ideological leanings of current USA supreme court judges https://en.wikipedia.org/wiki/Ideological_leanings_of_United_States_Supreme_Court_justices

supreme.r

install.packages("readr", quiet = TRUE)
library(readr)
data https://mqscores.wustl.edu/measures.php
justices_df <- read_csv("justices.csv")

library(dplyr)
library(ggplot2)

# 1. Sitting Justices as of 2026
keep <- c("CThomas", "SAAlito", "JGRoberts", "EKagan", "SSotomayor", 
          "ACBarrett", "KBJackson", "NMGorsuch", "BMKavanaugh")

dems <- c("SSotomayor", "EKagan", "KBJackson")

# 2. Manual Label Dataframe
label_data <- data.frame(
  Party = c("Rep", "Rep", "Rep", "Dem", "Dem", "Rep", "Rep", "Rep", "Dem"),
  Name = c("Thomas", "Roberts", "Alito", "Sotomayor", "Kagan", 
           "Gorsuch", "Kavanaugh", "Barrett", "Jackson"),
  nameYear = c(1995, 2006, 2004, 2007.5, 2009, 2015, 2017, 2019, 2021),
  nameNum = c(4.0, 0.8, 1.4, -2.0, -1.0, 1.0, 0.8, 1.2, -2.6)
)

# 3. Filtering and Processing
recent <- justices_df %>%
  filter(term >= 1991, justiceName %in% keep) %>%
  mutate(col = ifelse(justiceName %in% dems, "Democrat", "Republican"))

# 4. Compact Plot Construction
gg <- ggplot(recent) +
  geom_hline(yintercept = 0, alpha = 0.3, linetype = "dashed") +
  
  # Reference Labels
  annotate("label", x = 1991.5, y = 1.5, label = "More Conservative →", 
           angle = 0, fontface = "bold", size = 3, fill = "white", label.size = 0) +
  annotate("label", x = 1991.5, y = -1.5, label = "← More Liberal", 
           angle = 0, fontface = "bold", size = 3, fill = "white", label.size = 0) +

  # Data Layers
  geom_ribbon(aes(ymin = post_mn - post_sd, ymax = post_mn + post_sd, 
                  x = term, group = justiceName, fill = col), 
              color = NA, alpha = 0.15) +
  geom_line(aes(x = term, y = post_med, color = col, group = justiceName), 
            linewidth = 0.8) +

  # Justice Name Labels
  geom_text(
    data = label_data,
    aes(x = nameYear, y = nameNum, label = Name, 
        color = ifelse(Party == "Dem", "Democrat", "Republican")),
    size = 4.2, fontface = "bold", show.legend = FALSE
  ) +

  # Axis Controls
  scale_x_reverse(
    limits = c(2025, 1991), 
    breaks = c(2024, seq(2015, 1995, -10), 1991),
    labels = c(2024, seq(2015, 1995, -10), "1991↓"),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    limits = c(-5, 6), 
    breaks = seq(-4, 6, 2),
    labels = function(x) ifelse(x == 0, "0\n(Neutral)", as.character(x))
  ) +
  
  scale_color_manual(name = "Nominated by a", values = c(Democrat = "#2166ac", Republican = "#b2182b")) +
  scale_fill_manual(name = "Nominated by a", values = c(Democrat = "#2166ac", Republican = "#b2182b")) +
  
  coord_flip() + 
  theme_minimal() +
  labs(
    title = "Current Supreme Court Justices Ideology (1991–2024)",
    subtitle = "Negative scores favor progressive, positive scores favor traditionalist interpretations.",
    caption = "Data: mqscores.wustl.edu | @iamreddave",
    x = "Term Year", y = NULL # REMOVED redundant y-axis label
  ) +
  theme(
    legend.position = "bottom",
    legend.box = "horizontal",
    legend.margin = margin(t = -5), # Pull legend closer to the graph
    plot.caption = element_text(size = 8, color = "grey40", vjust = 5.5, hjust = 1), # Align caption with legend
    plot.title = element_text(face = "bold"),
    axis.title.x = element_blank() # Further compacting the bottom
  )

print(gg)