U.S. Census Counts Data
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As promised previously, I packaged up the U.S. Census data that I pulled together to make the population density and pyramid animations. The package is called uscenpops and it’s available to install via GitHub or with install.packages() if you set up drat first. The instructions are on the package homepage.
A small multiple plot of selected population pyramids
Instead of an animation, let’s make the less-flashy but, frankly, in all likelihood more useful small multiple plot seen here. With the package installed we can produce it as follows:
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library(tidyverse) library(uscenpops) uscenpops #> # A tibble: 10,520 x 5 #> year age pop male female #> <int> <dbl> <dbl> <dbl> <dbl> #> 1 1900 0 1811000 919000 892000 #> 2 1900 1 1835000 928000 907000 #> 3 1900 2 1846000 932000 914000 #> 4 1900 3 1848000 932000 916000 #> 5 1900 4 1841000 928000 913000 #> 6 1900 5 1827000 921000 906000 #> 7 1900 6 1806000 911000 895000 #> 8 1900 7 1780000 899000 881000 #> 9 1900 8 1750000 884000 866000 #> 10 1900 9 1717000 868000 849000 #> # … with 10,510 more rows |
That’s what the dataset looks like. We’ll lengthen it, calculate a relative frequency (that we won’t use in this particular plot) and add a base value that we’ll use for the ribbon boundaries below.
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pop_pyr <- uscenpops %>% select(year, age, male, female) %>%
pivot_longer(male:female, names_to = "group", values_to = "count") %>%
group_by(year, group) %>%
mutate(total = sum(count),
pct = (count/total)*100,
base = 0)
pop_pyr
#> # A tibble: 21,040 x 7
#> # Groups: year, group [240]
#> year age group count total pct base
#> <int> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1900 0 male 919000 38867000 2.36 0
#> 2 1900 0 female 892000 37227000 2.40 0
#> 3 1900 1 male 928000 38867000 2.39 0
#> 4 1900 1 female 907000 37227000 2.44 0
#> 5 1900 2 male 932000 38867000 2.40 0
#> 6 1900 2 female 914000 37227000 2.46 0
#> 7 1900 3 male 932000 38867000 2.40 0
#> 8 1900 3 female 916000 37227000 2.46 0
#> 9 1900 4 male 928000 38867000 2.39 0
#> 10 1900 4 female 913000 37227000 2.45 0
#> # … with 21,030 more rows
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Next we set up some little vectors of labels and colors, and then make a mini-dataframe of what we’ll use as labels in the plot area, rather than using the default strip labels in facet_wrap().
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## Axis labels
mbreaks <- c("1M", "2M", "3M")
## colors
pop_colors <- c("#E69F00", "#0072B2")
## In-plot year labels
dat_text <- data.frame(
label = c(seq(1900, 2015, 5), 2019),
year = c(seq(1900, 2015, 5), 2019),
age = rep(95, 25),
count = rep(-2.75e6, 25)
)
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As before, the trick to making the pyramid is to set all the values for one category (here, males) to negative numbers.
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pop_pyr$count[pop_pyr$group == "male"] <- -pop_pyr$count[pop_pyr$group == "male"]
p <- pop_pyr %>%
filter(year %in% c(seq(1900, 2015, 5), 2019)) %>%
ggplot(mapping = aes(x = age, ymin = base,
ymax = count, fill = group))
p + geom_ribbon(alpha = 0.9, color = "black", size = 0.1) +
geom_label(data = dat_text,
mapping = aes(x = age, y = count,
label = label), inherit.aes = FALSE,
vjust = "inward", hjust = "inward",
fontface = "bold",
color = "gray40",
fill = "gray95") +
scale_y_continuous(labels = c(rev(mbreaks), "0", mbreaks),
breaks = seq(-3e6, 3e6, 1e6),
limits = c(-3e6, 3e6)) +
scale_x_continuous(breaks = seq(10, 100, 10)) +
scale_fill_manual(values = pop_colors, labels = c("Females", "Males")) +
guides(fill = guide_legend(reverse = TRUE)) +
labs(x = "Age", y = "Population in Millions",
title = "Age Distribution of the U.S. Population, 1900-2019",
subtitle = "Age is top-coded at 75 until 1939, at 85 until 1979, and at 100 since then",
caption = "Kieran Healy / kieranhealy.org / Data: US Census Bureau.",
fill = "") +
theme(legend.position = "bottom",
plot.title = element_text(size = rel(2), face = "bold"),
strip.background = element_blank(),
strip.text.x = element_blank()) +
coord_flip() +
facet_wrap(~ year, ncol = 5)
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The calls to geom_ribbon() and geom_label() draw the actual plots, and everything else is just a little attention to detail in order to make it come out nicely.
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