https://github.com/caijun/ggcorrplot2
Implementation of corrplot using ggplot2
https://github.com/caijun/ggcorrplot2
Last synced: 29 days ago
JSON representation
Implementation of corrplot using ggplot2
- Host: GitHub
- URL: https://github.com/caijun/ggcorrplot2
- Owner: caijun
- License: mit
- Created: 2019-12-11T12:11:18.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-05-07T11:16:36.000Z (almost 3 years ago)
- Last Synced: 2025-03-16T10:41:19.682Z (30 days ago)
- Language: R
- Size: 18.9 MB
- Stars: 38
- Watchers: 5
- Forks: 11
- Open Issues: 8
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - caijun/ggcorrplot2 - Implementation of corrplot using ggplot2 (R)
README
---
output: github_document
---
# ggcorrplot2
[](https://travis-ci.org/caijun/ggcorrplot2)
Implementation of corrplot using ggplot2
## Introduction
Reinventing wheels is not what I like doing. [corrplot](https://CRAN.R-project.org/package=corrplot) is a great R package, but I am really tired of customizing the appearance of corrplot, for example, the space between colorbar and its tick labels, the space around the plot that I don't know how to control when writing it to PDF on my macOS. This is most likely because I am more familiar with the Grammar of Graphics implemented in ggplot2 than the base plotting system in R. There are several R packages (e.g., [ggcorrplot](https://github.com/kassambara/ggcorrplot) developed by Alboukadel Kassambara, [ggcorr](https://github.com/briatte/ggcorr) developed by François Briatte) that can visualize a correlation matrix into a [corrgram](https://www.tandfonline.com/doi/abs/10.1198/000313002533) using ggplot2; however, they are unable to visualize a correlation matrix using ellipse and mixed methods. **ggcorrplot2** has implemented only a subset of features of **corrplot** to meet my urgent needs. See examples in the **Getting started** section. More functionality will be added in the future as needed.
## Installation
Get the development version from github:
```r
if (!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("caijun/ggcorrplot2")
```
## Getting started
The `mtcars` dataset will be used to demonstrate the usages of **ggcorrplot2**. Most parameters of **ggcorrplot2** functions are the same as those of **corrplot**. Therefore, it's easy for users to migrate from **corrplot** to **ggcorrplot2**.
```{r, echo = FALSE}
knitr::opts_chunk$set(
dpi = 200,
fig.path = "figs/README-"
)
```
```{r, fig.height=4}
library(ggcorrplot2)
data(mtcars)
# Use corr.test() from psych package to calculate the correlation matrix and
# corresponding p value matrix without adjustment.
library(psych)
ct <- corr.test(mtcars, adjust = "none")
corr <- ct$r
p.mat <- ct$p
# Visualize the correlation matrix
# --------------------------------
# method = "circle" (default)
ggcorrplot(corr)
# method = "square"
ggcorrplot(corr, method = "square")
# method = "ellipse"
ggcorrplot(corr, method = "ellipse")
# method = "number", display the correlation coefficients
ggcorrplot(corr, method = "number")
# Visualize the upper or lower triangle of correlation matrix
# -----------------------------------------------------------
# the upper triangle
ggcorrplot(corr, type = "upper")
# the lower triangle
ggcorrplot(corr, type = "lower")
# Visualize the correlation matrix using mixed methods
# ----------------------------------------------------
# default: upper = "circle", lower = "number"
ggcorrplot.mixed(corr)
# upper = "ellipse", lower = "number"
ggcorrplot.mixed(corr, upper = "ellipse", lower = "number")
# Combine correlogram with the significance test
# ----------------------------------------------
# Insignificant coefficients according to the default significant level
# (sig.lvl = 0.05) are indicated by X by default.
ggcorrplot.mixed(corr, upper = "ellipse", lower = "number", p.mat = p.mat)
# Leave blank on insignificant coefficients
ggcorrplot.mixed(corr, upper = "ellipse", lower = "number", p.mat = p.mat,
insig = "blank")
# Label significant coefficients with asterisks (*, default) denoting the significance level
ggcorrplot.mixed(corr, upper = "ellipse", lower = "number", p.mat = p.mat,
insig = "label_sig", sig.lvl = c(0.05, 0.01, 0.001))
# Label significant coefficients with varying number of + denoting the significance level
(p <- ggcorrplot.mixed(corr, upper = "ellipse", lower = "number", p.mat = p.mat,
insig = "label_sig", sig.lvl = c(0.05, 0.01, 0.001), pch = "+",
pch.cex = 4))
```
The above examples reproduce some features of **corrplot**. In the following example, the added advantages of implementing **corrplot** using ggplot2, such as customizing the appearance of corrgram, combining a corrgram with other plots (including non-corrgrams) into one plot using [cowplot](https://github.com/wilkelab/cowplot), are demonstrated.
```{r, fig.width=9, fig.height=5, fig.align="center", message=FALSE}
# Customize the appearance of corrplot using functions from ggplot2
library(ggplot2)
# Use different color palette
col1 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "white",
"cyan", "#007FFF", "blue", "#00007F"))
# Change the colorbar direction to horizontal and place it at the bottom
# As mixed methods are used, there are two scales: color filled in ellipse and
# number color
p <- p + scale_fill_gradientn(colours = col1(10), limits = c(-1, 1),
guide = guide_colorbar(
direction = "horizontal",
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 15,
barheight = 1.5)) +
scale_colour_gradientn(colours = col1(10), limits = c(-1, 1),
guide = guide_colorbar(
direction = "horizontal",
title = "",
nbin = 1000,
ticks.colour = "black",
frame.colour = "black",
barwidth = 15,
barheight = 1.5)) +
theme(legend.position = "bottom")
p
# Combine a lower corrgram and a mixed corrgram side by side with a shared colorbar on the bottom
# NAs are allowed in correlation matrix and p value matrix, which are labelled as NA
# Assign NAs
rid <- c(2, 3, 1, 2)
cid <- c(3, 2, 2, 1)
pos <- cbind(rid, cid)
corr[pos] <- NA
p.mat[pos] <- NA
# a lower corrgram
p1 <- ggcorrplot(corr, type = "lower", method = "square", p.mat = p.mat,
insig = "label_sig", sig.lvl = c(0.05, 0.01, 0.001), show.diag = FALSE)
# a mixed corrgram
p2 <- ggcorrplot.mixed(corr, upper = "ellipse", lower = "number", p.mat = p.mat,
insig = "label_sig", sig.lvl = c(0.05, 0.01, 0.001),
pch = "+", pch.cex = 4)
library(cowplot)
prow <- plot_grid(p1 + theme(legend.position = "none"),
p2 + theme(legend.position = "none"),
rel_widths = c(1, 1), nrow = 1, align = 'hv',
labels = c("(a)", "(b)"), label_x = 0, label_y = 1)
# Extract the legend from the first corrgram
legend <- get_legend(p1)
# Add the legend to the bottom of the plot row we made earlier.
p <- cowplot::plot_grid(prow, legend, ncol = 1, rel_heights = c(1, 0.15))
p
```
## Citation
To cite the 'ggcorrplot2' package in publications use:
```
Jun Cai, Granville Matheson and Samson Leonard Daniël (2022). ggcorrplot2: Visualize a Correlation Matrix using ggplot2. R package version 0.1.2.
```
A BibTeX entry for LaTeX users is
```
@Manual{,
title = {ggcorrplot2: Visualize a Correlation Matrix using ggplot2},
author = {Jun Cai and Granville Matheson and Samson Leonard Daniël},
year = {2022},
note = {R package version 0.1.2},
url = {https://github.com/caijun/ggcorrplot2},
}
```
The above citation information can be generated by calling `citation("ggcorrplot2")` in R.
## Contact
Bugs and feature requests can be filed to
. Pull requests are also welcome.