如何用ggplot绘制分类内呈S形排列的geom_point散点图

我想要绘制每个分类下散点排列形成S形走势的可视化效果，请问是否可以在ggplot中实现该需求？
参考样图为每个分类下散点呈正弦S形排列的分组散点图，可获取同类公开示例数据用于测试。
我目前已完成的代码如下：

somatic.variants <- read.delim("data/Lawrence.S2.txt", stringsAsFactors=T)
cancer_rates <- tapply(somatic.variants$logn_coding_mutations, somatic.variants$tumor_type, median)
cancer_rates <- cancer_rates[order(cancer_rates, decreasing=F)]
somatic.variants$tumor_type <- factor(somatic.variants$tumor_type, levels = names(cancer_rates))

library(ggplot2)
library(GGally)
ggplot(data = somatic.variants,
       mapping = aes(x = tumor_type,
                     y = log10(n_coding_mutations))) +
  geom_point(position = position_dodge2()) +
  scale_x_discrete(position = "top") +
  scale_y_continuous(labels = c(0,10,100,1000,10000), expand = c(0,0)) +
  geom_stripped_cols() +
  theme_bw() +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 315, hjust = 1, size = 12),
        panel.grid = element_blank()) +
  labs(y = "Coding mutations count") +
  stat_summary(fun = median,
               geom="crossbar",
               size = 0.25,
               width = 0.9,
               group = 1,
               show.legend = FALSE,
               color = "#FF0000")

• 可以在ggplot中实现该效果，核心思路是对每个分组内的点按y值排序后，自定义x轴偏移量，使其按S形（正弦曲线走势）排列，具体修改步骤如下：

1. 安装加载数据处理所需的dplyr包（已安装可跳过安装步骤）：

install.packages("dplyr")
library(dplyr)

2. 对原始数据集做预处理，给每个分组内的点计算S形偏移量：

somatic.variants <- somatic.variants %>%
  group_by(tumor_type) %>%
  # 按y值排序后生成点序号
  arrange(log10(n_coding_mutations)) %>%
  mutate(
    point_id = row_number(),
    # 计算正弦偏移，幅度0.4可调整控制摇摆宽度，3*pi可调整控制弯曲次数
    x_offset = sin(seq(0, 3*pi, length.out = n())) * 0.4,
    # 分类变量转数值加上偏移得到实际x坐标
    x_pos = as.numeric(tumor_type) + x_offset
  ) %>%
  ungroup()

3. 修改ggplot绘图代码，替换x轴映射为计算好的偏移坐标，同时保留原分类标签显示：

ggplot(data = somatic.variants,
       mapping = aes(x = x_pos,
                     y = log10(n_coding_mutations))) +
  geom_point(size = 0.8) +
  # 手动设置x轴刻度与原分类对应
  scale_x_continuous(
    breaks = 1:length(levels(somatic.variants$tumor_type)),
    labels = levels(somatic.variants$tumor_type),
    position = "top",
    expand = c(0.05, 0.05)
  ) +
  scale_y_continuous(labels = c(0,10,100,1000,10000), expand = c(0,0)) +
  geom_stripped_cols() +
  theme_bw() +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 315, hjust = 1, size = 12),
        panel.grid = element_blank()) +
  labs(y = "Coding mutations count") +
  stat_summary(
    # 中位数线映射回原分类位置，保证显示在每组中间
    aes(x = as.numeric(tumor_type)),
    fun = median,
    geom="crossbar",
    size = 0.25,
    width = 0.9,
    group = 1,
    show.legend = FALSE,
    color = "#FF0000"
  )

• 参数调整说明：
  • 增大x_offset计算中sin()内的3*pi数值，可增加S形的弯曲次数
  • 增大x_offset计算中乘的系数0.4，可增大散点左右摇摆的宽度，反之则排列更窄

内容的提问来源于stack exchange，提问作者Yuriy Grabovsky