Exercise

# Finding outliers using IQR

Outliers can have big effects on statistics like mean, as well as statistics that rely on the mean, such as variance and standard deviation. 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.

In this exercise, you'll calculate IQR and use it to find some outliers. Both `dplyr`

and `ggplot2`

are loaded and `food_consumption`

is available.

Instructions 1/4

**undefined XP**

- Calculate the total
`co2_emission`

per country by grouping by country and taking the sum of`co2_emission`

. Call the sum`total_emission`

and store the resulting data frame as`emissions_by_country`

.