Often you may want to remove outliers from multiple columns at once in R.

One common way to define an observation as an outlier isĀ if it is 1.5 times the interquartile range greater than the third quartile (Q3) or 1.5 times the interquartile range less than the first quartile (Q1).

Using this definition, we can use the following steps to create a simple function to identify outliers and then apply this function across multiple columns in an R data frame.

**Step 1: Create data frame.**

First, let’s create a data frame in R:

df <- data.frame(index=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), var1=c(4, 4, 5, 4, 3, 2, 8, 9, 4, 5), var2=c(1, 2, 4, 4, 6, 9, 7, 8, 5, 29), var3=c(9, 9, 9, 5, 5, 3, 4, 5, 11, 34))

**Step 2: Define outlier function.**

Next, let’s define a function that can identify outliers and a function that can then remove outliers:

outliers <- function(x) { Q1 <- quantile(x, probs=.25) Q3 <- quantile(x, probs=.75) iqr = Q3-Q1 upper_limit = Q3 + (iqr*1.5) lower_limit = Q1 - (iqr*1.5) x > upper_limit | x < lower_limit } remove_outliers <- function(df, cols = names(df)) { for (col in cols) { df <- df[!outliers(df[[col]]),] } df }

**Step 3: Apply outlier function to data frame.**

Lastly, let’s apply this function across multiple columns of the data frame to remove outliers:

remove_outliers(df, c('var1', 'var2', 'var3')) index var1 var2 var3 1 1 4 1 9 2 2 4 2 9 3 3 5 4 9 4 4 4 4 5 5 5 3 6 5 9 9 4 5 11

*You can find more R tutorials here.*