A variety of reported crash data cross-classified by time (hour of the day) and day of the week, accumulated over 2009. These include fatalities and injuries (by car), trucks, motor cycles, bicycles and pedestrians. There are some alcohol-related data too.
data(crashi) data(crashf) data(crashtr) data(crashmc) data(crashbc) data(crashp) data(alcoff) data(alclevels)
Data frames with hourly times as rows and days of the week as columns.
alclevels dataset has hourly times and alcohol levels.
Day of the week.
Blood alcohol level (milligrams alcohol per 100 millilitres of blood).
Each cell is the aggregate number of crashes reported at each
hour-day combination, over the 2009 calendar year.
rownames of each data frame is the
start time (hourly from midnight onwards) on a 24 hour clock,
e.g., 21 means 9.00pm to 9.59pm.
chrashi are the number of injuries by car,
crashf are the number of fatalities by car
(not included in
crashtr are the number of crashes involving trucks,
crashmc are the number of crashes involving motorcyclists,
crashbc are the number of crashes involving bicycles,
crashp are the number of crashes involving pedestrians.
For alcohol-related offences,
alcoff are the number of alcohol offenders from
breath screening drivers,
alclevels are the blood alcohol levels of fatally injured drivers.
Thanks to Warwick Goold and Alfian F. Hadi for assistance.
Motor Vehicles Crashes in New Zealand 2009; Statistical Statement Calendar Year 2009. Ministry of Transport, NZ Government; Yearly Report 2010. ISSN: 1176-3949
## Not run: plot(unlist(alcoff), type = "l", frame.plot = TRUE, axes = FALSE, col = "blue", bty = "o", main = "Alcoholic offenders on NZ roads, aggregated over 2009", sub = "Vertical lines at midnight (purple) and noon (orange)", xlab = "Day/hour", ylab = "Number of offenders") axis(1, at = 1 + (0:6) * 24 + 12, labels = colnames(alcoff)) axis(2, las = 1) axis(3:4, labels = FALSE, tick = FALSE) abline(v = sort(1 + c((0:7) * 24, (0:6) * 24 + 12)), lty = "dashed", col = c("purple", "orange")) ## End(Not run) # Goodmans RC models ## Not run: fitgrc1 <- grc(alcoff) # Rank-1 model fitgrc2 <- grc(alcoff, Rank = 2, Corner = FALSE, Uncor = TRUE) Coef(fitgrc2) ## End(Not run) ## Not run: biplot(fitgrc2, scaleA = 2.3, Ccol = "blue", Acol = "orange", Clabels = as.character(1:23), xlim = c(-1.3, 2.3), ylim = c(-1.2, 1)) ## End(Not run)
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