library(readxl)
library(ggplot2)
ruta_excel <- "C:\\Users\\jdom3\\Desktop\\Datos tesis.xlsx"
Analisisprev1.2 <- read_excel(ruta_excel, sheet = 'Preventivo - Exp 1 vertical')
Analisisprev1.2 <- na.omit(Analisisprev1.2)
Analisisprev1.2
## # A tibble: 168 × 4
## Numero_petalo Tratamiento Dia Area_herida
## <dbl> <chr> <dbl> <dbl>
## 1 10 Control comercial 1 0
## 2 17 Control comercial 1 0.194
## 3 43 Control comercial 1 0.965
## 4 54 Control comercial 1 0
## 5 98 Control comercial 1 0.242
## 6 32 100 ppm 1 0
## 7 63 100 ppm 1 0.269
## 8 80 100 ppm 1 0.088
## 9 81 100 ppm 1 0.464
## 10 85 100 ppm 1 0
## # ℹ 158 more rows
library(agricolae)
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
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
Analisisprev1.2 <- Analisisprev1.2 %>%
group_by(Numero_petalo) %>% mutate(Max_dia = max(Dia))
Tablaaudpcprev1 <- Analisisprev1.2 %>%
group_by(Numero_petalo,Tratamiento, Max_dia) %>%
summarise(across(Area_herida, ~ audpc(.x, Dia)))
## `summarise()` has grouped output by 'Numero_petalo', 'Tratamiento'. You can
## override using the `.groups` argument.
Tablaaudpcprev1 <- Tablaaudpcprev1 %>%
group_by(Numero_petalo) %>% mutate(sAUDPC = Area_herida/Max_dia)
Tablaaudpcprev1 <- Tablaaudpcprev1[with(Tablaaudpcprev1,order(Tablaaudpcprev1$Tratamiento)), ]
Tablaaudpcprev1
## # A tibble: 39 × 5
## # Groups: Numero_petalo [39]
## Numero_petalo Tratamiento Max_dia Area_herida sAUDPC
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 5 1 ppm 5 12.5 2.50
## 2 14 1 ppm 5 17.0 3.40
## 3 21 1 ppm 4 8.31 2.08
## 4 27 1 ppm 4 9.41 2.35
## 5 29 1 ppm 5 16.6 3.32
## 6 36 1 ppm 5 16.9 3.39
## 7 48 1 ppm 5 16.8 3.36
## 8 32 100 ppm 4 6.52 1.63
## 9 63 100 ppm 5 10.2 2.05
## 10 80 100 ppm 5 14.2 2.85
## # ℹ 29 more rows
ggplot(data = Tablaaudpcprev1, aes(x = Tratamiento, y = sAUDPC, color = Tratamiento)) +
geom_boxplot() +
theme_bw()
anovapr1 <- aov(Tablaaudpcprev1$sAUDPC ~ Tablaaudpcprev1$Tratamiento )
summary(anovapr1)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tablaaudpcprev1$Tratamiento 4 16.47 4.118 9.779 2.24e-05 ***
## Residuals 34 14.32 0.421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aovpr1_residuals <- residuals(object = anovapr1 )
hist(aovpr1_residuals)
shapiro.test(x = aovpr1_residuals)
##
## Shapiro-Wilk normality test
##
## data: aovpr1_residuals
## W = 0.96628, p-value = 0.2867
plot(anovapr1,2)
bartlett.test(aovpr1_residuals ~ Tablaaudpcprev1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: aovpr1_residuals by Tablaaudpcprev1$Tratamiento
## Bartlett's K-squared = 0.84327, df = 4, p-value = 0.9326
plot(aovpr1_residuals, pch = 20)
Tukeypr1 <- TukeyHSD(anovapr1)
library(multcompView)
tukeyletraspr1 <- multcompLetters4(anovapr1, Tukeypr1)
print(tukeyletraspr1)
## $`Tablaaudpcprev1$Tratamiento`
## 1 ppm Control comercial 100 ppm
## "a" "a" "a"
## 50 ppm Control absoluto\r\n
## "a" "b"
Tablafinalpr1 <- group_by(Tablaaudpcprev1,Tratamiento) %>%
summarise(mean=mean(sAUDPC), sd=sd(sAUDPC)) %>%
arrange(desc(mean))
Tablafinalpr1
## # A tibble: 5 × 3
## Tratamiento mean sd
## <chr> <dbl> <dbl>
## 1 "1 ppm" 2.91 0.578
## 2 "Control comercial" 2.89 0.835
## 3 "100 ppm" 2.63 0.626
## 4 "50 ppm" 2.58 0.667
## 5 "Control absoluto\r\n" 1.27 0.598
letraspr1 <- as.data.frame.list(tukeyletraspr1$`Tablaaudpcprev1$Tratamiento`)
Tablafinalpr1f <- Tablafinalpr1 %>%
group_by() %>%
mutate( Tukey = letraspr1$Letters)
print(Tablafinalpr1f)
## # A tibble: 5 × 4
## Tratamiento mean sd Tukey
## <chr> <dbl> <dbl> <chr>
## 1 "1 ppm" 2.91 0.578 a
## 2 "Control comercial" 2.89 0.835 a
## 3 "100 ppm" 2.63 0.626 a
## 4 "50 ppm" 2.58 0.667 a
## 5 "Control absoluto\r\n" 1.27 0.598 b
ggplot(Tablafinalpr1f, aes(x = Tratamiento, y = mean, fill =Tratamiento)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.5, colour = "gray25") +
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), position = position_dodge(0.9), width = 0.25,
show.legend = FALSE, colour = "gray25") +
labs(x="Tratamientos", y="sAUDPC") +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
geom_text(aes(label=Tukey), position = position_dodge(0.90), size = 3,
vjust=-0.8, hjust=-0.5, colour = "gray25") +
ylim(0, 4) +
scale_fill_grey()
