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library(rpart)
library(rpart.plot)
## Warning: package 'rpart.plot' was built under R version 4.0.3
library(rattle)
## Warning: package 'rattle' was built under R version 4.0.3
## Loading required package: tibble
## Loading required package: bitops
## Rattle: A free graphical interface for data science with R.
## Version 5.4.0 Copyright (c) 2006-2020 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.
library(gganimate)
## Warning: package 'gganimate' was built under R version 4.0.3
## Loading required package: ggplot2
library(av)
## Warning: package 'av' was built under R version 4.0.3
library(cluster)
library(dendextend)
## Warning: package 'dendextend' was built under R version 4.0.3
##
## ---------------------
## Welcome to dendextend version 1.14.0
## Type citation('dendextend') for how to cite the package.
##
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
##
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## Or contact: <tal.galili@gmail.com>
##
## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
## ---------------------
##
## Attaching package: 'dendextend'
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## prune
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## cutree
library(factoextra)
## Warning: package 'factoextra' was built under R version 4.0.3
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(gridExtra)
library(sjPlot)
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## Registered S3 method overwritten by 'parameters':
## method from
## predict.kmeans rattle
library(FactoMineR)
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library(tidyverse)
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## v readr 1.3.1 v stringr 1.4.0
## v purrr 0.3.4 v forcats 0.5.0
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library(ggrepel)
library(kableExtra)
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## group_rows
library(knitr)
library(cluster)
library(factoextra)
library(PerformanceAnalytics)
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## as.Date, as.Date.numeric
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## legend
library(psych)
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## %+%, alpha
library(amap)
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## pca
library(ade4)
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## reconst
library(fastDummies)
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library(nnet)
library(kableExtra)
library(jtools)
library(fastDummies)
library(dplyr)
library(plotly)
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library(tidyverse)
library(ggrepel)
library(esquisse)
library(ggplot2)
library(gridExtra)
library(outliers)
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## outlier
library(readxl)
library(agricolae)
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## kurtosis, skewness
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## hcut
dt <- read_excel("C:/Users/Alex/Desktop/Angelica/exp.xlsx",
sheet = "sclerotinia")
attach(dt)
head(dt)
## # A tibble: 6 x 6
## VETOR TRAT BLOCO numplantasmortas mortes asen
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 MT inoc BLOCO1 1 20 0.464
## 2 VAZIO inoc BLOCO1 4 80 1.11
## 3 VF inoc BLOCO1 1 20 0.464
## 4 VF.1 inoc BLOCO1 5 100 1.57
## 5 VG inoc BLOCO1 1 20 0.464
## 6 MT seminoc BLOCO1 0 0 0
#2. ANOVA e normalidade Peso pupa##############
summary(aov(asen ~ VETOR*TRAT, data = dt))
## Df Sum Sq Mean Sq F value Pr(>F)
## VETOR 4 1.434 0.358 6.556 0.000647 ***
## TRAT 1 3.319 3.319 60.710 1.08e-08 ***
## VETOR:TRAT 4 1.434 0.358 6.556 0.000647 ***
## Residuals 30 1.640 0.055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
shapt <-shapiro.test(unlist(aov(asen ~ VETOR*TRAT, data = dt)["residuals"])); print(shapt)
##
## Shapiro-Wilk normality test
##
## data: unlist(aov(asen ~ VETOR * TRAT, data = dt)["residuals"])
## W = 0.82237, p-value = 2.053e-05
my_residuals_asen<-unlist(aov(asen ~ VETOR*TRAT, data = dt)["residuals"])
hist(my_residuals_asen)
ks.test(my_residuals_asen, pnorm)$statistic
## Warning in ks.test(my_residuals_asen, pnorm): ties should not be present for the
## Kolmogorov-Smirnov test
## D
## 0.3538612
a<-as.factor(VETOR)
b<-as.factor(TRAT)
inter<-as.factor(a:b)
kruskal.test(asen ~ inter, data = dt)
##
## Kruskal-Wallis rank sum test
##
## data: asen by inter
## Kruskal-Wallis chi-squared = 31.713, df = 9, p-value = 0.0002232
kruskal(asen, inter)["groups"]
## $groups
## asen groups
## VAZIO:inoc 37.500 a
## VF.1:inoc 33.500 ab
## MT:inoc 26.500 bc
## VG:inoc 25.375 c
## VF:inoc 17.125 d
## MT:seminoc 13.000 d
## VAZIO:seminoc 13.000 d
## VF.1:seminoc 13.000 d
## VF:seminoc 13.000 d
## VG:seminoc 13.000 d
my_mean<-LSD.test(aov(asen ~ inter, data = dt),
"inter", alpha = 0.05)["groups"]
datos<-as.data.frame(my_mean)
names(datos)<- c("trat", "mean")
datos2<-datos[1:5,]
datos2["vetores"]<-c("a", "b","c", "d", "e" )
attach(datos2)
vetores2<-factor(vetores, levels=c("c", "a", "e", "b", "d"))
dt <- read_excel("C:/Users/Alex/Desktop/Angelica/exp.xlsx",
sheet = "mediassclero")
my_labels<- c("WT", "pCAMBIA2201",
"Tc-PR1-f", "Tc-PR1-f.1","Tc-PR1-g")
attach(dt)
## The following object is masked from datos2:
##
## trat
ggplot(data=dt, aes(x=vetor, y=mmortes2, fill= trat )) +
geom_bar(stat="identity", color ="black", position = "dodge")+
geom_errorbar(data = dt, aes(ymin = mmortes2 , ymax = mmortes2 + errop),
width = 0.5, position = position_dodge(width = .9))+
labs(x = NULL, y = "Percentage of death plants (%)", fill = "Tratment",
position = position_dodge(1), size=5)+
scale_fill_manual(values=c("black", "white",position = "dodge"),
labels = c("Inoculated", "Mock") )+
scale_x_discrete(labels=my_labels)+
geom_text(data=dt, aes(x = vetor, y = mmortes2+errop +4, label = sigtest,
vjust=0, size=0), position = position_dodge(0.9),
size=5) +
theme_classic()
###########Figuras floração seca##########################
dt <- read_excel("C:/Users/Alex/Desktop/Angelica/exp.xlsx",
sheet = "secaexp1")
attach(dt)
head(dt)
## # A tibble: 6 x 5
## Vetor Planta CC Rept dias
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 VF 1 100 3 11
## 2 MT 2 30 3 10
## 3 MT 3 100 2 13
## 4 VG 4 100 1 18
## 5 VG 5 60 2 19
## 6 VØ 6 30 4 17
summary(aov(dias~Vetor*CC, data=dt))
## Df Sum Sq Mean Sq F value Pr(>F)
## Vetor 3 238.9 79.62 2.691 0.0557 .
## CC 1 0.0 0.03 0.001 0.9759
## Vetor:CC 3 93.2 31.05 1.050 0.3786
## Residuals 52 1538.5 29.59
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
shapt <-shapiro.test(unlist(aov(dias ~ Vetor*CC, data = dt)["residuals"])); print(shapt)
##
## Shapiro-Wilk normality test
##
## data: unlist(aov(dias ~ Vetor * CC, data = dt)["residuals"])
## W = 0.97375, p-value = 0.2217
my_residuals_asen<-unlist(aov(dias ~ Vetor*CC, data = dt)["residuals"])
hist(my_residuals_asen)
ks.test(my_residuals_asen, pnorm)$statistic
## Warning in ks.test(my_residuals_asen, pnorm): ties should not be present for the
## Kolmogorov-Smirnov test
## D
## 0.4041489
a<-as.factor(Vetor)
b<-as.factor(CC)
inter<-as.factor(a:b)
kruskal.test(dias ~ inter, data = dt)
##
## Kruskal-Wallis rank sum test
##
## data: dias by inter
## Kruskal-Wallis chi-squared = 11.389, df = 11, p-value = 0.4113
kruskal(dias, inter)["groups"]
## $groups
## dias groups
## VG:100 41.2 a
## VG:60 40.7 a
## MT:30 39.0 a
## MT:60 38.6 a
## VØ:30 32.6 a
## MT:100 31.7 a
## VF:100 28.8 a
## VG:30 26.3 a
## VF:60 22.6 a
## VF:30 22.2 a
## VØ:100 22.0 a
## VØ:60 20.3 a
my_mean<-LSD.test(aov(dias ~ inter, data = dt),
"inter", alpha = 0.05)["groups"];print(my_mean)
## $groups
## dias groups
## VG:100 16.6 a
## VG:60 16.6 a
## MT:30 16.0 ab
## MT:60 15.2 ab
## MT:100 13.8 ab
## VØ:30 13.4 ab
## VF:100 12.2 ab
## VG:30 12.2 ab
## VF:60 11.2 ab
## VF:30 10.8 ab
## VØ:100 9.6 ab
## VØ:60 9.4 b
###########Figuras floração Monilioptora ##########################
dt <- read_excel("C:/Users/Alex/Desktop/Angelica/exp2.xlsx",
sheet = "MP")
attach(dt)
## The following object is masked from dt (pos = 3):
##
## dias
## The following object is masked from dt (pos = 4):
##
## trat
## The following object is masked from datos2:
##
## trat
head(dt)
## # A tibble: 6 x 24
## Número genot Bloco trat dias `1` `2` `3` `4` `5` `6` `7` `8`
## <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 VF.1 Bloc~ Inoc~ 4 <NA> <NA> <NA> x <NA> <NA> <NA> <NA>
## 2 2 VF Bloc~ Mock 7 <NA> <NA> <NA> <NA> <NA> <NA> x <NA>
## 3 3 VF.1 Bloc~ Mock 2 <NA> x <NA> <NA> <NA> <NA> <NA> <NA>
## 4 4 MT Bloc~ Mock 17 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 5 VG Bloc~ Inoc~ 11 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 6 VF Bloc~ Mock 12 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## # ... with 11 more variables: `9` <chr>, `10` <chr>, `11` <chr>, `12` <chr>,
## # `13` <chr>, `14` <chr>, `15` <chr>, `16` <chr>, `17` <chr>, `18` <chr>,
## # `19` <chr>
summary(aov(dias~genot*trat, data=dt))
## Df Sum Sq Mean Sq F value Pr(>F)
## genot 4 543.7 135.91 5.782 0.000254 ***
## trat 1 0.0 0.00 0.000 0.991187
## genot:trat 4 161.2 40.31 1.715 0.150436
## Residuals 133 3126.5 23.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingness
shapt <-shapiro.test(unlist(aov(dias ~ genot*trat, data = dt)["residuals"])); print(shapt)
##
## Shapiro-Wilk normality test
##
## data: unlist(aov(dias ~ genot * trat, data = dt)["residuals"])
## W = 0.97293, p-value = 0.006175
my_residuals_asen<-unlist(aov(dias ~ genot*trat, data = dt)["residuals"])
hist(my_residuals_asen)
ks.test(my_residuals_asen, pnorm)$statistic
## Warning in ks.test(my_residuals_asen, pnorm): ties should not be present for the
## Kolmogorov-Smirnov test
## D
## 0.3626711
a<-as.factor(genot)
b<-as.factor(trat)
inter<-as.factor(a:b)
kruskal.test(dias ~ inter, data = dt)
##
## Kruskal-Wallis rank sum test
##
## data: dias by inter
## Kruskal-Wallis chi-squared = 25.853, df = 9, p-value = 0.002159
kruskal(dias, inter)["groups"]
## $groups
## dias groups
## MT:Inoculated 104.50000 a
## MT:Mock 86.26471 ab
## VF.1:Inoculated 82.75000 abc
## VF.1:Mock 82.17857 abcd
## VAZIO:Mock 81.80556 abcd
## VG:Mock 71.00000 bcd
## VAZIO:Inoculated 62.50000 bcde
## VF:Inoculated 57.13333 cde
## VG:Inoculated 53.42857 de
## VF:Mock 39.25000 e
my_mean<-LSD.test(aov(dias ~ inter, data = dt),
"inter", alpha = 0.05)["groups"];print(my_mean)
## $groups
## dias groups
## MT:Inoculated 14.090909 a
## MT:Mock 11.294118 ab
## VF.1:Mock 11.142857 abc
## VAZIO:Mock 11.111111 abc
## VF.1:Inoculated 11.000000 abcd
## VG:Mock 9.642857 bcd
## VAZIO:Inoculated 8.666667 bcde
## VF:Inoculated 7.800000 cde
## VG:Inoculated 7.428571 de
## VF:Mock 5.714286 e
my_mean<-LSD.test(aov(dias ~ genot, data = dt),
"genot", alpha = 0.05)["groups"];print(my_mean)
## $groups
## dias groups
## MT 12.392857 a
## VF.1 11.071429 ab
## VAZIO 10.133333 ab
## VG 8.535714 bc
## VF 6.793103 c
dt <- read_excel("C:/Users/Alex/Desktop/Angelica/exp2.xlsx",
sheet = "MP3")
my_labels<- c("WT", "pCAMBIA2201",
"Tc-PR1-f", "Tc-PR1-f.1","Tc-PR1-g")
attach(dt)
## The following object is masked from dt (pos = 3):
##
## genot
## The following objects are masked from dt (pos = 5):
##
## desv, errop, sigtest
head(dt)
## # A tibble: 5 x 5
## genot med desv errop sigtest
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 MT 12.4 4.76 0.674 a
## 2 VAZIO 10.1 5.41 0.765 ab
## 3 VF 6.79 4.20 0.594 ab
## 4 VF.1 11.1 5.35 0.757 bc
## 5 VG 8.54 4.61 0.653 c
ggplot(data=dt, aes(x=genot, y=med )) +
geom_bar(stat="identity", position = "dodge")+
geom_errorbar(data = dt, aes(ymin = med - errop, ymax = med + errop), width=0.5)+
labs(x = NULL, y = "Days to flowering",
size=5)+
scale_x_discrete(labels=my_labels)+
geom_text(data=dt, aes(x = genot, y = med+errop +0.5, label = sigtest,
vjust=0, size=0), size=5) +
theme_classic()
