# How to Create a Correlation Heatmap in R (With Example)

You can use the following basic syntax to create a correlation heatmap in R:

```#calculate correlation between each pairwise combination of variables
cor_df <- round(cor(df), 2)

#melt the data frame
melted_cormat <- melt(cor_df)

#create correlation heatmap
ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
geom_text(aes(Var2, Var1, label = value), size = 5) +
scale_fill_gradient2(low = "blue", high = "red",
limit = c(-1,1), name="Correlation") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank())
```

The following example shows how to use this syntax in practice.

## Example: Create Correlation Heatmap in R

Suppose we have the following data frame in R that shows various statistics for eight different basketball players:

```#create data frame
df <- data.frame(points=c(22, 25, 30, 16, 14, 18, 29, 22),
assists=c(4, 4, 5, 7, 8, 6, 7, 12),
rebounds=c(10, 7, 7, 6, 8, 5, 4, 3),
blocks=c(12, 4, 4, 6, 5, 3, 8, 5))

#view data frame
df

points assists rebounds blocks
1     22       4       10     12
2     25       4        7      4
3     30       5        7      4
4     16       7        6      6
5     14       8        8      5
6     18       6        5      3
7     29       7        4      8
8     22      12        3      5```

Suppose we would like to create a correlation heatmap to visualize the correlation coefficient between each pairwise combination of variables in the data frame.

Before we create the correlation heatmap, we must first calculate the correlation coefficient between each variable using cor() and then transform the results into a usable format using the melt() function from the reshape2 package:

```library(reshape2)

#calculate correlation coefficients, rounded to 2 decimal places
cor_df <- round(cor(df), 2)

#melt the data frame
melted_cor <- melt(cor_df)

#view head of melted data frame

Var1    Var2 value
1   points  points  1.00
2  assists  points -0.27
3 rebounds  points -0.16
4   blocks  points  0.10
5   points assists -0.27
6  assists assists  1.00```

Next, we can use the geom_tile() function from the ggplot2 package to create correlation heatmap:

```library(ggplot2)

#create correlation heatmap
ggplot(data = melted_cor, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
geom_text(aes(Var2, Var1, label = value), size = 5) +
scale_fill_gradient2(low = "blue", high = "red",
limit = c(-1,1), name="Correlation") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank())``` The result is a correlation heatmap that allows us to visualize the correlation coefficient between each pairwise combination of variables.

In this particular heatmap, the correlation coefficients take on the following colors:

• Blue if they are close to -1
• White if they are close to 0
• Red if they are close to 1

Feel free to use whatever colors you’d like for the low and high arguments within the scale_fill_gradient2() function.

For example, you could instead use “red” for the low value and “green” for the high value:

```library(ggplot2)

#create correlation heatmap
ggplot(data = melted_cor, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
geom_text(aes(Var2, Var1, label = value), size = 5) +
scale_fill_gradient2(low = "red", high = "green",
limit = c(-1,1), name="Correlation") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank())``` Note: You can also specify hex color codes to use if you’d like even more control over the exact colors in the correlation heatmap.