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

A4Q2 <- read_excel("C:/Assignment 4/A4Q2.xlsx")

enne_table <- table(A4Q2$status, A4Q2$scholarship)
observed <- enne_table
observed

##                
##                   0   1
##   Domestic       39 111
##   International 118  32

barplot(
  enne_table,
  beside = TRUE,
  col = rainbow(nrow(enne_table)),
  legend.text = rownames(enne_table))

chi_result <- chisq.test(enne_table)
chi_result

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  enne_table
## X-squared = 81.297, df = 1, p-value < 2.2e-16

rcompanion::cramerV(enne_table)

## Cramer V 
##   0.5272

# A Chi-Square Test of Independence was conducted to determine 
# whether there was an association between student status 
# (Domestic vs. International) and scholarship award (0 vs. 1).

# The results showed that there was a difference between the 
# observed and expected frequencies, χ²(1) = 81.30, 
# p-value < 2.2e-16 which is 0.00000000000000022.

# The difference was large (Cramer's V = .53).