To calculate sample and population variance in R, you can use **var()** function and **sd()** function.

The following methods show how you can do it with syntax.

**Method 1: Use var() Function**

```
var(data)
```

**Method 2: Use var() and length() Function**

```
var(data) * (length(data) - 1) / length(data)
```

The following examples show how to calculate sample and population variance in R.

## Using var() Function

Let’s see how to calculate sample variance using *var()* function:

```
# Create data frame
df <- data.frame(Machine_name=c("A","D","B","C","B","D","C","A"),
Pressure1=c(78.2, 81.21, 71.7, 80.21, 71.7, 81.21, 80.21, 78.2),
Temperature1=c(31, 33, 36, 37, 36, 33, 37, 31),
Status=c(TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE))
# Calculate sample variance
v <- var(df$Pressure1)
# Show sample variance
print(v)
```

Output:

```
[1] 15.65789
```

Here the output shows sample variance for **Pressure** column of dataframe.

## Using var() and length() Function

Let’s calculate population variance in R using **var()** and **length()** function:

```
# Create data frame
df <- data.frame(Pressure=c(78, 81, 71, 80, 71, 81, 80, 78),
Temperature=c(31, 33, 36, 37, 36, 33, 37, 31))
# Calculate sample variance
v <- var(df$Pressure) * (length(df$Pressure) - 1) / length(df$Pressure)
# Print population variance
print(v)
```

Output:

```
[1] 15.25
```

Here the output shows population variance for **Pressure** column of dataframe.