This article describes how to plot a **correlation network in R** using the corrr package.

Related article: Easy Correlation Matrix Analysis in R Using Corrr Package

Contents:

## Load required R packages

`tidyverse`

: easy data manipulation and visualization`corrr`

: correlation matrix analysis

```
library(tidyverse)
library(corrr)
```

## Data

```
data("airquality")
head(airquality)
```

```
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
```

## Compute correlation matrix

```
res.cor <- correlate(airquality)
res.cor
```

```
## # A tibble: 6 x 7
## rowname Ozone Solar.R Wind Temp Month Day
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Ozone NA 0.348 -0.602 0.698 0.165 -0.0132
## 2 Solar.R 0.348 NA -0.0568 0.276 -0.0753 -0.150
## 3 Wind -0.602 -0.0568 NA -0.458 -0.178 0.0272
## 4 Temp 0.698 0.276 -0.458 NA 0.421 -0.131
## 5 Month 0.165 -0.0753 -0.178 0.421 NA -0.00796
## 6 Day -0.0132 -0.150 0.0272 -0.131 -0.00796 NA
```

`fashion()`

the correlations for pleasant viewing:

`res.cor %>% fashion()`

```
## rowname Ozone Solar.R Wind Temp Month Day
## 1 Ozone .35 -.60 .70 .16 -.01
## 2 Solar.R .35 -.06 .28 -.08 -.15
## 3 Wind -.60 -.06 -.46 -.18 .03
## 4 Temp .70 .28 -.46 .42 -.13
## 5 Month .16 -.08 -.18 .42 -.01
## 6 Day -.01 -.15 .03 -.13 -.01
```

## Create a correlation network

The R function `network_plot()`

can be used to visualize and explore correlations.

```
airquality %>% correlate() %>%
network_plot(min_cor = 0.3)
```

The option `min_cor`

indicates the required minimum correlation value for a correlation to be plotted.

**Each point reprents a variable**. Variable that are highly correlated are clustered together. The positioning of variables is handled by multidimensional scaling of the absolute values of the correlations.

For example, it can be seen from the above plot that the variables `Ozone`

, `Wind`

and `Temp`

are clustering together (which makes sense).

**Each path represents a correlation** between the two variables that it joins. Blue color represents a positive correlation, and a red color corresponds to a negative correlation.

The width and transparency of the path represent the strength of the correlation (wider and less transparent = stronger correlation).

For example, it can be seen that the positive correlation between `Ozone`

and `Temp`

is stronger than the positive correlation between `Ozone`

and `Solar.R`

.

## Cleaning up the correlation network

We can clean this up by increasing the `min_cor`

, thus plotting fewer correlation paths:

```
mtcars %>% correlate() %>%
network_plot(min_cor = .7)
```

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