Slope Chart EV

library(ggplot2)
library(dplyr)

library(tidyr)

# New data for CRIS vs EV CRIS
data <- data.frame(
  Value = c(
    "Asian", 
    "Urbanized (200,000+)", 
    "4 Or More Lanes, Undivided", 
    "Male", 
    "White", 
    "Rural Prin Arterial"
  ),
  All_CRIS = c(3.448, 62.466, 29.057, 54.163, 37.273, 27.264),
  EV_CRIS = c(22.239, 80.103, 37.932, 60.85, 43.959, 32.842)
)

# Convert to long format
data_long <- data %>%
  pivot_longer(cols = c(All_CRIS, EV_CRIS),
               names_to = "Group", values_to = "Percentage")

# Use numeric x-axis to control spacing
data_long$GroupX <- ifelse(data_long$Group == "All_CRIS", 1, 1.5)

# Get labels at the right (EV_CRIS)
labels_df <- data_long %>% filter(Group == "EV_CRIS")

# Plot
ggplot(data_long, aes(x = GroupX, y = Percentage, group = Value)) +
  geom_line(aes(linetype = Value), color = "black", size = 1) +
  geom_point(aes(shape = Value), color = "black", size = 3) +
  geom_text(data = labels_df,
            aes(label = Value),
            hjust = 0, vjust = 0.5, size = 4.5,
            fontface = "bold", color = "black") +
  scale_x_continuous(
    breaks = c(1, 1.5),
    labels = c("All CRIS", "EV CRIS"),
    expand = expansion(mult = c(0.01, 0.8))
  ) +
  scale_linetype_manual(values = c("solid", "dashed", "dotdash", "twodash", "longdash", "twodash")) +
  scale_shape_manual(values = c(15, 16, 17, 18, 3, 4)) +
  labs(
    title = "",
    x = "",
    y = "Percentage",
    caption = ""
  ) +
  theme_minimal(base_size = 14) +
  theme(
    legend.position = "none",
    plot.title = element_text(face = "bold")
  )