Visualizing one year of mortality data

Now that you have some feeling with the 1999 life table, you can illustrate the evolution in female mortality rates \(q_x\) as a function of age \(x\). Cynthia's teacher usually plots the log of the mortality rates in his lecture sheets. Can you convince her that this is indeed a good approach?

The object life_table as well as the vectors age and qx defined in the previous exercise have been preloaded.

This exercise is part of the course

Life Insurance Products Valuation in R

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Exercise instructions

plot() (docs) the mortality rates for females in 1999. The first argument has to be the age and the second has to be the mortality rates qx.
Use a second call to plot() to display the mortality rates for females in 1999 on the logarithmic scale. Compared to the previous command, you have to transform the second argument by first taking the log() (docs).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Plot the female mortality rates in the year 1999
plot(___, ___, 
    main = "Mortality rates (Belgium, females, 1999)", 
    xlab = "Age x", 
    ylab = expression(paste("Mortality rate ", q[x])), 
    type = "l")

# Plot the logarithm of the female mortality rates in the year 1999
plot(___, ___, 
    main = "Log mortality rates (Belgium, females, 1999)", 
    xlab = "Age x", 
    ylab = expression(paste("Log mortality rate ", log(q[x]))), 
    type = "l")

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