HistData (version 0.9-1)

Breslau: Halley's Breslau Life Table

Description

Edmond Halley published his Breslau life table in 1693, which was arguably the first in the world based on population data. David Bellhouse (2011) resurrected the original sources of these data, collected by Caspar Neumann in the city of Breslau (now called Wroclaw), and then reconstructed in the 1880s by Jonas Graetzer, the medical officer in Breslau at that time.

The dataset here follows Graetzer, and gives the number of deaths at ages 1:100 recorded in each of the years 1687:1691. Halley's analysis was based on the total over those years.

Usage

data("Breslau")

Format

A data frame with 100 observations on the following 8 variables. The yearXXXX variables give the number of deaths for persons of a given age recorded in that year.

age

a numeric vector

year1687

a numeric vector

year1688

a numeric vector

year1689

a numeric vector

year1690

a numeric vector

year1691

a numeric vector

total

a numeric vector

average

a numeric vector

Details

This dataset was kindly provided by David Bellhouse.

References

Halley, E. (1693). An estimate of the degrees of mortality of mankind, drawn from the curious tables of births and funerals in the City of Breslaw; with an attempt to ascertain the price of annuities upon lives. Phil. Trans., 17, 596-610.

See Also

Arbuthnot, HalleyLifeTable

Examples

data(Breslau)

# Reproduce Figure 1 in Bellhouse (2011)
# He excluded age < 5 and made a point of the over-representation of deaths in quinquennial years.
library(ggplot2)
library(dplyr, warn.conflicts = FALSE)
Breslau5 <- Breslau |>
  filter(age >= 5) |>
  mutate(div5 = factor(age %% 5 == 0))

ggplot(Breslau5, aes(x=age, y=total), size=1.5) +
  geom_point(aes(color=div5)) +
  scale_color_manual(labels = c(FALSE, TRUE), 
                     values = c("blue", "red")) +
  guides(color=guide_legend("Age divisible by 5")) +
  theme(legend.position = "top") +
  labs(x = "Age current at death",
       y = "Total number of deaths") +
  theme_bw()