DATA 608: Story - 6 : What Is The State of Food Security and Nutrition in the US

The chart below examines trends in food insecurity among different household types over time. Using national household-level food security data, the visualization compares food insecurity rates for households with children and those without children. By plotting these categories across multiple years, the graph highlights persistent differences in vulnerability based on household composition. This helps reveal whether certain household structures consistently experience higher rates of food insecurity.

household <- read.csv("D://Cuny_sps//DATA_608//Story-6//foodsecurity_all_households_2023.csv")

trend_data <- household %>%
  filter(Category == "Household composition") %>%
  filter(is.na(`Sub.subcategory`) | `Sub.subcategory` == "") %>%
  select(Year, Subcategory, `Food.insecure.percent`) %>%
  filter(!is.na(`Food.insecure.percent`))

ggplot(trend_data, aes(x = Year, y = `Food.insecure.percent`, color = Subcategory, group = Subcategory)) +
  geom_line() +
  geom_point() +
  labs(
    title = "Households with Children Consistently Experience Higher Food Insecurity",
    subtitle = "Across all years, families raising children report significantly higher food insecurity rates.",
    caption = "Households with children—especially single-parent households—face greater economic vulnerabilities."
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(size = 13),
    plot.caption = element_text(size = 10, face = "italic")
  )

The line chart shows a clear and consistent pattern: households with children experience higher food insecurity rates than those without children across every year represented. Although both groups show some fluctuations over time, the gap between them remains noticeable, suggesting structural challenges faced by families raising children. These disparities may be influenced by childcare costs, housing expenses, limited wages, or gaps in access to nutrition programs. The trend emphasizes the importance of targeted policies and support programs aimed at families with children in order to reduce national food insecurity levels.

The visualization below explores food insecurity trends among single-parent households, comparing households led by a female head with no spouse to those led by a male head with no spouse. These two household types often face different economic pressures, including wage disparities, childcare responsibilities, and availability of support systems. By examining year-to-year changes in their food insecurity rates, the graph highlights how gender dynamics within single-parent households may influence household vulnerability.

trend_data <- household %>%
  filter(Sub.subcategory == "Female head, no spouse" |
         Sub.subcategory == "Male head, no spouse") %>%
  select(Year, Sub.subcategory, `Food.insecure.percent`) %>%
  filter(!is.na(`Food.insecure.percent`))

ggplot(trend_data, aes(x = Year, y = `Food.insecure.percent`, 
                       color = Sub.subcategory, group = Sub.subcategory)) +
  geom_line(size = 1) +
  geom_point(size = 2) +
  labs(
    title = "Single-Mother Households Face the Highest and Most Persistent Food Insecurity",
    subtitle = "Economic inequality, wage gaps, and childcare burdens leave female-headed households at elevated risk.",
    x = "Year",
    y = "Food Insecure Percent (%)",
    caption = "Across all years, food insecurity rates for female-headed households exceed those for male-headed households."
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(size = 16, face = "bold"),
    plot.subtitle = element_text(size = 12),
    plot.caption = element_text(size = 10, face = "italic")
  )

The results show a persistent and significant gap: single-parent households led by women experience higher food insecurity rates than those led by men across every year shown. While both groups face elevated levels of economic strain compared to two-parent households, the consistently higher rates for female-headed households reflect broader issues such as gender wage inequality, higher caregiving burdens, and limited access to economic opportunities. These findings underscore the need for targeted support—such as childcare subsidies, income assistance, and expanded nutrition programs—to address the disproportionate challenges faced by single mothers.

Food Insecurity Trends by Race

trend_data <- household %>%
  filter(Category == "Race/ethnicity of households") %>%
  select(Year, Subcategory, `Food.insecure.percent`) %>%
  filter(!is.na(`Food.insecure.percent`))  

ggplot(trend_data, aes(x = Year, y = `Food.insecure.percent`, color = Subcategory, group = Subcategory)) +
  geom_line() +
  geom_point()+
  labs(
    title = "Food Insecurity Remains Significantly Higher for Black and Hispanic Households",
    subtitle = "These disparities persist year after year, reflecting structural inequities.",
    caption = "Gaps do not close over time, indicating long-term systemic drivers of food insecurity."
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability