Take output from rcarbon::calibrate and plot with ggridges::geom_ridgeline

gistfile1.txt

# Load required libraries
library(rcarbon)
library(ggplot2)
library(ggridges)
library(dplyr)

# Step 1: Calibrate dates
# Example data: replace with your actual radiocarbon dates and errors
dates <- c(4500, 4800, 5000)  # Replace with your dates
errors <- c(30, 50, 100)      # Replace with your errors

calibrated_dates <- calibrate(dates, errors)

# Step 2: Prepare data for plotting
# Extract the grids for each calibrated sample and reshape for plotting
cal_data_long <- bind_rows(lapply(seq_along(calibrated_dates$grids), function(i) {
  # Extract each grid's calBP and PrDens, and add a sample identifier
  grid_data <- calibrated_dates$grids[[i]]
  data.frame(
    Sample = paste("Sample", i),
    Year = grid_data$calBP,
    Probability = grid_data$PrDens
  )
}))

# Step 3: Plot using ggridges::geom_ridgeline
ggplot(cal_data_long, 
       aes(x = Year, 
           y = Sample, 
           height = Probability, 
           group = Sample)) +
  geom_ridgeline(fill = "lightblue", 
                 color = "darkblue", 
                 scale = 100) +
  scale_x_reverse() +  # Reverse x-axis for cal BP to AD
  labs(
    x = "Calibrated Years BP",
    y = "Sample",
    title = "Calibrated Radiocarbon Date Distributions"
  ) +
  theme_minimal()