Title:	Visualize Population Pyramids Aggregated by Age
Version:	0.1.3
Description:	Provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a 'ggplot' object, allowing the user to further customize the output. This package is part of the 'R4Epis' project https://r4epis.netlify.app/.
License:	GPL-3
Depends:	R (≥ 3.2.0)
URL:	https://github.com/R4EPI/apyramid, https://r4epis.netlify.app/
BugReports:	https://github.com/R4EPI/apyramid/issues
Imports:	ggplot2 (≥ 3.0.0), tidyselect, rlang, forcats (≥ 1.0.0), dplyr, scales, glue
Suggests:	testthat (≥ 2.1.0), survey, srvyr, vdiffr, covr, outbreaks, knitr, rmarkdown
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2023-02-14 17:09:42 UTC; zhian
Author:	Zhian N. Kamvar [aut, cre], Alex Spina [ctb]
Maintainer:	Zhian N. Kamvar <zkamvar@gmail.com>
Repository:	CRAN
Date/Publication:	2023-02-14 17:30:02 UTC

Plot a population pyramid (age-sex) from a dataframe.

Description

Plot a population pyramid (age-sex) from a dataframe.

Usage

age_pyramid(
  data,
  age_group = "age_group",
  split_by = "sex",
  stack_by = NULL,
  count = NULL,
  proportional = FALSE,
  na.rm = TRUE,
  show_midpoint = TRUE,
  vertical_lines = FALSE,
  horizontal_lines = TRUE,
  pyramid = TRUE,
  pal = NULL
)

Arguments

Note

If the split_by variable is bivariate (e.g. an indicator for a specific symptom), then the result will show up as a pyramid, otherwise, it will be presented as a facetted barplot with with empty bars in the background indicating the range of the un-facetted data set. Values of split_by will show up as labels at top of each facet.

Examples

library(ggplot2)
old <- theme_set(theme_classic(base_size = 18))

# with pre-computed data ----------------------------------------------------
# 2018/2008 US census data by age and gender
data(us_2018)
data(us_2008)
age_pyramid(us_2018, age_group = age, split_by = gender, count = count)
age_pyramid(us_2008, age_group = age, split_by = gender, count = count)

# 2018 US census data by age, gender, and insurance status
data(us_ins_2018)
age_pyramid(us_ins_2018, 
  age_group = age,
  split_by = gender,
  stack_by = insured,
  count = count
)
us_ins_2018$prop <- us_ins_2018$percent/100
age_pyramid(us_ins_2018,
  age_group = age,
  split_by = gender,
  stack_by = insured,
  count = prop,
  proportion = TRUE
)

# from linelist data --------------------------------------------------------
set.seed(2018 - 01 - 15)
ages <- cut(sample(80, 150, replace = TRUE),
  breaks = c(0, 5, 10, 30, 90), right = FALSE
)
sex <- sample(c("Female", "Male"), 150, replace = TRUE)
gender <- sex
gender[sample(5)] <- "NB"
ill <- sample(c("case", "non-case"), 150, replace = TRUE)
dat <- data.frame(
  AGE = ages,
  sex = factor(sex, c("Male", "Female")),
  gender = factor(gender, c("Male", "NB", "Female")),
  ill = ill,
  stringsAsFactors = FALSE
)

# Create the age pyramid, stratifying by sex
print(ap <- age_pyramid(dat, age_group = AGE))

# Create the age pyramid, stratifying by gender, which can include non-binary
print(apg <- age_pyramid(dat, age_group = AGE, split_by = gender))

# Remove NA categories with na.rm = TRUE
dat2 <- dat
dat2[1, 1] <- NA
dat2[2, 2] <- NA
dat2[3, 3] <- NA
print(ap <- age_pyramid(dat2, age_group = AGE))
print(ap <- age_pyramid(dat2, age_group = AGE, na.rm = TRUE))

# Stratify by case definition and customize with ggplot2
ap <- age_pyramid(dat, age_group = AGE, split_by = ill) +
  theme_bw(base_size = 16) +
  labs(title = "Age groups by case definition")
print(ap)

# Stratify by multiple factors
ap <- age_pyramid(dat,
  age_group = AGE,
  split_by = sex,
  stack_by = ill,
  vertical_lines = TRUE
) +
  labs(title = "Age groups by case definition and sex")
print(ap)

# Display proportions
ap <- age_pyramid(dat,
  age_group = AGE,
  split_by = sex,
  stack_by = ill,
  proportional = TRUE,
  vertical_lines = TRUE
) +
  labs(title = "Age groups by case definition and sex")
print(ap)

# empty group levels will still be displayed
dat3 <- dat2
dat3[dat$AGE == "[0,5)", "sex"] <- NA
age_pyramid(dat3, age_group = AGE)
theme_set(old)

US Census data for population, age, and gender

Description

All of these tables were read directly from the excel sources via custom script located at https://github.com/R4EPI/apyramid/blob/master/scripts/read-us-pyramid.R.

Usage

us_2018

us_2008

us_ins_2018

us_ins_2008

us_gen_2018

us_gen_2008

Format

All tables are in long tibble format. There are three columns common to all of the tables:

• age [factor] 18 ordered age groups in increments of five years from "<5" to "85+"

• gender [factor] 2 reported genders (male, female).

• count [integer] Numbers in thousands. Civilian noninstitutionalized and military population.

Below are specifics of each table beyond the stated three columns with names as reported on the US census website

Population by Age and Sex (us_2018, us_2008)

A tibble with 36 rows and 4 columns. (us_2018 source: https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2018/age-sex-composition/2018gender_table1.xls) (us_2008 source: https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2008/age-sex-composition/2008gender_table1.xls)

Additional columns:

• percent [numeric] percent of the total US population rounded to the nearest 0.1%

Health Insurance by Sex and Age (us_ins_2018, us_ins_2008)

A tibble with 72 rows and 5 columns. (us_ins_2018 source: https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2018/age-sex-composition/2018gender_table14.xls) (us_ins_2008 source: https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2008/age-sex-composition/2008gender_table29.xls)

Additional columns:

• insured [factor] Either "Insured" or "Not insured" indicating insured status

• percent [numeric] percent of each age and gender category insured rounded to the nearest 0.1%

Generational Distribution of the Population by Sex and Age (us_gen_2018, us_gen_2008)

A tibble with 108 rows and 5 columns. (us_gen_2018 source: https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2018/age-sex-composition/2018gender_table13.xls) (us_gen_2008 source: https://www2.census.gov/programs-surveys/demo/tables/age-and-sex/2008/age-sex-composition/2008gender_table29.xls)

Additional columns:

• generation [factor] Three categories of generations in the US: First, Second, Third and higher (see note)

• percent [numeric] percent of the total US population rounded to the nearest 0.1%

Note: from the US Census Bureau: The foreign born are considered first generation. Natives with at least one foreign-born parent are considered second generation. Natives with two native parents are considered third-and-higher generation.

An object of class tbl_df (inherits from tbl, data.frame) with 36 rows and 4 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 72 rows and 5 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 72 rows and 5 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 108 rows and 5 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 108 rows and 5 columns.

Source

https://census.gov/data/tables/2018/demo/age-and-sex/2018-age-sex-composition.html https://census.gov/data/tables/2008/demo/age-and-sex/2008-age-sex-composition.html