Rows: 70
Columns: 5
$ Year <int> 2012, 2012, 2012, 2…
$ Age.Group <chr> "Adolescent (12-17)…
$ Category <chr> "All ", "Gender", "…
$ Type <chr> "Total", "Male", "F…
$ Five.or.more.servings.of.fruits.and.vegetables.... <dbl> 40.2, 37.3, 43.3, 3…Fruit & Vegetable Consumption and Obesity in California
INFO 201 Project Presentation
Tara Good, Luke, Kahin
2025-11-19
Overview
Guiding Question
Across demographic groups in California in 2012–2013, how do patterns of fruit and vegetable consumption relate to obesity rates?
INSERT PHOTO FOR OUR PROJECT??? 
Background
Public health research suggests that diet quality and obesity vary across demographic groups (age, gender, etc.).
We use two datasets from the California Department of Public Health to describe patterns in:
Fruit & vegetable consumption
Obesity percentages
Our goal is to look for patterns and associations, not proof that consumption causes obesity.
Analysis Outline
We address the following sub-questions:
How does fruit & vegetable consumption vary across age groups in California?
How do obesity rates vary across the same demographic groups?
Do demographic groups with higher fruit & vegetable consumption tend to show lower obesity percentages?
Fruit and Vegetable Consumption and Obesity Overview
Exploring the interaction between fruit and vegetable consumption and obesity is an important public health objective. To do that, we will use our data to explore the answers to some questions, including those below:
- How does fruit & vegetable consumption vary across age groups and genders in California?
- How do obesity rates vary across the same demographic groups?
- Do demographic groups with higher fruit & vegetable consumption tend to have lower obesity rates?
Fruit and Vegetable Consumption Data
Fruit and Vegetable Consumption was shared by the State of California, which organized this data pulling from the 2013 California Dietary Practices Surveys (CDPS), 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and 2013 California Children’s Healthy Eating and Exercise Practices Survey (CalCHEEPS).
- Population: The residents of the State of California
- Sample: California residents who consumed five or more servings of fruits and vegetables a day.
- Features: Age Group, Category, Type
Fruit and Vegetable Consumption Age Groups
According to the documentation shared by the State of California, there are three main age groups under consideration.
- Adolescent (12-17): Divided into 12-13, 14-15, 16-17.
- Adult (18+): Divded into 18-24, 25-34, 35-50, 51-64, 65
- Child (6-11): Divided into 6-8, 8-11
In our data, there are three different age categories!.
Age.Group
1 Adolescent (12-17)
2 Adult (18+)
3 Child (6-11)Fruit and Vegetable Consumption by Age Group and Gender
Obesity Dataset
Rows: 66
Columns: 5
$ Year <int> 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, …
$ Age.Group <chr> "Adolescent (12-17)", "Adolescent (12-17)", "Adolescent (12-…
$ Category <chr> "All ", "Gender", "Gender", "Gender by Age- Male", "Gender b…
$ Type <chr> "Total", "Male", "Female", "12-13", "14-15", "16-17", "12-13…
$ Obese.... <dbl> 11.2, 14.6, 7.8, 15.2, 14.2, 14.4, 7.6, 5.0, 11.1, 7.3, 14.5…The obesity dataset was published by the California Department of Public Health, using statewide survey data from 2012–2013.
Population: All California residents
Sample: Surveyed CA residents represented in CDPH estimates
Key variable: Obese (%)
Features used: Year, Age Group, Category, Type, Obesity (%)
Background Source
According to the California Department of Public Health and CDC guidelines, obesity rates differ across demographic groups such as age, gender, and lifestyle categories. Understanding these differences allows us to compare them with fruit and vegetable consumption trends.
Source used: https://www.cdc.gov/obesity/data/adult.html
Wrangling Steps (Obesity Data)
To prepare the obesity dataset, we:
Selected relevant variables
Renamed columns for consistency
Filtered to 2012–2013
Grouped by Age Group
Calculated mean obesity percentage
Obesity by Age Group (Visualization)
Interpretation
Obesity levels vary significantly across age groups.
Certain groups show higher obesity percentages than others.
These trends help set up our comparison with fruit and vegetable consumption patterns.
Conclusions
Overall Conclusions
Overall, we are confident that San Diego is a promising city for novice cyclists to practice the sport.
- Most routes will require riding with auto traffic, but 80+ miles of dedicated bike lanes exist
- Weather is comfortable for riding most of the year
Limitations
Bike Route Availability Estimates:
- City-provided data about bike routes does not include all potential routes you might practice on.
- Unrecorded routes might be less likely to be good for cycling, but the data presents a baseline or minimal level of access to good routes, so we aren’t going to overestimate access
- Our data doesn’t tell us how well connected the routes are – can we string good routes together to make long routes?
Weather and Comfort Estimates:
- Sample is only from San Diego Airport (by the water) and from the 2010s
- Inland areas often have more extreme weather, so we might overestimate comfort
- The current and near future may have more hot and extreme weather than the past, so we might overestimate comfort
Future Directions
In the future, we would like to address our limitations and additionally:
- Evaluate the risks of cycling by looking at traffic and injury data in the city
- Perform a comparison to see whether similar cities might be more or less well set up for novice cicylists
Acknowledgments
Thank you for listening!
Thank you to the UCSD Ocean Informatics lab for making weather data available and to the San Diego City data team for making data easy to find!
Project Summary
Group Members: Tara, Luke, Kahin
Lab Section: Wednesday 8:30am (TA: Melaya La Madrid)
Data Sets :
Basic Tools Used:
- filter(), summarize(), distinct()
Intermediate/Novel Tool Used:
- Dates
- Conditional Transformation