wages {brolgar} | R Documentation |

## Wages data from National Longitudinal Survey of Youth (NLSY)

### Description

This data contains measurements on hourly wages by years in
the workforce, with education and race as covariates. The population
measured was male high-school dropouts, aged between 14 and 17 years
when first measured. `wages`

is a time series `tsibble`

.
It comes from J. D. Singer and J. B. Willett.
Applied Longitudinal Data Analysis.
Oxford University Press, Oxford, UK, 2003.
https://stats.idre.ucla.edu/stat/r/examples/alda/data/wages_pp.txt

### Usage

wages

### Format

A `tsibble`

data frame with 6402 rows and 8 variables:

- id
1–888, for each subject. This forms the `key`

of the data

- ln_wages
natural log of wages, adjusted for inflation,
to 1990 dollars.

- xp
Experience - the length of time in the workforce (in years).
This is treated as the time variable, with t0 for each subject starting
on their first day at work. The number of time points and values of time
points for each subject can differ. This forms the `index`

of the data

- ged
when/if a graduate equivalency diploma is obtained.

- xp_since_ged
change in experience since getting a ged (if they get one)

- black
categorical indicator of race = black.

- hispanic
categorical indicator of race = hispanic.

- high_grade
highest grade completed

- unemploy_rate
unemployment rates in the local geographic region
at each measurement time

### Examples

# show the data
wages
library(ggplot2)
# set seed so that the plots stay the same
set.seed(2019-7-15-1300)
# explore a sample of five individuals
wages %>%
sample_n_keys(size = 5) %>%
ggplot(aes(x = xp,
y = ln_wages,
group = id)) +
geom_line()
# Explore many samples with `facet_sample()`
ggplot(wages,
aes(x = xp,
y = ln_wages,
group = id)) +
geom_line() +
facet_sample()
# explore the five number summary of ln_wages with `features`
wages %>%
features(ln_wages, feat_five_num)

[Package

*brolgar* version 0.1.1

Index]