[HF]-Outlier-Identification-&-Test-of-Normality.md

[HF] Outlier Identification and Test of Normality

Preparation Code

# Functions
library(dplyr)
library(glue)

# Sample Data
library(wooldridge)
G <- gpa2

Actual Code

1. Helper function. (Must be included. Do not edit.)

test.NormalityOutliers <- function(dataset_variable, variable.text, density_plot.remove_na = TRUE){

  # Test of normality
  shapiro.test(dataset_variable) %>% print()
  hist(dataset_variable, main = glue::glue("Histogram to Assess if {variable.text} is Normally Distributed"))
  plot(density(dataset_variable, na.rm = density_plot.remove_na), main = glue::glue("Scatterplot to Assess if {variable.text} is Normally Distributed"))
  qqnorm(dataset_variable,  main = glue::glue("qq-Plot to Assess if {variable.text} is Normally Distributed"))
  qqline(dataset_variable, col = 2)
  
  # Outlier Identification
  boxplot(dataset_variable, range = 3, main = glue::glue("Boxplot to Examine Distribution of Extreme Outliers"))
  boxplot(dataset_variable, range = 1.5, main = glue::glue("Boxplot to Examine Distribution of Moderate Outliers"))}

2. Inputs. (Must be included. Edit this.)

test.NormalityOutliers(G$sat, # dataset_variable
                       "SAT score") # variable.text

3. Optional keyword arguments for fine-tuning of output. (If you wish to include some of these, edit the rhs of = and add them at the back of your input.)

#               density_plot.remove_na = TRUE