1

I have a dataframe that has multiple outliers. I suspect that these ouliers have produced different results than expected.

I tried to use this tip but it didn't work as I still have very different values: https://www.r-bloggers.com/2020/01/how-to-remove-outliers-in-r/

I tried the solution with the rstatix package, but I can't remove the outliers from my data.frame

library(rstatix)
library(dplyr)

df <- data.frame(
  sample = 1:20,
  score = c(rnorm(19, mean = 5, sd = 2), 50))

View(df)

out_df<-identify_outliers(df$score)#identify outliers

df2<-df#copy df

df2<- df2[-which(df2$score %in% out_df),]#remove outliers from df2

View(df2)
wesleysc352
  • 579
  • 1
  • 8
  • 21
  • What is your expected output. You said you wanted to remove the outliers and that is what it was doing – akrun Sep 14 '21 at 21:37

2 Answers2

2

The identify_outliers expect a data.frame as input i.e. usage is

identify_outliers(data, ..., variable = NULL)

where

... - One unquoted expressions (or variable name). Used to select a variable of interest. Alternative to the argument variable.

df2 <- subset(df, !score %in% identify_outliers(df, "score")$score)
akrun
  • 874,273
  • 37
  • 540
  • 662
1

A rule of thumb is that data points above Q3 + 1.5xIQR or below Q1 - 1.5xIQR are considered outliers. Therefore you just have to identify them and remove them. I don't know how to do it with the dependency rstatix, but with base R can be achived following the example below:

# Generate a demo data
set.seed(123)
demo.data <- data.frame(
                         sample = 1:20,
                         score = c(rnorm(19, mean = 5, sd = 2), 50),
                         gender = rep(c("Male", "Female"), each = 10)
                        )
#identify outliers
outliers <- which(demo.data$score > quantile(demo.data$score)[4] + 1.5*IQR(demo.data$score) | demo.data$score < quantile(demo.data$score)[2] - 1.5*IQR(demo.data$score)) 

# remove them from your dataframe
df2 = demo.data[-outliers,]

Do a cooler function that returns to you the index of the outliers:

get_outliers = function(x){
   which(x > quantile(x)[4] + 1.5*IQR(x) | x < quantile(x)[2] - 1.5*IQR(x))
}

outliers <- get_outliers(demo.data$score)


df2 = demo.data[-outliers,]
Alejo
  • 315
  • 1
  • 10