# Remove Outliers from Data Set in R (Example)

In this article you’ll learn how to delete outlier values from a data vector in the R programming language.

Let’s dive into it.

## Creation of Example Data

Have a look at the following example data:

```set.seed(937573)                               # Create randomly distributed data
x <- rnorm(1000)
x[1:5] <- c(7, 10, - 5, 16, - 23)              # Insert outliers
x                                              # Print data
#    7.000000000  10.000000000  -5.000000000  16.000000000 -23.000000000  -0.413450746   0.801720348 ...```

The previous output of the RStudio console shows the structure of our example data – It’s a numeric vector consisting of 1000 values.

Now, we can draw our data in a boxplot as shown below:

`boxplot(x)                                     # Create boxplot of all data` As shown in Figure 1, the previous R programming syntax created a boxplot with outliers.

## Example: Removing Outliers Using boxplot.stats() Function in R

In this Section, I’ll illustrate how to identify and delete outliers using the boxplot.stats function in R. The following R code creates a new vector without outliers:

`x_out_rm <- x[!x %in% boxplot.stats(x)\$out]    # Remove outliers`

Let’s check how many values we have removed:

```length(x) - length(x_out_rm)                   # Count removed observations
# 10```

We have removed ten values from our data. Note that we have inserted only five outliers in the data creation process above. In other words: We deleted five values that are no real outliers (more about that below).

However, now we can draw another boxplot without outliers:

`boxplot(x_out_rm)                              # Create boxplot without outliers` The output of the previous R code is shown in Figure 2 – A boxplot that ignores outliers.

Important note: Outlier deletion is a very controversial topic in statistics theory. Any removal of outliers might delete valid values, which might lead to bias in the analysis of a data set.

Furthermore, I have shown you a very simple technique for the detection of outliers in R using the boxplot function (have a look at the documentation of boxplots.stats for more details). However, there exist much more advanced techniques such as machine learning based anomaly detection.

I strongly recommend having a look at the outlier detection literature (e.g. this article) to make sure that you are not removing the wrong values from your data set.

## Video, Further Resources & Summary

I have recently published a video on my YouTube channel, which explains the topics of this tutorial. You can find the video below.

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Furthermore, you may read the related tutorials on this website.

This tutorial showed how to detect and remove outliers in the R programming language. Please let me know in the comments below, in case you have additional questions.

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• Danielle
May 20, 2023 4:23 pm

Why criteria does boxplot.stats use to determine outliers? Thank you!

• May 22, 2023 9:01 am