Example - Matrix Highlighting - Correlation Matrix

library(dplyr)
library(ggplot2)
library(emphatic)
hl_opt_global(dark_mode = FALSE)

The cor() function can calculate the correlation between the variables in a data.frame.

A question which is often asked of this correlation information, is which variables have a strong correlation (positive or negative)?

Standard Correlation Matrix

Hunting for the strong and weak correlations in the raw cor() output is difficult.

cor(mtcars)
#>             mpg        cyl       disp         hp        drat         wt
#> mpg   1.0000000 -0.8521620 -0.8475514 -0.7761684  0.68117191 -0.8676594
#> cyl  -0.8521620  1.0000000  0.9020329  0.8324475 -0.69993811  0.7824958
#> disp -0.8475514  0.9020329  1.0000000  0.7909486 -0.71021393  0.8879799
#> hp   -0.7761684  0.8324475  0.7909486  1.0000000 -0.44875912  0.6587479
#> drat  0.6811719 -0.6999381 -0.7102139 -0.4487591  1.00000000 -0.7124406
#> wt   -0.8676594  0.7824958  0.8879799  0.6587479 -0.71244065  1.0000000
#> qsec  0.4186840 -0.5912421 -0.4336979 -0.7082234  0.09120476 -0.1747159
#> vs    0.6640389 -0.8108118 -0.7104159 -0.7230967  0.44027846 -0.5549157
#> am    0.5998324 -0.5226070 -0.5912270 -0.2432043  0.71271113 -0.6924953
#> gear  0.4802848 -0.4926866 -0.5555692 -0.1257043  0.69961013 -0.5832870
#> carb -0.5509251  0.5269883  0.3949769  0.7498125 -0.09078980  0.4276059
#>             qsec         vs          am       gear        carb
#> mpg   0.41868403  0.6640389  0.59983243  0.4802848 -0.55092507
#> cyl  -0.59124207 -0.8108118 -0.52260705 -0.4926866  0.52698829
#> disp -0.43369788 -0.7104159 -0.59122704 -0.5555692  0.39497686
#> hp   -0.70822339 -0.7230967 -0.24320426 -0.1257043  0.74981247
#> drat  0.09120476  0.4402785  0.71271113  0.6996101 -0.09078980
#> wt   -0.17471588 -0.5549157 -0.69249526 -0.5832870  0.42760594
#> qsec  1.00000000  0.7445354 -0.22986086 -0.2126822 -0.65624923
#> vs    0.74453544  1.0000000  0.16834512  0.2060233 -0.56960714
#> am   -0.22986086  0.1683451  1.00000000  0.7940588  0.05753435
#> gear -0.21268223  0.2060233  0.79405876  1.0000000  0.27407284
#> carb -0.65624923 -0.5696071  0.05753435  0.2740728  1.00000000

Correlation matrix with highlighting using {emphatic}

One way to highlight the correlations would be to colour the strong negative correlations in red, and the strong positive correlations in blue.

For this type of colouring, the ggplot2 colour scale scale_colour_gradient2() is a perfect fit.

Issue: everything is now coloured - including locations with low correlation.

cor(mtcars) %>%
  hl_mat(scale_colour_gradient2()) 

               mpg         cyl        disp          hp        drat          wt        qsec          vs          am        gear        carb
mpg     1.00000000 -0.85216196 -0.84755138 -0.77616837  0.68117191 -0.86765938  0.41868403  0.66403892  0.59983243  0.48028476 -0.55092507
cyl    -0.85216196  1.00000000  0.90203287  0.83244745 -0.69993811  0.78249579 -0.59124207 -0.81081180 -0.52260705 -0.49268660  0.52698829
disp   -0.84755138  0.90203287  1.00000000  0.79094859 -0.71021393  0.88797992 -0.43369788 -0.71041589 -0.59122704 -0.55556920  0.39497686
hp     -0.77616837  0.83244745  0.79094859  1.00000000 -0.44875912  0.65874789 -0.70822339 -0.72309674 -0.24320426 -0.12570426  0.74981247
drat    0.68117191 -0.69993811 -0.71021393 -0.44875912  1.00000000 -0.71244065  0.09120476  0.44027846  0.71271113  0.69961013 -0.09078980
wt     -0.86765938  0.78249579  0.88797992  0.65874789 -0.71244065  1.00000000 -0.17471588 -0.55491568 -0.69249526 -0.58328700  0.42760594
qsec    0.41868403 -0.59124207 -0.43369788 -0.70822339  0.09120476 -0.17471588  1.00000000  0.74453544 -0.22986086 -0.21268223 -0.65624923
vs      0.66403892 -0.81081180 -0.71041589 -0.72309674  0.44027846 -0.55491568  0.74453544  1.00000000  0.16834512  0.20602335 -0.56960714
am      0.59983243 -0.52260705 -0.59122704 -0.24320426  0.71271113 -0.69249526 -0.22986086  0.16834512  1.00000000  0.79405876  0.05753435
gear    0.48028476 -0.49268660 -0.55556920 -0.12570426  0.69961013 -0.58328700 -0.21268223  0.20602335  0.79405876  1.00000000  0.27407284
carb   -0.55092507  0.52698829  0.39497686  0.74981247 -0.09078980  0.42760594 -0.65624923 -0.56960714  0.05753435  0.27407284  1.00000000

Apply colouring with {emphatic} to only locations with high correlation

The selection of locations to colour is controlled with the selection argument to hl_mat().

The result still isn’t great because everything is now coloured - including locations with low correlation, as well as the diagonal (which is uninformative)

Issue: the diagonal of the matrix is still highlighted, but it is totally uninformative.

cor(mtcars) %>%
  hl_mat(scale_colour_gradient2(), selection = abs(.x) > 0.7) 

               mpg         cyl        disp          hp        drat          wt        qsec          vs          am        gear        carb
mpg     1.00000000 -0.85216196 -0.84755138 -0.77616837  0.68117191 -0.86765938  0.41868403  0.66403892  0.59983243  0.48028476 -0.55092507
cyl    -0.85216196  1.00000000  0.90203287  0.83244745 -0.69993811  0.78249579 -0.59124207 -0.81081180 -0.52260705 -0.49268660  0.52698829
disp   -0.84755138  0.90203287  1.00000000  0.79094859 -0.71021393  0.88797992 -0.43369788 -0.71041589 -0.59122704 -0.55556920  0.39497686
hp     -0.77616837  0.83244745  0.79094859  1.00000000 -0.44875912  0.65874789 -0.70822339 -0.72309674 -0.24320426 -0.12570426  0.74981247
drat    0.68117191 -0.69993811 -0.71021393 -0.44875912  1.00000000 -0.71244065  0.09120476  0.44027846  0.71271113  0.69961013 -0.09078980
wt     -0.86765938  0.78249579  0.88797992  0.65874789 -0.71244065  1.00000000 -0.17471588 -0.55491568 -0.69249526 -0.58328700  0.42760594
qsec    0.41868403 -0.59124207 -0.43369788 -0.70822339  0.09120476 -0.17471588  1.00000000  0.74453544 -0.22986086 -0.21268223 -0.65624923
vs      0.66403892 -0.81081180 -0.71041589 -0.72309674  0.44027846 -0.55491568  0.74453544  1.00000000  0.16834512  0.20602335 -0.56960714
am      0.59983243 -0.52260705 -0.59122704 -0.24320426  0.71271113 -0.69249526 -0.22986086  0.16834512  1.00000000  0.79405876  0.05753435
gear    0.48028476 -0.49268660 -0.55556920 -0.12570426  0.69961013 -0.58328700 -0.21268223  0.20602335  0.79405876  1.00000000  0.27407284
carb   -0.55092507  0.52698829  0.39497686  0.74981247 -0.09078980  0.42760594 -0.65624923 -0.56960714  0.05753435  0.27407284  1.00000000

Change text contrast

Since the magnitude of the correlations is now indicated by colour, we can de-emphasise the text by reducing its contrast with the fill colour.

Also, the diagonals are now excluded from the colouring i.e. highlighting is now further limited to whererow(.x) != col(.x)

cor(mtcars) %>%
  hl_mat(scale_colour_gradient2(), selection = abs(.x) > 0.7 & row(.x) != col(.x)) %>%
  hl_opt(text_contrast = 0.2)

               mpg         cyl        disp          hp        drat          wt        qsec          vs          am        gear        carb
mpg     1.00000000 -0.85216196 -0.84755138 -0.77616837  0.68117191 -0.86765938  0.41868403  0.66403892  0.59983243  0.48028476 -0.55092507
cyl    -0.85216196  1.00000000  0.90203287  0.83244745 -0.69993811  0.78249579 -0.59124207 -0.81081180 -0.52260705 -0.49268660  0.52698829
disp   -0.84755138  0.90203287  1.00000000  0.79094859 -0.71021393  0.88797992 -0.43369788 -0.71041589 -0.59122704 -0.55556920  0.39497686
hp     -0.77616837  0.83244745  0.79094859  1.00000000 -0.44875912  0.65874789 -0.70822339 -0.72309674 -0.24320426 -0.12570426  0.74981247
drat    0.68117191 -0.69993811 -0.71021393 -0.44875912  1.00000000 -0.71244065  0.09120476  0.44027846  0.71271113  0.69961013 -0.09078980
wt     -0.86765938  0.78249579  0.88797992  0.65874789 -0.71244065  1.00000000 -0.17471588 -0.55491568 -0.69249526 -0.58328700  0.42760594
qsec    0.41868403 -0.59124207 -0.43369788 -0.70822339  0.09120476 -0.17471588  1.00000000  0.74453544 -0.22986086 -0.21268223 -0.65624923
vs      0.66403892 -0.81081180 -0.71041589 -0.72309674  0.44027846 -0.55491568  0.74453544  1.00000000  0.16834512  0.20602335 -0.56960714
am      0.59983243 -0.52260705 -0.59122704 -0.24320426  0.71271113 -0.69249526 -0.22986086  0.16834512  1.00000000  0.79405876  0.05753435
gear    0.48028476 -0.49268660 -0.55556920 -0.12570426  0.69961013 -0.58328700 -0.21268223  0.20602335  0.79405876  1.00000000  0.27407284
carb   -0.55092507  0.52698829  0.39497686  0.74981247 -0.09078980  0.42760594 -0.65624923 -0.56960714  0.05753435  0.27407284  1.00000000