getwd()
## [1] "C:/Users/OCN/Documents"
setwd("C:/Users/OCN/Documents")

myamandamovie<-read.csv("C:/Users/OCN/Documents/Archie4.csv")

###Install Library Package for ANOVA##

library(agricolae)
names(myamandamovie)
## [1] "Treatment"       "Number.of.Roots"
head(myamandamovie)
str(myamandamovie)
## 'data.frame':    50 obs. of  2 variables:
##  $ Treatment      : chr  "T1-IBA 100 ppm" "T1-IBA 100 ppm" "T1-IBA 100 ppm" "T1-IBA 100 ppm" ...
##  $ Number.of.Roots: num  19 18 11 15 15 17 18 17 16 15 ...
hist(myamandamovie$Number.of.Roots)

##ANOVA and Shapiro Wilk Test for Normality###

Piolo<-aov(Number.of.Roots~Treatment, data=myamandamovie)
summary(Piolo)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## Treatment    3  267.5   89.16   17.55 9.81e-08 ***
## Residuals   46  233.7    5.08                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
shapiro.test(Piolo$residuals)
## 
##  Shapiro-Wilk normality test
## 
## data:  Piolo$residuals
## W = 0.9773, p-value = 0.4446

###Bartlett Test for Homogeneity of Variance##

bartlett.test(Number.of.Roots~Treatment, data=myamandamovie)
## 
##  Bartlett test of homogeneity of variances
## 
## data:  Number.of.Roots by Treatment
## Bartlett's K-squared = 6.2728, df = 3, p-value = 0.09907

###Checking of Assumptions###

plot (Piolo)

##Install Packages for Graphs and Analysis##

library(ggplot2)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## v purrr   0.3.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(ggpubr)
library(rstatix)
## 
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
## 
##     filter

##Boxplot with Jitter##

ggboxplot(myamandamovie, x = "Treatment", y = "Number.of.Roots", add = "jitter")

##Simple Boxplot##

boxplot(Number.of.Roots~Treatment, data = myamandamovie)

##Colored BOxplot##

ggplot(myamandamovie, aes(x=Treatment, y=Number.of.Roots)) + 
    geom_boxplot(color="red", fill="blue", alpha=0.5)

Post Hoc Analysis

TukeyHSD(Piolo, which = 'Treatment')
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Number.of.Roots ~ Treatment, data = myamandamovie)
## 
## $Treatment
##                                 diff        lwr        upr     p adj
## T2-35% GA -T1-IBA 100 ppm  -3.600000 -5.7939608 -1.4060392 0.0003923
## T3-Mykovam-T1-IBA 100 ppm   1.733333 -0.4606274  3.9272941 0.1664838
## T4- CONTROL-T1-IBA 100 ppm -3.800000 -6.9027291 -0.6972709 0.0107791
## T3-Mykovam-T2-35% GA        5.333333  3.1393726  7.5272941 0.0000003
## T4- CONTROL-T2-35% GA      -0.200000 -3.3027291  2.9027291 0.9981704
## T4- CONTROL-T3-Mykovam     -5.533333 -8.6360624 -2.4306043 0.0001147
tukey.test2 <- HSD.test(Piolo,trt = 'Treatment')
tukey.test2
## $statistics
##    MSerror Df  Mean       CV
##   5.081159 46 15.66 14.39427
## 
## $parameters
##    test    name.t ntr StudentizedRange alpha
##   Tukey Treatment   4         3.769581  0.05
## 
## $means
##                Number.of.Roots      std  r Min Max  Q25 Q50 Q75
## T1-IBA 100 ppm        16.60000 2.261479 15  11  21 15.5  17  18
## T2-35% GA             13.00000 1.603567 15   9  15 12.0  13  14
## T3-Mykovam            18.33333 2.919556 15  14  25 16.5  18  20
## T4- CONTROL           12.80000 1.303840  5  11  14 12.0  13  14
## 
## $comparison
## NULL
## 
## $groups
##                Number.of.Roots groups
## T3-Mykovam            18.33333      a
## T1-IBA 100 ppm        16.60000      a
## T2-35% GA             13.00000      b
## T4- CONTROL           12.80000      b
## 
## attr(,"class")
## [1] "group"