crosstable

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df <- data.frame(
  Gender = c("m", "m", "f", "f", "f", "m", "f"),
  Muslim = c("yes", "yes", "no", "yes", "yes", "yes", "no"),
  Result = c("p", "p", "p", "f", "p", "p", "p")
)
df

##   Gender Muslim Result
## 1      m    yes      p
## 2      m    yes      p
## 3      f     no      p
## 4      f    yes      f
## 5      f    yes      p
## 6      m    yes      p
## 7      f     no      p

ftable(Muslim+Gender~Result,data=df)

##        Muslim no   yes  
##        Gender  f m   f m
## Result                  
## f              0 0   1 0
## p              2 0   1 3

##chi_square test

# Create a sample dataframe
df <- data.frame(
  Gender = c("Male", "Male", "Female", "Female", "Female", "Male", "Female"),
  Muslim = c("Yes", "Yes", "No", "Yes", "Yes", "Yes", "No"),
  Result = c("Pass", "Pass", "Pass", "Fail", "Pass", "Pass", "Pass")
)

# Create a contingency table
contingency_table <- table(df$Gender, df$Muslim)

# Apply chi-square test
chi_square_result <- chisq.test(contingency_table)

## Warning in chisq.test(contingency_table): Chi-squared approximation may be
## incorrect

# Print the result
print(chi_square_result)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  contingency_table
## X-squared = 0.36458, df = 1, p-value = 0.546

my_datas<-matrix(c(3,9,12,7),nrow=2)
colnames(my_datas)<-c("pass","fail")
rownames(my_datas)<-c("male","female")
my_datas

##        pass fail
## male      3   12
## female    9    7

chi_square_result <- chisq.test(my_datas)
print(chi_square_result)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  my_datas
## X-squared = 2.8962, df = 1, p-value = 0.08879