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

The United Nations Food and Agriculture Organization publication, The State of Food Security and Nutrition in the World 2022 (https://www.fao.org/documents/card/en/c/cc0639en) might lead one to the conclusion that it’s an elsewhere problem. That the people who are suffering malnutrition and starvation are “elsewhere”, not in our backyard. For this assignment you will need to take a closer look here at home (the US) Notes: You will need to locate and source data that reflects food security and nutrition by state broken down by men, women, children and by age groups

Your analysis should demonstrate correlations that exist between level of poverty and food insecurity, malnutrition and starvation.

Your data and analysis should also indicate what happens to the children as they mature into adults. Will they become fully functional citizens or will they require continued support?

You data visualizations need to tell the story for a political audience that you were lobbying to address the issue of food insecurity in the US.

Load Required Libraries

library(readr)
library(plotly)
## Loading required package: ggplot2
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library(ggplot2)
library(reshape2)
library(tidyverse)
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library(rnaturalearthdata)
theme_set(theme_bw())
library(sf)
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

Data Cleaning and Preparation

Source: USDA, Economic Research Service, calculations using data from the Current Population Survey Food Security Supplement and https://www.feedingamerica.org.

#Import the data 
df <- read_csv("State of Food USA 2021.csv")
## Rows: 51 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): State Name, State, Overall Food Insecurity Rate, Food Insecurity R...
## dbl  (3): FIPS, Povery_Rate, Food_Insecurity_Prevalence
## 
## 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.
df <- df %>%
  rename(
    Overall_Food_Inecurity_Rate = 'Overall Food Insecurity Rate',
    Food_Insecurity_Rate_all_ethnicities = 'Food Insecurity Rate among Black Persons (all ethnicities)',
    Food_Insecurity_Rate_Children = 'Child Food Insecurity Rate',
    Food_Insecurity_Rate_Adult = 'Older Adult Food Insecurity Rate\n(State of Senior Hunger)',
    Food_Insecurity_Rate_Senior = 'Senior Food Insecurity Rate\n(State of Senior Hunger)'
    
  )

#Remove the percentage sign
df$Overall_Food_Inecurity_Rate <- gsub("%", "", df$Overall_Food_Inecurity_Rate)
df$Food_Insecurity_Rate_Children <- gsub("%", "", df$Food_Insecurity_Rate_Children)
df$Food_Insecurity_Rate_Adult <- gsub("%", "", df$Food_Insecurity_Rate_Adult)
df$Food_Insecurity_Rate_Senior <- gsub("%", "", df$Food_Insecurity_Rate_Senior)

# Convert the variable to numeric
df$Overall_Food_Inecurity_Rate <- as.numeric(df$Overall_Food_Inecurity_Rate)
df$Food_Insecurity_Rate_Children <- as.numeric(df$Food_Insecurity_Rate_Children)
df$Food_Insecurity_Rate_Adult <- as.numeric(df$Food_Insecurity_Rate_Adult)
df$Food_Insecurity_Rate_Senior <- as.numeric(df$Food_Insecurity_Rate_Senior)
head(df)
## # A tibble: 6 × 19
##    FIPS `State Name` State Povery_Rate Food_Insecurity_Prevalence
##   <dbl> <chr>        <chr>       <dbl>                      <dbl>
## 1     1 Alabama      AL           16.1                       12.4
## 2     2 Alaska       AK           10.5                        9.5
## 3     4 Arizona      AZ           12.8                       10.2
## 4     5 Arkansas     AR           16.3                       16.6
## 5     6 California   CA           12.3                       10.3
## 6     8 Colorado     CO            9.7                        8.9
## # ℹ 14 more variables: Overall_Food_Inecurity_Rate <dbl>,
## #   Food_Insecurity_Rate_all_ethnicities <chr>,
## #   `Food Insecurity Rate among Hispanic Persons (any race)` <chr>,
## #   `Food Insecurity Rate among White, non-Hispanic Persons` <chr>,
## #   Food_Insecurity_Rate_Senior <dbl>,
## #   `Senior Very Low Food Security Rate\n(State of Senior Hunger)` <chr>,
## #   Food_Insecurity_Rate_Adult <dbl>, …

US Poverty Rate

Poverty means having little to no income and the inability to provide basic needs, like shelter, food, and security. According to the United States Census Bureau, over 34 million Americans lived in poverty in 2021. Poverty is measured based on a household’s income level, the number of people in the household, and the ages of people in the household.

# Create a choropleth map
map <- plot_geo(df, locationmode = 'USA-states') %>%
  add_trace(
    z = ~Povery_Rate,
    locations = ~State,
    color = ~Povery_Rate,
    colorscale = 'Viridis',
    colorbar = list(title = 'Poverty_Rate (%)')
  ) %>%
  layout(
    title = 'US Poverty Rate in 2021',
    geo = list(scope = 'usa')
  )

map

US Food Insecurity Rate

The United States Department of Agriculture (USDA) measures food insecurity in the United States through a national survey that is an addition to the monthly Current Population Survey (CPS). According to the U.S. Department of Agriculture (USDA), 89.8% of households were classified as food secure households. 6.4% of households were classified as low food security, and very low food security households were at 3.8% in 2021

# Create a choropleth map
map1 <- plot_geo(df, locationmode = 'USA-states') %>%
  add_trace(
    z = ~Overall_Food_Inecurity_Rate,
    locations = ~State,
    color = ~Overall_Food_Inecurity_Rate,
    colorscale = 'Viridis',
    colorbar = list(title = 'Overall_Food_Inecurity_Rate')
  ) %>%
  layout(
    title = 'US Food Insecurity Rate in 2021',
    geo = list(scope = 'usa')
  )

map1

Poverty Rate Vs Food Insecurity Prevalence

# Create a line plot 
ggplot(df, aes(x = Povery_Rate, y = Food_Insecurity_Prevalence)) +
  geom_point(color = "blue") +
  geom_smooth(method = "lm", formula = y ~ x, se = FALSE, color = "red") +  
  geom_abline(intercept = 0, slope = 0, linetype = "dashed", color = "black") +  
  labs(x = "Poverty Rate", y = "Food Insecurity Prevalence", title = "Correlation between Poverty Rate and Food Insecurity Prevalence")

There is a direct correlation between poverty and food insecurity, as they are related while poverty is a significant risk factor for food insecurity, not all individuals living in poverty experience food insecurity, and conversely, some individuals above the poverty line may still experience food insecurity. Data from the USDA indicates that many individuals living in poverty are indeed food secure, and conversely, many individuals facing hunger may have incomes above the federal poverty line. This demonstrates the importance of these federal aids in addressing food insecurity and the need for continued support and advocacy to ensure they reach those who need them most.

Food Insecurity Rate by Age Range

# Melt the data frame to long format
df_long <- melt(df, id.vars = "State")

# Subset the data 
df_long <- subset(df_long, variable %in% c("Food_Insecurity_Rate_Children", "Food_Insecurity_Rate_Adult", "Food_Insecurity_Rate_Senior"))

# Plot the line chart with Plotly and wider X-axis scales
plot_ly(df_long, x = ~State, y = ~value, color = ~variable, type = 'scatter', mode = 'lines') %>%
  layout(title = "Food Insecurity Rate by Age Group",
         xaxis = list(title = "State", tickangle = 35),  # Adjust tick angle for better visibility
         yaxis = list(title = "Food Insecurity Rate"),
         legend = list(title = "Age Group"),
         margin = list(l = 45, r = 45, b = 45, t = 45))  # Adjust margins for wider X-axis scales
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The trajectory for children growing up in households affected by food insecurity and poverty can vary significantly depending on various factors such as access to education, healthcare, social support networks, and economic opportunities.

The senior citizens facing food insecurity and poverty requires a comprehensive and compassionate federal support and political law makers should promote healthy aging programs that provide essential services for the elderly people to ensure that all seniors have the opportunity to age with dignity, security, and fulfillment.

Food Insecurity Percentage by Gender

HH <- read_csv("Food Insecurity HH.csv")
## New names:
## Rows: 317 Columns: 5
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Subcategory, ...5 dbl (2): Year, Food-insecure households-percent lgl (1):
## ...4
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## • `` -> `...4`
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HH <- HH[, -c((ncol(HH) - 1):ncol(HH))]  # Remove the last two columns

# Subset the data 
HH_filtered <- subset(HH, Subcategory %in% c("Male head, no spouse", "Female head, no spouse"))


# Plot Food Insecurity percent value from 2010 to 2021
ggplot(HH_filtered, aes(x = Year, y = `Food-insecure households-percent`, color = Subcategory)) +
  geom_line() +
  labs(x = "Year", y = "Food-insecurity households-percent", title = "Household Food Insecurity grouped by Female and Male from 2008 to 2021") +
  theme_minimal() +
  scale_x_continuous(breaks = seq(2008, 2022, by = 2)) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.05)))

The line graph depicts that women are more food insecure than men and gender gap has widened , particularly in the rural areas and we find that the current gap in food insecurity between women and men would be reduced by eliminating gender gaps in education, labour force participation and income.

Conclusion

Addressing poverty requires not only income support alone but also education, healthcare, affordable housing, job opportunities, and social support services are all critical components of poverty alleviation efforts. We also need to strengthen our federal nutrition programs o fight hunger throughout America.