If you are using the package, please consider citing it, along with its companion publication: Ho, B. N., Ramdas, V. and Sarkar, A. (2026). HEI analysis of NHANES dietary data: Exploring the diet quality of Americans with R package heiscore. Journal of Data Science. DOI: https://doi.org/10.6339/24-JDS1160
The heiscore package aims to make it easier to evaluate a population’s dietary habits using the Healthy Eating Index (HEI) and to enable straightforward comparisons of diet quality across demographic subgroups. It allows users with minimal technical experience to access preloaded dietary recall data from the National Health and Nutrition Examination Survey (NHANES) and calculate HEI-2020 scores representative of the U.S. population using three methods prescribed by the National Cancer Institute. Additionally, heiscore includes functions that visualize this multidimensional diet quality data via various graphing techniques, including histograms, bar charts, and radar charts. These plots facilitate clear comparisons of dietary patterns between sub-populations in the U.S. and across several years.
heiscore’s four functions:
selectDataset() retrieves a tibble containing NHANES dietary recall data converted to relevant food groups (the U.S. Department of Agriculture’s Food Patterns Components) and demographic data for the chosen years.score() calculates HEI scores for the chosen time period and demographic subgroups.plotScore() graphs HEI score distributions for the chosen time period and demographic subgroups.runShinyApp() launches an interactive R Shiny app that plots the distributions of raw dietary data, demographic information, and HEI scores with options to compare years and demographic subgroups of interest# Install the heiscore package from CRAN
install.packages("heiscore")
# OR
# Install the heiscore package from GitHub
library(devtools)
# install_github("abhrastat/heiscore")
install.packages("heiscore", repos = c('https://abhrastat.r-universe.dev', 'https://cloud.r-project.org'))
library(heiscore)
# Retrieve NHANES dietary data converted to Food Patterns components and
# demographic data for 2017 and 2018
head(selectDataset(years = '1718'))
#> SEQN WTDRD1 WTDR2D SEX RACE_ETH AGE FAMINC DR1TKCAL
#> 1 93703 0.000 NA Female Asian 2 >100000 NA
#> 2 93704 81714.005 82442.869 Male White 2 >100000 1230
#> 3 93705 7185.561 5640.391 Female Black 66 [10000, 15000) 1202
#> 4 93706 6463.883 0.000 Male Asian 18 <NA> 1987
#> 5 93707 15333.777 22707.067 Male Other 13 [65000, 75000) 1775
#> 6 93708 10825.545 22481.854 Female Asian 66 [25000, 35000) 1251
#> DR2TKCAL DR1T_F_TOTAL DR2T_F_TOTAL DR1_FWHOLEFRT DR2_FWHOLEFRT DR1T_F_JUICE
#> 1 NA NA NA NA NA NA
#> 2 1356 0.99 1.94 0.00 1.07 0.99
#> 3 1235 0.00 0.03 0.00 0.00 0.00
#> 4 NA 0.00 NA 0.00 NA 0.00
#> 5 1794 0.00 0.00 0.00 0.00 0.00
#> 6 842 0.49 0.00 0.49 0.00 0.00
#> DR2T_F_JUICE DR1_VTOTALLEG DR2_VTOTALLEG DR1_VDRKGRLEG DR2_VDRKGRLEG
#> 1 NA NA NA NA NA
#> 2 0.87 0.82 0.02 0.00 0.00
#> 3 0.03 1.65 0.52 0.46 0.23
#> 4 NA 0.72 NA 0.00 NA
#> 5 0.00 0.94 0.70 0.00 0.00
#> 6 0.00 2.33 0.26 0.00 0.00
#> DR1_VNONDRKGR DR2_VNONDRKGR DR1T_V_DRKGR DR2T_V_DRKGR DR1T_V_LEGUMES
#> 1 NA NA NA NA NA
#> 2 0.82 0.02 0.00 0 0.00
#> 3 1.19 0.29 0.31 0 0.15
#> 4 0.72 NA 0.00 NA 0.00
#> 5 0.94 0.70 0.00 0 0.00
#> 6 2.33 0.26 0.00 0 0.00
#> DR2T_V_LEGUMES DR1T_G_WHOLE DR2T_G_WHOLE DR1T_D_TOTAL DR2T_D_TOTAL
#> 1 NA NA NA NA NA
#> 2 0.00 0.00 0.72 1.73 1.74
#> 3 0.23 0.00 0.00 0.22 0.52
#> 4 NA 0.00 NA 2.03 NA
#> 5 0.00 4.12 0.90 0.26 0.04
#> 6 0.00 0.34 0.00 0.06 1.14
#> DR1_PFALLPROTLEG DR2_PFALLPROTLEG DR1_PFSEAPLANTLEG DR2_PFSEAPLANTLEG
#> 1 NA NA NA NA
#> 2 3.52 0.46 1.18 0.00
#> 3 1.38 3.66 1.37 1.31
#> 4 8.79 NA 0.00 NA
#> 5 5.61 10.30 0.00 0.07
#> 6 4.73 2.34 2.57 1.42
#> DR1_PF_MPE DR2_PF_MPE DR1_PF_SSNS DR2_PF_SSNS DR1T_PF_LEGUMES DR2T_PF_LEGUMES
#> 1 NA NA NA NA NA NA
#> 2 0.22 0.46 1.18 0.00 0.00 0.00
#> 3 0.01 2.35 0.79 0.40 0.58 0.91
#> 4 0.00 NA 0.00 NA 0.00 NA
#> 5 3.55 8.94 0.00 0.07 0.00 0.00
#> 6 2.16 0.03 2.57 1.42 0.00 0.00
#> DR1_TFACIDS DR2_TFACIDS DR1T_G_REFINED DR2T_G_REFINED DR1TSODI DR2TSODI
#> 1 NA NA NA NA NA NA
#> 2 2.3601 2.2851 3.03 4.28 2198 1345
#> 3 2.2037 2.6248 2.10 5.39 3574 2447
#> 4 2.7205 NA 4.51 NA 3657 NA
#> 5 1.3875 1.7117 1.14 2.04 2450 2581
#> 6 2.5119 1.4630 3.19 2.59 2135 1241
#> DR1T_ADD_SUGARS DR2T_ADD_SUGARS DR1TSFAT DR2TSFAT DR1_MONOPOLY DR2_MONOPOLY
#> 1 NA NA NA NA NA NA
#> 2 9.12 17.25 11.372 10.368 26.839 23.692
#> 3 19.42 8.67 16.435 14.079 36.218 36.954
#> 4 1.64 NA 35.169 NA 95.678 NA
#> 5 18.65 18.15 33.252 31.098 46.136 53.232
#> 6 6.00 6.44 17.446 14.962 43.823 21.889
# Produce 2017-March 2020 Pre-pandemic HEI scores for the Total Fruit component
# using the Mean Ratio scoring method. Only include white and black women aged
# 50 to 100. Display the results by race/ethnicity.
score(method = "Mean Ratio",
years = "1720",
component = "Total Fruit",
demo = "Race",
sex = ("Female"),
race = c("White", "Black"),
age = c(50, 100))
#> # A tibble: 2 × 2
#> RACE_ETH score
#> <fct> <dbl>
#> 1 Black 3.57
#> 2 White 3.43
# Create a radar plot that displays the breakdown of HEI score components by
# family income level. Use 2017-18 NHANES data and the Population Ratio Method
# for scoring.
plotScore(graph = "Radar",
method = "Pop Ratio",
years = "1718",
component = "Total Score",
demo = "income",
income = c("[0, 5000)", "[15000, 20000)", "[35000, 45000)", "[45000, 55000)",
"[55000, 65000)", "[65000, 75000)", "[75000, 100000)", ">100000"))
# Launch the interactive Shiny app
runShinyApp()