A quick look at the racial/ethnic composition and age distribution of American generations. Per Pew Research definitions & US Census data.
library(dplyr)
gen <- c('Alpha',
'Gen Z',
'Millennial',
'Gen X',
'Boomers',
'Silent',
'Greatest')
start <- c(2013, 1997, 1981, 1965, 1946, 1928, 1901)
end <- c(2028, 2012, 1996, 1980, 1964, 1945, 1927)
gen_desc <- data.frame(rank = 7:1,
gen = gen,
start = start,
end = end)
gen_desc |> knitr::kable()| rank | gen | start | end |
|---|---|---|---|
| 7 | Alpha | 2013 | 2028 |
| 6 | Gen Z | 1997 | 2012 |
| 5 | Millennial | 1981 | 1996 |
| 4 | Gen X | 1965 | 1980 |
| 3 | Boomers | 1946 | 1964 |
| 2 | Silent | 1928 | 1945 |
| 1 | Greatest | 1901 | 1927 |
Monthly Postcensal Resident Population, 7/1/2024 to 12/1/2024. Made available by the US Census as CSV file here.
U.S. Census Bureau. (2023). Monthly Population Estimates by Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2020, to July 1, 2023 (with short-term projections to December 2024). National population estimates. U.S. Department of Commerce. Retrieved from https://www.census.gov
dlink <- 'https://www2.census.gov/programs-surveys/popest/datasets/2020-2023/national/asrh/nc-est2023-alldata-p-file10.csv'
pops <- read.csv (
url(dlink)) |>
filter(MONTH == '10' & YEAR == '2024') |>
tidyr::gather(key = 'race',
value = 'pop',
-UNIVERSE:-AGE)A more detailed description of the population estimates can be found here. Note: Race categories reflect non-Hispanic populations.
race <- c('NHWA', 'NHBA', 'NHIA',
'NHAA', 'NHNA', 'NHTOM', 'H')
race1 <- c('White Alone',
'Black Alone',
'American Indian Alone',
'Asian Alone',
'Native Hawaiian Alone',
'Two or More Races',
'Hispanic')
labels <- data.frame(race = race,
race1 = race1)
search <- paste(paste0('^',race, '_'), collapse = '|')The following table details a random sample of the data set – with Pew Research defined generations & estimated year-of-birth.
gen_pops <- pops |>
filter(grepl(search, race)) |>
mutate(race = gsub('_.*$', '', race)) |>
group_by(AGE, race) |>
summarise(pop = sum(pop))|>
left_join(labels) |>
filter(AGE != '999') |>
mutate(yob = 2024 - AGE)
# Join gen_pops with gen_desc based on yob within start and end range
data.table::setDT(gen_pops)[gen_desc,
on = .(yob >= start, yob <= end),
gen := i.gen]
gen_pops1 <- gen_pops |> left_join(gen_desc)gen_pops1 |>
select(race1, yob, AGE, gen, pop) |>
sample_n(7) |>
knitr::kable()| race1 | yob | AGE | gen | pop |
|---|---|---|---|---|
| American Indian Alone | 1945 | 79 | Silent | 11158 |
| Asian Alone | 1953 | 71 | Boomers | 171729 |
| Asian Alone | 2024 | 0 | Alpha | 193309 |
| White Alone | 1975 | 49 | Gen X | 2212520 |
| Native Hawaiian Alone | 1983 | 41 | Millennial | 10592 |
| White Alone | 1987 | 37 | Millennial | 2475205 |
| Hispanic | 1999 | 25 | Gen Z | 1033154 |
library(ggplot2)
gen_pops1 |>
group_by(gen, rank) |>
summarize(pop = sum(pop)) |>
mutate(lab = round(pop/1000000, 1)) |>
ggplot(aes(x = reorder(gen, rank),
y = pop,
fill = gen)) +
geom_col(show.legend = FALSE,
alpha = 0.75) +
geom_text(aes(label = lab),
size = 3.5)+
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
xlab('') + ylab('') +
coord_flip()+
ggthemes::scale_fill_stata() +
theme_minimal() +
labs(title = 'Population by American Generation',
caption = 'SOURCE: US Census, Monthly Postcensal Resident Population, October 2024.')
gg <- gen_pops1 |>
group_by(yob, AGE, gen) |>
summarize(tot = sum(pop)) |>
group_by(gen) |>
mutate(tot = max(tot)) |> #For labels below.
filter(yob %in% c('1919', '1928', '1946', '1965',
'1981', '1997', '2013'))The figure below illustrates the US population by single year of age, ranging from the population aged less than a year to the population over 100 (as of October 2024). Generation membership per single year of age is specified by color.
gen_pops |>
ggplot(aes(x = AGE,
y = pop,
fill = gen)) +
geom_vline(xintercept = gg$AGE,
linetype =2,
color = 'gray',
linewidth = .25)+
geom_col(show.legend = FALSE,
alpha = 0.85,
width = .7) +
annotate(geom="text",
x = gg$AGE - 2.5,
y = gg$tot + 70000,
label = gg$gen,
size = 3.25) +
xlab('Age')+
ylab('') +
theme_minimal() +
theme(legend.position="bottom",
legend.title = element_blank(),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank()) +
ggthemes::scale_fill_stata()+
scale_x_reverse(breaks = rev(gg$AGE)) +
labs(title = 'American population by single-year age & generation')
gen_pal <- c('#b0bcc1','#ead8c3', '#437193',
'#c66767', '#55752f', '#dae2ba',
'#7b9bb3')gen_pops |>
ggplot(aes(x = AGE,
y = pop,
fill = race1)) +
geom_area(stat = "identity",
color = 'white',
alpha = 0.85) +
scale_fill_manual(values = gen_pal) +
geom_vline(xintercept = gg$AGE,
linetype = 2,
color = 'gray',
linewidth = .25)+
annotate(geom="text",
x = gg$AGE - 4.5,
y = gg$tot + 70000,
label = gg$gen,
size = 3.25) +
xlab('')+ ylab('') +
theme_minimal() +
theme(legend.position="bottom",
legend.title = element_blank(),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
panel.grid.minor.y=element_blank()) +
scale_x_reverse(breaks = rev(gg$AGE) )+
labs(title ='American population by age, race & generation')
white_label <- gen_pops |>
group_by(gen, AGE) |>
mutate(per = pop/sum(pop))|>
filter(race1 == 'White Alone') |>
group_by(gen) |>
mutate(per = max(per)) |> #For labels below.
arrange(yob) |>
filter(yob %in% c('1919', '1928', '1946', '1965',
'1981', '1997', '2013'))The final figure provides a proportional breakdown of race and ethnicity in the United States, organized by single year of age.
The chart illustrates a clear and ongoing demographic shift in the racial and ethnic composition of the American population across generations. Older cohorts, such as the Silent Generation and Baby Boomers, are overwhelmingly composed of individuals categorized as “White Alone,” with minimal representation from other racial and ethnic groups. As we move to younger generations, starting with Generation X, there is a noticeable increase in diversity.
Millennials, Generation Z, and Generation Alpha demonstrate significant changes, with the proportion of “White Alone” individuals decreasing steadily. At the same time, the share of Hispanic, Asian, and mixed-race populations (“Two or More Races”) grows considerably. This pattern underscores a broader trend toward greater racial and ethnic diversity in the younger cohorts. By the time we reach Generation Alpha, the demographic composition becomes the most diverse, reflecting the cumulative impact of immigration, intermarriage, and evolving cultural identities.
This progression highlights a generational shift in the United States, where younger populations represent a more multicultural and pluralistic society, contrasting sharply with the more homogenous makeup of older generations. The chart encapsulates how societal and demographic changes are reshaping the fabric of the nation over time.
gen_pops |>
group_by(gen, AGE) |>
mutate(per = pop/sum(pop)) |>
ggplot(aes(x = (AGE),
y = per,
fill = race1)) +
geom_area(stat = "identity",
color = 'white',
alpha = 0.85) +
geom_hline(yintercept = .5,
linetype = 4,
color = 'white') +
scale_fill_manual(values = gen_pal) +
geom_vline(xintercept = gg$AGE,
linetype = 2,
color = 'gray',
linewidth = .25)+
annotate(geom="text",
x = gg$AGE-4.5,
y = white_label$per - .05,
label = gg$gen,
size = 3.25) +
xlab('')+ ylab('') +
theme_minimal() +
theme(legend.position="bottom",
legend.title = element_blank(),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank()) +
scale_x_reverse(breaks = rev(gg$AGE)) +
labs(title = 'American population by age, race & generation')