R comes with a number of built-in functions to compute common descriptive statistics like the mean, median, variance, standard deviation, etc. In this tutorial, we will look at how to get the variance of values in an R vector with the help of some examples.
How to get the variance of values in a vector in R?
You can use the R
var() function to get the variance of values in a vector. Pass the vector as an argument to the function. The following is the syntax –
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# variance of values in a vector var(x, na.rm=FALSE)
The following are the arguments that you can give to the
var() function in R.
- x – The vector for which you want to compute the variance.
- na.rm – (Optional argument) Indicates whether to remove missing values before computing the variance. It is
The function returns the sample variance of values in the passed vector.
If you want to get the population variance instead (considering vector values as population values) multiply the result from the
var() function by
Let’s look at some examples of using the above method to get the variance of a vector.
Variance of values in a numeric vector
Let’s create a vector of numbers (and without any
NA values) and apply the
# create a vector vec <- c(1, 2, 3, 4, 5) # variance of values in the vector var(vec)
We get the sample variance of the values in the above vector as 2.5. If you want to get the population variance, multiply this result by
# population variance n <- length(vec) var(vec) * (n-1)/n
The population variance of the above vector is 2.
Variance of values in a vector with
What would happen if there are some
NA present values in the vector?
Let’s find out.
First, we will create a vector with some
NA values and then apply the
var() function without any additional arguments.
# create a vector with NA values vec <- c(1, 2, NA, 3, 4, 5, NA) # variance of values in the vector var(vec)
You can see that we get
NA as the output. This is because performing an arithmetic operation with
NA results in an
NA in R.
You can pass
TRUE to the
na.rm parameter of the
var() function to exclude missing values when computing the variance of a vector.
# create a vector with NA values vec <- c(1, 2, NA, 3, 4, 5, NA) # variance of values in the vector var(vec, na.rm = TRUE)
Now we get the variance of the values in the above vector as 2.5.
Standard deviation of values in a Vector
Standard deviation is defined as the square root of the variance. You can use the
sqrt() function in R on the result of the
var() function to get the standard deviation of a vector. Let’s look at an example.
# create a vector vec <- c(1, 2, 3, 4, 5) # standard deviation of values in the vector sqrt(var(vec))
Here, we get the standard deviation of the values in the above vector as 1.5811, which is the square root of the variance, 2.5. Alternatively, you can also use the
sd() function in R to compute the standard deviation directory without using the
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