Introducing correlation coefficient
We will now visualize our correlation matrix by adding the correlation coefficient using the ggcorrplot function and providing correlation matrix, hc.order, type, and lower variables as arguments.
Syntax :
ggcorrplot(correlation_matrix, hc.order = TRUE, type = “lower”, lab = TRUE)
Parameters :
- correlation_matrix : The correlation matrix used for visualization.
- hc.order : If it is true, then the correlation matrix will be ordered.
- type : It is the arrangement of the character to display.
- lab : It is a logical value. If it is true, then we add the correlation coefficient to our matrix.
Example: Introducing correlation coefficient
R
library (ggplot2) library (ggcorrplot) # Reading the data data (USArrests) # Computing correlation matrix correlation_matrix <- round ( cor (USArrests),1) # Computing correlation matrix with p-values corrp.mat <- cor_pmat (USArrests) # Adding the correlation coefficient ggcorrplot (correlation_matrix, hc.order = TRUE , type = "lower" , lab = TRUE ) |
Output :
Visualization of a correlation matrix using ggplot2 in R
In this article, we will discuss how to visualize a correlation matrix using ggplot2 package in R programming language.
In order to do this, we will install a package called ggcorrplot package. With the help of this package, we can easily visualize a correlation matrix. We can also compute a matrix of correlation p-values by using a function that is present in this package. The corr_pmat() is used for computing the correlation matrix of p-values and the ggcorrplot() is used for displaying the correlation matrix using ggplot.
Syntax :
corr_pmat(x,..)
Where x is the dataframe or the matrix
Syntax:
ggcorrplot(corr, method = c(“circle”, “square”), type = c(“full”, “lower”, “upper”), title = “”, ggtheme=ggplot2::theme_minimal, 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”, p.mat = NULL,.. )