# Column Standard Deviation in R (Step by Step)

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 –

1. Create a dataframe (skip this step if you already have a dataframe on which you want to operate).
2. Use the `sd()` function to compute the standard deviation of column values. Pass the column values vector as an argument.
3. If your column contains any `NA` values, pass `na.rm=TRUE` to the `sd()` function to calculate the standard deviation excluding the `NA` values in the column.

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## Author

• Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

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