library(readxl)
library(ggpubr)

## Loading required package: ggplot2

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(effectsize)
library(effsize)
A6Q4_Sheet1_ <- read_excel(
  "C:/Users/hp/Desktop/digital Forensics/AA 5221/A6Q4(Sheet1).xlsx"
)

View(A6Q4_Sheet1_)
unique(A6Q4_Sheet1_$Exercise)  # Check the exact group names

## [1] "nolift" "lift"

# Descriptive statistics
A6Q4_Sheet1_ %>%
  group_by(Exercise) %>%
  summarise(
    Mean = mean(Weight, na.rm = TRUE),
    Median = median(Weight, na.rm = TRUE),
    SD = sd(Weight, na.rm = TRUE),
    N = sum(!is.na(Weight)),
    .groups = "drop"
  )

## # A tibble: 2 × 5
##   Exercise  Mean Median    SD     N
##   <chr>    <dbl>  <dbl> <dbl> <int>
## 1 lift     120.   116.   53.3    25
## 2 nolift    33.0   40.8  56.7    25

hist(A6Q4_Sheet1_$Weight[A6Q4_Sheet1_$Exercise == "lift"],
     breaks = 15,
     col = "skyblue",
     border = "white")

hist(A6Q4_Sheet1_$Weight[A6Q4_Sheet1_$Exercise == "nolift"],
     breaks = 15,
     col = "firebrick",
     border = "white")

#Data for Weight of nolift appears abnormally distributed.
#Data for weight of lift appears abnormally distributed.

ggboxplot(A6Q4_Sheet1_, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

# The lift group  boxplot has 2 outliers.
# The No-lift group boxplot has 1 outlier.

shapiro.test(A6Q4_Sheet1_$Weight[A6Q4_Sheet1_$Exercise == "lift"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4_Sheet1_$Weight[A6Q4_Sheet1_$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436

shapiro.test(A6Q4_Sheet1_$Weight[A6Q4_Sheet1_$Exercise == "nolift"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4_Sheet1_$Weight[A6Q4_Sheet1_$Exercise == "nolift"]
## W = 0.70002, p-value = 7.294e-06

#The lift group is abnormally distributed, (p < .05).
#The no-lift group is abnormally distributed, (p < .05)


wilcox.test(Weight ~ Exercise, data = A6Q4_Sheet1_)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Weight by Exercise
## W = 603, p-value = 7.132e-11
## alternative hypothesis: true location shift is not equal to 0

mw_effect <- cliff.delta(Weight ~ Exercise, data = A6Q4_Sheet1_)
print(mw_effect)

## 
## Cliff's Delta
## 
## delta estimate: 0.9296 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.7993841 0.9764036

# A Mann-Whitney U test was conducted to determine if there was a difference in Weight between lift and nolift.
# Lift scores (Mdn = 116.00) were significantly different from nolift scores (Mdn = 40.80), W = 603, p < .001.
# The effect size was large, Cliff's Delta = 0.930.#