• ggcorrplot(): A graphical display of a correlation matrix using ggplot2.

  • cor_pmat(): Compute a correlation matrix p-values.

ggcorrplot(corr, method = c("square", "circle"), type = c("full",
  "lower", "upper"), ggtheme = ggplot2::theme_minimal, title = "",
  show.legend = TRUE, legend.title = "Corr", show.diag = FALSE,
  colors = c("blue", "white", "red"), outline.color = "gray",
  hc.order = FALSE, hc.method = "complete", lab = FALSE,
  lab_col = "black", lab_size = 4, p.mat = NULL, sig.level = 0.05,
  insig = c("pch", "blank"), pch = 4, pch.col = "black",
  pch.cex = 5, tl.cex = 12, tl.col = "black", tl.srt = 45,
  digits = 2)

cor_pmat(x, ...)

Arguments

corr

the correlation matrix to visualize

method

character, the visualization method of correlation matrix to be used. Allowed values are "square" (default), "circle".

type

character, "full" (default), "lower" or "upper" display.

ggtheme

ggplot2 function or theme object. Default value is `theme_minimal`. Allowed values are the official ggplot2 themes including theme_gray, theme_bw, theme_minimal, theme_classic, theme_void, .... Theme objects are also allowed (e.g., `theme_classic()`).

title

character, title of the graph.

show.legend

logical, if TRUE the legend is displayed.

legend.title

a character string for the legend title. lower triangular, upper triangular or full matrix.

show.diag

logical, whether display the correlation coefficients on the principal diagonal.

colors

a vector of 3 colors for low, mid and high correlation values.

outline.color

the outline color of square or circle. Default value is "gray".

hc.order

logical value. If TRUE, correlation matrix will be hc.ordered using hclust function.

hc.method

the agglomeration method to be used in hclust (see ?hclust).

lab

logical value. If TRUE, add correlation coefficient on the plot.

lab_col, lab_size

size and color to be used for the correlation coefficient labels. used when lab = TRUE.

p.mat

matrix of p-value. If NULL, arguments sig.level, insig, pch, pch.col, pch.cex is invalid.

sig.level

significant level, if the p-value in p-mat is bigger than sig.level, then the corresponding correlation coefficient is regarded as insignificant.

insig

character, specialized insignificant correlation coefficients, "pch" (default), "blank". If "blank", wipe away the corresponding glyphs; if "pch", add characters (see pch for details) on corresponding glyphs.

pch

add character on the glyphs of insignificant correlation coefficients (only valid when insig is "pch"). Default value is 4.

pch.col, pch.cex

the color and the cex (size) of pch (only valid when insig is "pch").

tl.cex, tl.col, tl.srt

the size, the color and the string rotation of text label (variable names).

digits

Decides the number of decimal digits to be displayed (Default: `2`).

x

numeric matrix or data frame

...

other arguments to be passed to the function cor.test.

Value

  • ggcorrplot(): Returns a ggplot2

  • cor_pmat(): Returns a matrix containing the p-values of correlations

Examples

# Compute a correlation matrix data(mtcars) corr <- round(cor(mtcars), 1) corr
#> mpg cyl disp hp drat wt qsec vs am gear carb #> mpg 1.0 -0.9 -0.8 -0.8 0.7 -0.9 0.4 0.7 0.6 0.5 -0.6 #> cyl -0.9 1.0 0.9 0.8 -0.7 0.8 -0.6 -0.8 -0.5 -0.5 0.5 #> disp -0.8 0.9 1.0 0.8 -0.7 0.9 -0.4 -0.7 -0.6 -0.6 0.4 #> hp -0.8 0.8 0.8 1.0 -0.4 0.7 -0.7 -0.7 -0.2 -0.1 0.7 #> drat 0.7 -0.7 -0.7 -0.4 1.0 -0.7 0.1 0.4 0.7 0.7 -0.1 #> wt -0.9 0.8 0.9 0.7 -0.7 1.0 -0.2 -0.6 -0.7 -0.6 0.4 #> qsec 0.4 -0.6 -0.4 -0.7 0.1 -0.2 1.0 0.7 -0.2 -0.2 -0.7 #> vs 0.7 -0.8 -0.7 -0.7 0.4 -0.6 0.7 1.0 0.2 0.2 -0.6 #> am 0.6 -0.5 -0.6 -0.2 0.7 -0.7 -0.2 0.2 1.0 0.8 0.1 #> gear 0.5 -0.5 -0.6 -0.1 0.7 -0.6 -0.2 0.2 0.8 1.0 0.3 #> carb -0.6 0.5 0.4 0.7 -0.1 0.4 -0.7 -0.6 0.1 0.3 1.0
# Compute a matrix of correlation p-values p.mat <- cor_pmat(mtcars) p.mat
#> mpg cyl disp hp drat #> mpg 0.000000e+00 6.112687e-10 9.380327e-10 1.787835e-07 1.776240e-05 #> cyl 6.112687e-10 0.000000e+00 1.802838e-12 3.477861e-09 8.244636e-06 #> disp 9.380327e-10 1.802838e-12 0.000000e+00 7.142679e-08 5.282022e-06 #> hp 1.787835e-07 3.477861e-09 7.142679e-08 0.000000e+00 9.988772e-03 #> drat 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03 0.000000e+00 #> wt 1.293959e-10 1.217567e-07 1.222320e-11 4.145827e-05 4.784260e-06 #> qsec 1.708199e-02 3.660533e-04 1.314404e-02 5.766253e-06 6.195826e-01 #> vs 3.415937e-05 1.843018e-08 5.235012e-06 2.940896e-06 1.167553e-02 #> am 2.850207e-04 2.151207e-03 3.662114e-04 1.798309e-01 4.726790e-06 #> gear 5.400948e-03 4.173297e-03 9.635921e-04 4.930119e-01 8.360110e-06 #> carb 1.084446e-03 1.942340e-03 2.526789e-02 7.827810e-07 6.211834e-01 #> wt qsec vs am gear #> mpg 1.293959e-10 1.708199e-02 3.415937e-05 2.850207e-04 5.400948e-03 #> cyl 1.217567e-07 3.660533e-04 1.843018e-08 2.151207e-03 4.173297e-03 #> disp 1.222320e-11 1.314404e-02 5.235012e-06 3.662114e-04 9.635921e-04 #> hp 4.145827e-05 5.766253e-06 2.940896e-06 1.798309e-01 4.930119e-01 #> drat 4.784260e-06 6.195826e-01 1.167553e-02 4.726790e-06 8.360110e-06 #> wt 0.000000e+00 3.388683e-01 9.798492e-04 1.125440e-05 4.586601e-04 #> qsec 3.388683e-01 0.000000e+00 1.029669e-06 2.056621e-01 2.425344e-01 #> vs 9.798492e-04 1.029669e-06 0.000000e+00 3.570439e-01 2.579439e-01 #> am 1.125440e-05 2.056621e-01 3.570439e-01 0.000000e+00 5.834043e-08 #> gear 4.586601e-04 2.425344e-01 2.579439e-01 5.834043e-08 0.000000e+00 #> carb 1.463861e-02 4.536949e-05 6.670496e-04 7.544526e-01 1.290291e-01 #> carb #> mpg 1.084446e-03 #> cyl 1.942340e-03 #> disp 2.526789e-02 #> hp 7.827810e-07 #> drat 6.211834e-01 #> wt 1.463861e-02 #> qsec 4.536949e-05 #> vs 6.670496e-04 #> am 7.544526e-01 #> gear 1.290291e-01 #> carb 0.000000e+00
# Visualize the correlation matrix # -------------------------------- # method = "square" or "circle" ggcorrplot(corr)
ggcorrplot(corr, method = "circle")
# Reordering the correlation matrix # -------------------------------- # using hierarchical clustering ggcorrplot(corr, hc.order = TRUE, outline.color = "white")
# Types of correlogram layout # -------------------------------- # Get the lower triangle ggcorrplot(corr, hc.order = TRUE, type = "lower", outline.color = "white" )
# Get the upeper triangle ggcorrplot(corr, hc.order = TRUE, type = "upper", outline.color = "white" )
# Change colors and theme # -------------------------------- # Argument colors ggcorrplot(corr, hc.order = TRUE, type = "lower", outline.color = "white", ggtheme = ggplot2::theme_gray, colors = c("#6D9EC1", "white", "#E46726") )
# Add correlation coefficients # -------------------------------- # argument lab = TRUE ggcorrplot(corr, hc.order = TRUE, type = "lower", lab = TRUE, ggtheme = ggplot2::theme_dark(), )
# Add correlation significance level # -------------------------------- # Argument p.mat # Barring the no significant coefficient ggcorrplot(corr, hc.order = TRUE, type = "lower", p.mat = p.mat )
# Leave blank on no significant coefficient ggcorrplot(corr, p.mat = p.mat, hc.order = TRUE, type = "lower", insig = "blank" )
# Changing number of digits for correlation coeffcient # -------------------------------- ggcorrplot(cor(mtcars), type = "lower", insig = "blank", lab = TRUE, digits = 3 )