Is there a difference in body weight (kg) before versus after all participants tried the keto diet?

library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(effsize)
library(rstatix)
## 
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
## 
##     filter
A6Q2 <- read_excel("~/downloads/A6Q2.xlsx")

Before <- A6Q2$Before
After <- A6Q2$After

Differences <- After - Before

mean(Before, na.rm = TRUE)
## [1] 76.13299
median(Before, na.rm = TRUE)
## [1] 75.95988
sd(Before, na.rm = TRUE)
## [1] 7.781323
mean(After, na.rm = TRUE)
## [1] 57.17874
median(After, na.rm = TRUE)
## [1] 58.36459
sd(After, na.rm = TRUE)
## [1] 14.39364
hist(Differences,
     main = "Histogram of Difference Weights",
     xlab = "Value",
     ylab = "Frequency",
     col = "blue",
     border = "black",
     breaks = 20)

Histogram of Difference Scores The difference weight look abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.

boxplot(Differences,
        main = "Distribution of Weight Differences (After - Before)",
        ylab = "Difference in Wwight",
        col = "blue",
        border = "darkblue")

Boxplot There is one dots outside the boxplot. The dots are not close to th e whiskers. Based on these findings, the boxplot is normal.(Correction, the boxplot is not normal)

shapiro.test(Differences)
## 
##  Shapiro-Wilk normality test
## 
## data:  Differences
## W = 0.89142, p-value = 0.02856

Shapiro-Wilk Difference in weight The data is normally distributed, (p = 0.33). (Correction, The data is abnormally distributed)

t.test(Before, After, paired = TRUE, na.action = na.omit)
## 
##  Paired t-test
## 
## data:  Before and After
## t = 6.1382, df = 19, p-value = 6.704e-06
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  12.49121 25.41730
## sample estimates:
## mean difference 
##        18.95425

A Dependent T-Test was conducted to determine if there was a difference in Weight before trying Keto diet versus after trying Keto diet. Before scores M = 76.13, SD = 7.78 were not significantly different from after scores (M = 71.59, SD = 6.64), t(19) = 1.90, p = 0.072

(Correction: running a Wilcox test & effect size to run the correct report)

wilcox.test(Before, After, paired = TRUE, na.action = na.omit)
## 
##  Wilcoxon signed rank exact test
## 
## data:  Before and After
## V = 210, p-value = 1.907e-06
## alternative hypothesis: true location shift is not equal to 0
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 tibble: 1 × 7
##   .y.   group1 group2 effsize    n1    n2 magnitude
## * <chr> <chr>  <chr>    <dbl> <int> <int> <ord>    
## 1 score After  Before   0.877    20    20 large

A Wilcoxon Signed-Rank Test was conducted to determine if there was a difference in body weigh before keto diet versus after keto diet. Before scores (Mdn = 75.96) was asignificant difference from after scores (Mdn = 58.36), V = 210, p < .001 The effect size was large, r = .88