Orange County NC Energy Burden Sample Data

A sample dataset containing energy burden data for Orange County, North Carolina (FIPS code 37135). This dataset includes both Federal Poverty Line (FPL) and Area Median Income (AMI) cohort data for 2018 and 2022 vintages.

Usage

orange_county_sample

Format

A named list with 4 data frames:

fpl_2018

Federal Poverty Line cohort data for 2018 (135 rows)

fpl_2022

Federal Poverty Line cohort data for 2022 (206 rows)

ami_2018

Area Median Income cohort data for 2018 (259 rows)

ami_2022

Area Median Income cohort data for 2022 (149 rows)

Each data frame contains:

geoid

11-digit census tract identifier (character)

income_bracket

Income bracket category (character)

households

Number of households in this cohort (numeric)

total_income

Total household income in dollars (numeric)

total_electricity_spend

Total electricity spending in dollars (numeric)

total_gas_spend

Total gas spending in dollars (numeric)

total_other_spend

Total other fuel spending in dollars (numeric)

Source

U.S. Department of Energy Low-Income Energy Affordability Data (LEAD) Tool

• 2018 vintage: https://data.openei.org/submissions/573

• 2022 vintage: https://data.openei.org/submissions/6219

Details

This sample data is provided for quick demos, testing, and vignettes without requiring a large download. For full state or national analysis, use load_cohort_data() to download complete datasets from OpenEI.

Orange County NC (Chapel Hill, Carrboro, Hillsborough):

• 2018: 27 census tracts

• 2022: 42 census tracts (tract boundaries changed)

Income Brackets:

• FPL: 0-100%, 100-150%, 150-200%, 200-400%, 400%+

• AMI: very_low, low_mod, mid_high (aggregated from 6 AMI categories)

See also

• load_cohort_data - Load full datasets for any state

• compare_energy_burden - Compare energy burden across vintages

• calculate_weighted_metrics - Calculate weighted metrics with grouping

Examples

# Load sample data
data(orange_county_sample)

# View structure
names(orange_county_sample)
#> [1] "fpl_2018" "fpl_2022" "ami_2018" "ami_2022"

# Quick analysis of 2022 FPL data
library(dplyr)
orange_county_sample$fpl_2022 %>%
  group_by(income_bracket) %>%
  summarise(
    households = sum(households),
    avg_energy_burden = sum(total_electricity_spend + total_gas_spend + total_other_spend) /
                        sum(total_income)
  )
#> # A tibble: 5 × 3
#>   income_bracket households avg_energy_burden
#>   <chr>               <dbl>             <dbl>
#> 1 0-100%              5342.            0.163 
#> 2 100-150%            3612.            0.0811
#> 3 150-200%            3004.            0.0481
#> 4 200-400%           12926.            0.0296
#> 5 400%+              30650.            0.0104