2

I have two corr matrices that I would like to combine in 1 plot:

Sample code for Corr Matrix 1:

matrix_values <- c(-0.07, -0.03, 0.1, 0.11, 0.06, 0.16, 0.16, 0.13, 0.04, 0.06, 0.05, 0.04, 0.16, 0.07, 0.1, 0.08, 0.08, 0.17, 0.07, -0.13, 0.16, -0.07, 0.09, 0.07, -0.08, 0, 0.09, -0.02, 0.18, 0.09, 0.01, -0.1, -0.04, -0.12, -0.03, 0.03, 0.09, 0.09, 0.15, -0.01, 0.15, 0.09, 0.11, 0.09, 0.15, 0.19, -0.07, -0.04, 0, -0.12, NaN, -0.02, -0.11, 0.01, 0.1, -0.1, -0.1, 0.01, 0.04, 0.08, -0.02, -0.12, 0.09, -0.05, -0.07, -0.03, -0.19, -0.07, -0.16, -0.08, -0.05, -0.04, 0.03, -0.09, -0.09, -0.12, -0.07, 0.04, 0.07, 0.04, 0.02, -0.08, -0.03, -0.18, -0.02, 0.03, -0.06, 0.03, -0.07, 0.09, 0.04, -0.06, -0.1, -0.07, 0.1, 0.02, 0.06, -0.13, -0.14, -0.06, NaN, NaN, -0.07, -0.12, 0.02, -0.02, 0.01, 0.02, -0.01, -0.08, -0.03, -0.06, -0.05, -0.15, 0, -0.12, 0.13, -0.09, -0.05, 0.05, 0.08, -0.06, 0.16, 0.16, 0, 0.06, -0.05, -0.05, 0.14, -0.02, 0.12, 0.01, -0.07, -0.06, 0.07, 0.07, -0.13, 0.06, -0.05, -0.06, -0.15, -0.07, 0.11, 0.03, 0.1, 0.05, -0.12, 0.13, -0.1, 0.04, NaN, NaN, NaN, -0.03, -0.12, -0.02, 0.23, 0.13, 0.04, 0.01, 0.1, -0.01, 0.04, 0.03, -0.02, 0, -0.01, -0.08, -0.17, -0.05, 0, -0.07, -0.13, 0.1, -0.04, -0.01, 0.05, -0.03, -0.03, 0.13, -0.03, 0.01, 0.03, -0.03, 0.06, -0.01, -0.08, 0.05, 0.12, 0.09, 0.08, 0.07, -0.04, 0.09, 0.05, 0.1, 0.03, 0.05, 0.09, 0, NaN, NaN, NaN, NaN, 0.03, -0.03, 0.13, 0.14, 0.04, -0.03, 0.05, 0.14, 0.02, 0, -0.09, 0, 0, 0.01, -0.1, -0.14, 0, 0.02, 0.04, -0.07, -0.03, -0.07, -0.08, 0.1, 0.02, 0.18, 0.07, -0.16, 0.08, 0.03, -0.01, 0.03, -0.01, -0.07, 0.01, 0.1, 0.11, -0.11, 0.04, -0.08, -0.01, -0.03, -0.02, 0.09, 0.03, 0.13, NaN, NaN, NaN, NaN, NaN, -0.01, -0.05, 0.24, 0.02, 0, 0.11, 0.22, 0.22, 0.09, 0.06, 0.1, 0.09, 0.21, 0.16, 0.08, 0.08, 0.14, 0.05, 0.14, 0.15, -0.01, 0.05, 0.23, 0.13, 0.04, 0.06, 0.11, 0.05, 0.16, 0.03, 0.06, 0.01, -0.02, 0.23, -0.05, -0.09, 0.01, -0.02, 0.08, -0.07, 0.06, -0.01, -0.02, -0.03, 0.06, NaN, NaN, NaN, NaN, NaN, NaN, 0.05, -0.02, 0.08, -0.03, 0.02, -0.05, 0.13, 0.08, 0.08, 0.11, -0.04, -0.08, 0.03, 0.09, 0.1, -0.04, 0.12, 0.12, -0.06, 0.07, -0.09, 0.03, 0.03, -0.03, -0.02, 0.05, 0.04, -0.14, -0.05, 0.15, 0.06, -0.03, 0.04, -0.06, 0.21, 0.12, 0.2, -0.04, 0.05, 0.02, 0.14, 0, 0.12, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.13, 0, 0.12, 0.13, 0.05, 0.03, 0.09, 0.13, -0.05, 0.1, 0.14, 0.05, 0.06, 0.11, 0.03, 0.09, 0.17, 0.04, 0.15, 0.03, 0.03, -0.1, 0.07, 0.01, 0.02, 0.04, -0.08, 0.06, 0.05, 0.14, 0.07, 0.03, 0, 0.14, 0.02, -0.01, 0.02, 0.13, 0.09, -0.16, 0.1, -0.06, -0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.14, -0.05, 0.2, 0.05, -0.07, 0.1, 0.21, 0.14, -0.04, 0.01, 0.11, 0.1, 0.17, 0.21, 0.06, 0.09, 0.17, 0.17, 0.26, -0.04, 0.04, -0.01, 0.06, 0.14, -0.11, 0.05, 0.13, -0.05, 0.14, 0.06, 0.01, -0.05, 0.03, 0.04, 0.02, -0.08, -0.09, 0, -0.08, -0.21, -0.02, -0.03, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.16, -0.1, 0.03, 0.06, 0.03, 0.16, 0.07, 0.09, -0.05, 0.02, 0.02, 0.02, 0.15, 0.04, 0.11, 0.04, 0.03, 0.08, 0.1, 0.06, -0.09, -0.03, 0.25, 0.11, -0.12, -0.12, 0.07, 0.03, 0.12, 0.11, 0.07, -0.07, 0.1, 0.11, -0.08, -0.05, -0.1, 0.1, -0.04, 0.07, 0.07, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.06, -0.04, 0.19, 0.04, -0.04, 0.07, 0.09, 0.07, -0.04, 0.03, 0.06, 0.1, 0.01, 0, 0.16, -0.07, 0.12, 0.07, 0.11, 0, 0.02, 0.17, 0.19, 0.13, -0.15, -0.14, 0.26, 0.08, 0.02, 0.08, 0.17, -0.03, -0.02, 0.17, 0.03, 0.03, -0.1, 0.1, -0.02, -0.2, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.02, 0.15, -0.01, -0.02, -0.19, 0, 0.05, -0.08, -0.09, -0.15, 0.16, 0.12, 0.08, -0.03, 0.11, 0.09, 0.08, 0.06, 0.11, -0.07, 0.2, 0.05, 0.22, 0.05, -0.1, -0.07, -0.08, 0.07, 0.18, -0.06, 0.12, -0.06, -0.06, 0.09, -0.12, -0.15, -0.16, 0, -0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.07, -0.1, 0.23, -0.08, 0.01, -0.02, 0.13, 0.13, -0.04, 0.14, 0.03, 0.14, 0.07, 0.15, -0.02, 0.01, 0.05, 0.03, 0, 0.15, -0.15, 0.1, 0.11, 0.17, 0, -0.06, 0.14, -0.14, 0.03, 0.16, -0.12, -0.15, -0.1, 0.17, 0.2, -0.13, -0.11, -0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.02, 0, 0.13, 0.03, -0.04, 0.03, 0.06, -0.08, -0.11, -0.08, -0.09, 0.12, 0.1, -0.01, 0.04, -0.12, -0.1, 0.01, 0.09, 0.02, 0.04, -0.03, 0.04, 0.11, -0.11, -0.15, 0.07, -0.13, -0.05, 0.15, 0.02, -0.07, 0.12, 0, 0.06, -0.05, 0.09, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.25, -0.05, 0.29, -0.04, -0.06, 0.11, 0.16, 0.07, 0.05, 0.06, 0.12, 0.09, 0.22, 0.11, 0.17, 0.1, 0.19, 0.12, 0.17, 0.03, 0.03, 0.11, 0.19, 0.17, 0.02, 0.07, 0.27, -0.02, -0.05, 0.19, 0.16, 0, 0.11, 0.14, 0.04, 0.14, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.12, -0.08, 0.36, -0.08, 0.02, -0.03, -0.04, 0, -0.14, 0.02, -0.07, 0.05, 0.01, 0.03, -0.06, -0.03, 0.04, -0.05, 0.15, -0.03, -0.2, 0.03, 0.01, 0.1, 0.15, 0.21, 0.02, -0.2, -0.03, -0.01, -0.1, 0.02, 0.05, 0.1, -0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.04, 0.08, 0.2, -0.06, 0.06, 0.12, 0.2, 0.12, 0.03, 0.06, 0.08, 0.12, 0.16, 0.11, 0.15, 0.18, 0.1, 0.09, 0.04, 0.11, 0.03, 0.06, 0.11, -0.05, -0.06, 0.04, 0.04, -0.06, 0.11, 0.18, 0.12, -0.06, -0.06, 0.13, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.09, 0.04, -0.05, 0.12, 0.13, 0.13, 0.13, 0.07, 0.16, 0.05, 0.07, -0.1, 0.08, -0.05, -0.01, -0.06, -0.07, 0.01, -0.07, -0.05, 0.13, -0.06, -0.01, -0.07, -0.06, -0.02, 0.11, -0.07, 0.13, -0.02, -0.03, 0.03, -0.09, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.13, -0.12, 0.07, -0.03, -0.03, -0.06, -0.1, 0.04, -0.12, 0.07, -0.04, -0.08, -0.16, -0.03, -0.11, -0.24, -0.08, -0.04, -0.04, -0.13, -0.19, -0.01, -0.01, 0, -0.08, -0.03, -0.06, -0.15, -0.11, -0.05, -0.05, -0.02, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.18, -0.13, 0.03, 0.09, -0.03, -0.09, 0.14, 0.02, 0, 0.05, -0.11, -0.08, 0.04, -0.04, -0.03, -0.16, 0.01, -0.03, 0.11, -0.11, -0.1, 0.02, 0.01, 0.06, -0.05, -0.01, 0.15, -0.05, 0.08, 0.01, -0.07, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.04, -0.13, 0.13, 0.15, 0.23, 0.23, 0.13, 0.1, 0.01, 0.04, 0.04, 0.08, 0.09, 0.08, 0.03, 0.03, 0.13, 0.14, 0.04, 0.01, 0.09, -0.03, 0.12, 0.01, -0.06, -0.11, 0.09, -0.13, 0.02, 0.17, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.07, 0.11, 0.09, -0.08, 0.01, -0.04, 0.05, 0.16, -0.03, 0.08, 0.02, 0.05, -0.11, 0.1, 0.01, -0.07, 0.05, 0, 0.05, 0.09, -0.22, -0.09, 0.05, -0.05, -0.05, -0.04, -0.02, -0.11, -0.09, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.24, 0.07, 0.05, 0.07, 0.11, -0.11, -0.08, -0.16, -0.13, -0.07, -0.03, 0.01, -0.06, -0.07, -0.01, -0.07, 0.04, 0.04, -0.1, -0.04, 0.06, 0.04, 0.16, 0.08, -0.05, -0.09, 0.13, 0.14, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.04, 0.01, 0, 0.05, 0, 0.07, -0.02, -0.06, -0.07, -0.12, -0.02, 0.08, -0.01, -0.07, -0.14, -0.11, -0.14, -0.04, 0.01, -0.15, 0.15, -0.15, -0.02, 0.02, -0.14, -0.1, -0.06, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.14, 0.08, 0.34, 0.02, 0.16, 0.04, 0.12, 0.21, 0.03, 0.07, 0.18, 0.02, 0.02, 0.03, 0.04, 0, 0.02, 0.05, 0.1, 0.01, -0.05, -0.07, 0.08, -0.08, -0.02, -0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.06, -0.12, -0.02, 0.06, 0.08, -0.11, -0.05, -0.07, -0.06, -0.08, -0.12, 0, -0.03, -0.08, -0.11, -0.17, -0.02, -0.05, 0.01, -0.15, -0.21, -0.03, -0.04, 0.03, -0.03, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.02, -0.07, -0.03, 0.02, -0.08, -0.1, -0.08, -0.01, -0.07, -0.02, -0.15, 0.04, -0.07, -0.04, -0.22, -0.09, -0.1, -0.02, -0.14, -0.15, -0.22, -0.06, -0.07, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.09, -0.04, 0.02, 0.14, 0.15, 0.13, 0.02, 0.07, -0.01, 0.08, 0.1, -0.13, 0.1, -0.02, 0.02, 0.01, 0.05, 0.07, -0.07, 0.01, 0.04, -0.13, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.06, 0.14, 0.07, 0.15, 0.1, 0.09, 0.14, 0.09, 0.03, 0.04, 0.13, 0.02, 0.13, -0.02, 0.21, -0.03, 0.03, 0.12, -0.06, 0.08, 0.13, 0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.04, -0.09, 0.08, 0.01, 0.04, 0.01, 0, 0.06, 0.04, 0.03, 0.09, -0.12, -0.06, -0.01, -0.09, -0.11, -0.07, -0.04, -0.05, -0.1, 0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.12, 0.09, -0.05, 0, 0.08, 0.02, -0.08, -0.15, -0.14, -0.16, 0.03, 0, -0.03, -0.11, 0.04, -0.09, -0.17, -0.09, -0.05, -0.05, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.07, -0.07, 0.09, -0.07, 0.07, 0.05, 0.06, 0.23, 0, 0.11, -0.01, 0.03, 0.04, 0.07, 0.04, -0.01, 0.01, 0.04, -0.03, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.03, -0.15, -0.01, 0.14, 0.19, 0.03, -0.1, -0.03, -0.12, 0.04, -0.14, 0.05, -0.15, -0.09, 0.03, -0.16, 0.05, 0.12, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.08, -0.05, 0.09, -0.08, 0.09, 0.05, 0.08, 0.05, -0.08, 0.03, -0.04, 0.06, -0.15, 0.06, 0.07, 0.09, 0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.02, 0.04, 0.1, -0.16, 0.05, 0, 0.06, 0.06, -0.05, -0.01, -0.13, 0.11, -0.1, 0.03, 0.08, -0.09, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.02, -0.18, -0.11, 0.02, 0.06, 0.01, -0.18, -0.03, -0.19, -0.01, -0.23, 0.02, -0.11, -0.06, 0.03, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.04, -0.07, 0.07, 0.09, 0.08, 0.1, 0.06, 0.12, -0.06, -0.04, 0.12, 0.14, -0.03, 0.04, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0, -0.01, 0.23, -0.09, 0.1, -0.07, -0.01, 0.13, -0.05, 0.07, -0.11, 0.01, -0.17, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.1, 0.04, 0.05, -0.06, 0.17, 0, -0.03, 0.01, -0.14, 0.08, -0.05, 0.16, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.03, 0.04, -0.1, 0.1, 0.17, 0.12, 0.19, 0.1, 0.24, 0.15, 0.03, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0, 0.13, -0.11, -0.02, 0.14, 0.01, -0.07, -0.07, -0.08, -0.1, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.05, 0.06, 0.1, 0.06, 0.18, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.09, 0.02, 0.3, 0.11, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.02, 0.06, 0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.01, -0.01, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.21, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN)

 cor_matrix1 <- matrix(matrix_values, ncol = 51, nrow = 51)
 dat <- melt(cor_matrix1[-52, ])


  r <- ggplot(data =  dat, aes(x = Var1, y = Var2)) +
   geom_tile(aes(fill = value), color = "white") +
   scale_fill_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#770000", "red", "#ff8000", "#ffff00", "#ffffe5"))+
   theme( axis.title.x = element_blank(),
    axis.title.y = element_blank(),
   panel.background = element_blank())

Sample code for Corr Matrix 2:

 cor_matrix2 <- matrix(matrix_values, ncol = 51, nrow = 51)
 dat <- melt(cor_matrix2[-52, ])
 p <- ggplot(data =  dat, aes(x = Var1, y = Var2)) +
 geom_tile(aes(fill = value), color = "white") +
scale_fill_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#00007f", "#1212b2", "cyan", "#b4b4cc", "white"))

1) combine the _r_ and _p_ matrices so that _r_ shows up in the lower diagonal and _p_ in the upper diagonnal; 2) have the main diagonal to be a *black* line (a line that separates the two matrices); 3)certain values can be highlighted

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3
  • You will need to combine the data before plotting, but having multiple legends for the same thing is not easy to do in ggplot. Furthermore, why do they need to go in the same plot when they have different ranges/represent different entities? Commented Jan 22, 2016 at 17:16
  • They represents data from 2 different populations and I want to have them side by side for easy comparison. How can I have multiple legends? A sample code is highly appreciated! Commented Jan 22, 2016 at 17:29
  • Also how can I have the diagonal in black and fixed (min, max) range for my c values? and also how can I highlight some values in the matrix (i.e. black hollow rectangles)? Commented Jan 22, 2016 at 17:31

1 Answer 1

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2

To my knowledge, ggplot does not let you use multiple colour scales for the same plot. It also makes your graph harder to interpret. However, you can get clever with shapes:

Some preprocessing to handle your data, I'm sure you can avoid some of this when generating your dataset:

cor_matrix1 <- matrix(matrix_values, ncol = 51, nrow = 51)
 dat1 <- melt(cor_matrix1[-52, ])
 cor_matrix2 <- matrix(matrix_values, ncol = 51, nrow = 51)
 dat2 <- melt(cor_matrix2[-52, ])
 dat2$Var1 <- 52 - dat2$Var1
 dat2$Var2 <- 52 - dat2$Var2
 dat1$Class <- "A"
 dat2$Class <- "B"
 dat <- rbind(dat1,dat2)
 dat <- dat[!is.nan(dat$value),]

Instead of using geom_tile, try geom_point. This will give you the flexibility to use shapes. (I.e. an additional dimension on which to segment data):

   ggplot(data = dat, aes(x = Var1, y = Var2)) +
   geom_point(size = 4, aes(color = value, pch = Class)) +
   scale_color_gradientn(values=c(1, .6, .5, .4, 0), colours=c("#00007f", "#1212b2", "cyan", "#b4b4cc", "white")) + 
   geom_abline(slope = -1,intercept = 52 , size = 2) + 
    geom_rect(xmin = 30, xmax = 31, ymin = 30, ymax = 31, color = "red", fill = NA) + 
     theme( axis.title.x = element_blank(),
            axis.title.y = element_blank(),
            panel.background = element_blank())

Which gives:

enter image description here

A couple of things going on here:

  • The pch argument to geom_point sets a different shape for each population
  • geom_abline gives you the black line down the center of the graph (you can also do this with points, but I think this is clearer
  • The geom_rect argument creates the red rectangle. Adjust the mins/maxes as needed and put in as many as you need.

Also, note that negative correlations here are going to white (as per the color scale you defined). I would find this misleading.

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