gggibbous带你绘制月亮散点图

library(data.table)
library(tidyverse)
library(ggforce)
library(ggtext)
library(ggnewscale)
library(paletteer)
library(packcircles)
library(gggibbous)

detectors <- fread("detectors.csv")

detectors$native = ifelse(detectors$native == "Yes", TRUE, FALSE)
# 对数据进行聚合，按'detector'、'kind'、和'.pred_class'列组合，并计算每个组合的计数
df = detectors[, by = .(detector, kind, `.pred_class`, native), .N]
# 对数据再次聚合，按'detector'、'kind'、和'.pred_class'列组合，并计算每个组合的频率和总计数
df = df[, by = .(detector, kind, `.pred_class`), c("Freq", "N2") := list(N / sum(N), sum(N))]

df$kind = df$kind |> factor(levels = c("Human", "AI"))
df$.pred_class = df$.pred_class |> factor(levels = c("AI", "Human"))

max_value = max(df$N2) # 计算'df'数据框中'N2'列的最大值
# 根据'kind'和'.pred_class'列创建新的'class'列，用于描述组合类型
df$class = paste0(df$kind, " - ", df$.pred_class)
df = df |> split(df$class) # 根据'class'列将'df'数据框分割成多个子数据框
# 对每个子数据框进行操作
packing <-  lapply(df, function(x) {
  # 按'detector'列对子数据框进行排序
  x = x[order(detector)]
  # 从子数据框中提取'detector'和'N2'列，并保留唯一的行
  radius = x[, c("detector", "N2"), with = FALSE] |> unique()
  # 计算用于圆形布局的值，并将结果存储到'out'数据框中
  out = circleProgressiveLayout( (radius$N2 / max_value) / 20 )
  out = setDT(out)   # 将'out'数据框转换为数据表格式
  index = match(x$detector, radius$detector)   # 通过'detector'列匹配'x'数据框中的位置索引
  x$x0 = out[index]$x + x$kind |> as.numeric()   # 计算x数据框中每个元素的横坐标，并存储在'x0'列中
  x$y0 = out[index]$y + x$`.pred_class` |> as.numeric() # 计算x数据框中每个元素的纵坐标，并存储在'y0'列中
  x$r  = out[index]$radius   # 将x数据框中每个元素的半径信息存储在'r'列中
  return(x)
})
packing <- rbindlist(packing) # 数据合并

ggplot() +
  # 添加散点图图层，其中数据来自packing数据框中具有缺失'native'列的行
  geom_point(
    data = packing[which(is.na(native))],
    aes(x = x0, y = y0, size = r), fill = "#00A087",
    color = "white", shape = 21, stroke = .25) +  # 设置点的颜色、形状、填充等属性
  # 添加自定义的"moon"（月亮）图层，其中数据来自packing数据框中具有非缺失'native'列的行
  geom_moon(data = packing[which(!is.na(native))],
    aes(x0, y0, ratio = Freq, right = native, fill = native, size = r),
    color = "white", stroke = .25) +  # 设置"moon"图层的颜色、填充等属性
  scale_size_continuous(range = c(5, 15)) + # 设置点大小的连续缩放范围
  new_scale("size") +   # 创建一个新的点大小比例尺
  # 设置新点大小比例尺的连续缩放范围
  scale_size_continuous(range = c(.75, 3)) +
  # 添加垂直虚线，x轴交叉点为1.5
  geom_vline(xintercept = 1.5, linetype = "dashed", linewidth = .3) +
  # 添加水平虚线，y轴交叉点为1.5
  geom_hline(yintercept = 1.5, linetype = "dashed", linewidth = .3) +
  # 设置x轴的刻度位置、标签和文本样式
  scale_x_continuous(breaks = c(1, 2), labels = c("Human", "AI"), position = "top") +
  # 设置y轴的刻度位置、标签和文本样式
  scale_y_continuous(breaks = c(1, 2), labels = c("AI", "Human")) +
  # 设置填充颜色的手动映射，并使用特定的调色板
  scale_fill_manual(values = paletteer_d("ggsci::nrc_npg"),
    guide = guide_legend(title.position = "top",
      title.theme = element_text(face = "bold", size = 10),
      nrow = 2)) +  # 设置图例的标题位置和样式
  labs(x=NULL, y=NULL) +   # 设置x轴和y轴的标题为空
  theme_minimal() +
  theme(panel.grid = element_blank(),  # 隐藏网格线
    axis.text.x.top = element_text(size = 10, margin = margin(b = 5), color = "black"),  # 设置x轴顶部文本样式
    axis.text.y = element_text(size = 10, margin = margin(r = 5), color = "black"),  # 设置y轴文本样式
    plot.background = element_rect(fill = "#f5f5f5", color = NA),  # 设置图形背景颜色
    plot.margin = margin(10, 2, 2, 2)  # 设置图形的边距
  )