cor_plot {rstatix} | R Documentation |
Visualize Correlation Matrix Using Base Plot
Description
Provide a tibble-friendly framework to visualize a correlation
matrix. Wrapper around the R base function
corrplot()
. Compared to
corrplot()
, it can handle directly the output of the
functions cor_mat() (in rstatix)
, rcorr() (in Hmisc)
,
correlate() (in corrr)
and cor() (in stats)
.
The p-values contained in the outputs of the functions
cor_mat()
and rcorr()
are automatically detected and
used in the visualization.
Usage
cor_plot(
cor.mat,
method = "circle",
type = "full",
palette = NULL,
p.mat = NULL,
significant.level = 0.05,
insignificant = c("cross", "blank"),
label = FALSE,
font.label = list(),
...
)
Arguments
cor.mat |
the correlation matrix to visualize |
method |
Character, the visualization method of correlation matrix to be
used. Currently, it supports seven methods, named The areas of circles or squares show the absolute value of corresponding
correlation coefficients. Method |
type |
Character, |
palette |
character vector containing the color palette. |
p.mat |
matrix of p-value corresponding to the correlation matrix. |
significant.level |
significant level, if the p-value is bigger than
|
insignificant |
character, specialized insignificant correlation coefficients, "cross" (default), "blank". If "blank", wipe away the corresponding glyphs; if "cross", add crosses (X) on corresponding glyphs. |
label |
logical value. If TRUE, shows the correlation coefficient labels. |
font.label |
a list with one or more of the following elements: size
(e.g., 1), color (e.g., "black") and style (e.g., "bold"). Used to
customize the correlation coefficient labels. For example |
... |
additional options not listed (i.e. "tl.cex") here to pass to corrplot. |
See Also
Examples
# Compute correlation matrix
#::::::::::::::::::::::::::::::::::::::::::
cor.mat <- mtcars %>%
select(mpg, disp, hp, drat, wt, qsec) %>%
cor_mat()
# Visualize correlation matrix
#::::::::::::::::::::::::::::::::::::::::::
# Full correlation matrix,
# insignificant correlations are marked by crosses
cor.mat %>% cor_plot()
# Reorder by correlation coefficient
# pull lower triangle and visualize
cor.lower.tri <- cor.mat %>%
cor_reorder() %>%
pull_lower_triangle()
cor.lower.tri %>% cor_plot()
# Change visualization methods
#::::::::::::::::::::::::::::::::::::::::::
cor.lower.tri %>%
cor_plot(method = "pie")
cor.lower.tri %>%
cor_plot(method = "color")
cor.lower.tri %>%
cor_plot(method = "number")
# Show the correlation coefficient: label = TRUE
# Blank the insignificant correlation
#::::::::::::::::::::::::::::::::::::::::::
cor.lower.tri %>%
cor_plot(
method = "color",
label = TRUE,
insignificant = "blank"
)
# Change the color palettes
#::::::::::::::::::::::::::::::::::::::::::
# Using custom color palette
# Require ggpubr: install.packages("ggpubr")
if(require("ggpubr")){
my.palette <- get_palette(c("red", "white", "blue"), 200)
cor.lower.tri %>%
cor_plot(palette = my.palette)
}
# Using RcolorBrewer color palette
if(require("ggpubr")){
my.palette <- get_palette("PuOr", 200)
cor.lower.tri %>%
cor_plot(palette = my.palette)
}