如何获得每个分布具有不同形状和颜色的混合数据的散点图？

Question

I am running a simulation of mixture data. My function is harder than Gaussian distribution. Hence, here, I simplified my question to be in Gaussian form. That is, if I simulated a mixture data like this:

  N=2000
 U=runif(N, min=0,max=1)
 X = matrix(NA, nrow=N, ncol=2)
         for (i in 1:N){
              if(U[i] < 0.7){
               X[i,] <-   rnorm(1,0.5,1)
                    } else {
               X[i,] <- rnorm(1,3,5)
      }
 }

How can I have a scatter plot with different colour and shape (type of the plot point) for each cluster or distribution? I would like to have this manually since my function is hard and complex. I tried plot(X[,1],X[,2],col=c("red","blue")) but it does not work.

Answer 1

Here's what I got, but I'm not sure if this what you are looking for - the location of the observations for both clusters are exactly the same.

library(tidyverse)
df <- data.frame(X = X, U = U)
df <- gather(df, key = cluster, value = X, -U)
ggplot(df, aes(x = X, y = U, colour = cluster)) + geom_point() + facet_wrap(~cluster)

EDIT: I don't seem to be understanding what you are looking to map onto a scatter plot, so I'll indicate how you need to shape your data in order to create a chart like the above with the proper X and Y coordinates:

 head(df)
            U cluster          X
 1 0.98345408     X.1  2.3296047
 2 0.33939935     X.1 -0.6042917
 3 0.66715421     X.1 -2.2673422
 4 0.06093674     X.1  2.4007376
 5 0.48162959     X.1 -2.3118850
 6 0.50780007     X.1 -0.7307929

So you want one variable for the Y coordinate (I'm using variable U here), one variable for the X coordinate (using X here), and a 3rd variable that indicates whether the observation belongs to cluster 1 or cluster 2 (variable cluster here).

Answer 2

I think this is what you want. Note that I had to do a bit of guesswork here to figure out what was going on, because your example code seems to have an error in it, you weren't generating different x1 and x2 values in each row:

N=2000
U=runif(N, min=0,max=1)
X = matrix(NA, nrow = N, ncol=2)
for (i in 1:N){
    if(U[i] < 0.7){
        # You had rnorm(n=1, ...) which gives 2 identical values in each row
        # Change that to 2 and you get different X1 and X2 values
        X[i,] <-   rnorm(2, 0.5, 1)
    } else {
        X[i,] <- rnorm(2, 3, 5)
    }
}

df = data.frame(
    source = ifelse(U < 0.7, "dist1", "dist2"),
    x = X[, 1],
    y = X[, 2]
)

library(ggplot2)
ggplot(df, aes(x = x, y = y, colour = source, shape = source)) +
    geom_point()

Result: