RCBD Factorial with Inteaction Plot

library(knitr)

## Warning: package 'knitr' was built under R version 4.0.5

##Get working directory##

getwd()

## [1] "C:/Users/Acer/Desktop"

setwd("C:/Users/Acer/Desktop")

##Set data frame###

dataframe<-read.csv("C:/Users/Acer/Desktop/eggplant.csv")

###load prerequesite##
library(agricolae)

## Warning: package 'agricolae' was built under R version 4.0.5

##Check the data###

names(dataframe)

## [1] "Factor.A"     "Factor.B"     "Samples"      "Plant.ht.2wk" "NoLvs"

##run ANOVA Factorial##

Anova<-aov(Plant.ht.2wk~Factor.A*Factor.B, data=dataframe)

summary(Anova)

##                   Df Sum Sq Mean Sq F value  Pr(>F)   
## Factor.A           1   7.37   7.370   6.376 0.02012 * 
## Factor.B           1   0.77   0.770   0.666 0.42389   
## Factor.A:Factor.B  1  10.27  10.270   8.885 0.00739 **
## Residuals         20  23.12   1.156                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

###Assumption checking##

shapiro.test(Anova$residuals)

## 
##  Shapiro-Wilk normality test
## 
## data:  Anova$residuals
## W = 0.9645, p-value = 0.5352

###Assumption plot###

par(mfrow=c(1,1))

plot (Anova)

## hat values (leverages) are all = 0.1666667
##  and there are no factor predictors; no plot no. 5

###interaction plot###

interaction.plot(x.factor = dataframe$Factor.B, #x-axis variable
                 trace.factor = dataframe$Factor.A, #variable for lines
                 response = dataframe$Plant.ht.2wk, #y-axis variable
                 fun = median, #metric to plot
                 ylab = "Number of Leaves",
                 xlab = "Growing Media",
                 col = c("pink", "blue"),
                 lty = 1, #line type
                 lwd = 2, #line width
                 trace.label = "Container Size")