https://github.com/kassambara/ggcorrplot

Visualization of a correlation matrix using ggplot2
https://github.com/kassambara/ggcorrplot

Last synced: 3 months ago
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Visualization of a correlation matrix using ggplot2

• Host: GitHub
• URL: https://github.com/kassambara/ggcorrplot
• Owner: kassambara
• Created: 2016-01-12T16:51:33.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2022-11-07T12:17:21.000Z (over 2 years ago)
• Last Synced: 2024-08-03T21:02:28.637Z (6 months ago)
• Language: R
• Homepage: https://rpkgs.datanovia.com/ggcorrplot/
• Size: 6.14 MB
• Stars: 185
• Watchers: 6
• Forks: 35
• Open Issues: 29
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

• awesome-r-dataviz - ggcorrplot - Visualization of a correlation matrix using ggplot2. [[Tutorial]](http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2) (ggplot / Additional Plot Types)

README

        
---

output: github_document

---

```{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"

)

```

    

[![R build status](https://github.com/kassambara/ggcorrplot/workflows/R-CMD-check/badge.svg)](https://github.com/kassambara/ggcorrplot/actions)

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/ggcorrplot)](https://cran.r-project.org/package=ggcorrplot)

[![CRAN Checks](https://cranchecks.info/badges/summary/ggcorrplot)](https://cran.r-project.org/web/checks/check_results_ggcorrplot.html)

[![Downloads](https://cranlogs.r-pkg.org/badges/ggcorrplot)](https://cran.r-project.org/package=ggcorrplot)

[![Total Downloads](https://cranlogs.r-pkg.org/badges/grand-total/ggcorrplot?color=orange)](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"

)

```