There are multiple methods to find outliers in R, but a common method is using the Interquartile Range (IQR).
The values that fall outside 1.5 times the interquartile range (IQR) below the first quartile (Q1) or above the third quartile (Q3) are identified as outliers.
In this article, we will explore how to find outliers in R using the IQR method with examples.
Method: Use IQR() Function
The IQR() function in R is used to calculate the interquartile range. Here’s the syntax:
q1 <- quantile(df$column, 0.25) # Compute the first quartile (25th percentile)
q3 <- quantile(df$column, 0.75) # Compute the third quartile (75th percentile)
iqr <- IQR(df$column) # Calculate the interquartile range
outliers <- subset(df, df$column < (q1 - 1.5 * iqr) | df$column > (q3 + 1.5 * iqr))
The following example shows how to find outliers in R using the IQR() function.
Find Outliers Using IQR() Function in R
Let’s see how we can find outliers for one of the columns of a data frame in R:
# Create data frame
df <- data.frame(Machine_name=c("A","B","C","D","E","F","G","H"),
Pressure1=c(78.2, 28, 71.7, 80.21, 72.7, 30, 84.21, 76.2),
Temperature1=c(31, 33, 36, 37, 36, 33, 37, 31),
Status=c(TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE))
# Find Q1, Q3, and interquartile range for Pressure1 column
Q1 <- quantile(df$Pressure1, 0.25)
Q3 <- quantile(df$Pressure1, 0.75)
IQR <- IQR(df$Pressure1)
# Subset data where points value is outside 1.5*IQR of Q1 and Q3
outliers <- subset(df, df$Pressure1 < (Q1 - 1.5 * IQR) | df$Pressure1 > (Q3 + 1.5 * IQR))
# Print outliers
print(outliers)
Output: 👇️
Machine_name Pressure1 Temperature1 Status
2 B 28 33 TRUE
6 F 30 33 TRUE
In this example, the IQR() function calculates the interquartile range for the Pressure1 column of the data frame.
The subset() function is then used to identify the outliers, which are values that fall outside 1.5 times the IQR below the first quartile or above the third quartile.
