library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(effectsize)
library(rstatix)
##
## Attaching package: 'rstatix'
## The following objects are masked from 'package:effectsize':
##
## cohens_d, eta_squared
## The following object is masked from 'package:stats':
##
## filter
A6Q2 <- read_excel("A6Q2.xlsx")
Before <- A6Q2$Before
After <- A6Q2$After
Differences <- After - Before
mean(Before, na.rm = TRUE)
## [1] 76.13299
median(After, na.rm = TRUE)
## [1] 58.36459
sd(Before, na.rm = TRUE)
## [1] 7.781323
mean(Before, na.rm = TRUE)
## [1] 76.13299
median(After, na.rm = TRUE)
## [1] 58.36459
sd(After, na.rm = TRUE)
## [1] 14.39364
hist(Differences,
main = "body weight",
xlab = "After",
ylab = "Before",
col = "blue",
border = "black",
breaks = 20)

#Histogram of Difference Scores
#The difference scores look normally distributed.
#The data is symmetrical.
#The data has a proper bell curve.
boxplot(Differences,
main = "body weight (After - Before)",
ylab = "Difference in Scores",
col = "blue",
border = "darkblue")

#Boxplot
#There are dots outside the boxplot.
#The dots are not close to the whiskers.
#The dots are very far away from the whiskers.
#Based on these findings, the boxplot is normal.
shapiro.test(Differences)
##
## Shapiro-Wilk normality test
##
## data: Differences
## W = 0.89142, p-value = 0.02856
#Shapiro-Wilk Difference Scores
#The data is abnormally distributed, (p = .02856).
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 OutcomeVariable before Independent Variable versus after Independent Variable.
#Before scores (Mdn = 76.13299) were significantly different from after scores (Mdn = 76.13299), V = 210, p = [< .001 / = .0.1 / > .05].
#The effect size was large , r = .0.877