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)
Team1vTeam2 <- read_excel("//apporto.com/dfs/SLU/Users/brentgallagher_slu/Desktop/Team1vTeam2.xlsx")
Team1vTeam2 %>%
  group_by(Team) %>%
  summarise(
    Mean = mean(CommScore, na.rm = TRUE),
    Median = median(CommScore, na.rm = TRUE),
    SD = sd(CommScore, na.rm = TRUE),
    N = n()
  )

## # A tibble: 2 × 5
##    Team  Mean Median    SD     N
##   <dbl> <dbl>  <dbl> <dbl> <int>
## 1     1  75.1     76  11.9   100
## 2     2  73.1     76  12.0   100

# A tibble: 2 x 5
# Team 1: Mean 75.1, Median 76, SD 11.9, N 100
# Team 2: Mean 73.1, Median 76, SD 12.0, N 100

hist(Team1vTeam2$CommScore[Team1vTeam2$Team == "1"],
     breaks = 15,
     col = "skyblue",
     border = "white")

hist(Team1vTeam2$CommScore[Team1vTeam2$Team == "2"],
     breaks = 15,
     col = "firebrick",
     border = "white")

# Data for Team 1 appears abnormally distributed.
# Data for Team 2 appears abnormally distributed.

ggboxplot(Team1vTeam2, x = "Team", y = "CommScore",
          color = "Team",
          palette = "jco",
          add = "jitter")

# Team 1 does have outliers.
# Team 2 does not have outliers.

shapiro.test(Team1vTeam2$CommScore[Team1vTeam2$Team == "1"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Team1vTeam2$CommScore[Team1vTeam2$Team == "1"]
## W = 0.98466, p-value = 0.2999

shapiro.test(Team1vTeam2$CommScore[Team1vTeam2$Team == "2"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Team1vTeam2$CommScore[Team1vTeam2$Team == "2"]
## W = 0.96301, p-value = 0.006629

# Shapiro-wilk normality test, Team 1: W = 0.98, p-value = 0.2999
# Shapiro-wilk normality test, Team 2: w = 0.96, p-value = 0.006629

# Team 1 is normally distributed p > .05.
# Team 2 is abnormally distributed p < .05.

wilcox.test(CommScore ~ 
Team, data = Team1vTeam2)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  CommScore by Team
## W = 5422, p-value = 0.3021
## alternative hypothesis: true location shift is not equal to 0

# Wilcoxon rank sum test: w = 5422, p-value = 0.3021. Alternative hypothesis: true location shift is not equal to 0.

mw_effect <- cliff.delta(CommScore
~ Team, data = Team1vTeam2)
print(mw_effect)

## 
## Cliff's Delta
## 
## delta estimate: 0.0844 (negligible)
## 95 percent confidence interval:
##       lower       upper 
## -0.07620657  0.24073778

# Cliff's Delta: Delta estimate: 0.0844 (negligible),
# 95% confidence interval
# Lower: -0.07620657
# Upper: 0.24073778

# A Mann-Whitney test was conducted to determine if there was a difference in CommScore between Team 1 and Team 2.
# Team 1 scores (Mdn:76) were not significantly different from Team 2 scores (Mdn: 76).
# W = 5422, p-value = 0.3021.
# The effect size was small, Cliff's Delta = .08.