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

```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.

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]