```{r} library(readxl) library(ggpubr) library(effsize) library(rstatix) A6Q2 <- read_excel(“C:/Users/User/Downloads/A6Q2.xlsx”) Before <- A6Q2\(Before After <- A6Q2\)After Differences <- After - Before mean(Before, na.rm = TRUE) median(Before, na.rm = TRUE) sd(Before, na.rm = TRUE)

mean(After, na.rm = TRUE) median(After, na.rm = TRUE) sd(After, na.rm = TRUE) hist(Differences, breaks = 15, col = “blue”, border = “white”) #Data for the difference scores appears abnormally distributed. boxplot(Differences, main = “Distribution of Score Differences (After - Before)”, ylab = “Difference in Scores”, col = “blue”, border = “darkblue”) # The difference scores boxplot does not have outliers. shapiro.test(Differences) #Shapiro-Wilk Difference Scores #The data is abnormally distributed, (p = .029). wilcox.test(Before, After, paired = TRUE, na.action = na.omit) library(TH.data) library(coin) df_long <- data.frame(id = rep(1:length(Before), 2), time = rep(c(“Before”, “After”), each = length(Before)), score = c(Before, After))

wilcox_effsize(df_long, score ~ time, paired = TRUE) #A Wilcoxon Signed-Rank Test was conducted to determine if there was a difference in weight before keto diet versus after keto diet. #Before scores (Mdn = 75.96) were significantly different from after scores (Mdn = 58.36), V = 210, p = < .001. #The effect size was large, r = .877.```