Updated: 2026-01-19
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.
January 2026 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. 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.
- American political data & R
- Quick preliminaries
- Data sources
- Historical presidential election
results
- National popular vote is becoming more competitive
- National popular vote and electoral landslides in the 20th century
- Voting margins in Presidential elections by state 1980-2024
- When each state last voted for a Democratic presidential nominee
- Presidential elections and vote shares by state
- Presidential elections and the disappearance of competitive counties
- Historical composition of the Senate
- Historical composition of the House
- Age, generations & freshman classes in the House
- Towards 2026
- Fin
library(dplyr)
library(ggplot2)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") 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) }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)) +
labs(title = 'American South = Dixie + Kentucky + Oklahoma',
caption = 'Data source: tigris')
The VoteView project provides roll call-based political ideology scores for all lawmakers in the history of the US Congress. The R package
Rvoteviewprovides access to these data.
## NOTE: election years. term begins year + 1
ccr <- data.frame(year = c(1786 + 2*rep(c(1:119))),
congress = c(1:119)) con <- 66 #66
vvo <- lapply(c('house', 'senate'), function(x) {
Rvoteview::download_metadata(type = 'members',
chamber = x) |>
filter(chamber != 'President') }) ## [1] "/tmp/RtmpVdPlzr/Hall_members.csv"
## [1] "/tmp/RtmpVdPlzr/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)The
PresElectionResultspackage includes US Presidential Election Results by county (2000-2024), congressional district (2024), and state (1864-2024). 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/PresElectionResults")leg_dets <- 'https://unitedstates.github.io/congress-legislators/legislators-current.csv'
leg_dets0 <- read.csv((url(leg_dets)), stringsAsFactors = FALSE) |>
rename(state_abbrev = state)National popular and electoral presidential election results made available at britannica.com.
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 == 4, 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$d20 |> rev()) +
theme_minimal() +
theme(axis.ticks = element_blank(),
legend.position = 'none') +
xlim(-30, 30) +
labs(title ='National popular vote margins, 1868 to 2024',
caption = 'Data source: Britannica')
PresElectionResults::pres_results |>
filter(year > 1828, !is.na(ec_votes)) |>
group_by(year) |>
filter(ec_votes == max(ec_votes)) |> ungroup() |>
mutate(ec_per = round(ec_votes/ec_total*100,1)) |>
tidyr::pivot_longer(cols = c(pop_per, ec_per)) |>
ggplot(aes(x = value,
y = year)) +
geom_line(aes(group = year),
color = 'lightgray',
size = 2) +
geom_point(aes(color = name)) +
xlim(0,100) +
coord_flip() +
theme_minimal() +
ggthemes::scale_colour_economist() +
theme(axis.ticks = element_blank(),
legend.position = 'none') +
scale_y_continuous(breaks = seq(1824, 2024, by = 20)) +
labs(title ="President-elect's share of electoral and popular votes, 1828 to 2024",
caption = 'Data source: Britannica')
Historical Presidential election results by state via Wikipedia. Equal-area state geometry via Daily Kos.
mp <- PresElectionResults::xsf_TileOutv10 |>
left_join(PresElectionResults::pres_by_state |>
filter(year >= 1980, year <= 2024) |>
mutate(margins = republican - democrat))
mp |>
ggplot() +
geom_sf(aes(fill = margins),
color = 'darkgray', lwd = .15) +
geom_sf(data = PresElectionResults::xsf_TileInv10,
fill = NA,
show.legend = F,
color = NA,
lwd=.5) +
geom_sf_text(data = PresElectionResults::xsf_TileInv10,
aes(label = state_abbrev),
size = 1.5,
color='black') +
scale_fill_distiller(palette = "RdBu",
limit = max(abs(mp$margins)) * c(-1, 1)) +
facet_wrap(~year, ncol = 4) +
theme_minimal()+ theme_guide() +
labs(title = "Voting margins in Presidential elections 1980-2024",
caption = 'Data sources: Wikipedia & Daily Kos')
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$label),
size = 3,
color = 'black') +
theme_minimal() +
theme_guide() +
ggthemes::scale_fill_economist()+
labs(title = "When each state last voted for a Democratic presidential nominee, 1864 to 2024",
caption = 'Data sources: Wikipedia & Daily Kos')
Shows the maximum vote share (percentage of votes received) by any presidential nominee in each state from 1864 to 2024, not the margin of victory.
vote_share <- clean_prex |>
select(-party_win) |>
tidyr::gather(key = 'party', value = 'per', democrat:republican) |>
group_by(state_abbrev) |>
slice(which.max(per)) |>
ungroup() |>
mutate(label = paste0(year, ' - ', winner))new <- PresElectionResults::xsf_TileInv10 |>
left_join(vote_share, by ='state_abbrev') |>
mutate(per = round(per, 1)) |>
mutate(label = paste0(state_abbrev,
'\n',
year,
'\n',
gsub('^.* ', '', winner),
'\n',
per))
PresElectionResults::xsf_TileOutv10 |>
left_join(vote_share, by ='state_abbrev') |>
ggplot() +
geom_sf(aes(fill = paste0(year, ' - ', gsub('^.* ', '', winner))),
color = 'white' ,
alpha = .65) +
geom_sf_text(data = new,
aes(label = new$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 by state, 1864 to 2024",
caption = 'Data sources: Wikipedia & Daily Kos')
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 |
| 2024 | 312 | 361 | 2514 |
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, Pennsylvania in the (present) 119th 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(71, 77, 83,
89, 95, 101,
107, 113, 119)) |>
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, 1929 to 2025",
caption = 'Data sources: Daily Kos & VoteView')
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(1921, 2025, 4)) +
labs(title = "Split Senate delegations, 1921 to 2025",
caption = 'Data source: VoteView')
Note: King (Maine) and Sanders (Vermont) are Independents that caucus with Democrats. There are three actual splits with different party control: King/Collins in Maine, Fetterman/McCormick in Pennsylvania, and Baldwin/Johnson in Wisconsin.
split_senate_details <- sens |>
filter(congress > con) |>
filter(congress == max(congress)) |>
group_by(year, congress, state_abbrev) |>
filter(n_distinct(party_name) == 2) |>
arrange(party_name) |>
mutate(senator_num = row_number()) |>
ungroup() |>
select(congress, year, state_abbrev, bioname, party_name, senator_num) |>
tidyr::pivot_wider(names_from = senator_num,
values_from = c(bioname, party_name),
names_sep = "_") |>
select(state_abbrev,
senator_1 = bioname_1, party_1 = party_name_1,
senator_2 = bioname_2, party_2 = party_name_2) |>
arrange(state_abbrev)
split_senate_details |> knitr::kable()| state_abbrev | senator_1 | party_1 | senator_2 | party_2 |
|---|---|---|---|---|
| ME | COLLINS, Susan Margaret | Republican | KING, Angus Stanley, Jr. | other |
| PA | FETTERMAN, John Karl | Democrat | MCCORMICK, David Harold | Republican |
| VT | WELCH, Peter | Democrat | SANDERS, Bernard | other |
| WI | BALDWIN, Tammy | Democrat | JOHNSON, Ron | Republican |
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(1921, 2025, 4)) +
xlab('') +
labs(title = 'US Senate delegations, by party composition, 1921 to 2025',
caption = 'Data source: VoteView')
Senate seats held by Republicans via VoteView; population of states represented by Reublican senators via FRED.
wpops_wide <- sens |>
filter(congress > con) |>
left_join(PresElectionResults::fred_pop_by_state,
by = c('year', 'state_abbrev')) |>
group_by(year, party_name) |>
summarize(n = 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)
# Identify periods where GOP has Senate majority but represents < 50% of population
gray_years_df <- wpops_wide |>
filter(Senate_share > 50, Population_share < 50) |>
arrange(year)
if(nrow(gray_years_df) > 0) {
# Only group truly consecutive years (gap = 1 or 2 for biennial elections)
# Break period if gap > 2 years (to avoid including years that don't meet condition)
gray_years <- gray_years_df$year
gaps <- diff(gray_years)
# Find where to break periods (gaps > 2)
break_points <- which(gaps > 2)
if(length(break_points) > 0) {
# Create periods between break points
period_starts <- c(1, break_points + 1)
period_ends <- c(break_points, length(gray_years))
} else {
# All years are consecutive
period_starts <- 1
period_ends <- length(gray_years)
}
gray_periods <- data.frame(
period_id = seq_along(period_starts),
xmin = gray_years[period_starts],
xmax = gray_years[period_ends]
) |>
# Make single-year periods span 2 years, multi-year periods get 0.5 padding
mutate(
is_single = (xmin == xmax),
xmin = xmin - 0.5,
xmax = ifelse(is_single, xmax + 1.5, xmax + 0.5)
) |>
select(-is_single)
} else {
gray_periods <- data.frame(xmin = numeric(0), xmax = numeric(0))
}
wpops <- wpops_wide |>
tidyr::gather(-year, key = 'var', value = 'per')Gray highlight: Congresses in which (1) GOP senators hold a majority in the Senate AND (2) Republican senators collectively represent less than half of the U.S. population. We are presently in a Republican-controlled Senate where Republican senators represent a minority of Americans.
p <- wpops |>
ggplot()
if(nrow(gray_periods) > 0) {
p <- p + geom_rect(data = gray_periods,
aes(xmin = xmin,
xmax = xmax,
ymin = -Inf,
ymax = Inf),
fill = 'lightgray',
inherit.aes = FALSE)
}
p <- p +
geom_hline(yintercept = 50, color = 'black', lwd = .2) +
geom_line(aes(x = year,
y = per,
color = var),
size = 1) +
geom_point(aes(x = year,
y = per,
color = var),
size = 1.5) +
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(title = "Republican Senate share v. Population share, 1921 to 2025",
subtitle = "Gray areas reflect periods where GOP holds Senate majority but represents < 50% of population",
caption = 'Data sources: VoteView & FRED')
p 
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)) Prior to the Civil Rights Act, Republicans were virtually nonexistent in the South at the congressional level. Following the Civil Rights era, conservative Southern whites gradually realigned with the Republican Party over the course of several decades, transforming the “Solid South” from reliably Democratic to reliably Republican.
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) +
geom_vline(xintercept = 1964, color = 'black', linetype = 2, size = 0.5) +
annotate("text", x = 1965, y = 0.95, label = "Civil Rights Act 1964",
angle = 0, hjust = 0, vjust = 0, size = 3) +
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, 1920 to 2025",
caption = 'Data source: VoteView')
DW-NOMINATE ideal points in two dimensions. The first dimension represents the familiar left-right ideological spectrum. The second dimension captures a separate axis of political division—before the 1960s, this primarily reflected the North-South split on civil rights. Southern Democrats (shown in light blue) cluster separately from other Democrats due to their socially conservative positions, appearing as a distinct voting bloc in earlier Congresses. After the Civil Rights Act, this regional-conservative coalition gradually realigned into the Republican Party.
congress_south |>
mutate(year = year + 1) |>
filter (congress %in% c(87, 91, 95,
99, 103, 107,
111, 115, 119)) |>
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, 1959 to 2023",
subtitle = 'In two dimensions',
caption = 'Data source: VoteView')
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))gens1 <- gens |>
mutate(age = 2026 - as.integer(end)) |>
filter(order %in% c(15:19))
gens1 |> knitr::kable()| generation | start | end | model | order | age |
|---|---|---|---|---|---|
| Silent | 1928 | 1945 | pew | 15 | 81 |
| Boomers | 1946 | 1964 | pew | 16 | 62 |
| Gen X | 1965 | 1980 | pew | 17 | 46 |
| Millenials | 1981 | 1996 | pew | 18 | 30 |
| Gen Z | 1997 | 2012 | pew | 19 | 14 |
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, NA_real_)) |>
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(1921, 2025, 4)) +
labs(title = "Average age of congress members by party, 1921 to 2025",
caption = 'Data source: VoteView') 
freshmen <- congress |>
group_by(icpsr, bioname) |>
mutate(n = length(congress)) |>
ungroup() |>
filter(congress == 119) |> # 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)congress |>
filter (party_code %in% c('100', '200'),
congress == 119) |>
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 =gens1$age - 0.5,
linetype =2,
color = 'black',
size = .25) +
geom_text(data = gens1,
aes(x = age + 2.25,
y = 0.95,
label = generation),
size = 3) +
theme_minimal() +
ggthemes::scale_fill_economist() +
ggthemes::scale_color_economist() +
scale_x_continuous(breaks = gens1$age) +
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 119th House (2025) by party, generation & class",
caption = 'Data sources: VoteView & AmericanGenerations')
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'))
# Add Trump's first midterm (2018 election, 2019 Congress)
trump_midterm <- freshmen1 |>
filter(min == 2018) |>
arrange(desc(count)) |>
slice(1) |>
mutate(txt = 'Trump 1st midterm')
labs <- bind_rows(labs, trump_midterm)
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(1963,2025,2)) +
labs(title = "Freshman House members by party, 1963 to 2025",
caption = 'Data source: VoteView')
class1 <- states |> filter(!state_code %in% nonx[1:5]) |>
left_join(leg_dets0 |> filter(senate_class == 2)) |>
left_join(PresElectionResults::pres_by_state |>
filter(year == 2024)) |>
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 II Senators, 2026",
subtitle = 'With 2024 Harris margins',
caption = 'Data sources: unitedstates.io, Wikipedia & Daily Kos')
House Republicans in 119th representing districts Harris won in 2024.
Note: There are a few new re-worked districts in California from mid-census redistricting that are not included here.
vrs <- PresElectionResults::pres_by_cd |>
filter(house_rep_party == 'republican',
party_win == 'democrat') |>
left_join(freshmen) |>
mutate(Harris_Margin = democrat - republican,
district = paste0(state_abbrev, '-', district_code)) |>
select(district, house_rep, Class, Harris_Margin) |>
arrange(-Harris_Margin)
vrs |> knitr::kable()| district | house_rep | Class | Harris_Margin |
|---|---|---|---|
| NE-02 | Don Bacon | Upper-class | 4.62 |
| NY-17 | Michael Lawler | Sophmore | 0.56 |
| PA-01 | Brian K. Fitzpatrick | Upper-class | 0.32 |