How to Remove Outliers in R

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Outliers are data points that are significantly different from the other observations in a dataset. While they can sometimes be genuine, at other times they may be due to errors. In either case, they can have a dramatic effect on the statistical analysis and data visualization, thus it becomes imperative to address them. In this article, we will learn the various techniques for identifying and removing outliers in R.

Table of Contents

  1. Understanding Outliers
  2. Setting Up the Environment
  3. Importing Data
  4. Visualizing Outliers
  5. Statistical Methods for Detecting Outliers
  6. Handling Outliers
  7. Reassessing Data
  8. Conclusion

1. Understanding Outliers

Before jumping into how to remove outliers, it is important to understand what they are. Outliers are typically those data points that lie at an abnormal distance from other values in a random sample from a population. Their presence can skew and mislead the training process of machine learning algorithms resulting in longer training times, less accurate models, and ultimately poorer results.

2. Setting Up the Environment

First, ensure that R and RStudio (optional, but highly recommended) are installed on your computer. You should also set the working directory to the folder containing your dataset. You can set the working directory using the setwd() function:


Make sure that the required libraries are installed using the install.packages() function and loaded using the library() function.


3. Importing Data

For this tutorial, we will work with a hypothetical dataset. You can read data from a CSV file using the read.csv() function.

data <- read.csv("your_file.csv")

4. Visualizing Outliers

Visualization is often one of the best ways to identify outliers. The two common plots used are the box plot and the scatter plot.

Box Plot

ggplot(data, aes(x = " ", y = your_variable)) +

This will give you a visual representation of the data and any data points that are outside the whiskers of the boxplot can be considered as outliers.

Scatter Plot

ggplot(data, aes(x = variable_1, y = variable_2)) +

5. Statistical Methods for Detecting Outliers


Z-score is a measure of how many standard deviations an element is from the mean. Generally, data points with a z-score above 3 or below -3 are considered as outliers.

z_scores <- scale(data$your_variable)
data <- data[abs(z_scores) < 3, ]

IQR Method

IQR (Interquartile Range) is the range within which the central 50% of the data lies.

Q1 <- quantile(data$your_variable, 0.25)
Q3 <- quantile(data$your_variable, 0.75)
IQR <- Q3 - Q1
data <- data[(data$your_variable > Q1 - 1.5 * IQR) & (data$your_variable < Q3 + 1.5 * IQR), ]

6. Handling Outliers

Once we have identified outliers, there are several ways to handle them.

Removing Outliers

As seen in the Z-score and IQR methods above, you can directly filter out the outliers.


This involves setting the outliers to a specified percentile of the data. For example, you can set the outliers to the 5th and 95th percentiles.

data$your_variable <- Winsorize(data$your_variable, probs = c(0.05, 0.95))


You can also replace the outlier values with statistical measures such as mean, median, or mode.

data$your_variable[data$your_variable > upper_bound] <- mean(data$your_variable)

7. Reassessing Data

After handling outliers, it is essential to reassess your data by visualizing and analyzing the data again. This will help you understand the impact of the changes made on the dataset.

8. Conclusion

Handling outliers is an essential step in data preprocessing. R provides several robust functions and packages that make this process easier. While there is no one-size-fits-all approach to dealing with outliers, understanding your data, the domain, and the purpose of the analysis will guide you in choosing the best approach for your specific case. Always remember to reassess your data after performing outlier treatment to ensure that the data integrity is maintained.

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