library(knitr)
## Warning: package 'knitr' was built under R version 4.0.5
getwd()
## [1] "C:/Users/Acer/Desktop"
setwd( "C:/Users/Acer/Desktop")
###Creating dataframe##Dataframe name spp##
facto<-read.csv( "C:/Users/Acer/Desktop/eggplant.csv")
###Verification of imported data###
names(facto)
## [1] "Factor.A" "Factor.B"
## [3] "Samples" "Week"
## [5] "Plant.ht..2wk" "Plant.ht..3wk"
## [7] "NoLvs..2wk" "NLvs..3wk"
## [9] "Canopy.Cover..1MAT" "Chlorophyll.Top...3wk"
## [11] "Chloropyll.Bottom..3.wk"
head(facto)
## Factor.A Factor.B Samples Week Plant.ht..2wk Plant.ht..3wk NoLvs..2wk
## 1 A1 B1 1 2 9.2 9.6 5
## 2 A1 B1 2 2 8.8 7.5 6
## 3 A1 B1 3 2 8.2 9.6 5
## 4 A1 B1 4 2 8.2 10.6 6
## 5 A1 B1 5 2 7.1 9.8 5
## 6 A1 B1 6 2 9.6 11.4 7
## NLvs..3wk Canopy.Cover..1MAT Chlorophyll.Top...3wk Chloropyll.Bottom..3.wk
## 1 6 6.28 NA NA
## 2 NA 11.82 NA NA
## 3 NA 8.34 NA NA
## 4 NA 5.80 NA NA
## 5 NA 9.17 NA NA
## 6 NA 12.71 NA NA
str(facto)
## 'data.frame': 24 obs. of 11 variables:
## $ Factor.A : chr "A1" "A1" "A1" "A1" ...
## $ Factor.B : chr "B1" "B1" "B1" "B1" ...
## $ Samples : int 1 2 3 4 5 6 1 2 3 4 ...
## $ Week : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Plant.ht..2wk : num 9.2 8.8 8.2 8.2 7.1 9.6 5.5 5.5 8.5 7.5 ...
## $ Plant.ht..3wk : num 9.6 7.5 9.6 10.6 9.8 11.4 10.4 11.3 11.6 10 ...
## $ NoLvs..2wk : int 5 6 5 6 5 7 4 4 5 6 ...
## $ NLvs..3wk : int 6 NA NA NA NA NA 9 6 3 7 ...
## $ Canopy.Cover..1MAT : num 6.28 11.82 8.34 5.8 9.17 ...
## $ Chlorophyll.Top...3wk : logi NA NA NA NA NA NA ...
## $ Chloropyll.Bottom..3.wk: logi NA NA NA NA NA NA ...
###installing libraries###
library (agricolae)
## Warning: package 'agricolae' was built under R version 4.0.5
library(ggplot2)
###Anova Factorial##
factoanov<-aov(Canopy.Cover..1MAT~Factor.A*Factor.B, data = facto)
summary(factoanov)
## Df Sum Sq Mean Sq F value Pr(>F)
## Factor.A 1 2.7 2.7 0.081 0.7791
## Factor.B 1 2347.1 2347.1 69.737 5.97e-08 ***
## Factor.A:Factor.B 1 155.2 155.2 4.613 0.0442 *
## Residuals 20 673.1 33.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
###interaction plots###
with(facto, interaction.plot(x.factor = Factor.B,
trace.factor = Factor.A,
response = Canopy.Cover..1MAT))
###interaction plots with error bars###
library (ggpubr)
## Warning: package 'ggpubr' was built under R version 4.0.5
ggline(facto, x = "Factor.B", y = "Canopy.Cover..1MAT", color = "Factor.A",
add = c("mean_se", "dotplot"),
palette = c("#0384fc", "gold"))
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
####Factorial Post Hoc, Tukey HSD##
tukey<-TukeyHSD(factoanov)
library(multcompView)
tukey.cld <- multcompLetters4(factoanov, tukey)
print(tukey.cld)
## $Factor.A
## $Factor.A$Letters
## A1 A2
## "a" "a"
##
## $Factor.A$LetterMatrix
## a
## A1 TRUE
## A2 TRUE
##
##
## $Factor.B
## B2 B1
## "a" "b"
##
## $`Factor.A:Factor.B`
## A2:B2 A1:B2 A1:B1 A2:B1
## "a" "a" "b" "b"