Mortality rates over time

Cynthia downloads the most up-to-date mortality data for Belgium from the Human Mortality Database (HMD). This data set is preloaded as life_table.

In this exercise, you will use two common R functions. with() (docs) allows you to evaluate an R expression in a local environment constructed from a data frame. This avoids the need of repeatedly typing life_table$ to extract columns. For instance, the log mortality rates of 18-year-olds over the years can be extracted using:

with(life_table, log(qx[age == 18]))

with() is particularly handy in combination with subset() (docs). For instance, the log mortality rate of 18-year-olds from the life table of year 1999 can be returned using:

with(subset(life_table, year == 1999), log(qx[age == 18]))

This exercise is part of the course

Life Insurance Products Valuation in R

View Course

Exercise instructions

Explore life_table. Print out the first 6 rows using head() and compute the range() (docs) of the year variable.
Complete the code with proper use of subset() on life_table such that the mortality rates of an 18-year-old female throughout the years are plotted.
Again make use of subset() to select the life table of 1950 and plot the mortality rate curve in that year.

Hands-on interactive exercise

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

# Explore life_table
___
___

# Plot the logarithm of the female mortality rates for (18) by year
with(___(___, ___), 
     plot(year, log(qx), 
          type = "l", main = "Log mortality rates (Belgium, females, 18-year-old)", 
          xlab = "Year t", ylab = expression(paste("Log mortality rate ", log(q[18])))))

# Plot the logarithm of the female mortality rates in the year 1950 by age
with(___(___, ___), 
     plot(age, log(qx), 
          type = "l", main = "Log mortality rates (Belgium, females, 1950)",
          xlab = "Age x", ylab = expression(paste("Log mortality rate ", log(q[x])))))

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