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Age pyramid construction and plotting
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R4EPI/apyramid
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The goal of {apyramid} is to provide a quick method for visualizingcensus data stratified by age and one or two categorical variables(e.g. gender and health status). This is a product of the R4EPIsproject; learn more athttps://r4epis.netlify.app/.
You can install {apyramid} from CRAN:
install.packages("apyramid")Click here for alternative installation options
If there is a bugfix or feature that is not yet on CRAN, you can installit via the {drat} package:
# install.packages("drat")drat::addRepo("R4EPI")install.packages("apyramid")
You can also install the in-development version from GitHub using the{remotes} package (but there’s no guarantee that it will be stable):
# install.packages("remotes")remotes::install_github("R4EPI/apyramid")
The {apyramid} package was primarily designed for quick visualisation ofun-aggregated linelist data in field epidemiological situations. It hasone available function:
age_pyramid()returns age pyramid visualizations of linelist,survey, or pre-aggregated census data as aggplotobject.
library("apyramid")library("ggplot2")# load ggplot2 to control plot aestheticslibrary("outbreaks")# load the outbreaks package for linelist dataold_theme<- theme_set(theme_classic(base_size=18))
We can demonstrate plotting of un-aggregated data with thefluH7N9_china_2013 data set in the {outbreaks} package that records136 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)#> case_id date_of_onset date_of_hospitalisation date_of_outcome outcome gender#> 1 1 2013-02-19 <NA> 2013-03-04 Death Male#> 2 2 2013-02-27 2013-03-03 2013-03-10 Death Male#> 3 3 2013-03-09 2013-03-19 2013-04-09 Death Female#> 4 4 2013-03-19 2013-03-27 <NA> <NA> Female#> 5 5 2013-03-19 2013-03-30 2013-05-15 Recover Female#> 6 6 2013-03-21 2013-03-28 2013-04-26 Death Female#> age province age_group#> 1 87 Shanghai (50,60]#> 2 27 Shanghai [0,10]#> 3 35 Anhui (10,20]#> 4 45 Jiangsu (10,20]#> 5 48 Jiangsu (10,20]#> 6 32 Jiangsu [0,10]flup<- age_pyramid(flu,age_group,split_by=gender)#> Warning: 2 missing rows were removed (0 values from `age_group` and 2 values#> from `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" )#> Scale for fill is already present.#> Adding another scale for fill, which will replace the existing scale.
One of the advantages of {apyramid} is that it will adjust to accountfor non-binary categorical variables. For example, in the flu data set,there are two cases with no gender reported. If we setna.rm = FALSE,we can the age distribution of these two cases:
age_pyramid(flu,age_group,split_by=gender,na.rm=FALSE)
{apyramid} can also be used to visualize pre-aggregated data. Thisexample is the US census data from 2018:
us_labels<- labs(x="Age group",y="Thousands of people",title="US Cenus Data 2018",caption="source: https://census.gov/data/tables/2018/demo/age-and-sex/2018-age-sex-composition.html")data(us_2018)us_2018#> # A tibble: 36 × 4#> age gender count percent#> <fct> <fct> <int> <dbl>#> 1 <5 male 10193 6.4#> 2 <5 female 9736 5.9#> 3 5-9 male 10338 6.5#> 4 5-9 female 9905 6#> 5 10-14 male 10607 6.7#> 6 10-14 female 10204 6.2#> 7 15-19 male 10617 6.7#> 8 15-19 female 10291 6.2#> 9 20-24 male 10809 6.8#> 10 20-24 female 10625 6.4#> # … with 26 more rowsp<- age_pyramid(us_2018,age_group=age,split_by=gender,count=count)p+us_labels
You can also use another factor to split the data:
data(us_ins_2018)# stratified by gender and health insurance statusdata(us_gen_2018)# stratified by gender and generational statusp_ins<- age_pyramid(us_ins_2018,age_group=age,split_by=gender,stack_by=insured,count=count)p_gen<- age_pyramid(us_gen_2018,age_group=age,split_by=gender,stack_by=generation,count=count)p_ins+us_labels
p_gen+us_labels
Beyond that, survey data can be incorporated with the help of srvyr.Note that while it will show the weighted counts, it will not show theconfidence intervals as that highly depends on the appropriate choice ofCI estimator. This is meant as more of quick visualization tool for EDA.
library(srvyr,warn.conflicts=FALSE)data(api,package="survey")dstrata<-apistrat %>% mutate(apicat= cut(api00, pretty(api00),include.lowest=TRUE,right=TRUE)) %>% as_survey_design(strata=stype,weights=pw) age_pyramid(dstrata,apicat,split_by=stype)
theme_set(old_theme)Population pyramids are common tools for epidemiologists and there areseveral solutions in R. To our knowledge, the {apyramid} package is theonly one that allows for non-binary splitting variable.
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