大型数据集跨因子的时间序列密度图

Question

我想为数据集的因子用水管理制作基于时间的密度图，如下所示

Seed(123)
ID = rep(c("BAU","IMP","SGR","CR"), each=25)
Time = rep (c(1,2,3,4,5), each = 20)
data <- data.frame( ID, Time,  profits = runif(100,0,1))

我正在使用以下代码制作跨 ID 利润的密度图。或者可能是组或时间的 facet_wrap？

library(ggridges)
ggplot(
  data, aes(x = profits, y=as.factor( Time), group = ID, fill=stat(x))) +
  geom_density_ridges_gradient(scale = 3, size = 0.3, rel_min_height = 0.01) + 
  scale_fill_viridis_c(name = "Profits", option = "C") +
  labs(title = 'Total Profits')  + facet_wrap(~ID, scales = "free")+
  theme_classic()

它给出了很好的密度图。但是对于我原来拥有的百万行的大数据；例如如下所示：

  Seed(123)
ID = rep(c("BAU","IMP","SGR","CR"), each=5000)
Time = rep (c(1:1000), each = 20)
data <- data.frame( ID, Time,  profits = runif(20000,0,1))

代码给出了一个不整洁的图表。我们可以让时间因素以 20 的间隔表示，以使图表易于理解吗？或者如果任何其他刻面或包装可以提高图形的可见性。请帮忙 谢谢

Answer 1

看看这个： https://stackoverflow.com/a/55852972/13142581

Seed(123)
ID = rep(c("BAU","IMP","SGR","CR"), each=25)
Time = rep (c(1,2,3,4,5), each = 20)
data <- data.frame( ID, Time,  profits = runif(100,0,1))

library(ggridges)
ggplot(
  data, aes(x = profits, y=as.character(ID), group = as.character(Time), fill=stat(x))) +
  geom_density_ridges_gradient(scale = 3,  rel_min_height = 0.01) + 
  scale_fill_viridis_c(name = "Profits", option = "C") +
  labs(title = 'Total Profits')  + facet_wrap(~ID, scales = "free")+
  theme_classic()