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The heiscore package aims to increase the accessibility of evaluating a population’s dietary habits using the Healthy Eating Index (HEI) and enable straightforward comparisons of the diet quality of different demographic subgroups. It allows users with minimal technical experience to obtain preloaded dietary recall data from the National Health and Nutrition Examination Survey (NHANES) and use it calculate HEI scores that are representative of the U.S. population using three distinct 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 GitHub
install.packages("devtools")
library(devtools)
install_github("author/package")This is a basic example which shows you how to solve a common problem:
library(heiscore)
library(tidyr)
suppressPackageStartupMessages(library(dplyr))
library(stringr)
library(shiny)
library(shinythemes)
library(ggplot2)
library(tibble)
library(ggpubr)
library(grDevices)
library(RColorBrewer)
library(rlang)
# Retrieve NHANES dietary data converted to Food Patterns components and
# demographic data for 2017 and 2018
selectDataset(year = '1718')
#> # A tibble: 8,704 × 51
#> SEQN WTDRD1 WTDR2D SEX RACE_ETH AGE FAMINC DR1TKCAL DR2TKCAL
#> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 93703 0 NA Female Asian 2 ">100000" NA NA
#> 2 93704 81714. 82443. Male White 2 ">100000" 1230 1356
#> 3 93705 7186. 5640. Female Black 66 "[10000,… 1202 1235
#> 4 93706 6464. 0 Male Asian 18 "" 1987 NA
#> 5 93707 15334. 22707. Male Other 13 "[65000,… 1775 1794
#> 6 93708 10826. 22482. Female Asian 66 "[25000,… 1251 842
#> 7 93709 0 NA Female Black 75 "[5000, … NA NA
#> 8 93710 8616. 7185. Female White 0 ">100000" 900 1195
#> 9 93711 9098. 8230. Male Asian 56 ">100000" 2840 2819
#> 10 93712 60947. 89066. Male Mexican American 18 "[15000,… 2045 3348
#> # ℹ 8,694 more rows
#> # ℹ 42 more variables: DR1T_F_TOTAL <dbl>, DR2T_F_TOTAL <dbl>,
#> # DR1_FWHOLEFRT <dbl>, DR2_FWHOLEFRT <dbl>, DR1T_F_JUICE <dbl>,
#> # DR2T_F_JUICE <dbl>, DR1_VTOTALLEG <dbl>, DR2_VTOTALLEG <dbl>,
#> # DR1_VDRKGRLEG <dbl>, DR2_VDRKGRLEG <dbl>, DR1_VNONDRKGR <dbl>,
#> # DR2_VNONDRKGR <dbl>, DR1T_V_DRKGR <dbl>, DR2T_V_DRKGR <dbl>,
#> # DR1T_V_LEGUMES <dbl>, DR2T_V_LEGUMES <dbl>, DR1T_G_WHOLE <dbl>, …
# Produce 2011-12 HEI scores for the Total Fruit component using the Mean Ratio
# scoring method. Only include white and black women aged 50 to 100 with a
# family income of $75,000 or more. Display the results by race/ethnicity.
score(scoringMethod = "Mean Ratio",
years = "1112",
heiComponent = "Total Fruit",
demographicGroup = "Race/Ethnicity",
sex = ("Female"),
raceEthnicity = c("White", "Black"),
age = c(50, 100),
familyIncome = c("[75000, 100000)", "75000+", ">100000"))
#> # A tibble: 2 × 2
#> RACE_ETH score
#> <chr> <dbl>
#> 1 Black 4.30
#> 2 White 4.18
# 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",
scoringMethod = "Pop Ratio",
years = "1718",
heiComponent = "Total Score",
demographicGroup = "Family Income",
familyIncome = c("[0, 5000)", "[15000, 20000)", "[35000, 45000)", "[45000, 55000)",
"[55000, 65000)", "[65000, 75000)", "[75000, 100000)", ">100000"))
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.