Description Usage Arguments Value Examples
ggcorrplot(): A graphical display of a correlation matrix using ggplot2.
cor_pmat(): Compute a correlation matrix p-values.
1 2 3 4 5 6 7 8 9 10 11 | 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, ...)
|
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. |
ggcorrplot(): Returns a ggplot2
cor_pmat(): Returns a matrix containing the p-values of correlations
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # Compute a correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
corr
# Compute a matrix of correlation p-values
p.mat <- cor_pmat(mtcars)
p.mat
# 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
)
|
Loading required package: ggplot2
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
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
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