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]))

Diese Übung ist Teil des Kurses

Life Insurance Products Valuation in R

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Anleitung zur Übung

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.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# 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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