library(readxl)
ruta_excel <- "C:\\Users\\jdom3\\Desktop\\Datos tesis.xlsx"
Conteo_curativo <- read_excel(ruta_excel, sheet = 'Curativo - Conteo de conidias')
Conteo_curativo
## # A tibble: 25 × 3
## `Número de petalo` Tratamiento Conteo_conidias
## <dbl> <chr> <dbl>
## 1 1 1 ppm 168000
## 2 9 1 ppm 206000
## 3 15 1 ppm 199000
## 4 19 1 ppm 221000
## 5 21 1 ppm 254000
## 6 13 50 ppm 253000
## 7 11 50 ppm 127000
## 8 17 50 ppm 154000
## 9 23 50 ppm 169000
## 10 25 50 ppm 122000
## # ℹ 15 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
Conteocuraprom <- Conteo_curativo %>%
group_by(Tratamiento) %>%
summarise(Conidias_por_mL= mean(Conteo_conidias))
library(ggplot2)
Grafico_conteo_curativo <- ggplot(Conteocuraprom, aes(Tratamiento, Conidias_por_mL)) + geom_bar(width = 0.5, stat='identity')
Grafico_conteo_curativo
ggplot(data = Conteo_curativo, aes(x = Tratamiento, y = Conteo_conidias, color = Tratamiento)) +
geom_boxplot() +
theme_bw()
anova_conteo_curativo <- aov(Conteo_curativo$Conteo_conidias ~ Conteo_curativo$Tratamiento )
summary(anova_conteo_curativo)
## Df Sum Sq Mean Sq F value Pr(>F)
## Conteo_curativo$Tratamiento 4 1.71e+11 4.276e+10 18.01 2.08e-06 ***
## Residuals 20 4.75e+10 2.375e+09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
aovconcur_residuals <- residuals(object = anova_conteo_curativo )
hist(aovconcur_residuals)
shapiro.test(x = aovconcur_residuals)
##
## Shapiro-Wilk normality test
##
## data: aovconcur_residuals
## W = 0.81683, p-value = 0.0004398
kruskal.test(Conteo_curativo$Conteo_conidias ~ Conteo_curativo$Tratamiento)
##
## Kruskal-Wallis rank sum test
##
## data: Conteo_curativo$Conteo_conidias by Conteo_curativo$Tratamiento
## Kruskal-Wallis chi-squared = 15.158, df = 4, p-value = 0.004384
pairwise.wilcox.test(Conteo_curativo$Conteo_conidias, Conteo_curativo$Tratamiento,p.adjust.method = "bonf", paired = F)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum exact test
##
## data: Conteo_curativo$Conteo_conidias and Conteo_curativo$Tratamiento
##
## 1 ppm 100 ppm 50 ppm Control
## 100 ppm 1.000 - - -
## 50 ppm 1.000 1.000 - -
## Control 0.079 0.079 0.079 -
## Control comercial 0.159 1.000 1.000 0.079
##
## P value adjustment method: bonferroni
library(ggpubr)
ggplot(Conteo_curativo, aes(x = Tratamiento, y = Conteo_conidias, color = Tratamiento))+
geom_boxplot()+
geom_point()+
stat_compare_means(method = "kruskal",label.y = 500000)+
stat_compare_means(method = "wilcox.test", ref.group = 'Control', label = "p.signif", label.y = 400000)+ xlab('Tratamientos') + ylab('Conidias/mL')
theme_bw()
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