library(readxl)
library(effsize)
library(rstatix)

## 
## Attaching package: 'rstatix'

## The following object is masked from 'package:stats':
## 
##     filter

library(ggpubr)

## Loading required package: ggplot2

library(effectsize)

## 
## Attaching package: 'effectsize'

## The following objects are masked from 'package:rstatix':
## 
##     cohens_d, eta_squared, omega_squared

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

Team1Data <- read_excel("//apporto.com/dfs/SLU/Users/brentgallagher_slu/Desktop/Team1Data.xlsx")

BeforeComm <- Team1Data$BeforeComm
AfterComm <- Team1Data$AfterComm

Differences <- AfterComm - BeforeComm

mean(BeforeComm, na.rm = TRUE)

## [1] 64.48

median(BeforeComm, na.rm = TRUE)

## [1] 65

sd(BeforeComm, na.rm = TRUE)

## [1] 10.42189

# BeforeComm: Mean: 64.48, Median: 65, SD: 10.42

mean(AfterComm, na.rm = TRUE)

## [1] 75.12

median(AfterComm, na.rm = TRUE)

## [1] 76

sd(AfterComm, na.rm = TRUE)

## [1] 11.90644

# AfterComm: Mean: 75.12, Median: 76, SD: 11.90

hist(Differences,
     breaks = 15,
     col = "blue",
     border = "white")

boxplot(Differences,
        main = "Differences in Scores",
        col = "blue",
        border = "darkblue")

# The difference scores boxplot has no outliers.

shapiro.test(Differences)

## 
##  Shapiro-Wilk normality test
## 
## data:  Differences
## W = 0.95856, p-value = 0.003177

# W = 0.96, p-value = 0.003177
# The data is not normally distributed. p-value 0.003177 < 0.05

wilcox.test(BeforeComm, AfterComm, paired = TRUE, na.action = na.omit)

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  BeforeComm and AfterComm
## V = 22, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0

# Data: Before and AfterComm: v = 22, p-value < 2.2e-16.
# Alternative hypothesis: true location shift is not equal to 0.

df_long <- data.frame(id = 
rep(1:length(BeforeComm), 2), time = 
rep(c("BeforeComm", "AfterComm"), each =
length(BeforeComm)), score = c(BeforeComm,
AfterComm))

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 AfterComm BeforeComm   0.850   100   100 large

# A tibble: 1 x 7
# Effsize: 0.85, n1: 100, n2: 100, magnitude large

# A Wilcoxon Signed-Rank Test was conducted to determine if there was a difference in communication effectiveness increase in Team 1,after versus before workshop.
# Before scores (Mdn = 65) were significantly different from after scores (Mdn = 76), v = 22, p-value = 2.2 e-16.
# The effect size was large, 0.85.
# Communication effectiveness increased for Team 1 after the workshop.