library(readxl) 
library(ggplot2) 
library(rcompanion)
DatasetA1 <- read_excel("C:/Users/Leyav/Downloads/DatasetA2.xlsx")
table2 <- table(DatasetA1$FavoriteDrink)
table2
## 
## Coffee   Soda    Tea  Water 
##     26     29     28     17
ggplot(DatasetA1, aes(x =FavoriteDrink , fill = FavoriteDrink)) +
  geom_bar() +
  labs(
    x = "FavouriteDrink",
    y = "Frequency",
    title = "Distribution of favourite drink"
  ) +
  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"             
  )

observed <- c(26, 29, 28, 17) 
expected <- c(0.25, 0.25, 0.25, 0.25)
chisq.test(x = observed, p = expected)
## 
##  Chi-squared test for given probabilities
## 
## data:  observed
## X-squared = 3.6, df = 3, p-value = 0.308

p-value is 0.308 that means the data is not significant and effect size is calculated only if the p-value was statistically significant hence Cohen’s W(effect size) test is not required.

A chi-square goodness-of-fit test indicated that the observed frequencies were different from the expected frequencies, χ²(2) = 3.6, p = .308.