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Outliers in R

Kind Kinkajou answered on August 30, 2023 Popularity 3/10 Helpfulness 3/10

answer Outliers in R

Outliers in R

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# Load necessary libraries
library(readr)
library(dplyr)

# Read the data file (replace "data.csv" with your actual file name)
data <- read_csv("data.csv")

# Function to detect outliers using Tukey's method
detect_outliers <- function(vector) {
  Q1 <- quantile(vector, 0.25)
  Q3 <- quantile(vector, 0.75)
  IQR <- Q3 - Q1
  lower_bound <- Q1 - 1.5 * IQR
  upper_bound <- Q3 + 1.5 * IQR
  
  outliers <- vector[vector < lower_bound | vector > upper_bound]
  return(outliers)
}

# Function to determine distribution type
determine_distribution <- function(vector) {
  skew <- skewness(vector)
  if (skew < -1) {
    return("Strongly Left Skewed")
  } else if (skew > 1) {
    return("Strongly Right Skewed")
  } else if (skew < 0) {
    return("Slightly Left Skewed")
  } else if (skew > 0) {
    return("Slightly Right Skewed")
  } else {
    return("Normal")
  }
}

# Iterate through columns
for (col in names(data)) {
  if (is.numeric(data[[col]])) {
    outliers <- detect_outliers(data[[col]])
    distribution <- determine_distribution(data[[col]])
    
    cat("Column:", col, "\n")
    cat("Distribution:", distribution, "\n")
    
    if (length(outliers) > 0) {
      cat("Outliers:", outliers, "\n")
    } else {
      cat("No outliers found.\n")
    }
    
    cat("\n")
  }
}

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Contributed on Aug 30 2023

Kind Kinkajou

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