In statistics, the **standard error of the mean** is a way to measure how spread out values are in a dataset.

It is calculated as:

**Standard error = s / √n**

where:

**s**: sample standard deviation**n**: sample size

One of the easiest ways to calculate the standard error of the mean in R is by using the **std.error()** function from the **plotrix** package, which is designed to perform this exact task.

The **std.error()** function uses the following basic syntax:

**std.error(x, na.rm)**

where:

**x**: A vector of numerical calculations**na.rm**: Whether or not to remove NA values

The following example shows how to use the **std.error()** function in practice in R.

**Note**: Before using the **std.error****()** function, you will first need to install the **plotrix **package. You can use the following syntax to do so:

**install.packages('plotrix') **

Once the **plotrix **package is successfully installed, you will be able to use the **std.error****()** function without encountering any errors.

**Example: How to Use std.error() Function in R**

Suppose that we create the following data frame that contains information about various basketball players:

**#create data frame
df <- data.frame(team=c('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'),
points=c(22, 39, 24, 18, 15, 10, 28, 23),
assists=c(3, 8, 8, 6, 10, 14, 8, 17))
#view data frame
df
team points assists
1 A 22 3
2 B 39 8
3 C 24 8
4 D 18 6
5 E 15 10
6 F 10 14
7 G 28 8
8 H 23 17
**

The dataset contains the following columns:

**team**: The team name they player belongs on**points**: The total points scored by the player**assists**: The total assists made by the player

Suppose that we would like to calculate the standard error of the values in the **points** column of the data frame.

We can use the following syntax to do so:

**library(plotrix)
#calculate standard error of values in points column
std.error(df$points)
[1] 3.099179**

The output tells us that the standard error of values in the **points** column of the data frame is **3.099179**.

Note that we can also use the **sapply()** function to calculate the standard error of values across multiple columns of a data frame at once if we would like.

For example, we can use the following syntax to calculate the standard error of both the **points** and **assists** columns of the data frame:

**library(plotrix)
#calculate standard error of values in points and assists columns
sapply(df[c('points', 'assists')], std.error)
points assists
3.099179 1.566958**

From the output we can see:

- The standard error of the mean of
**points**is**3.099179**. - The standard error of the mean of
**assists**is**1.566958**.

Note that in this example we specified two column names to calculate the standard error for, but you can use similar syntax with the **sapply()** function to calculate the standard error of the mean for as many columns as you would like at once.

Also note that if you pass a character vector to the **std.error()** function that the function will simply return **NA** as a result.

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Calculate Conditional Mean in R

How to Calculate a Trimmed Mean in R

How to Calculate Geometric Mean in R

How to Calculate Standard Error of the Mean in R