R Dataset / Package datasets / UKDriverDeaths

On this R-data statistics page, you will find information about the UKDriverDeaths data set which pertains to Road Casualties in Great Britain 1969–84. The UKDriverDeaths data set is found in the datasets R package. You can load the UKDriverDeaths data set in R by issuing the following command at the console data("UKDriverDeaths"). This will load the data into a variable called UKDriverDeaths. If R says the UKDriverDeaths data set is not found, you can try installing the package by issuing this command install.packages("datasets") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the UKDriverDeaths R data set. The size of this file is about 3,608 bytes.

Road Casualties in Great Britain 1969–84

Description

UKDriverDeaths is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

Seatbelts is more information on the same problem.

Usage

UKDriverDeaths
Seatbelts

Format

Seatbelts is a multiple time series, with columns

DriversKilled: car drivers killed.
drivers: same as UKDriverDeaths.
front: front-seat passengers killed or seriously injured.
rear: rear-seat passengers killed or seriously injured.
kms: distance driven.
PetrolPrice: petrol price.
VanKilled: number of van (‘light goods vehicle’) drivers.
law: 0/1: was the law in effect that month?

Source

Harvey, A.C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, pp. 519–523.

Durbin, J. and Koopman, S. J. (2001) Time Series Analysis by State Space Methods. Oxford University Press. http://www.ssfpack.com/dkbook/

References

Harvey, A. C. and Durbin, J. (1986) The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series B, 149, 187–227.

Examples

require(stats); require(graphics)
## work with pre-seatbelt period to identify a model, use logs
work <- window(log10(UKDriverDeaths), end = 1982+11/12)
par(mfrow = c(3, 1))
plot(work); acf(work); pacf(work)
par(mfrow = c(1, 1))
(fit <- arima(work, c(1, 0, 0), seasonal = list(order = c(1, 0, 0))))
z <- predict(fit, n.ahead = 24)
ts.plot(log10(UKDriverDeaths), z$pred, z$pred+2*z$se, z$pred-2*z$se,
lty = c(1, 3, 2, 2), col = c("black", "red", "blue", "blue"))## now see the effect of the explanatory variables
X <- Seatbelts[, c("kms", "PetrolPrice", "law")]
X[, 1] <- log10(X[, 1]) - 4
arima(log10(Seatbelts[, "drivers"]), c(1, 0, 0),
seasonal = list(order = c(1, 0, 0)), xreg = X)

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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