Motivation

The code below was originally written by Ian McDonald. If you’re interested in learning more about the data, this page has an excellent overview.

library(readr)

metadata <- read_csv(
    "col_name      , col_width
     congress      , 4
     icpsr         , 6
     st_code       , 3
     cd            , 2
     st_name       , 8
     party_code    , 5
     mc_name       , 15
     dim_1         , 10
     dim_2         , 10
     dim_1_se      , 10
     dim_2_se      , 10
     dim_1_2_corr  , 8
     log_lik       , 11
     num_votes     , 5
     num_class_err , 5
     geo_mean_prob , 10"
)

members <- read_fwf(
    "ftp://k7moa.com/junkord/HL01113D21_BSSE.DAT",
    fwf_widths(widths = metadata$col_width, col_names = metadata$col_name)
)

library(dplyr)

representatives <- members %>%
    filter(
        congress >= 88,
        !(cd %in% c(0, 98, 99)),
        party_code == 100 | party_code == 200
        ) %>%
    mutate(year1 = congress * 2 + 1787) %>%
    arrange(desc(year1))

democrats <- representatives %>% filter(party_code == 100)
republicans <- representatives %>% filter(party_code == 200)

library(ggjoy)

ggplot(representatives, aes(x = dim_1, y = year1, group = year1)) +
    geom_joy(data = democrats, fill = "blue", scale = 7, size = 0.25, rel_min_height = 0.01, alpha = 0.2) +
    geom_joy(data = republicans, fill = "red", scale = 7, size = 0.25, rel_min_height = 0.01, alpha = 0.2) +
    theme_classic() +
    scale_x_continuous(limits = c(-1, 1.3), expand = c(0.01, 0), breaks = seq(-1, 1, 0.5)) +
    scale_y_reverse(breaks = seq(2013, 1963, -10)) +
    ggtitle("Distribution of DW-NOMINATE of U.S. House by Party: 1963-2013") +
    ylab("First Year of Each Congress") +
    xlab("First Dimension DW-NOMINATE")