X1 X2 Y
2  B  1
1  C  0
6  A  0
3  B  1
3  C  0

set.seed(123)
X1 <- sample (1:6, 1000, rep = TRUE)
X2 <- sample(c('A', 'B', 'C'), 1000, rep = TRUE)
Y <- sample(c(0, 1), 1000, rep = TRUE)
df <- data.frame(X1, X2, Y)

> library(caret)
> df2 <- data.frame(predict(dummyVars(~., df), df))
> str(df2)

> str(df2)
'data.frame':   1000 obs. of  5 variables:
 $ X1  : num  2 5 3 6 6 1 4 6 4 3 ...
 $ X2.A: num  1 0 1 0 0 0 0 1 1 0 ...
 $ X2.B: num  0 1 0 0 0 1 0 0 0 1 ...
 $ X2.C: num  0 0 0 1 1 0 1 0 0 0 ...
 $ Y   : num  0 0 0 1 0 1 0 0 0 0 ...

> library(corrplot)
> myCor <- cor(df2)
> print(myCor)

              X1        X2.A         X2.B        X2.C            Y
X1    1.00000000  0.05282849 -0.034155072 -0.01812134  0.035527106
X2.A  0.05282849  1.00000000 -0.517131108 -0.47597892 -0.010821102
X2.B -0.03415507 -0.51713111  1.000000000 -0.50658893 -0.005726481
X2.C -0.01812134 -0.47597892 -0.506588926  1.00000000  0.016784564
Y     0.03552711 -0.01082110 -0.005726481  0.01678456  1.000000000

> corrplot(myCor)