[[!meta date="2017-10-10 16:56:56 +0800"]]
[[!tag R analysis]]

Road fatalities in Australia
----------------------------

Recently inspired to doing a little analysis again, I landed on a
dataset from
<https://bitre.gov.au/statistics/safety/fatal_road_crash_database.aspx>,
which I downloaded on 5 Oct 2017. Having open datasets for data is a
great example of how governments are moving with the times!

Trends
------

I started by looking at the trends - what is the approximate number of
road fatalities a year, and how is it evolving over time? Are there any
differences noticeable between states? Or by gender?

[[Overall trend line|/pics/explore-AU-road-fatalities_files/fatalitiesTrends-1.png]][[Trend lines by Australian state|/pics/explore-AU-road-fatalities_files/fatalitiesTrends-2.png]][[Trend lines by gender|/pics/explore-AU-road-fatalities_files/fatalitiesTrends-3.png]]

What age group is most at risk in city traffic?
-----------------------------------------------

Next, I wondered if there were any particular ages that were more at
risk in city traffic. I opted to quickly bin the data to produce a
histogram.

    fatalities %>%
      filter(Year != 2017, Speed_Limit <= 50) %>%
      ggplot(aes(x=Age))+
      geom_histogram(binwidth = 5) +
      labs(title = "Australian road fatalities by age group",
           y = "Fatalities") +
      theme_economist()

    ## Warning: Removed 2 rows containing non-finite values (stat_bin).

[[histogram|/pics/explore-AU-road-fatalities_files/fatalities.cityTraffic-1.png]]

Hypothesis
----------

Based on the above, I wondered - are people above 65 more likely to die
in slow traffic areas? To make this a bit easier, I added two variables
to the dataset - one splitting people in younger and older than 65, and
one based on the speed limit in the area of the crash being under or
above 50 km per hour - city traffic or faster in Australia.

    fatalities.pensioners <- fatalities %>%
      filter(Speed_Limit <= 110) %>% # less than 2% has this - determine why
      mutate(Pensioner = if_else(Age >= 65, TRUE, FALSE)) %>%
      mutate(Slow_Traffic = ifelse(Speed_Limit <= 50, TRUE, FALSE)) %>%
      filter(!is.na(Pensioner))

To answer the question, I produce a density plot and a boxplot.

[[density plot|/pics/explore-AU-road-fatalities_files/fatalitiesSegmentation-1.png]][[box plot|/pics/explore-AU-road-fatalities_files/fatalitiesSegmentation-2.png]]

Some further statistical analysis does confirm the hypothesis!

    # Build a contingency table and perform prop test
    cont.table <- table(select(fatalities.pensioners, Slow_Traffic, Pensioner))
    cont.table

    ##             Pensioner
    ## Slow_Traffic FALSE  TRUE
    ##        FALSE 36706  7245
    ##        TRUE   1985   690

    prop.test(cont.table)

    ## 
    ##  2-sample test for equality of proportions with continuity
    ##  correction
    ## 
    ## data:  cont.table
    ## X-squared = 154.11, df = 1, p-value < 2.2e-16
    ## alternative hypothesis: two.sided
    ## 95 percent confidence interval:
    ##  0.07596463 0.11023789
    ## sample estimates:
    ##    prop 1    prop 2 
    ## 0.8351573 0.7420561

    # Alternative approach to using prop test
    pensioners <- c(nrow(filter(fatalities.pensioners, Slow_Traffic == TRUE, Pensioner == TRUE)), nrow(filter(fatalities.pensioners, Slow_Traffic == FALSE, Pensioner == TRUE)))
    everyone <- c(nrow(filter(fatalities.pensioners, Slow_Traffic == TRUE)), nrow(filter(fatalities.pensioners, Slow_Traffic == FALSE)))
    prop.test(pensioners,everyone)

    ## 
    ##  2-sample test for equality of proportions with continuity
    ##  correction
    ## 
    ## data:  pensioners out of everyone
    ## X-squared = 154.11, df = 1, p-value < 2.2e-16
    ## alternative hypothesis: two.sided
    ## 95 percent confidence interval:
    ##  0.07596463 0.11023789
    ## sample estimates:
    ##    prop 1    prop 2 
    ## 0.2579439 0.1648427

Conclusion
----------

It's possible to conclude older people are over-represented in the
fatalities in lower speed zones. Further ideas for investigation are
understanding the impact of the driving age limit on the fatalities -
the position in the car of the fatalities (driver or passenger) was not
yet considered in this quick look at the contents of the dataset.

[[quantile-quantile plot|/pics/explore-AU-road-fatalities_files/fatalitiesDistComp-1.png]]