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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)
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