Intro

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

Data Prep

# Load the data directly from the GitHub repository
url <- "https://github.com/yinaS1234/data-608/blob/main/S6/c1.csv?raw=true"
url2 <- "https://github.com/yinaS1234/data-608/blob/main/S6/c2.csv?raw=true"
data <- read.csv(url, stringsAsFactors = FALSE)
cd <- read.csv(url2, stringsAsFactors = FALSE)

# Ensure the state abbreviations are in a column for mapping
data$State <- state.abb[match(data$state, state.name)]
data$State <- ifelse(is.na(data$State), "DC", data$State)

print(data)
head(cd)
# Rename the columns
cd <- cd %>%
  rename(
    Poverty_Rate_overall_2021 = PCTPOVALL_2021,
    Poverty_Rate_under17_2021 = PCTPOV017_2021,
    Poverty_Rate_5to17_2021 = PCTPOV517_2021,
    Poverty_Rate_under4_2021 = PCTPOV04_2021,
    Median_Inc_2021 = MEDHHINC_2021,
    SeniorFoodInsecurityRate = `SeniorFoodInsecurityRate.StateofSeniorHunger.`,
    OlderAdultFoodInsecurityRate = `OlderAdultFoodInsecurityRate.StateofSeniorHunger.`
  )

# Calculate the mean food insecurity rate for each category across all years
mean_rate_children <- mean(cd$ChildFoodInsecurityRate, na.rm = TRUE)
mean_rate_older_adults <- mean(cd$OlderAdultFoodInsecurityRate, na.rm = TRUE)
mean_rate_seniors <- mean(cd$SeniorFoodInsecurityRate, na.rm = TRUE)

# Prepare data for plotting
categories <- c('Children', 'Older Adults', 'Seniors')
mean_rates <- c(mean_rate_children, mean_rate_older_adults, mean_rate_seniors)

Data Visuals



Poverty~Food Insecurity
































Age

































Geographic