library(readxl)
ruta_excel <- "C:\\Users\\jdom3\\Desktop\\Datos tesis.xlsx"
Curativo2df <- read_excel(ruta_excel, sheet = 'Curativo - Exp 2 vertical')
Curativo2df
## # A tibble: 143 × 4
## Numero_petalo Tratamiento Dia Area_herida
## <dbl> <chr> <dbl> <dbl>
## 1 5 "Control\r\n" 1 0.035
## 2 8 "Control\r\n" 1 0.161
## 3 30 "Control\r\n" 1 0
## 4 44 "Control\r\n" 1 0.113
## 5 48 "Control\r\n" 1 0.242
## 6 10 "Control comercial" 1 0.202
## 7 26 "Control comercial" 1 0.075
## 8 28 "Control comercial" 1 0.016
## 9 37 "Control comercial" 1 0.109
## 10 40 "Control comercial" 1 0
## # ℹ 133 more rows
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
dfcu2_100ppm = Curativo2df|>
group_by(Dia,Area_herida) |>
filter (Tratamiento=="100 ppm")
dfcu2_100ppm <- na.omit(dfcu2_100ppm)
dfcu2_100ppm
## # A tibble: 24 × 4
## # Groups: Dia, Area_herida [24]
## Numero_petalo Tratamiento Dia Area_herida
## <dbl> <chr> <dbl> <dbl>
## 1 3 100 ppm 1 0.114
## 2 15 100 ppm 1 0
## 3 29 100 ppm 1 0.152
## 4 34 100 ppm 1 0.083
## 5 42 100 ppm 1 0.068
## 6 46 100 ppm 1 0.133
## 7 3 100 ppm 2 0.249
## 8 15 100 ppm 2 0.202
## 9 29 100 ppm 2 0.565
## 10 34 100 ppm 2 0.363
## # ℹ 14 more rows
Boxplotcu2100ppm <- boxplot(dfcu2_100ppm$Area_herida ~ dfcu2_100ppm$Dia, frame.plot=F)
df_cu2100ppm <- dfcu2_100ppm[!(dfcu2_100ppm$Area_herida %in% Boxplotcu2100ppm$out),]
dfcu2100ppmprom <- dfcu2_100ppm %>%
group_by(Dia) %>%
summarise(Area_prom_cu2100ppm = mean(Area_herida))
dfcu2_1ppm = Curativo2df|>
group_by(Dia,Area_herida) |>
filter (Tratamiento=="1 ppm")
dfcu2_1ppm <- na.omit(dfcu2_1ppm)
dfcu2_1ppm
## # A tibble: 36 × 4
## # Groups: Dia, Area_herida [35]
## Numero_petalo Tratamiento Dia Area_herida
## <dbl> <chr> <dbl> <dbl>
## 1 1 1 ppm 1 0.108
## 2 4 1 ppm 1 0.06
## 3 9 1 ppm 1 0.186
## 4 16 1 ppm 1 0.293
## 5 18 1 ppm 1 0.179
## 6 19 1 ppm 1 0.169
## 7 31 1 ppm 1 0.047
## 8 35 1 ppm 1 0.061
## 9 38 1 ppm 1 0
## 10 1 1 ppm 2 0.305
## # ℹ 26 more rows
Boxplotcu21ppm <- boxplot(dfcu2_1ppm$Area_herida ~ dfcu2_1ppm$Dia, frame.plot=F)
dfcu2_1ppm <- dfcu2_1ppm[!(dfcu2_1ppm$Area_herida %in% Boxplotcu21ppm$out),]
dfcu21ppmprom <- dfcu2_1ppm %>%
group_by(Dia) %>%
summarise(Area_prom_cu21ppm = mean(Area_herida))
dfcu2_50ppm = Curativo2df|>
group_by(Dia,Area_herida) |>
filter (Tratamiento=="50 ppm")
dfcu2_50ppm <- na.omit(dfcu2_50ppm)
dfcu2_50ppm
## # A tibble: 31 × 4
## # Groups: Dia, Area_herida [31]
## Numero_petalo Tratamiento Dia Area_herida
## <dbl> <chr> <dbl> <dbl>
## 1 2 50 ppm 1 0
## 2 7 50 ppm 1 0.163
## 3 11 50 ppm 1 0.091
## 4 13 50 ppm 1 0.189
## 5 17 50 ppm 1 0.37
## 6 23 50 ppm 1 0.06
## 7 25 50 ppm 1 0.076
## 8 43 50 ppm 1 0.12
## 9 2 50 ppm 2 0.145
## 10 7 50 ppm 2 0.319
## # ℹ 21 more rows
Boxplotcu250ppm <- boxplot(dfcu2_50ppm$Area_herida ~ dfcu2_50ppm$Dia, frame.plot=F)
dfcu2_50ppm <- dfcu2_50ppm[!(dfcu2_50ppm$Area_herida %in% Boxplotcu250ppm$out),]
dfcu250ppmprom <- dfcu2_50ppm %>%
group_by(Dia) %>%
summarise(Area_prom_cu250ppm = mean(Area_herida))
dfcu2_Controlcomercial = Curativo2df|>
group_by(Dia,Area_herida) |>
filter (Tratamiento=="Control comercial")
dfcu2_Controlcomercial <- na.omit(dfcu2_Controlcomercial)
dfcu2_Controlcomercial
## # A tibble: 32 × 4
## # Groups: Dia, Area_herida [32]
## Numero_petalo Tratamiento Dia Area_herida
## <dbl> <chr> <dbl> <dbl>
## 1 10 Control comercial 1 0.202
## 2 26 Control comercial 1 0.075
## 3 28 Control comercial 1 0.016
## 4 37 Control comercial 1 0.109
## 5 40 Control comercial 1 0
## 6 47 Control comercial 1 0.151
## 7 49 Control comercial 1 0.171
## 8 50 Control comercial 1 0.168
## 9 10 Control comercial 2 0.919
## 10 26 Control comercial 2 0.338
## # ℹ 22 more rows
Boxplotcu2Controlcomercial <- boxplot(dfcu2_Controlcomercial$Area_herida ~ dfcu2_Controlcomercial$Dia, frame.plot=F)
dfcu2_Controlcomercial <- dfcu2_Controlcomercial[!(dfcu2_Controlcomercial$Area_herida %in% Boxplotcu2Controlcomercial$out),]
dfcu2Controlcomercialprom <- dfcu2_Controlcomercial %>%
group_by(Dia) %>%
summarise(Area_prom_cu2Controlcomercial = mean(Area_herida))
dfcu2_Control = Curativo2df|>
group_by(Dia,Area_herida) |>
filter (Tratamiento=="Control\r\n")
dfcu2_Control <- na.omit(dfcu2_Control)
dfcu2_Control
## # A tibble: 20 × 4
## # Groups: Dia, Area_herida [20]
## Numero_petalo Tratamiento Dia Area_herida
## <dbl> <chr> <dbl> <dbl>
## 1 5 "Control\r\n" 1 0.035
## 2 8 "Control\r\n" 1 0.161
## 3 30 "Control\r\n" 1 0
## 4 44 "Control\r\n" 1 0.113
## 5 48 "Control\r\n" 1 0.242
## 6 5 "Control\r\n" 2 0.066
## 7 8 "Control\r\n" 2 0.502
## 8 30 "Control\r\n" 2 0
## 9 44 "Control\r\n" 2 0.303
## 10 48 "Control\r\n" 2 0.736
## 11 5 "Control\r\n" 3 2.32
## 12 8 "Control\r\n" 3 2.40
## 13 30 "Control\r\n" 3 0.317
## 14 44 "Control\r\n" 3 4.20
## 15 48 "Control\r\n" 3 4.02
## 16 5 "Control\r\n" 4 7.16
## 17 8 "Control\r\n" 4 6.59
## 18 30 "Control\r\n" 4 2.87
## 19 44 "Control\r\n" 4 11.0
## 20 48 "Control\r\n" 4 9.41
Boxplotcu2Control <- boxplot(dfcu2_Control$Area_herida ~ dfcu2_Control$Dia, frame.plot=F)
dfcu2_Control <- dfcu2_Control[!(dfcu2_Control $Area_herida %in% Boxplotcu2Control),]
dfcu2Controlprom <- dfcu2_Control %>%
group_by(Dia) %>%
summarise(Area_prom_cu2Control = mean(Area_herida))
library(agricolae)
library(ggplot2)
audpccu2100ppm <- agricolae::audpc(dfcu2100ppmprom$Area_prom_cu2100ppm,dfcu2100ppmprom$Dia)
Graficocu2100ppm <- ggplot(dfcu2100ppmprom, aes(Dia, Area_prom_cu2100ppm)) + geom_rect(aes(xmin = Dia - 0.5, xmax = Dia + 0.5, ymin = 0, ymax = Area_prom_cu2100ppm),fill = "#1874CD", color = "black", alpha = 0.1 ) + geom_line()+
geom_text(data = data.frame(x = 2, y = 2.5, label = paste("AUDPC", round(audpccu2100ppm,2))),
aes(x = x, y = y, label = label),
size = 4, hjust = 1, vjust = 1)+
theme_minimal() + labs(x="Día", y= "Área promedio afectada", title = "100 ppm")
Graficocu2100ppm
audpccu21ppm <- agricolae::audpc(dfcu21ppmprom$Area_prom_cu21ppm,dfcu21ppmprom$Dia)
Graficocu21ppm <- ggplot(dfcu21ppmprom, aes(Dia, Area_prom_cu21ppm)) + geom_rect(aes(xmin = Dia - 0.5, xmax = Dia + 0.5, ymin = 0, ymax = Area_prom_cu21ppm),fill = "#1874CD", color = "black", alpha = 0.1 ) + geom_line()+
geom_text(data = data.frame(x = 2, y = 2.5, label = paste("AUDPC", round(audpccu21ppm,2))),
aes(x = x, y = y, label = label),
size = 4, hjust = 1, vjust = 1)+
theme_minimal() + labs(x="Día", y= "Área promedio afectada", title = "1 ppm")
Graficocu21ppm
audpccu250ppm <- agricolae::audpc(dfcu250ppmprom$Area_prom_cu250ppm,dfcu250ppmprom$Dia)
Graficocu250ppm <- ggplot(dfcu250ppmprom, aes(Dia, Area_prom_cu250ppm)) + geom_rect(aes(xmin = Dia - 0.5, xmax = Dia + 0.5, ymin = 0, ymax = Area_prom_cu250ppm),fill = "#1874CD", color = "black", alpha = 0.1 ) + geom_line()+
geom_text(data = data.frame(x = 2, y = 2.5, label = paste("AUDPC", round(audpccu250ppm,2))),
aes(x = x, y = y, label = label),
size = 4, hjust = 1, vjust = 1)+
theme_minimal() + labs(x="Día", y= "Área promedio afectada", title = "50 ppm")
Graficocu250ppm
audpccu2Controlcomercial <- agricolae::audpc(dfcu2Controlcomercialprom$Area_prom_cu2Controlcomercial,dfcu2Controlcomercialprom$Dia)
Graficocu2Controlcomercial <- ggplot(dfcu2Controlcomercialprom, aes(Dia, Area_prom_cu2Controlcomercial)) + geom_rect(aes(xmin = Dia - 0.5, xmax = Dia + 0.5, ymin = 0, ymax = Area_prom_cu2Controlcomercial),fill = "#1874CD", color = "black", alpha = 0.1 ) + geom_line()+
geom_text(data = data.frame(x = 2, y = 2.5, label = paste("AUDPC", round(audpccu2Controlcomercial,2))),
aes(x = x, y = y, label = label),
size = 4, hjust = 1, vjust = 1)+
theme_minimal() + labs(x="Día", y= "Área promedio afectada", title = "Control comercial")
Graficocu2Controlcomercial
audpccu2Control <- agricolae::audpc(dfcu2Controlprom$Area_prom_cu2Control,dfcu2Controlprom$Dia)
Graficocu2Control <- ggplot(dfcu2Controlprom, aes(Dia, Area_prom_cu2Control)) + geom_rect(aes(xmin = Dia - 0.5, xmax = Dia + 0.5, ymin = 0, ymax = Area_prom_cu2Control),fill = "#1874CD", color = "black", alpha = 0.1 ) + geom_line()+
geom_text(data = data.frame(x = 2, y = 2.5, label = paste("AUDPC", round(audpccu2Control,2))),
aes(x = x, y = y, label = label),
size = 4, hjust = 1, vjust = 1)+
theme_minimal() + labs(x="Día", y= "Área promedio afectada", title = "Control")
Graficocu2Control
barplotcu2 <- data.frame(
"Tratamiento" = as.factor(c("100 ppm","50 ppm", "1 ppm","Control comercial", "Control" )), "AUDPC" = c(audpccu2100ppm,audpccu250ppm,audpccu21ppm,audpccu2Controlcomercial,audpccu2Control))
barplotcu2 <- as.data.frame(barplotcu2)
barplotcu2
## Tratamiento AUDPC
## 1 100 ppm 7.594917
## 2 50 ppm 6.834000
## 3 1 ppm 6.510611
## 4 Control comercial 9.904563
## 5 Control 6.730300
barplotcu2f <-ggplot(barplotcu2, aes(Tratamiento, AUDPC)) + geom_bar(width = 0.5, stat='identity')
barplotcu2f
library(patchwork)
Combcu2_plot <- Graficocu21ppm+Graficocu250ppm+Graficocu2100ppm+Graficocu2Controlcomercial+Graficocu2Control+barplotcu2f
Combcu2_plot
