wages: Wages of male high-school dropouts
In ggobi/cranvas: Interactive statistical graphics based on Qt

Description Format Source Examples

The data was collected to track the labor experiences of male high-school dropouts. The men were between 14 and 17 years old at the time of the first survey.

Number of subjects: 888; Number of variables: 15; Number of observations, across all subjects: 6402

id: id numbers for each subject
lnw: natural log of wages, adjusted for inflation, to 1990 dollars
exper: length of time in the workforce (in years). This is treated as the time variable, with t = 0 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
ged: when/if a graduate equivalency diploma is obtained
black: categorical indicator of race is black
hispanic: categorical indicator of race is hispanic
hgc: highest grade completed
uerate: unemployment rates in the local geographic region at each measurement time

Singer, J. D. & Willett, J. B. (2003), Applied Longitudinal Data Analysis, Oxford University Press, Oxford, UK. It is a subset of data collected in the National Longitudinal Survey of Youth (NLSY) described at http://www.bls.gov/nls/nlsdata.htm.

library(cranvas)

## Not run: 

data(wages)
qwages <- qdata(wages)
qtime(exper, lnw, qwages, group = id)  # Too slow right now

## this is a very simple linear regression assuming each man has the same slope,
## which may not be entirely reasonable
fit <- lm(lnw ~ exper + id, data = wages)
wage2 <- subset(wages, !duplicated(wages$id), c(id, hispanic))
## predict wages at year 0 and 6 respectively
wage2$lnw0 <- predict(fit, data.frame(id = unique(wages$id), exper = 0))
wage2$lnw6 <- predict(fit, data.frame(id = unique(wages$id), exper = 6))
qwage2 <- qdata(wage2, color = hispanic)

qhist(lnw0, data = qwage2, main = "Wages on the first day")
qhist(lnw6, data = qwage2, main = "Wages after 6 years")

## End(Not run)