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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
# out.width = "100%",
# dpi = 300,
fig.path = "tools/README-",
fig.cap = "ggcorrplot: visualize correlation matrix using ggplot2"
)
```
[](https://github.com/kassambara/ggcorrplot/actions)
[](https://cran.r-project.org/package=ggcorrplot)
[](https://cran.r-project.org/web/checks/check_results_ggcorrplot.html)
[](https://cran.r-project.org/package=ggcorrplot)
[](https://cranlogs.r-pkg.org/badges/grand-total/ggcorrplot)
# ggcorrplot: Visualization of a correlation matrix using ggplot2
The **ggcorrplot** package can be used to **visualize easily** a **correlation matrix** using **ggplot2**. It provides a solution for **reordering** the correlation matrix and displays the **significance level** on the correlogram. It includes also a function for computing a matrix of **correlation p-values**.
Find out more at http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2.
## Installation and loading
ggcorrplot can be installed from `CRAN` as follow:
```{r, eval = FALSE}
install.packages("ggcorrplot")
```
Or, install the latest version from GitHub:
```{r, eval = FALSE}
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
```
```{r, message = FALSE, warning = FALSE}
# Loading
library(ggcorrplot)
```
## Getting started
### Compute a correlation matrix
The *mtcars* data set will be used in the following R code. The function
**cor_pmat()** [in **ggcorrplot**] computes a matrix of correlation p-values.
```{r, fig.show = "asis"}
# Compute a correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
head(corr[, 1:6])
# Compute a matrix of correlation p-values
p.mat <- cor_pmat(mtcars)
head(p.mat[, 1:4])
```
## Correlation matrix visualization
```{r demo-ggcorrplot, fig.show = "asis", fig.width=5, fig.height=5}
# Visualize the correlation matrix
# --------------------------------
# method = "square" (default)
ggcorrplot(corr)
# method = "circle"
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 upper 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)
# 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"
)
```