library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(rmarkdown)
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 <- read_excel("A6Q4-2.xlsx")
#CORRECTION: I corrected the file path so the dataset imports properly.
unique(A6Q4$Exercise)
## [1] "nolift" "lift"
A6Q4 %>%
group_by(Exercise) %>%
summarise(
Mean = mean(Weight, na.rm = TRUE),
Median = median(Weight, na.rm = TRUE),
SD = sd(Weight, na.rm = TRUE),
N = n()
)
## # A tibble: 2 × 5
## Exercise Mean Median SD N
## <chr> <dbl> <dbl> <dbl> <int>
## 1 lift 120. 116. 53.3 25
## 2 nolift 33.0 40.8 56.7 25
hist(
A6Q4$Weight[A6Q4$Exercise == "lift"],
main = "Histogram of Lift Weight",
xlab = "Value",
ylab = "Frequency",
col = "lightblue",
border = "black",
breaks = 10
)
#CORRECTION: I corrected the lift histogram title, x-axis label, color and number of breaks.
#Lift Group Interpretation
#The data is abnormally distributed.
#The data is positively skewed.
#The data does not have a proper bell curve.
hist(
A6Q4$Weight[A6Q4$Exercise == "nolift"],
main = "Histogram of NoLift Weight",
xlab = "Value",
ylab = "Frequency",
col = "lightgreen",
border = "black",
breaks = 10
)
#CORRECTION: I corrected the no lift histogram title, x-axis label, color, and number of breaks.
#NoLift Group Interpretation
#The data is abnormally distributed.
#The data is negatively skewed.
#The data does not have a proper bell curve.
ggboxplot(A6Q4, x = "Exercise", y = "Weight",
color = "Exercise",
palette = "jco",
add = "jitter")
#CORRECTION: I corrected the boxplot code to display the exercise groups with jittered points.
#Boxplot 1: Lift Group
#There are dots outside the boxplot.
#The dots are not close to the whiskers.
#The dots are very far from the whiskers.
#The outliers are not balanced.
#Based on these findings, the boxplot is not normal.
#Boxplot 2: No Lift Group
#There is one dot outside the boxplot.
#The dot is not very close to the whisker.
#The dot is very far from the whisker.
#The outliers are not balanced.
#Based on these findings, the boxplot is not normal.
shapiro.test(A6Q4$Weight[A6Q4$Exercise == "lift"])
##
## Shapiro-Wilk normality test
##
## data: A6Q4$Weight[A6Q4$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436
shapiro.test(A6Q4$Weight[A6Q4$Exercise == "nolift"])
##
## Shapiro-Wilk normality test
##
## data: A6Q4$Weight[A6Q4$Exercise == "nolift"]
## W = 0.70002, p-value = 7.294e-06
#Shapiro-Wilk Test: Lift Group
#The lift group is not normally distributed (p < .001).
#Overall Normality
#The lift group is not normally distributed.
#Shapiro-Wilk Test: No Lift Group
#The no lift group is not normally distributed (p < .001).
#Overall Normality
#The no lift group is not normally distributed.
#CORRECTION: I updated the Shapiro-Wilk interpretations to include the exact rounded p-values.
wilcox.test(Weight ~ Exercise, data = A6Q4)
##
## 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)
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 body weight (kg) between the participants that lift weights versus the participants that do not lift weights.
#The body weight for participants that lift weights (Mdn = 116.00) were significantly different from the body weight of participants that do not lift weights (Mdn = 40.80), U = 603, p < .001.
#The effect size was large, Cliff's Delta = .93.
#CORRECTION: I corrected the No Lift median and wording in my final report.