AQ63

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) 


A6Q32<- read_excel("C:/Users/NITHIN KUMAR/OneDrive/Desktop/Form 1098-T/A6Q32.xlsx")  
colnames(A6Q32) <- c("exercise", "bodyweight_kg")
A6Q32 %>%
  group_by(exercise) %>%
  summarise(
    Mean = mean(bodyweight_kg, na.rm = TRUE),
    Median = median(bodyweight_kg, na.rm = TRUE),
    SD = sd(bodyweight_kg, na.rm = TRUE),
    N = n()
  )  

## # A tibble: 2 × 5
##   exercise  Mean Median    SD     N
##   <chr>    <dbl>  <dbl> <dbl> <int>
## 1 cardio    74.7   73.3  7.57    25
## 2 nocardio  70.8   69.5  7.35    25

hist(A6Q32$bodyweight_kg[A6Q32$exercise == "cardio"],
     main = "Histogram of cardio",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q32$bodyweight_kg[A6Q32$exercise == "nocardio"],
     main = "Histogram of nocardio",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 10) 

#Group 1: cardio
#The first variable looks normally distributed.
#The data is symmetrical.
#The data has  a proper bell curve.


#Group 2: NOCARDIO
#The second variable looks normally distributed.
#The data is POSTIVE SKEWED.
#The data has a proper bell curve. 
ggboxplot(A6Q32, x = "exercise", y = "bodyweight_kg",
          color = "exercise",
          palette = "jco",
          add = "jitter") 

#Boxplot 1: Nocardio
#There are NO dots outside the boxplot.
#The dots are close to the whiskers.
#The dots are not very far away from the whiskers.
#The outliers  are  balanced.
#Based on these findings, the boxplot is   normal.

#Boxplot 2: Cardio
#There are NO dots outside the boxplot.
#The dots are close to the whiskers.
#The dots are not very far away from the whiskers.
#The outliers are balanced.
#Based on these findings, the boxplot is normal

shapiro.test(A6Q32$bodyweight_kg[A6Q32$exercise == "nocardio"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q32$bodyweight_kg[A6Q32$exercise == "nocardio"]
## W = 0.97686, p-value = 0.8166

shapiro.test(A6Q32$bodyweight_kg[A6Q32$exercise == "cardio"])  

## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q32$bodyweight_kg[A6Q32$exercise == "cardio"]
## W = 0.96745, p-value = 0.5812

#Group 1: nocardio
#The first group is normally distributed, (p = 0.8166).

#Group 2: cardio
#The second group is normally distributed, (p = 0.5812). 

t.test(bodyweight_kg ~ exercise, data = A6Q32, var.equal = TRUE) 

## 
##  Two Sample t-test
## 
## data:  bodyweight_kg by exercise
## t = 1.8552, df = 48, p-value = 0.06971
## alternative hypothesis: true difference in means between group cardio and group nocardio is not equal to 0
## 95 percent confidence interval:
##  -0.3280454  8.1605622
## sample estimates:
##   mean in group cardio mean in group nocardio 
##               74.73336               70.81710

#An Independent T-Test was conducted to determine if there was a difference in OutcomeVariable between nocardio and cardio.
#nocardio scores (M = 70.8, SD = 7.35) were significantly different from cardio scores (M = 74.7, SD = 7.57), t(df#) = 48, p =0.06971.