R/wages.R

In numbats/yowie: Longitudinal Wages Data from the National Longitudinal Survey of Youth 1979

#' Wages Data from the National Longitudinal Survey of Youth (NLSY79)
#'
#' A data set contains longitudinal data of mean hourly wages along with several
#' demographic variables of Americans from the National Longitudinal Survey of Youth
#' (NLSY79) held by the U.S. Bureau of Labor Statistics from Round 1 (1979 survey year)
#' to Round 28 (2018 survey year). The cohort provided in this data set of the NLSY79 cohort who participated in at least 3 survey rounds.
#'
#' @source The U.S. Bureau of Labor Statistics. (2021, January 6). *National Longitudinal Survey of Youth 1979*. https://www.nlsinfo.org/content/cohorts/nlsy79/get-data
#'
#' @format A tsibble with 206,795 rows and 18 variables:
#' \describe{
#'   \item{id}{A unique individual's ID number. This is the `key` of the data.}
#'   \item{year}{The year the observation was taken. This could be the `index` of the data.}
#'   \item{wage}{The mean of the hourly wages the individual gets at
#'                           each of their different jobs. The value could be a
#'                           weighted or an arithmetic mean. The weighted mean is used
#'                           when the information of hours of work as the weight
#'                           is available. The mean hourly wage could also be a predicted
#'                           value if the original value is considered influential
#'                           by the robust linear regression as part of data cleaning.}
#'   \item{age_1979}{The age of the subject in 1979.}
#'   \item{sex}{Sex of the subject: f = Female and m = Male.}
#'   \item{race}{Race of the subject: NBH = Non-Black,Non-Hispanic; H = Hispanic; B = Black.}
#'   \item{grade}{Integer value of the highest grade completed corresponding to `year`.}
#'   \item{hgc}{Highest grade completed.}
#'   \item{hgc_i}{Integer of highest grade completed.}
#'   \item{hgc_1979}{The highest grade completed in 1979 (integer value).}
#'   \item{ged}{Wether the respondent had a high school diploma or Graduate Equivalency Degree (GED). 1: High school diploma; 2: GED; 3: Both}
#'   \item{njobs}{Number of jobs that an individual has.}
#'   \item{hours}{The total number of hours the individual usually works per week.}
#'   \item{stwork}{The year when the individual starting to work.}
#'   \item{yr_workforce}{The length of time in the workforce in years (year - stwork).}
#'   \item{exp}{Work experience, i.e., the number of years worked}
#'   \item{is_wm}{Whether the mean hourly wage is weighted mean, using the hour work
#'                as the weight, or regular/arithmetic mean. TRUE = is weighted mean.
#'                FALSE = is regular mean.}
#'   \item{is_pred}{Whether the mean hourly wage is a predicted value or not.}
#' }
#'
#' @examples
#' # data summary
#' wages
#'
#' library(ggplot2)
#' library(dplyr)
#' library(tsibble)
#' wages_ids <- key_data(wages) %>% select(id)
#' wages %>%
#'   dplyr::filter(id %in% sample_n(wages_ids, 10)$id) %>%
#'   ggplot() +
#'     geom_line(aes(x = year,
#'                   y = wage,
#'                   group = id), alpha = 0.8)
#' @docType data
#' @name wages
#' @import tsibble
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