How To Draw A Box Plot Deleting Outliers In R

Remove Outliers from Data Set up in R (Instance)

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

Table of contents:

Allow's swoop into information technology.

Creation of Example Data

Accept a look at the following instance data:

                gear up                .                seed                (                937573                )                # Create randomly distributed data                ten                <-                rnorm(                thou                )                x[                one                :                5                ]                <-                c(                7,                ten,                -                five,                16,                -                23                )                # Insert outliers                10                # Print information                # [1]   7.000000000  x.000000000  -5.000000000  16.000000000 -23.000000000  -0.413450746   0.801720348 ...

set.seed(937573) # Create randomly distributed data x <- rnorm(1000) x[ane:five] <- c(7, ten, - 5, sixteen, - 23) # Insert outliers x # Print information # [i] 7.000000000 10.000000000 -v.000000000 xvi.000000000 -23.000000000 -0.413450746 0.801720348 ...

The previous output of the RStudio console shows the construction of our example data – It'due south a numeric vector consisting of 1000 values.

Now, we can draw our data in a boxplot every bit shown beneath:

boxplot(x)                # Create boxplot of all information

boxplot(x) # Create boxplot of all data

r graph figure 1 remove outliers from data set

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 Department, I'll illustrate how to identify and delete outliers using the boxplot.stats function in R. The post-obit R code creates a new vector without outliers:

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

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

Permit'due south check how many values we have removed:

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

length(ten) - length(x_out_rm) # Count removed observations # 10

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

However, now nosotros tin draw some other boxplot without outliers:

boxplot(x_out_rm)                # Create boxplot without outliers

boxplot(x_out_rm) # Create boxplot without outliers

r graph figure 2 remove outliers from data set

The output of the previous R lawmaking is shown in Effigy 2 – A boxplot that ignores outliers.

Of import note: Outlier deletion is a very controversial topic in statistics theory. Whatsoever 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 (accept a look at the documentation of boxplots.stats for more than details). However, in that location be much more advanced techniques such as machine learning based anomaly detection.

I strongly recommend having a expect at the outlier detection literature (e.g. this commodity) to make sure that you lot are not removing the wrong values from your information 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.

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

Remove Duplicated Rows from Information Frame in R
Ignore Outliers in ggplot2 Boxplot in R
Create a Box-and-Whisker Plot
R Programming Examples

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.