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.

high_grade

highest grade completed

unemploy_rate

unemployment rates in the local geographic region at each measurement time

Examples

# show the data wages
#> # A tsibble: 6,402 x 9 [!] #> # Key: id [888] #> id ln_wages xp ged xp_since_ged black hispanic high_grade #> <int> <dbl> <dbl> <int> <dbl> <int> <int> <int> #> 1 31 1.49 0.015 1 0.015 0 1 8 #> 2 31 1.43 0.715 1 0.715 0 1 8 #> 3 31 1.47 1.73 1 1.73 0 1 8 #> 4 31 1.75 2.77 1 2.77 0 1 8 #> 5 31 1.93 3.93 1 3.93 0 1 8 #> 6 31 1.71 4.95 1 4.95 0 1 8 #> 7 31 2.09 5.96 1 5.96 0 1 8 #> 8 31 2.13 6.98 1 6.98 0 1 8 #> 9 36 1.98 0.315 1 0.315 0 0 9 #> 10 36 1.80 0.983 1 0.983 0 0 9 #> # … with 6,392 more rows, and 1 more variable: unemploy_rate <dbl>
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)
#> # A tibble: 888 x 6 #> id min q25 med q75 max #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 31 1.43 1.48 1.73 2.02 2.13 #> 2 36 1.80 1.97 2.32 2.59 2.93 #> 3 53 1.54 1.58 1.71 1.89 3.24 #> 4 122 0.763 2.10 2.19 2.46 2.92 #> 5 134 2.00 2.28 2.36 2.79 2.93 #> 6 145 1.48 1.58 1.77 1.89 2.04 #> 7 155 1.54 1.83 2.22 2.44 2.64 #> 8 173 1.56 1.68 2.00 2.05 2.34 #> 9 206 2.03 2.07 2.30 2.45 2.48 #> 10 207 1.58 1.87 2.15 2.26 2.66 #> # … with 878 more rows