A function to generate outlier free dataset by imputing them with mean, median or trim entire row with outlier from dataset.

Usage

trim_outliers(dataframe, columns = NULL, identifier = "IQR", method = "trim")

Arguments

dataframe: A target dataframe where the function is performed.
columns: The target columns where the function needed to be performed. Default is None, the function will check all columns.
identifier: The method of identifying outliers.
method: The method of dealing with outliers. - if "trim" : remove completely rows with data points having outliers. - if "median" : replace outliers with median values - if "mean" : replace outliers with mean values

Value

a dataframe with the summary of the outlier identified by the method

Examples

library(tidyverse)

df = as.data.frame(tibble(SepalLengthCm = c(5.1, 4.9, 4.7, 5.5, 5.1, 50, 54, 5.0, 5.2, 5.3, 5.1),
                          SepalWidthCm = c(1.4, 1.4, 20, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1),
                          PetalWidthCm = c(0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5))
                  )

trim_outliers(df,identifier='Z_score', method='trim')
#>    SepalLengthCm SepalWidthCm PetalWidthCm
#> 1            5.1          1.4          0.2
#> 2            4.9          1.4          0.2
#> 4            5.5          2.0          0.3
#> 5            5.1          0.7          0.4
#> 6           50.0          1.6          0.5
#> 7           54.0          1.2          0.5
#> 8            5.0          1.4          0.6
#> 9            5.2          1.8          0.4
#> 10           5.3          1.5          0.2
#> 11           5.1          2.1          5.0