Chi-squared tests of independence [Musculoskeletal injuries treated], R

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X <- matrix(c(58,81,61,42,19,39),ncol=3,byrow=TRUE) # create table
colnames(X) <- c("Acetaminophen","Ibuprofen","Codeine") 
rownames(X) <- c(" Significant Improvement"," Slight Improvement")
X <- as.table(X)

X
df = (nrow(X)-1)*(ncol(X)-1)
alpha = 0.1

RS = rowSums(X) # sum of rows
E1 = c(colSums(X)*RS[1]/sum(X)) # Expected frequencies of Significant Improvement
E2 = c(colSums(X)*RS[2]/sum(X)) # Expected frequencies of Slight Improvement

x1 = sum((X[1,]-E1)^2/E1)
x2 = sum((X[2,]-E2)^2/E2)
X2 = x1 + x2
pv = pchisq(X2,df,lower.tail=FALSE)
cv = qchisq(alpha,df,lower.tail=FALSE)

X2 # Chi-squared
df # degree of freedom
pv # p-value
cv # critical value

# Make a conlusion
if(X2 > cv){
print("There is enough statistical evidence to REJECT the null hypothesis and to believe that there is a relationship between the treatment and response.")
}else {
print("There is not enough statistical evidence to reject the null hypothesis and to believe that there is no relationship between the treatment and response.")
}

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