To calculate spearman rank correlation in R, you can use **cor.test()** function with **method** argument.

The following method shows how you can do it with syntax.

**Method 1: Use cor.test() Function**

```
cor.test(vector1,vector2,method="spearman")
```

The following example shows how to calculate spearman rank correlation in R.

## Using cor.test() Function

Let’s see how we can calculate spearman rank correlation in R using **cor.test()** function:

```
# Create data frame
df <- data.frame(Machine_name=c("A","B","C","D","E","F","G","H"),
Pressure=c(12.39,11.25,12.15,13.48,13.78,12.89,12.21,12.58),
Temperature=c(78,89,85,84,81,79,77,85),
Status=c(TRUE,TRUE,FALSE,TRUE,FALSE,FALSE,TRUE,FALSE))
# Calculate Spearman Rank Correlation
x <- cor.test(df$Pressure,df$Temperature,method="spearman")
# Print Spearman Rank Correlation
print(x)
```

Output:

```
Spearman's rank correlation rho
data: df$Pressure and df$Temperature
S = 109.15, p-value = 0.4713
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.2994066
```

Here the output shows spearman rank correlation between **Pressure** and **Temperature** column of dataframe.