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Finding outliers using IQR

Interquartile range, or IQR, is another way of measuring spread that's less influenced by outliers. IQR is also often used to find outliers. If a value is less than \(\text{Q1} - 1.5 \times \text{IQR}\) or greater than \(\text{Q3} + 1.5 \times \text{IQR}\), it's considered an outlier. In fact, this is how the lengths of the whiskers in a ggplot2 box plot are calculated.

Diagram of a box plot showing median, quartiles, and outliers

In this exercise, you'll calculate IQR and use it to find some outliers. Both dplyr and ggplot2 libraries are loaded and food_consumption is available.

This exercise is part of the course

Introduction to Statistics in R

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Compute the 25th percentile and 75th percentile of co2_emission
q1 <- quantile(___$___, ___)
q3 <- ___

# Compute the IQR of co2_emission
iqr <- ___
iqr
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