#Mcnamar test for 2 related populations
library(dplyr)
library(ggplot2)
library(reshape2)
library(DescTools)

# Table entries
a <- 20
b <- 5
c <- 7
d <- 3

#create contingency table
ba_tab = as.table(matrix(
  c(20, 7, 5, 3),
  ncol=2, dimnames = list(c("Satisfactory","Unsatisfactory"),
                          c("Satisfactory","Unsatisfactory"))))

ba_tab
#set up data to generate bar charts
ba_tab_m <- melt(ba_tab, varnames = c("Before", "After"), id.vars = "After")

#set up data frame to generate bar charts
ggplot(ba_tab_m %>% group_by(Before) %>%   
         mutate(Percentage = round(value/sum(value),2)), 
       aes(x=Before, y = Percentage, 
           fill = After, cumulative = TRUE)) +
  geom_col() +
  geom_text(aes(label = paste0(Percentage*100,"%")), 
            position = position_stack(vjust = 0.5))

#column percentages
PercTable(ba_tab, row.vars = NULL, col.vars = NULL, justify = "right",
          freq = TRUE, rfrq = "001", expected = FALSE, residuals = FALSE,
          stdres = FALSE, margins = c(1,2), digits = 0)

#row percentages
PercTable(ba_tab, row.vars = NULL, col.vars = NULL, justify = "right",
          freq = TRUE, rfrq = "010", expected = FALSE, residuals = FALSE,
          stdres = FALSE, margins = c(1,2), digits = 0)

#Mcnamar Test
mcnemar.test(ba_tab,correct = FALSE)

#Since b+c < 25, we will use the exact  test
#To use the binomial test we use b as the number of successes and
# n=b+c as the sample size
binom.test(x=5, n=12 , p = 0.5)