Topic: SHIP Adolescents Who Have Obesity 2010, 2013-2014, 2016, 2018, 2021
This dataset was published by the Maryland Department of Health, and it tracks the percentage of adolescents diagnosed with obesity across various Maryland jurisdictions from 2010 to 2021. It provides annual data that allows for the analysis of long-term trends in adolescent obesity and highlights disparities based on race and ethnicity. My objective is to explore the dataset to uncover how obesity rates have shifted over time and where significant health obesity persists. Such insights are essential to determine public health interventions and policies that aim to reduce adolescent obesity in Maryland. The primary measure of interest is percentage of “Adolescents Who have Obesity” which is specified as Value in point form. The dataset has nine Race/Ethnicity groups. Geographic Area specifies as jurisdiction.
Source:Source: Maryland Department of Health https://opendata.maryland.gov/Health-and-Human-Services/SHIP-Adolescents-Who-Have-Obesity-2010-2013-2014-2/hedp-3fxm/about_data
load tidyverse
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.2 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Rows: 658 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): jurisdiction, Race ethnicity, Measure
dbl (2): value, Year
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##Data cleaning and inspection for the obesity dataset: Converts all column names to lowercase for consistency and to avoid case-sensitivity issues in later analysis. Replace spaces in column names with underscores, making them easier to reference in code. Perform quick preview of its structure and values.
# A tibble: 6 × 5
jurisdiction value race_ethnicity year measure
<chr> <dbl> <chr> <dbl> <chr>
1 State 12.6 All races/ ethnicities (aggregated) 2016 Adolescents …
2 Allegany 16.1 All races/ ethnicities (aggregated) 2016 Adolescents …
3 Anne Arundel 13 All races/ ethnicities (aggregated) 2016 Adolescents …
4 Baltimore City 19 All races/ ethnicities (aggregated) 2016 Adolescents …
5 Baltimore County 14.7 All races/ ethnicities (aggregated) 2016 Adolescents …
6 Calvert 11.3 All races/ ethnicities (aggregated) 2016 Adolescents …
remove the na.’s in value column:
Ensuring the Obesity dataset only includes complete data.
Obesity_new <- obesity |>filter(!is.na(value))
rename columns
for clarity, making it easier to interpret the data as representing obesity percentages.
Obesity_new |>rename(percentage=value)
# A tibble: 551 × 5
jurisdiction percentage race_ethnicity year measure
<chr> <dbl> <chr> <dbl> <chr>
1 State 12.6 All races/ ethnicities (aggregated) 2016 Adoles…
2 Allegany 16.1 All races/ ethnicities (aggregated) 2016 Adoles…
3 Anne Arundel 13 All races/ ethnicities (aggregated) 2016 Adoles…
4 Baltimore City 19 All races/ ethnicities (aggregated) 2016 Adoles…
5 Baltimore County 14.7 All races/ ethnicities (aggregated) 2016 Adoles…
6 Calvert 11.3 All races/ ethnicities (aggregated) 2016 Adoles…
7 Caroline 16 All races/ ethnicities (aggregated) 2016 Adoles…
8 Carroll 9.4 All races/ ethnicities (aggregated) 2016 Adoles…
9 Cecil 16.3 All races/ ethnicities (aggregated) 2016 Adoles…
10 Charles 13 All races/ ethnicities (aggregated) 2016 Adoles…
# ℹ 541 more rows
Used RColorBrewer palette created sequential palette
Create a graph using a sequential RColorBrewer palette (“OrRd”) to create a colored column chart that displays annual adolescent obesity percentages by race/ethnicity
ggplot(Obesity_new, aes(x=year, y=value, fill=race_ethnicity)) +geom_col(alpha=0.9,color="white")+scale_fill_brewer(palette ="OrRd") +labs(title="Adolescent Obesity by Race/Ethnicity",x ="Year", y ="Obesity Percentage", fill ="race_ethnicity", caption="Source: Maryland State Health Improvement Process") +theme_minimal()
Create visualization: To display adolescent obesity percentages over time, separated by each racial and ethnic group.
ggplot(Obesity_new, aes(x=year, y=value, fill=race_ethnicity)) +geom_col(alpha=0.9,color="white")+scale_fill_brewer(palette ="OrRd") +labs(title="Adolescent Obesity by Race/Ethnicity",x ="Year", y ="Obesity Percentage", fill ="race_ethnicity", caption="Source: Maryland State Health Improvement Process") +facet_wrap((~race_ethnicity)) +theme_minimal()
# A tibble: 6 × 5
jurisdiction value race_ethnicity year measure
<chr> <dbl> <chr> <dbl> <chr>
1 State 12.6 All races/ ethnicities (aggregated) 2016 Adolescents …
2 Allegany 16.1 All races/ ethnicities (aggregated) 2016 Adolescents …
3 Anne Arundel 13 All races/ ethnicities (aggregated) 2016 Adolescents …
4 Baltimore City 19 All races/ ethnicities (aggregated) 2016 Adolescents …
5 Baltimore County 14.7 All races/ ethnicities (aggregated) 2016 Adolescents …
6 Calvert 11.3 All races/ ethnicities (aggregated) 2016 Adolescents …
by_race_ethnicity |>ggplot(aes(race_ethnicity, value, fill = race_ethnicity)) +geom_bar(stat ="identity") +labs(title="Adolescent Obesity by Race/Ethnicity",x ="Year", y ="Obesity Percentage", fill ="race_ethnicity", caption="Source: Maryland State Health Improvement Process") +theme_minimal()
Brief Essay
The graph reveals a notable upward trend in adolescent obesity across most racial and ethnic groups in Maryland from 2010 to 2021. Black or African American Non-Hispanic/Latino and Hispanic/Latino adolescents consistently show the highest obesity rate, indicating they are disproportionately affected. The overall rates have climbed across nearly all groups, suggesting a broad public health. According to the State of Childhood Obesity organization, high school students’ obesity rate was 12.8 % in Maryland, between 2021 and 2022.However, some racial groups, such as Asian or Asian/Pacific Islander Non-Hispanic/Latino, appear to have relatively lower obesity rates, which could prompt further investigation into protective factors or reporting differences. In summary, adolescent obesity is rising, but it’s not rising equally. This points to the importance of culturally tailored interventions and sustained public health efforts. The challenge was to convert point values to percentages. To isolate trends for each group, I tried to use a ( facet_wrap,) the view was not clear.
