R中的Sankey图，冲积层，冲积层–三个数据块：基线流（多个时间点）-结果

Question

在研究结束时，我们想介绍由于不同年龄组的锻炼而产生的肌肉质量变化以及比赛中的最终表现/结果。
我们有几个时间点测量肌肉质量。在此示例中，我仅显示了三个时间点，但是该研究折衷了12个时间点。
为了显示肌肉质量的变化以及与平均值的偏差，我可以使用geom_flow()。但是，在图表左侧添加年龄组以及在右侧添加效果会变得非常棘手。这些数据位于不同的变量中。 请帮助我们找到一种呈现数据的好方法。谢谢。

数据结构：
ID Age_at_start月的Deviation_muscle效果
1的36的3的59的优秀
1的36的6的104的优秀
1，36，9，200，优秀
2，29，3，-40 2，29，6，-109的平均值
2月29日9月-30日 3，22，3，310，高于平均水平

library(ggplot2)
library(ggalluvial)

df.san$age<-factor(df.san$age)
df.san$age<-factor(df.san$age, levels=c(1,2,3,4), labels=c("20 to 24 years","25 to 29 years","30 to 34 years","35 to 39 years"))
df.san$dev_group <-factor(df.san$dev_group,levels=c(1,2,3,4,5,6,7),labels=c("≥250g","≥150 to <250g","≥50 to <150g","> -50 to <50g","> -150 to ≤ -50","> -250 to ≤ -150", "≤ -250g"))
df.san$month <- factor(df.san$month,labels=c("1mo","2mo","3mo"))
df.san$perform<-factor(df.san$perform,levels=c(1,2,3,4),labels=c("outstanding "," above average "," average "," below average"))
ggplot(df.san,aes(x = month,stratum = dev_group, alluvium = ID, fill = dev_group,label = dev_group)) +
scale_fill_brewer(type = "qual", palette = "Set2") +
geom_flow(stat = "alluvium", lode.guidance = "rightleft", color = "darkgray") +
geom_stratum() +
theme(legend.position = "bottom") +
ggtitle("Effect of Exercice on Muscle Growth on Performance in 4 Different Age Groups ")

df.san的数据：

structure(list(ID = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L), age = c(2L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 4L, 1L, 1L, 3L, 1L, 4L, 4L, 3L, 4L, 3L, 4L, 2L, 2L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 3L, 1L, 2L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 4L, 3L, 3L, 2L), month = c(2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 2L, 4L, 6L), dev_muscle = c(-109.3, -236.2, -275.4, -44.5, -202.6, -436, 3, -115.8, -136.2, -142.1, -429, -561.4, -49, -248.8, -232.6, -15.9, -171.5, -391.6, -5.8, -21.7, -104.1, 12.6, -33.4, -25.4, -57.3, -50.7, -103.6, -124, -221.4, -457.2, 22.1, -126.9, -79.5, -76.8, -113.2, -129.7, -86.1, -126, -82.9, -10.8, -2.8, 88.3, 41.6, 0.2, 184.7), perform = c(1L, 2L, 1L, 2L, 4L, 1L, 1L, 4L, 3L, 4L, 2L, 4L, 4L, 4L, 2L, 2L, 4L, 3L, 3L, 4L, 1L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 4L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 4L, 4L), dev_group = c(5L, 6L, 7L, 4L, 6L, 7L, 4L, 5L, 5L, 5L, 7L, 7L, 4L, 6L, 6L, 4L, 6L, 7L, 4L, 4L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 7L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 3L, 4L, 4L, 2L)), class = "data.frame", row.names = c(NA, -45L))