ggplot2在一张3d图表中绘制了几条密度线

Question

I conducted IQ test on the same group (100 students) over 5 five years. like:

In 2005, Jack's IQ is 100, Amy' IQ is 99, John...

in 2006, Jack's IQ is 105, Amy' IQ is 95, John...

...

I want to estimate the density of IQ in different years. And plot the density lines of different years in one chart. Here is some data example.

year2005<-rnorm(100,100,2)
year2006<-rnorm(100,98,1)
year2006<-rnorm(100,101,4)

How can I draw them like the following chart?

above is the 2D chart. It is very difficult to read the trend between years beacuse I have to know red is 2016 and black is 2015. And there is no difficult with 3D and this is the reason I insist in 3D

Answer 1

Is there any reason you want to make a hard-to-read 3D plot instead of something like this?

library("ggplot2")

n <- 100
year2005<-rnorm(n,100,2)
year2006<-rnorm(n,98,1)
year2007<-rnorm(n,101,4)

dt <- data.frame(year = rep(c("2005", "2006", "2007"), each = n),
                 value = c(year2005, year2006, year2007))

ggplot(dt, aes(value, fill = year, colour = year)) +
  geom_density(alpha = 0.1)

See more examples here.

Even if you insist on a 3D plot like you show, how you do propose to do the interpolation between the years?

Edit

Here is a very related stack overflow post. I am aware this is not 3D, like you request. But in order to do so, you still need to consider how to interpolate between years (which seem to be a categorical variable in your case).

Answer 2

Thank @Anders Ellern Bilgrau for the website he/she suggested from which I learnt something about ggridges

I use ggridges to finish my work. Can get help from

https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html

library(devtools)
install_github("clauswilke/ggridges")
ggplot(dt, aes(x = value, y = year, fill =0.5-abs(0.5-..ecdf..))) +
  stat_density_ridges(geom="density_ridges_gradient",scale=1, calc_ecdf=TRUE,
                      jittered_points = T) + 
  scale_fill_viridis(name = "Tail Probability", direction = -1) 
  +theme_minimal()+
  theme(plot.title=element_text(size=17,color="black",face="bold",hjust=0.5), 
        axis.title.y = element_blank())+ 
  labs(x="...")+
  scale_y_discrete(expand = c(0.01, 0)) +   
  scale_x_continuous(breaks=seq(0,240,40),expand = c(0, 0)) 

Although it do not fully meet my requirement, it can help to analyse the change of the probability shape.