The R programming language comes with a number of helpful functions to work with the data stored in data structures like vectors, lists, dataframes, etc. In this tutorial, we will look at one such function that helps us get the standard deviation of the values in a column of an R dataframe.
How to get the standard deviation of an R dataframe column?

You can use the built-in sd()
function in R to compute the standard deviation of values in a dataframe column. Pass the column values as an argument to the function.
The following is the syntax –
sd(dataframe[[column_name]])
It returns the standard deviation of the passed vector.
Steps to compute the standard deviation of values in an R column
Let’s now look at a step-by-step example of using the above syntax to compute the std dev of a numeric column in R.
Step 1 – Create a dataframe
First, we will create an R dataframe that we will be using throughout this tutorial.
# create a dataframe employees_df = data.frame( "Name"= c("Jim", "Dwight", "Angela", "Tobi", "Kevin"), "Age"= c(26, 28, 29, 32, 30) ) # display the dataframe print(employees_df)
Output:
Team_A Team_B 1 70 65 2 80 95 3 90 91
We now have a dataframe containing information about some employees in an office. The dataframe has two columns – “Name” and “Age”.
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Step 2 – Calculate the standard deviation column values using the sd()
function
To calculate the standard deviation of values in a column, pass the column values as an argument to the sd()
function. You can use the [[]]
notation to access the values of a column.
Let’s compute the standard deviation in the “Age” column.
# std dev in "Age" column sd_age = sd(employees_df[["Age"]]) # display the std dev print(sd_age)
Output:
[1] 2.236068
We get the standard deviation in the “Age” column as 2.236068.
Standard deviation of a column with NA
values in R
What if there are NA
values in a column?
Let’s find out.
# create a dataframe employees_df = data.frame( "Name"= c("Jim", "Dwight", "Angela", "Tobi", "Kevin"), "Age"= c(26, 28, NA, 32, 30) ) # display the dataframe print(employees_df)
Output:
Name Age 1 Jim 26 2 Dwight 28 3 Angela NA 4 Tobi 32 5 Kevin 30
Here, we created a new dataframe such that the “Age” column now contains some NA
values.
Now, let’s apply the sd()
function to the “Age” column.
# std dev in "Age" column sd_age = sd(employees_df[["Age"]]) # display the std dev print(sd_age)
Output:
[1] NA
We get NA
as the standard deviation for the “Age” column. This happened because performing any mathematical operation with NA
results in an NA
in R.
If you want to calculate the standard deviation of a column with NA
values, pass na.rm=TRUE
to the sd()
function to skip the NA
values when computing the standard deviation.
# std dev in "Age" column sd_age = sd(employees_df[["Age"]], na.rm=TRUE) # display the std dev print(sd_age)
Output:
[1] 2.581989
We now get the standard deviation of the “Age” column excluding the NA
values.
Summary – Standard Deviation of Column Values in R
In this tutorial, we looked at how to compute the standard deviation of values in a column of an R dataframe. The following is a short summary of the steps –
- Create a dataframe (skip this step if you already have a dataframe on which you want to operate).
- Use the
sd()
function to compute the standard deviation of column values. Pass the column values vector as an argument. - If your column contains any
NA
values, passna.rm=TRUE
to thesd()
function to calculate the standard deviation excluding theNA
values in the column.
You might also be interested in –
- How to Add a Column to a Dataframe in R?
- Rename Column Name in R Dataframe
- Select One or More Columns From R Dataframe
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