Step 2 : load necessary packages

library(ggplot2)
library(rcompanion)
library(readxl)

Step 3: Load the dataset into R environment

datasetb2 <- read_excel("/Users/sarva/Desktop/DatasetB2.xlsx")

Step 3: import the table as an object in R

tab <- table(datasetb2$StudentType, datasetb2$PetOwnership)

Step 4: Visualising Bar Chart

p <- ggplot(datasetb2, aes(x = StudentType, fill = PetOwnership)) +
  geom_bar(position = "dodge") +               
  labs(
    x = "Student Type",
    y = "Frequency",
    title = "Pet Ownership by Student Type"
  ) +
  theme(
    text = element_text(size = 14),
    axis.title = element_text(size = 14),
    axis.text = element_text(size = 14),
    plot.title = element_text(size = 14),
    legend.position = "none"                 
  )
p

Step 5: Conducting the Chi Square Test Of Independence to determine P-Value

chisq.test(tab)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  tab
## X-squared = 0.040064, df = 1, p-value = 0.8414

Step 6: Conduct the Cohen’s W Effect Size test

cramerV(tab)
## Cramer V 
##  0.04003

#The Chi-Square Test of Independence indicated there was not a significant association between Student Type and Pet Ownership, χ²(df) = 0.040064, p = 0.8414. The association between the two variables was weak/moderate/strong (Cramer’s V = 0.04003).