Below we provide broad categorizations of each module in terms of the skill levels required (beginner, intermediate, or advanced).
Here, we are using the following interpretations of “beginner”, “intermediate”, and “advanced”:
| Level | Skills |
|---|---|
| Beginner | No experience with importing data into any programming language. No experience wrangling and cleaning raw data in any programming language. No experience visualizing data in any programming language. No experience with statistical concepts. |
| Intermediate | Some experience with importing common data formats (e.g. CSVs) into R or significant experience in another programming language. Some experience wrangling or cleaning raw data in common formats (e.g. numerical data) in R or significant experience in another programming language. Some experience with common visualization packages in R (e.g. ggplot) or significant experience in another programming language. Some familiarity with common statistical concepts (e.g. summary statistics, hypothesis testing) and techniques (e.g. t-test). |
| Advanced | Experience with importing uncommon data types (e.g. PDFs or web-scraping) and comfort with troubleshooting import challenges. Experience cleaning and wrangling raw data in uncommon formats (e.g. regular expressions) in R and comfort with troubleshooting wrangling challenges. Experience with creating complex data visualizations in R and comfort with visualization challenges.Good understanding of foundational statistical concepts and comfort with applying foundational statistical techniques. |
The following table lists a few example case studies that would be suitable for each experience level.
| Module | Skill Level |
|---|---|
| Intro | Beginner |
| Basic R | Beginner |
| RStudio | Beginner |
| Reproducibility | Beginner but also appropriate for Intermediate or Advanced audiences that are new to this topic |
| Data Input | Beginner |
| Subsetting Data in R | Intermediate |
| Data Summarization | Intermediate |
| Data Classes | Beginner |
| Data Cleaning | Intermediate |
| Manipulating Data in R | Intermediate |
| Intro to Data Visualization | Beginner |
| Data Visualization | Intermediate |
| Factors | Intermediate |
| Statistics | Beginner * Note that we do not focus on statistical theory, but rather on using R to perform tests |
| Data Output | Beginner |
| Functions | Intermediate and Advanced if a new topic |
| Version Control (codeathon resource) | Intermediate and Advanced if a new topic |
| Open Data | Beginner |
| Code Review | All levels if new topic |
| Data Ethics | All levels |
| Mapping mini-module | Intermediate |
3.4 Troubleshooting
You may encounter errors trying to render our materials.
R packages versions can have updates to arguments and function names that can cause code to work differently or can break the code.
If you encounter an error, this is likely the reason. We try to update our materials when we can, but updates to packages may happen in the meantime. You can either use the error message from trying to knit the Rmd file to determine what function may have been updated or deprecated (we recommend this option to help you or your students learn the most up-to-date information).
3.5 Additional Use Cases
Our materials can be used in a variety of ways that cater to the learner’s goals, experience, and interests. Below, we provide a few examples of how they could be used . If you use DaSEH resources in a new way, we would love to hear about it!
- Using materials for assignments
Assignments could include: - Scientific writing like writing scientific journal sections (e.g. Introduction, Methods, Results, Discussion) based on the the data - Extending analyses based on results presented in the lectures and labs. - Additional data visualization - Presentations
Our final project guidelines could also be expanded to create a more involved project.
- Independent Study
Our materials and recordings be used for learners to gain experience in statistics and data science independently. We strongly recommend that independent learners aim to actively engage with the recordings by running the analyses independently, and exploring additional data to investigate their own hypotheses. Furthermore, creating a finished product, such as a blog post or a presentation, can be an excellent demonstration of the skills learned.
3.6 Additional Resources
See here guidelines about considerations for effective and ethical data visualizations from the Advanced Data Science Course taught by Jeff leek and Roger Peng at Johns Hopkins Bloomberg School of Public Health.
We also include a resource for drafting and evaluating scientific writing here.
3.7 Session info
## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
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## time zone: Etc/UTC
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## [53] pkgconfig_2.0.3 htmltools_0.5.7
All illustrations