R/data-riskfactors.R

In njtierney/narnia: Data Structures, Summaries, and Visualisations for Missing Data

#' The Behavioral Risk Factor Surveillance System (BRFSS) Survey
#' Data, 2009.
#'
#' The data is a subset of the 2009 survey from BRFSS, an ongoing data
#'   collection program designed to measure behavioral risk factors for the
#'   adult population (18 years of age or older) living in households.
#'
#' @seealso the codebook:
#' \url{https://www.cdc.gov/brfss/annual_data/annual_2009.htm}
#'
#' Format: a data frame with 245 observations on the following 34
#' variables.
#'
#' \describe{
#' \item{`state`}{A factor with 52 levels. The labels and states
#'   corresponding to the labels are as follows: 1:Alabama, 2:Alaska, 4:Arizona,
#'   5:Arkansas, 6:California,8:Colorado, 9:Connecticut, 10:Delaware, 11:District
#'   of Columbia,12:Florida, 13:Georgia, 15:Hawaii, 16:Idaho, 1
#'   :Illinois,18:Indiana, 19:Iowa, 20:Kansas, 21:Kentucky, 22:Louisiana,23:Maine,
#'   24:Maryland, 25:Massachusetts, 26:Michigan,27:Minnesota, 28:Mississippi,
#'   2:Missouri, 30:Montana,31:Nebraska, 32:Nevada, 33:New Hampshire, 34:New
#'   Jersey, 35:NewMexico, 36:New York, 37:North Carolina, 38:North Dakota,
#'   39:Ohio,40:Oklahoma, 41:Oregon, 42:Pennsylvania, 44:Rhode Island,
#'   45:SouthCarolina, 46:South Dakota, 47:Tennessee, 48:Texas, 49:Utah,
#'   50:Vermont, 51:Virginia, 53:Washington, 54:West  Virginia,55:Wisconsin,
#'   56:Wyoming, 66:Guam, 72:Puerto Rico, 78:Virgin Islands}
#' \item{`sex`}{A factor with levels `Male` `Female`.}
#' \item{`age`}{A numeric vector from 7 to 97.}
#' \item{`weight_lbs`}{The weight without shoes in pounds.}
#' \item{`height_inch`}{The weight without shoes in inches.}
#' \item{`bmi`}{Body Mass Index (BMI). Computed by weight in Kilogram
#'     /(height in Meters * height in Meters). Missing if any of weight or
#'     height is missing.}
#' \item{`marital`}{A factor with levels `Married` `Divorced`
#'     `Widowed` `Separated` `NeverMarried` `UnmarriedCouple`.}
#' \item{`pregnant`}{Whether pregnant now with two levels `Yes` and
#'   `No`.}
#' \item{`children`}{A numeric vector giving the number of children less
#'   than 18 years of age in household.}
#' \item{`education`}{A factor with the education levels `1`
#'     `2` `3` `4` `5` `6` as 1: Never attended
#'     school or only kindergarten; 2: Grades 1 through 8 (Elementary);
#'     3: Grades 9 through 11 (Some high school); 4: Grade 12 or GED
#'     (High school graduate); 5: College 1 year to 3 years (Some college
#'     or technical school); 6: College 4 years or more  (College
#'     graduate).}
#' \item{`employment`}{A factor showing the employment status with levels
#'    `1` `2` `3` `4` `5` `7` `8`. The labels
#'    mean --  1: Employed for wages; 2: Self-employed; 3: Out of work for more
#'    than 1 year; 4: Out of work for less that 1 year;  5: A homemaker; 6: A
#'    student; 7:Retired; 8: Unable to work.}
#' \item{`income`}{The annual household income from all sources with
#'    levels `<10k` `10-15k` `15-20k` `20-25k` `25-35k`
#'    `35-50k` `50-75k` `>75k` `Dontknow` `Refused`.}
#' \item{`veteran`}{A factor with levels `1` `2` `3`
#'     `4` `5`. The question for this variable is: Have you ever
#'     served on active duty in the United States Armed Forces, either in the
#'     regular military or in a National Guard or military reserve unit? Active
#'     duty does not include training for the Reserves or National Guard, but
#'     DOES include activation, for example, for the Persian Gulf War. And the
#'     labels are meaning: 1: Yes, now on active duty; 2: Yes, on active duty
#'     during the last 12 months, but not now; 3: Yes, on active duty in the
#'     past, but not during the last 12 months; 4: No, training for Reserves or
#'     National Guard only; 5: No, never served in the military.}
#' \item{`hispanic`}{A factor with levels `Yes` `No`
#'     corresponding to the question: are you Hispanic or Latino?}
#'   \item{`health_general`}{Answer to question "in general your health is"
#'     with levels  `Excellent` `VeryGood` `Good` `Fair`
#'     `Poor` `Refused`.}
#'   \item{`health_physical`}{The number of days during the last 30 days
#'     that the respondent's physical health was not good.  -7 is for "Don't
#'     know/Not sure", and -9 is for "Refused".}
#'   \item{`health_mental`}{The number of days during the last 30 days
#'   that the respondent's mental health was not good.  -7 is for
#'   "Don't know/Not sure", and -9 is for "Refused".}
#'   \item{`health_poor`}{The number of days during the last 30 days
#'      that poor physical or mental health keep the respondent from doing
#'      usual activities, such as self-care, work, or recreation. -7 is
#'      for "Don't know/Not sure", and -9 is for "Refused".}
#'   \item{`health_cover`}{Whether having any kind of health care
#'      coverage, including health insurance, prepaid plans such as HMOs,
#'      or government plans such as Medicare. The answer has two levels:
#'      `Yes` and `No`.}
#'   \item{`provide_care`}{Whether providing any such care or assistance
#'   to a friend or family member during the past month, with levels `Yes`
#'   and `No`.}
#'   \item{`activity_limited`}{ Whether being limited in any way in any
#'     activities because of physical, mental, or emotional problems,
#'     with levels  `Yes` and `No`.}
#'   \item{`drink_any`}{Whether having had at least one drink of
#'     any alcoholic beverage such as beer, wine, a malt beverage or
#'     liquor during the past 30 days, with levels `Yes` and
#'     `No`.}
#'   \item{`drink_days`}{The number of days during the past 30 days that
#'     the respondent had at least one drink of any alcoholic beverage. -7 is
#'     for "Don't know/Not sure", and -9 is for "Refused".}
#'   \item{`drink_avg`}{The number of drinks on the average the respondent
#'     had on the days when he/she drank, during the past 30 days. -7 is for
#'      "Don't know/Not sure", and -9 is for "Refused".}
#'   \item{`smoke_100`}{ Whether having smoked at least
#'     100 cigarettes in the entire life, with levels `Yes` and
#'     `No`.}
#'   \item{`smoke_days`}{ The frequency of days now
#'     smoking, with levels `Everyday` `Somedays` and
#'     `NotAtAll`(not at all).}
#'   \item{`smoke_stop`}{Whether
#'     having stopped smoking for  one day or longer during the past 12
#'     months because the respondent was trying to quit smoking, with
#'     levels `Yes` and `No`.}
#'   \item{`smoke_last`}{A factor
#'     with levels `3` `4` `5` `6` `7` `8`
#'     corresponding to the question: how long has it been since last
#'     smoking cigarettes regularly? The labels mean: 3: Within the past
#'     6 months (3 months but less than 6 months ago); 4: Within the past
#'     year (6 months but less than 1 year ago); 5: Within the past 5
#'     years (1 year but less than 5 years ago); 6: Within the past 10
#'     years (5 years but less than 10 years ago); 7: 10 years or more;
#'     8: Never smoked regularly.}
#'   \item{`diet_fruit`}{The number of
#'     fruit the respondent eat every year, not counting juice. -7 is for
#'     "Don't know/Not sure", and -9 is for "Refused".}
#'   \item{`diet_salad`}{The number of servings of green   salad the
#'     respondent eat every year. -7 is for "Don't  know/Not sure",
#'     and -9 is for "Refused".}
#'   \item{`diet_potato`}{ The number of
#'     servings of potatoes, not including french fries, fried potatoes,
#'     or potato chips, that the respondent eat every year. -7 is for
#'     "Don't know/Not sure", and -9 is for "Refused".}
#'   \item{`diet_carrot`}{The number of carrots the respondent eat
#'     every year. -7 is for  "Don't know/Not sure", and -9 is for
#'     "Refused".}
#'    \item{`diet_vegetable`}{The number of servings of
#'     vegetables   the respondent eat every year, not counting carrots,
#'     potatoes, or salad. -7 is for "Don't know/Not sure", and -9 is
#'     for "Refused".}
#'   \item{`diet_juice`}{The number of fruit juices
#'     such as orange, grapefruit, or tomato that the respondent drink
#'     every year. -7 is   for "Don't know/Not sure", and -9 is for
#'     "Refused".}
#'     }
#'
#' @name riskfactors
#' @docType data
#' @usage data(riskfactors)
#' @source \url{https://www.cdc.gov/brfss/annual_data/annual_2009.htm}
#' @keywords datasets
#' @seealso library(MissingDataGUI) (named brfss)
#' @examples
#'
#' vis_miss(riskfactors)
#'
#' # Look at the missingness in the variables
#' miss_var_summary(riskfactors)
#'
#' # and now as a plot
#' gg_miss_var(riskfactors)
#'
#' \dontrun{
#' # Look at the missingness in bmi and poor health
#' library(ggplot2)
#' p <-
#' ggplot(riskfactors,
#'        aes(x = health_poor,
#'            y = bmi)) +
#'      geom_miss_point()
#'
#'  p
#'
#'  # for each sex?
#'  p + facet_wrap(~sex)

#'  # for each education bracket?
#'  p + facet_wrap(~education)
#' }
"riskfactors"