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London Cycling Deaths

CyclingDeaths.Rd

A data frame containing the number of deaths of cyclists in London from 2005 through 2012 in each fortnightly period. Aberdein & Spiegelhalter (2013) discuss these data in relation to the observation that six cyclists died in London between Nov. 5 and Nov. 13, 2013.

Usage

data(CyclingDeaths)

Format

A data frame with 208 observations on the following 2 variables.

date

a Date

deaths

number of deaths, a numeric vector

Source

https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data, STATS 19 data, 2005-2012, using the files Casualty0512.csv and Accidents0512.csv

References

Aberdein, Jody and Spiegelhalter, David (2013). Have London's roads become more dangerous for cyclists? Significance, 10(6), 46–48.

Examples

data(CyclingDeaths)

plot(deaths ~ date, data=CyclingDeaths, 
  type="h", 
  lwd=3, 
  ylab="Number of deaths", 
  axes=FALSE)
axis(1, at=seq(as.Date('2005-01-01'), 
               by='years', 
               length.out=9), 
     labels=2005:2013)
axis(2, at=0:3)


# make a one-way frequency table
CyclingDeaths.tab <- table(CyclingDeaths$deaths)

gf <- goodfit(CyclingDeaths.tab)
gf
#> 
#> Observed and fitted values for poisson distribution
#> with parameters estimated by `ML' 
#> 
#>  count observed     fitted pearson residual
#>      0      114 117.946412       -0.3633792
#>      1       75  66.911907        0.9887681
#>      2       14  18.979820       -1.1430562
#>      3        5   3.589133        0.4108395
summary(gf)
#> 
#> 	 Goodness-of-fit test for poisson distribution
#> 
#>                       X^2 df  P(> X^2)
#> Likelihood Ratio 4.151738  2 0.1254474

rootogram(gf, xlab="Number of Deaths")

distplot(CyclingDeaths.tab)


# prob of 6 or more deaths in one fortnight
lambda <- gf$par$lambda
ppois(5, lambda, lower.tail=FALSE)
#> [1] 2.854305e-05