[M]-Boxplot.md

Boxplot

Base R Graphic

Preparation Code

# Sample Data
library(wooldridge)
G <- gpa2

Actual Code

Sample Task 1

Identify extreme and moderate outliers (outside 3 IQR and 1.5 IQR respectively).

verbmath.extreme_outliers <- boxplot(G$verbmath, range = 3, main = "Boxplot to Examine Distribution of Extreme Outliers (outside 3 IQR)")
verbmath.extreme_outliers$out

verbmath.moderate_outliers <- boxplot(G$verbmath, range = 1.5, main = "Boxplot to Examine Distribution of Moderate Outliers (outside 1.5 IQR)")
verbmath.moderate_outliers$out

Sample Task 2

Remove extreme outliers (outside 3 IQR).

G.wo_extreme_outliers_verbmath <- G[-which(G$verbmath %in% verbmath.extreme_outliers$out),]

ggplot2 Graphic

Preparation Code

# Functions
library(ggplot2)

# Sample Data
library(wooldridge)
G <- gpa2
G$ethnicity <- ifelse(gpa2$black == 1, "Black",
                 ifelse(gpa2$white == 1, "White",
                   ifelse(gpa2$black == 0 & gpa2$white == 0, "Others", -1)))

Actual Code

Sample Task 1

Identify moderate outliers (outside 1 IQR) for verbmath.

ggplot(G, aes(y = verbmath)) + 
  geom_boxplot() +
  labs(title = "Boxplot to Examine Distribution of Moderate Outliers (outside 1 IQR)")

Sample Task 2

Identify moderate outliers (outside 1 IQR) for verbmath for each ethnicity.

ggplot(G, aes(x = ethnicity, y = verbmath)) + 
  geom_boxplot() +
  labs(title = "Boxplot to Examine Distribution of Moderate Outliers (outside 1 IQR)")