Skip to content

A guide to analyzing & visualizing American political data using R. Winter 2023 Version.

Notifications You must be signed in to change notification settings

jaytimm/american-political-data-and-r

Repository files navigation

American political data & R

Updated: 2023-02-24


An R-based guide to accessing, exploring & visualizing US political data via a collection of publicly available resources.

Election returns have been collated from Daily Kos, MIT Election Data and Science Lab and Wikipedia; the R package Rvoteview is used extensively to characterize lawmakers and congress.

Winter 2023 version. Most plots have been updated, some added, some abandoned. I have removed all census based examples this time around. And focused more on national-level Presidential election results, as well as generational control in congress. Previous versions are available here.

Hopefully a useful open source & transparent framework for investigating past & future election results and congresses using R. All work presented here can be reproduced in its entirety.

Quick preliminaries

library(dplyr)
library(ggplot2)

Some geo-spatial data

State-based geo-data

library(sf)
library(tigris)
options(tigris_use_cache = TRUE, tigris_class = "sf")

nonx <- c('78', '69', '66', '72', '60', '15', '02')

states_sf <- tigris::states(cb = TRUE) |>
  rename(state_code = STATEFP, state_abbrev = STUSPS)

states <- states_sf |>
  data.frame() |>
  select(state_code, state_abbrev)

laea <- sf::st_crs("+proj=laea +lat_0=30 +lon_0=-95") 

A simple add-on map theme

theme_guide <- function () {
  
    theme(axis.title.x=element_blank(), 
          axis.text.x=element_blank(),
          axis.title.y=element_blank(),
          axis.text.y=element_blank(),
          legend.title=element_blank(),
          legend.position = 'none', 
          complete = F) }

Some quick definitions

Per VoteView definition: The South = Dixie + Kentucky + Oklahoma

south <- c('SC', 'MS', 'FL', 
           'AL', 'GA', 'LA', 'TX', 
           'VA', 'AR', 'NC', 'TN',
           'OK', 'KY')
states_sf |>
  filter(!state_code %in% nonx) |>
  mutate(south = ifelse(state_abbrev %in% south, 
                        'south', 'not')) |>
  select(state_abbrev, geometry, south) |>
  mutate(label = ifelse(!grepl('not', south), state_abbrev, NA)) |>
  sf::st_transform(laea) |>
  
  ggplot() + 
  geom_sf(aes(fill = south),
          color = 'white', size = .15,
          alpha = 0.65) +
  geom_sf_text(aes(label = label),
                          size = 2.25,
                          color='black') +
  
  scale_fill_manual(values = c('#8faabe', 
                               '#1a476f', 
                               '#55752f')) +  
  theme_minimal() + 
  theme_guide() +
  theme(panel.background = 
          element_rect(fill = '#d5e4eb', color = NA)) +
  ggtitle('Dixie + Kentucky + Oklahoma')


Data sources

VoteView

The VoteView project provides roll call-based political ideology scores for all lawmakers in the history of the US Congress. The R package Rvoteview provides access to these data.

## NOTE: election years.  term begins year + 1
ccr <- data.frame(year = c(1786 + 2*rep(c(1:118))), 
                  congress = c(1:118)) 
con <- 65 #66

vvo <- lapply(c('house', 'senate'), function(x) {
              Rvoteview::download_metadata(type = 'members', 
                                    chamber = x) |>
    filter(chamber != 'President') }) 
## [1] "/tmp/RtmpjA3XR2/Hall_members.csv"
## [1] "/tmp/RtmpjA3XR2/Sall_members.csv"
congress00 <- vvo |>
  bind_rows() |>
  mutate(x = length(unique(district_code))) |>
    ungroup() |>
    mutate(district_code = ifelse(x==1, 0, district_code)) |>
    mutate(district_code = 
             stringr::str_pad (as.numeric(district_code), 
                               2, pad = 0),
           
           southerner = ifelse(state_abbrev %in% south, 
                        'South', 'Non-south'),
           party_name = case_when (party_code == 100 ~ 'Democrat',
                                   party_code == 200 ~ 'Republican',
                                   !party_code %in% c(100, 200) ~ 'other')) |>
  
  left_join(ccr, by = 'congress') |>
  filter(!is.na(born))

congress <- congress00 |>
  filter(congress > con)

PresElectionData

The PresElectionResults package includes US Presidential Election Results by county (2000-2020), congressional district (2020), and state (1864-2020). Additionally included are FRED population data and equal-area simple feature geometries (via Daily Kos). Full package build details are available here.

devtools::install_github("jaytimm/PresElectionData")

Legislator details

Via theuntiedstates.io

leg_dets <- 'https://theunitedstates.io/congress-legislators/legislators-current.csv'
leg_dets0 <- read.csv((url(leg_dets)), stringsAsFactors = FALSE) |>
  rename(state_abbrev = state)

Historical presidential election results

National popular and electoral presidential election results made available at britannica.com.

National popular vote is becoming more competitive

library(ggplot2)
pres1 <- PresElectionResults::pres_results |>
  filter(year > 1864, 
         party %in% c('Democratic', 'Republican'),
         !is.na(pop_per)) |>
  select(year, party, pop_per) |>
  tidyr::spread(party, pop_per) |>
  
  mutate(delta = Republican - Democratic,
         d20 = ifelse(year %% 20 == 0, year, '')) 

pres1 |> 
  ggplot2::ggplot(aes(x = delta,
                      y = year |> as.character(),
                      group=1,
                      color = delta)) + 

  geom_vline(xintercept = 0, color = 'gray') +
  geom_path(color = 'gray', size = 0.5) + 
  geom_point(size = 4) +
  scale_color_gradient2(low = "#5f8bd7", 
                        mid = "#8366a5", 
                        high = "#e75848",
                        midpoint = 0) +
  
  scale_y_discrete(limits = rev, labels = pres1$d20 |> rev()) + 
  theme_minimal() + 
  theme(axis.ticks = element_blank(),
        legend.position = 'none') +
  
  xlim(-30, 30) +
  labs(title ='National popular vote margins',
       subtitle = '1868 to 2020')

National popular vote and electoral landslides in the 20th century

PresElectionResults::pres_results |>
  filter(year > 1828, !is.na(ec_votes)) |>
  group_by(year) |>
  filter(ec_votes == max(ec_votes)) |> ungroup() |>
  mutate(ec_per = round(ec_votes/ec_total*100,1)) |>
  tidyr::pivot_longer(cols = c(pop_per, ec_per)) |>
  
  ggplot(aes(x = value, 
             y = year)) +
  
  geom_line(aes(group = year), 
            color = 'lightgray', 
            size = 2) +
  geom_point(aes(color = name)) +
  xlim(0,100) +
  coord_flip() +
  theme_minimal() + 
  ggthemes::scale_colour_economist() +
  theme(axis.ticks = element_blank(),
      legend.position = 'none') +
  labs(title ="President-elect's share of electoral and popular votes",
       subtitle = '1828 to 2020')

Voting margins in Presidential elections by state since 1976

Historical Presidential election results by state via Wikipedia. Equal-area state geometry via Daily Kos.

mp <- PresElectionResults::xsf_TileOutv10 |>
  left_join(PresElectionResults::pres_by_state |>
              filter(year > 1975) |>
              mutate(margins = republican - democrat)) 

mp |> 
  ggplot() +  
  geom_sf(aes(fill = margins),
           color = 'darkgray', lwd = .15) +
  geom_sf(data = PresElectionResults::xsf_TileInv10, 
          fill = NA, 
          show.legend = F, 
          color = NA, 
          lwd=.5) +
  
  geom_sf_text(data = PresElectionResults::xsf_TileInv10,
                          aes(label = state_abbrev),
                          size = 1.5,
                          color='black') +
  
  scale_fill_distiller(palette = "RdBu",  
                        limit = max(abs(mp$margins)) * c(-1, 1)) +
  facet_wrap(~year, ncol = 4) +
  theme_minimal()+ theme_guide() +
  labs(title = "Voting margins in Presidential elections since 1976")

When each state last voted for a Democratic presidential nominee

clean_prex <-  PresElectionResults::pres_by_state |>
  mutate(winner = gsub('Franklin D. Roosevelt', 'FDR', winner),
         winner = gsub('Lyndon B. Johnson', 'LBJ', winner),
         winner = gsub('Hillary Clinton', 'HRC', winner)) 
last_dem <- clean_prex |>
  group_by(state_abbrev, party_win) |>
  filter(year == max(year),
         party_win == 'democrat') |>
  ungroup() |>
  mutate(lab = paste0(year, ' - ', winner))

Nine US states have not voted for a Democratic Presidential candidate since LBJ.

new1 <- PresElectionResults::xsf_TileInv10 |> 
  left_join(last_dem, by ='state_abbrev') |>
  mutate(label = paste0(state_abbrev, 
                        '\n', 
                        year,
                        '\n', 
                        gsub('^.* ', '', winner)))

PresElectionResults::xsf_TileOutv10 |> 
  left_join(last_dem, by ='state_abbrev') |>
  arrange(desc(year)) |>
  ggplot() + 
  geom_sf(aes(fill = paste0(year, ' - ', gsub('^.* ', '', winner))),
          color = 'gray' , 
          alpha = .5) + 
  
  geom_sf_text(data = new1,
                          aes(label = new1$label), 
                          size = 3,
                          color = 'black') +
  theme_minimal() + 
  theme_guide() + 
  ggthemes::scale_fill_economist()+
  labs(title = "When each state last voted for a Democratic presidential nominee")

Presidential elections and vote shares by state

vote_share <- clean_prex |>
  select(-party_win) |>
  tidyr::gather(key = 'party', value = 'per', democrat:republican) |>
  group_by(state_abbrev) |>
  slice(which.max(per)) |>
  ungroup() |>
  mutate(label = paste0(year, ' - ', winner))
new <- PresElectionResults::xsf_TileInv10 |> 
  left_join(vote_share, by ='state_abbrev') |>
  mutate(per = round(per, 1)) |>
  mutate(label = paste0(state_abbrev, 
                        '\n', 
                        year,
                        '\n', 
                        gsub('^.* ', '', winner), 
                        '\n',
                        per))

PresElectionResults::xsf_TileOutv10 |> 
  left_join(vote_share, by ='state_abbrev') |>
  ggplot() + 
  geom_sf(aes(fill = paste0(year, ' - ', gsub('^.* ', '', winner))),
          color = 'white' , 
          alpha = .65) + 
  
  geom_sf_text(data = new,
                          aes(label = new$label), 
                          size = 2.5,
                          color = 'black') +
  scale_fill_manual(
      values = colorRampPalette(ggthemes::economist_pal()(8))(18)) +
  
  theme_minimal() + theme_guide() + 
  labs(title = "Largest vote share for Presidential nominee",
  subtitle = "By state since 1864")

Presidential elections and the disappearance of competitive counties

counties <- tigris::counties(cb = TRUE) |> 
  filter(!STATEFP %in% nonx) |>
  sf::st_transform(laea)
cutoff <- 10

cl2 <- PresElectionResults::pres_by_county |>
  mutate(delta = republican - democrat,
         dcat = case_when (delta < -(cutoff -1) ~ 'DEM > +10',
                           delta > (cutoff -1) ~ 'REP > +10',
                           delta > -cutoff & delta < cutoff ~ 'competitive'))

cl2 |> 
  count(year, dcat) |>
  filter(!is.na(dcat)) |>
  tidyr::spread(dcat, n) |>
  knitr::kable()
year competitive DEM > +10 REP > +10
2000 734 382 2036
2004 586 332 2236
2008 663 577 1914
2012 516 503 2138
2016 299 370 2488
2020 302 410 2443
p1 <- counties |>
  left_join(cl2, by = 'GEOID') |>
  filter(year %in% c(2000)) |>
  ggplot() +
  geom_sf(aes(fill = dcat),
          color = 'white',
          size = .1) + 
  
 scale_fill_manual(values = c("#819c70", # competitive 
                              "#5f8bd7",
                              "#e75848")) +
  
  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 = 'none') +
  labs(title = '2000')


Historical composition of the Senate

Split Senate delegations and shifting ideologies

A Senate delegation for a given state is said to be split when comprised of Senators from different parties, eg, one Republican and one Democrat – as is the case with, eg, West Virginia in the (present) 118th Congress.

sens <- congress00 |>
  filter(chamber == 'Senate') |>
  mutate(party_name = as.factor(party_name)) |>
  mutate(party_name = forcats::fct_relevel(party_name, 
                                       'other', 
                                        after = 2)) |>
  mutate(year = year + 1) |>
  group_by(year, congress, state_abbrev) |>
  slice(1:2) |>
  ungroup() 
sens2 <- sens |>
  filter(congress %in% c(70, 76, 82,
                         88, 94, 100, 
                         106, 112, 118)) |>
  
  group_by(year, congress, state_abbrev) |>
  arrange (party_name) |>
  mutate(layer = row_number()) |>
  ungroup()
PresElectionResults::xsf_TileOutv10 |>
  left_join(sens2 |> filter(layer == 2)) |>
  ggplot() + 
  geom_sf(aes(fill = party_name),
          color = 'white', 
          lwd = 0.2,
          alpha = .85) + 
  
  geom_sf(data = PresElectionResults::xsf_TileInv10 |>
            left_join(sens2 |> filter (layer == 1)), 
          aes(fill = party_name),
          color = 'white', 
          lwd = 0.2,
          alpha = .7) +
  
  geom_sf_text(data = PresElectionResults::xsf_TileInv10,
                          aes(label = state_abbrev), 
                          size = 1.55,
                          color = 'white') +
  
  ggthemes::scale_fill_stata()+
  theme_minimal() + 
  theme_guide() +
  theme(legend.position = 'bottom') +
  
  facet_wrap(~year + congress) +
  labs(title = "Senate composition by state since 1927",
       caption = 'Data sources: Daily Kos & VoteView')

Split Senate delegations on the wane again

With three more in danger come 2024 – Montanta, West Virginia, and Ohio.

split_senate <- sens |>
  filter(congress > con) |>
  group_by(year, congress, state_abbrev) |>
  summarize(splits = length(unique(party_name)),
            parts = paste0(party_name, collapse = '-')) |>
  mutate(parts = ifelse(splits == 2, 
                        'Split', 
                        paste0('Both ', 
                               gsub('-.*$', '', parts)))) 

split_senate$parts <- factor(split_senate$parts, 
                             levels = c('Both Democrat', 
                                        'Split',
                                        'Both Republican', 
                                        'Both other')) 
split_senate |>
  filter(splits == 2) |>
  group_by(year, congress) |>
  summarize(n = n()) |>
  ggplot() +
  geom_bar(aes(x = year, 
               y = n), 
           color = 'white',
           fill = 'steelblue',
           stat = 'identity') +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1),
        axis.title.x=element_blank()) +
  scale_x_continuous(breaks = seq(1919, 2023, 4)) +
  labs(title = "Split Senate delegations since 1919")

US Senate delegations by party composition

split_pal <- c('#395f81', '#ead8c3', '#9e5055', '#b0bcc1')

split_senate |>
  group_by(year, congress, parts) |>
  summarize(n = n()) |>

  ggplot(aes(x = year, 
             y = n, 
             fill = parts))+
  geom_bar(alpha = 0.85, 
           color = 'gray', 
           lwd = .25,
           stat = 'identity') +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(legend.position = "bottom",
        legend.title=element_blank())+
  scale_fill_manual(values = split_pal) +
  scale_x_continuous(breaks = seq(1919, 2023, 4)) +
  xlab('') +
  ggtitle('US Senate delegations, by party composition')

Republican Senators and a minority of Americans

Senate seats held by Republicans via VoteView; population of states represented by Reublican senators via FRED.

wpops <- sens |> #yy |> 
  filter(congress > con) |>
  left_join(PresElectionResults::fred_pop_by_state,
            by = c('year', 'state_abbrev')) |> #yy |>
  group_by(year, party_name) |>
  summarize(n = n(),
            #n = sum(n),
            pop = sum(population)) |>
  group_by(year) |>
  mutate(Senate_share = round(n/sum(n) * 100, 1),
         Population_share = round(pop/sum(pop) * 100, 1)) |>
  filter(party_name == 'Republican') |>
  select(year, Senate_share, Population_share) |>
  tidyr::gather(-year, key = 'var', value = 'per')

Gray highlight: Congresses in which (1) GOP senators hold a majority in the Senate AND (2) a minority of Americans are represented by a Repbulican senator.

wpops |>
  ggplot() +
  geom_rect(aes(xmin = 2015, 
                xmax = 2019,
                ymin = -Inf, 
                ymax = Inf),
            fill = 'lightgray') +
  
  geom_hline(yintercept = 50, color = 'black', lwd = .2) +
  geom_line(aes(x = year, 
                y = per, 
                color = var), 
            size = 1) +
  ggthemes::scale_color_few()+
  theme_minimal() +
  theme(legend.position = 'bottom',
        axis.text.x = element_text(angle = 90),
        axis.title.x = element_blank(),
        legend.title = element_blank()) +
  
  scale_x_continuous(breaks = seq(min(wpops$year), max(wpops$year), 4)) +
  labs(subtitle = "Republican Senate share v. Share Americans represented by Republican senator") 


Historical composition of the House

congress_south <- congress |> 
  filter(party_code %in% c(100, 200), chamber == 'House') |>
  mutate(Member = as.factor(paste0(party_name, ', ', southerner))) |>
  mutate(Member = forcats::fct_relevel(Member, 
                                       'Republican, Non-south', 
                                       after = 3)) 

Political realignment in the South

congress_south |>
  group_by(year, Member) |>
  summarize(n = n()) |>
  mutate(n = n/sum(n)) |>
  
  ggplot(aes(x = year+1, 
             y = n, 
             fill = Member)) +
  geom_area(alpha = 0.65, color = 'gray') +
  
  geom_hline(yintercept = 0.5, color = 'white', linetype = 2) +

  scale_x_continuous(breaks=seq(min(congress_south$year+1),
                                max(congress_south$year+ 1), 4)) +
  scale_fill_manual(values = c('#1a476f', '#8faabe',
                                '#e19463', '#913a40')) +
  
  theme_minimal() + 
  theme(legend.position = 'top',
        legend.title=element_blank(),
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.y=element_blank(),
        axis.text.x = element_text(angle = 90, hjust = 1)) +
  
  labs(title = "House composition since 1919")

On the evolution of the Southern Republican

DW-NOMINATE ideal points in two dimensions. The first dimension captures ideological variation based in the standard liberal-conservative divide. The second captures variation based in social conservatism that crosscuts political affiliation.

congress_south |>
  mutate(year = year + 1) |>
  filter (congress %in% c(86, 90, 94, 
                          98, 102, 106, 
                          110, 114, 118)) |>
  
  ggplot(aes(x = nominate_dim1, 
             y = nominate_dim2) ) +
  
          annotate("path",
               x=cos(seq(0,2*pi,length.out=300)),
               y=sin(seq(0,2*pi,length.out=300)),
               color='gray',
               size = .25) +
  
  geom_point(aes(color = Member), 
             size= 1.25,
             shape = 17) + 
  
  scale_color_manual(values = c('#1a476f', '#8faabe',
                                '#e19463', '#913a40')) +
  
  facet_wrap(~year + congress) +
  theme_minimal() +
  theme(legend.title=element_blank(),
        legend.position = 'bottom') +
  labs(title="The evolution of the Southern Republican",
       subtitle = 'In two dimensions: from 1959 to 2023')


Fourteen generations of American Senators

Pew Research generations & Strauss-Howe generations

gens <- read.csv('https://raw.githubusercontent.com/jaytimm/AmericanGenerations/main/data/pew-plus-strauss-generations.csv') |>
  mutate(order = row_number()) |>
  filter(order %in% c(5:19))

gens |> knitr::kable()
generation start end model order
Awakening 1701 1723 strauss 5
Liberty 1724 1741 strauss 6
Republican 1742 1766 strauss 7
Compromise 1767 1791 strauss 8
Transcendental 1792 1821 strauss 9
Gilded 1822 1842 strauss 10
Progressive 1843 1859 strauss 11
Missionary 1860 1882 strauss 12
Lost 1883 1900 strauss 13
Greatest 1901 1927 pew 14
Silent 1928 1945 pew 15
Boomers 1946 1964 pew 16
Gen X 1965 1980 pew 17
Millenials 1981 1996 pew 18
Gen Z 1997 2012 pew 19

Generational control in the Senate

Each column represents a congress; each tile represents a Senator. See this post for a cool, interactive version.

sens00 <- sens

sens00$generation <- gens$generation[
  findInterval(x = sens00$born, vec = gens$start)]

sens00 <- sens00 |>
  left_join(gens) |>
  arrange(order) |>
  mutate(generation = as.factor(generation)) |>
  mutate(generation = forcats::fct_relevel(generation, 
                                           gens$generation)) |>
  group_by(congress) |>
  arrange(desc(order)) |>
  mutate(nn = row_number()) |> ungroup() |>
  select(year, nn, generation, order)

sens00  |>
  ggplot(aes(x = year, y = (nn), fill = generation)) +
  geom_tile(color = 'white', size = .35) +
  ggthemes::scale_fill_stata() +
  theme_minimal() +
  theme(legend.position = 'top',
        legend.title=element_blank()) +
  ggtitle('Generational control of the Senate')

Profiling control over generational lifespans

The Transendental generation’s run in the Senate lasted just over 40 years; at its peak (in 1859), members of this generation comprised ~ 95% of the Senate.

sens00 |>
  count(year, generation) |>
  group_by(generation) |>
  mutate(t = row_number()) |>
  group_by(year) |>
  mutate(per = round(n/sum(n), 2)) |> ungroup() |>
  
  ggplot() +
  geom_line(aes(x = t, 
                y = per, 
                color = generation), 
            size = 1) +
  ggthemes::scale_color_stata() +
  theme_minimal() +
  theme(legend.position = 'top',
        legend.title=element_blank()) +
  ggtitle('Generational profiles in the US Senate')

Age, generations & freshman classes in the House

Average age of House members

congress |>
  mutate(age = year - born) |>
  filter (party_code %in% c('100', '200')) |>
  #filter(year > 1960) |>
  group_by(party_name, year) |>
  summarize(age = round(mean(age, na.rm = T), 1)) |>
  mutate(label = if_else(year == max(year) | year == min(year), 
                         age, NULL)) |>
  
  ggplot() +
  geom_line(aes(x = year + 1, 
                y = age, 
                color = party_name), 
            size = .8) +
  
    ggrepel::geom_text_repel(aes(x = year + 1, 
                                 y = age, 
                                 label = label),
                             size= 3.25,
                             nudge_x = 1,
                             na.rm = TRUE) +

  ggthemes::scale_color_stata()+
  theme_minimal() +
  theme(legend.position = 'none',
        axis.text.x = element_text(angle = 90, hjust = 1),
        axis.title.x=element_blank()) +
  
  scale_x_continuous(breaks=seq(1919, 2023, 4)) +
  labs(title = "Average age of congress members by party") 

Introducing Generation Z

freshmen <- congress |>
  group_by(icpsr, bioname) |>
  mutate(n = length(congress)) |>
  ungroup() |>
  filter(congress == 118) |> # only correct here -- not older congresses
  
  mutate (Class = case_when (n == 1 ~ 'Freshman',
                             n == 2 ~ 'Sophmore',
                             n > 2 ~ 'Upper-class')) |>
  
  select(icpsr, party_code, Class)

gens1 <- gens |>
  mutate(age = 2023 - as.integer(end)) |>
  filter(order %in% c(15:19))
congress |>
  filter (party_code %in% c('100', '200'), 
          congress == 118) |> 
  mutate(age = year - born,
         party_code = ifelse(party_code == '100', 
                             'House Democrats', 
                             'House Republicans')) |>
  left_join(freshmen |> select(-party_code), by = "icpsr") |>
  
  ## 100 == democrat --
  ggplot() +
  
  geom_dotplot(aes(x = age, 
                   color = Class,
                   fill = Class),
               method="histodot",
               dotsize = .9, 
               binpositions = 'all', 
               stackratio = 1.3, 
               stackgroups=TRUE,
               binwidth = 1) + 
  
  geom_vline(xintercept =gens$age - 0.5,
             linetype =2, 
             color = 'black', 
             size = .25) +
  
  geom_text(data = gens1, 
            aes(x = age + 2.25, 
                y = 0.95,
                label = generation),
            size = 3) +
  
  theme_minimal() + 
  ggthemes::scale_fill_economist() +
  ggthemes::scale_color_economist() +
  
  facet_wrap(~party_code, 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 118th House by party, generation & class")

First-timers in the House

freshmen1 <- congress |>
  group_by(icpsr, bioname, party_name) |>
  summarize(min = min(year),
            max = max(year)) |>
  group_by(min, party_name) |>
  summarise(count = n()) |>
  ungroup() |>
  filter(min > 1960, party_name != 'other')
  

labs <- freshmen1 |>
  arrange(desc(min)) |>
  top_n(4, count) |>
  mutate(txt = c('Obama 1st midterm',  
                 'Clinton 1st midterm', 
                 '"Watergate babies"', 
                 'LBJ atop ticket'))

freshmen1 |>
  ggplot() +
  geom_line(aes(x = min + 1, 
                y = count, 
                color = party_name),
            size = 0.8) +
  
  geom_text(data = labs,
            aes(x = min, 
                y = count, 
                label = txt),
            size = 3, nudge_y = 3) +
  ggthemes::scale_color_stata()+
  theme_minimal() +
  theme(legend.position = 'none',
        axis.title.x=element_blank(),
        axis.text.x = element_text(angle = 90, hjust = 1)) +
  
  scale_x_continuous(breaks=seq(1961,2023,2)) +
  labs(title = "Freshman House members by party")


Towards 2024

Class I Senators

class1 <- states |> filter(!state_code %in% nonx[1:5]) |>
  left_join(leg_dets0 |> filter(senate_class == 1)) |>
  left_join(PresElectionResults::pres_by_state |>
              filter(year == 2020)) |>
  mutate(bmar = round(democrat - republican, 1),
         bmar = ifelse(bmar < 0, paste0('(', gsub('-', '', bmar), ')'), bmar)) |>
  mutate(label = ifelse(is.na(senate_class), state_abbrev,
                        paste0(state_abbrev, 
                        '\n', 
                        last_name,
                        '\n',
                        bmar
                        ))) |>
  mutate(party = ifelse(is.na(party), 'x-Class I', party)) |>
  mutate(party = as.factor(party)) |>
  mutate(party = forcats::fct_relevel(party, 
                                       'Independent', 
                                        after = 2))
new2 <- PresElectionResults::xsf_TileInv10 |> 
  left_join(class1)


PresElectionResults::xsf_TileOutv10 |> 
  left_join(class1, by ='state_abbrev') |>
  ggplot() + 
  geom_sf(aes(fill = party),
          color = 'darkgray' , 
          alpha = .65) + 
  
  geom_sf_text(data = new2,
                          aes(label = label), 
                          size = 2.75,
                          color = 'black') +

  theme_minimal() + 
  theme_guide() + 
  #theme(legend.position = 'none') +
  ggthemes::scale_fill_stata()+
  #scale_fill_brewer(palette = 'YlGnBu') +
  labs(title = "Class I Senators, 2024",
       subtitle = 'With 2020 Biden margins')

Vulnerable Republican House Members

House Republicans in 118th representing districts Biden won in 2020.

vrs <- PresElectionResults::pres_by_cd |>
  filter(house_rep_party == 'republican',
         party_win == 'democrat') |>
  left_join(freshmen) |>
  mutate(Biden_Margin = democrat - republican,
         district = paste0(state_abbrev, '-', district_code)) |>
  select(1, 13, 4, 11:12) |> arrange(-Biden_Margin)
 
vrs |> knitr::kable()
icpsr district house_rep Class Biden_Margin
22313 NY-04 Anthony D’Esposito Freshman 14.6
21307 CA-22 David Valadao Upper-class 13.0
21988 CA-27 Mike Garcia Upper-class 12.4
22317 CA-13 John Duarte Freshman 10.9
22340 NY-17 Mike Lawler Freshman 10.1
22308 OR-05 Lori Chavez-DeRemer Freshman 8.8
22362 NY-03 George Santos Freshman 8.2
22372 NY-22 Brandon Williams Freshman 7.4
21701 NE-02 Don Bacon Upper-class 6.4
22152 CA-45 Michelle Steel Sophmore 6.1
22351 NY-19 Marc Molinaro Freshman 4.6
21718 PA-01 Brian Fitzpatrick Upper-class 4.6
22334 NJ-07 Tom Kean Jr. Freshman 3.8
22129 CA-40 Young Kim Sophmore 1.9
22335 VA-02 Jen Kiggans Freshman 1.9
21105 AZ-01 David Schweikert Upper-class 1.5
22337 NY-01 Nick LaLota Freshman 0.2
22309 AZ-06 Juan Ciscomani Freshman 0.1

Fin

About

A guide to analyzing & visualizing American political data using R. Winter 2023 Version.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published