this old house

A quick look at age and generation distributions in the 117th US House of Representatives.

---

1 Current US lawmakers

leg_dets <- 'https://theunitedstates.io/congress-legislators/legislators-current.csv'
details <- read.csv((url(leg_dets)), stringsAsFactors = FALSE) 
details$birthday <- as.Date(details$birthday)

k1 <- c('bioguide_id', 
        'party', 
        'birthday', 
        'gender', 
        'senate_class',
        'twitter', 
        'wikipedia_id')

nominate <- Rvoteview::download_metadata(type = 'members',
                                         #chamber = 'house',
                                         congress = '117') |>
  subset(chamber != 'President')

k2 <- c('bioguide_id', 
        'congress', 
        'chamber', 
        'district_code', 
        'state_abbrev', 
        'bioname',
        'nominate_dim1', 
        'nominate_dim2')

members <- merge(x = details[,k1], 
                 y = nominate[,k2],
                 by = "bioguide_id", 
                 all.y = TRUE)

members$age <- trunc(as.numeric(difftime(Sys.Date(), 
                                         members$birthday, 
                                         units = "days")) / 365.25)

2 American Generations

generation <- c(#'Missionary',
                #'Lost',
                'Greatest',
                'Silent',
                'Boomers',
                'Gen X',
                'Millenials',
                'Gen Z',
                'Post-Z')

start_yr <- c(#'1860-01-01',
              #'1883-01-01',
              '1901-01-01',
              '1928-01-01',
              '1946-01-01',
              '1965-01-01',
              '1981-01-01',
              '1997-01-01',
              '2013-01-01')

end_yr <- c(#'1882-01-01',
            #'1900-01-01',
            '1927-12-31',
            '1945-12-31',
            '1964-12-31',
            '1980-12-31',
            '1996-12-31',
            '2012-12-31',
            '2028-12-31')

Note that zero members of Gen Z are old enough to serve in the US House of Representatives– and all Millenials now are.

pgs <- data.frame(generation = factor(generation, 
                                      levels = unique(generation)),
                  start = as.Date(start_yr), 
                  end = as.Date(end_yr))

pgs$youngest <- trunc(as.numeric(difftime(Sys.Date(), 
                                          pgs$end,
                                          units = "days")) / 365.25)

pgs |> knitr::kable()

generation	start	end	youngest
Greatest	1901-01-01	1927-12-31	94
Silent	1928-01-01	1945-12-31	76
Boomers	1946-01-01	1964-12-31	57
Gen X	1965-01-01	1980-12-31	41
Millenials	1981-01-01	1996-12-31	25
Gen Z	1997-01-01	2012-12-31	9
Post-Z	2013-01-01	2028-12-31	-6

2.1 Assign generations

members$generation <- pgs$generation[
  findInterval(x = members$birthday, vec = pgs$start)]

library(dplyr)
members |>
  filter(chamber == 'House' & !is.na(age)) |>
  count(party, gender, generation) |>
  tidyr::spread(generation, n) |>
  knitr::kable()

party	gender	Silent	Boomers	Gen X	Millenials
Democrat	F	11	40	34	5
Democrat	M	9	75	37	9
Republican	F	2	13	11	6
Republican	M	3	97	65	13

2.2 Dotplot

library(ggplot2)
hs0 <- members |> filter(chamber == 'House' & !is.na(age)) |> 
  arrange(generation)
 
hs0 |>
  ggplot() +
  geom_dotplot(aes(x = age, 
                   color = gender,
                   fill = gender,),
               method="histodot",
               dotsize = .9, 
               binpositions = 'all', 
               stackratio = 1.3, 
               stackgroups=TRUE,
               binwidth = 1) + 
  
  geom_vline(xintercept = pgs$youngest[2:5] - 0.5,
             linetype =2, 
             color = 'black', 
             size = .25) +
  
  geom_text(data = pgs[2:5,], 
            aes(x = youngest + 2.25, 
                y = 0.95,
                label = generation),
            size = 3) +
  
  theme_minimal() + 
  ggthemes::scale_fill_economist() +
  ggthemes::scale_color_economist() +
  
  facet_wrap(~party, nrow = 2) +
  theme(legend.position = "bottom",
        axis.title.y=element_blank(),
        axis.text.y=element_blank()) +
  #ylim (0, .5) +
  
  labs(title = "Age distribution of the 117th House by party")

3 Parliamentary Plot

A real nice function below for drawing ‘parliamentary graphs’ – stolen from this SO post.

seats <- function(N, M, r0 = 2){ 
  radii <- seq(r0, 1, len=M)
  counts <- numeric(M)
  pts = do.call(rbind,
            lapply(1:M, function(i){
              counts[i] <<- round(N*radii[i]/sum(radii[i:M]))
              theta <- seq(0, pi, len = counts[i])
              N <<- N - counts[i]
              data.frame(x=radii[i]*cos(theta), y=radii[i]*sin(theta), r=i,
                         theta=theta)}))
   pts = pts[order(-pts$theta,-pts$r),]
   pts}

sx <- seats(430,8) |> mutate(Generation = hs0$generation)

sx |>
  ggplot() +
  geom_point(aes(x, y, 
                 color = Generation), 
             size = 3) +
  ggthemes::scale_color_stata()+
  theme_minimal()+
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        legend.position = 'bottom',
        plot.title = element_text(size=12)
        )  +
  labs(title="Composition of 117th US House by Generation")