In apyramid: Visualize Population Pyramids Aggregated by Age

Example

We can demonstrate plotting of un-aggregated data with the fluH7N9_china_2013 data set in the {outbreaks} package that records 136 cases of Influenza A H7N9 in China in 2013 (source: https://doi.org/10.5061/dryad.2g43n)

flu <- outbreaks::fluH7N9_china_2013

# data preparation (create age groups from ages)
autocut <- function(x) {
  cut(x, breaks = pretty(x), right = TRUE, include.lowest = TRUE)
}
flu$age_group <- autocut(as.integer(flu$age))
levels(flu$gender) <- c("Female", "Male")
head(flu)

flup <- age_pyramid(flu, age_group, split_by = gender)
flup

Since the result is a ggplot2 object, it can be customized like one:

flup + 
  scale_fill_grey(guide = guide_legend(order = 1)) + 
  theme(text = element_text(size = 18, family = "serif")) + 
  theme(panel.background = element_rect(fill = "#ccffff")) + 
  theme(plot.background = element_rect(fill = "#ffffcc")) + 
  theme(legend.background = element_blank()) +
  labs(
    x       = "Age group (years)",
    y       = "Number of cases",
    fill    = "Gender",
    title   = "136 cases of influenza A H7N9 in China",
    caption = "Source: https://doi.org/10.5061/dryad.2g43n"
  )

One of the advantages of {apyramid} is that it will adjust to account for non-binary categorical variables. For example, in the flu data set, there are two cases with no gender reported. If we set na.rm = FALSE, we can the age distribution of these two cases:

age_pyramid(flu, age_group, split_by = gender, na.rm = FALSE)

apyramid documentation built on Feb. 16, 2023, 10:53 p.m.