library(readxl) 
library(ggplot2) 
library(rcompanion)
DatasetB1 <- read_excel("C:/Users/Leyav/Downloads/DatasetB2.xlsx")
tab <- table(DatasetB1$StudentType, DatasetB1$PetOwnership)
tab
##                
##                 No Yes
##   Domestic      27  25
##   International 23  25
ggplot(DatasetB1, aes(x =StudentType, fill =PetOwnership)) +
  geom_bar(position = "dodge") +               
  labs(
    x = "Student type",
    y = "Frequency",
    title = "Petownership by Studenttype"
  ) +
  theme(
    text = element_text(size = 14),
    axis.title = element_text(size = 14),
    axis.text = element_text(size = 14),
    plot.title = element_text(size = 14),
  )

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

p-value = 0.8414 that means the data is not significant, hence Cramer’s V (Effect Size) test is not required.

The Chi-Square Test of Independence indicated there was not a significant association between petownership and student type, χ²(1) = 0.040, p = .841.