National Health and Nutrition Examination Survey (NHANES)

Demographic survey data from NHANES 2015 to 2016, with data on 9971 participants, including sampling weights.

Usage

nhanes

Format

A data frame with 9971 rows and 13 variables:

PSU: SDMVPSU - Masked variance pseudo-PSU
weights: WTINT2YR - Full sample 2 year interview weight
strata: SDMVSTRA - Masked variance pseudo-stratum
gender: RIAGENDR - Gender
age: RIDAGEYR - Age in years at screening
birth_country: DMDBORN4 - Country of birth
marital_status: DMDMARTL - Marital status
interview_lang: SIALANG - Language of interview
edu_level: DMDHREDU - Household reference person's education level
household_size: DMDHHSIZ - Total number of people in the Household
family_size: DMDFMSIZ - Total number of people in the Family
annual_household_income: INDHHIN2 - Annual household income
annual_family_income: INDFMIN2 - Annual family income

Source

NHANES 2015-2016

Note

The data sets provided in this package are derived from the NHANES database and have been adapted for educational purposes. As such, they are NOT suitable for use as a research database. For research purposes, you should download original data files from the NHANES website and follow the analysis instructions given there.

References

CDC

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
glimpse(nhanes)
#> Rows: 9,971
#> Columns: 13
#> $ PSU                     <dbl> 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 1…
#> $ weights                 <dbl> 134671.370, 24328.560, 12400.009, 102717.996, …
#> $ strata                  <dbl> 125, 125, 131, 131, 126, 128, 120, 124, 119, 1…
#> $ gender                  <fct> Male, Male, Male, Female, Female, Female, Fema…
#> $ age                     <dbl> 62, 53, 78, 56, 42, 72, 11, 4, 1, 22, 32, 18, …
#> $ birth_country           <fct> US, Other, US, US, US, Other, US, US, US, US, …
#> $ marital_status          <fct> Married, Divorced, Married, Living with partne…
#> $ interview_lang          <fct> English, English, English, English, English, S…
#> $ edu_level               <fct> College graduate or above, High School, High S…
#> $ household_size          <dbl> 2, 1, 2, 1, 5, 5, 5, 5, 7, 3, 4, 3, 1, 3, 4, 2…
#> $ family_size             <dbl> 2, 1, 2, 1, 5, 5, 5, 5, 7, 3, 4, 3, 1, 3, 4, 2…
#> $ annual_household_income <dbl> 10, 4, 5, 10, 7, 14, 6, 15, 77, 7, 6, 15, 3, 4…
#> $ annual_family_income    <dbl> 10, 4, 5, 10, 7, 14, 6, 15, 77, 7, 6, 15, 3, 4…
nhanes |> dplyr::count(edu_level)
#> # A tibble: 7 × 2
#>   edu_level                     n
#>   <fct>                     <int>
#> 1 College degree             2908
#> 2 College graduate or above  2331
#> 3 High School                2015
#> 4 9-11th Grade               1200
#> 5 Less Than 9th Grade        1087
#> 6 Missing                     396
#> 7 Don't Know                   34