This articles describes how to create an **interactive correlation matrix heatmap in R**. You will learn two different approaches:

- Using the
*heatmaply*R package - Using the combination of the
`ggcorrplot`

and the`plotly`

R packages.

Contents:

## Prerequisites

Install required R packages:

```
install.packages("plotly")
install.packages("heatmaply")
install.packages("ggcorrplot")
```

## Data preparation

`df <- mtcars`

## Correlation heatmaps using heatmaply

### Load R packages

`library(heatmaply)`

### Basic correlation matrix heatmap

Use the arguments `k_col`

and `k_row`

to specify the desired number of groups by which to color the dendrogram’s branches in the columns and rows, respectively.

```
heatmaply_cor(
cor(df),
xlab = "Features",
ylab = "Features",
k_col = 2,
k_row = 2
)
```

### Change the point size according to the correlation test p-values

```
# Compute correlation coefficients
cor.coef <- cor(df)
# Compute correlation p-values
cor.test.p <- function(x){
FUN <- function(x, y) cor.test(x, y)[["p.value"]]
z <- outer(
colnames(x),
colnames(x),
Vectorize(function(i,j) FUN(x[,i], x[,j]))
)
dimnames(z) <- list(colnames(x), colnames(x))
z
}
p <- cor.test.p(df)
```

```
# Create the heatmap
heatmaply_cor(
cor.coef,
node_type = "scatter",
point_size_mat = -log10(p),
point_size_name = "-log10(p-value)",
label_names = c("x", "y", "Correlation")
)
```

## Correlation heatmaps using ggcorrplot

### Load R packages

`library(ggcorrplot)`

### Static heatmap of the correlation matrix

```
# Compute a correlation matrix
corr <- round(cor(df), 1)
# Compute a matrix of correlation p-values
p.mat <- cor_pmat(df)
# Visualize the lower triangle of the correlation matrix
# Barring the no significant coefficient
corr.plot <- ggcorrplot(
corr, hc.order = TRUE, type = "lower", outline.col = "white",
p.mat = p.mat
)
corr.plot
```

### Make the correlation heatmap interactive

```
library(plotly)
ggplotly(corr.plot)
```

`

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