Convert birth years to generation names.
This package contains functions that will convert years of birth into
names of generational cohorts. For example, 1989 becomes
"Millennial". The package contains several helper functions to modify
the underlying lookup table as well as way to query it.
The package currently lives on GitHub, so you can install it like you would with any other package on GitHub:
remotes::install_github("joeystanley/generations")You can then load it like you can with any library.
library(generations)For the purposes of this tutorial, I’ll load ggplot as well.
library(dplyr)
library(ggplot2)The main function in this package is generations(). Given a vector of
integers, it’ll return a factor of generation names. First, I’ll
generate some random years of birth.
yobs <- floor(runif(10, 1900, 2020))
yobs## [1] 1908 1998 2013 1932 1920 1904 1921 1976 1902 1900
I can now easy convert that into generations.
generations(yobs)## [1] G.I. Millennial Gen Z Silent G.I. Lost
## [7] G.I. Gen X Lost Lost
## Levels: Lost G.I. Silent Gen X Millennial Gen Z
This function works on any year betwen 1435 and 2030. Numbers outside
that range return NA.
Note that by default, the function will return the vector as factor,
with the levels ordered so that the oldest generation in the vector is
first. To get a character vector instead, add the argument as_factor = FALSE.
There are some tweaks you can do to adjust the output of
generations(). First, you can return longer forms of the generational
names by specifying full_names = TRUE.
generations(yobs, full_names = TRUE)## [1] G.I. Generation Millennial Generation Generation Z
## [4] Silent Generation G.I. Generation Lost Generation
## [7] G.I. Generation Generation X Lost Generation
## [10] Lost Generation
## 6 Levels: Lost Generation G.I. Generation Silent Generation ... Generation Z
What this does is simply add "Generation" to the end of each one,
unless it’s "Gen X" (or Y, or Z), in which case it’ll expand it out to
simply "Generation X".
You can also show the years included in each generation by adding the
years = TRUE argument. This will add a space and, inside a pair of
parentheses, the start and end years of that generation, separated by an
en dash.
generations(yobs, years = TRUE)## [1] G.I. (1908–1928) Millennial (1984–2007) Gen Z (2008–2030)
## [4] Silent (1929–1945) G.I. (1908–1928) Lost (1886–1907)
## [7] G.I. (1908–1928) Gen X (1964–1983) Lost (1886–1907)
## [10] Lost (1886–1907)
## 6 Levels: Lost (1886–1907) G.I. (1908–1928) ... Gen Z (2008–2030)
The primary purpose of this is for visualizations, since not everyone is familiar with (or agrees with) the year ranges. For example, if you’ve got a bunch of people and want to visualize the distribution of when they were born, you could have very informative legends.
many_yobs <- tibble(yob = floor(rnorm(1000, 1975, 15))) %>%
mutate(gen = generations(yob, full_names = TRUE, years = TRUE))
ggplot(many_yobs, aes(yob, fill = gen)) +
geom_histogram(binwidth = 1) +
scale_fill_brewer(name = NULL, palette = "Set1")
How this additional portion is formatted can be adjusted. If rendering
an en dash is troublesome for you, you can change it to something else
with years_range_sep. You may also want to change the space between
the generation name and the opening parenthesis into a newline character
with years_sep, again for visualization purposes.
many_yobs <- many_yobs %>%
mutate(gen = generations(yob, full_names = TRUE, years = TRUE,
years_sep = "\n", years_range_sep = " to "))
ggplot(many_yobs, aes(yob, fill = gen)) +
geom_histogram(binwidth = 1) +
scale_fill_brewer(name = NULL, palette = "Set1") +
labs(x = NULL) +
theme(legend.key.height = unit(1, "cm"))
If you want to get really fancy, you can make the legend keys approximate the width they take up on the x-axis and put better tics marks.
widths <- many_yobs %>%
group_by(gen) %>%
summarize(width = max(yob) - min(yob)) %>%
ungroup() %>%
mutate(width = width / max(width) * 1.4) # you may have to fudge this a little more
ggplot(many_yobs, aes(yob, fill = gen)) +
geom_histogram(binwidth = 1) +
scale_fill_brewer(name = NULL, palette = "Set1") +
scale_x_continuous(breaks = c(1929, 1946, 1964, 1984, 2008, 2030)) +
labs(x = NULL) +
theme(legend.position = "bottom") +
guides(fill = guide_legend(nrow = 1, label.position = "bottom",
keywidth = widths$width, default.unit = "inches"))
To see a list of the generational data, you can use
show_generations(), which will return a data frame containing the
names, start years, and end years.
show_generations()## name start end
## 1 Gen Z 2008 2030
## 2 Millennial 1984 2007
## 3 Gen X 1964 1983
## 4 Boomer 1946 1963
## 5 Silent 1929 1945
## 6 G.I. 1908 1928
## 7 Lost 1886 1907
## 8 Missionary 1865 1885
## 9 Progressive 1844 1864
## 10 Gilded 1822 1843
## 11 Transcendental 1794 1821
## 12 Compromise 1773 1793
## 13 Republican 1746 1772
## 14 Liberty 1727 1745
## 15 Awakening 1704 1726
## 16 Enlightenment 1675 1703
## 17 Glorious 1649 1674
## 18 Cavalier 1621 1648
## 19 Puritan 1594 1620
## 20 Parliamentary 1569 1593
## 21 Elizabethan 1542 1568
## 22 Reprisal 1517 1541
## 23 Reformation 1497 1516
## 24 Humanist 1459 1496
## 25 Aurthurian 1435 1458
You can also get simple information. For example, if you want to know
when the start or end year of a particular generation is, you can use
get_start() or get_end():
get_start("Silent")## [1] 1929
get_end("Millennial")## [1] 2007
You can also find the names of neighboring generations with
get_prev_gen() and get_next_gen(), though these were mostly created
for internal purposes only rather than for you to use.
get_next_gen("Millennial")## [1] "Gen Z"
get_prev_gen("Missionary")## [1] "Progressive"
Note that if ask for something newer than Gen Z or older than Aurthurian
it will return NA.
The data that this package uses is loaded as a hidden object when you
load the package. You may modify it with the functions described in this
section. These changes will affect the dataset so long as the
generations package is loaded. You’ll have to reset the data each time
to reload it.
The labels and years for each generation are mostly borrowed from Howe &
Strauss’ Generational Theory books. However, not everyone agrees on the
names and year ranges for the various generations. For this reason, the
generations package makes it easy to modify the generations data to
your liking.
To rename a generation, use rename_generation(), with the old name
first and the new name second. For example, if you want to use Zoomer
instead of Gen Z, you can do so.
rename_generation("Gen Z", "Zoomer")## Gen Z has been renamed Zoomer
You’ll get a message informing you that the change has been made. If you
now run show_generations() you’ll see that the change has been made
and if you rerun generations(), you’ll get updated results.
show_generations()## name start end
## 1 Zoomer 2008 2030
## 2 Millennial 1984 2007
## 3 Gen X 1964 1983
## 4 Boomer 1946 1963
## 5 Silent 1929 1945
## 6 G.I. 1908 1928
## 7 Lost 1886 1907
## 8 Missionary 1865 1885
## 9 Progressive 1844 1864
## 10 Gilded 1822 1843
## 11 Transcendental 1794 1821
## 12 Compromise 1773 1793
## 13 Republican 1746 1772
## 14 Liberty 1727 1745
## 15 Awakening 1704 1726
## 16 Enlightenment 1675 1703
## 17 Glorious 1649 1674
## 18 Cavalier 1621 1648
## 19 Puritan 1594 1620
## 20 Parliamentary 1569 1593
## 21 Elizabethan 1542 1568
## 22 Reprisal 1517 1541
## 23 Reformation 1497 1516
## 24 Humanist 1459 1496
## 25 Aurthurian 1435 1458
generations(yobs)## [1] G.I. Millennial Zoomer Silent G.I. Lost
## [7] G.I. Gen X Lost Lost
## Levels: Lost G.I. Silent Gen X Millennial Zoomer
Because many people may want to use the term Zoomer instead of Gen
Z, a shortcut function, use_zoomer(), which is just a wrapper around
rename_generation("Gen Z", "Zoomer"), is included in the package. The
other shortcut functions are use_gen_y(), use_13th(),
use_baby_boom() as well as their reciprocals use_gen_z(),
use_millennial(), use_gen_x() and use_boomer().
You may also want to change the years. For example, many people consider
1997 as the end of the Millennial Generation. You can make this change
with redefine_generation(). With this function, you must specify the
new start and the new end year.
redefine_generation("Millennial", 1983, 1997)## Gen X is now from 1964 to 1982
## Millennial is now from 1983 to 1997
## Zoomer is now from 1998 to 2030
Since changing one generation impacts adjacent generations, you’ll get a message showing you what the new ranges are for this, the previous, and the next generations.
You can reset the data back to its original form with
reset_generations().
That’s the package so far! I plan on adding more things in the future, primarily to handle stability issues and to include some error catching. Hopefully, if you use generational cohorts in your data, this package is useful for you.