library(readxl)
df <- read_excel("C:/Users/User/Documents/MANEJOS/MANEJO INTEGRADO DE PLAGAS/datos_m.xlsx",
sheet = "Datos")
df
## # A tibble: 63 × 5
## Muestreo dds Nombre `%ninfas` `#adultos/foliolo`
## <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 29 CTR 3 37
## 2 1 29 CTR 1 12
## 3 1 29 CTR 3 858
## 4 1 29 JP 3 619
## 5 1 29 JP 1 166
## 6 1 29 JP 3 25
## 7 1 29 CAP 3 454
## 8 1 29 CAP 1 232
## 9 1 29 CAP 3 176
## 10 1 29 BUP 3 794
## # ℹ 53 more rows
library(nortest)
library(moments)
skewness(rnorm(n=df$`#adultos/foliolo`, mean=mean(df$`#adultos/foliolo`), sd=sd(df$`#adultos/foliolo`)))
## [1] -0.1256072
kurtosis(df$`#adultos/foliolo`)
## [1] 7.181306
shapiro.test(df$`#adultos/foliolo`)
##
## Shapiro-Wilk normality test
##
## data: df$`#adultos/foliolo`
## W = 0.66519, p-value = 1.035e-10
lillie.test(df$`#adultos/foliolo`)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: df$`#adultos/foliolo`
## D = 0.24983, p-value = 1.395e-10
library(agricolae)
##
## Attaching package: 'agricolae'
## The following objects are masked from 'package:moments':
##
## kurtosis, skewness
mod <- aov(`#adultos/foliolo`~Nombre*dds, df)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 290239 48373 1.056 0.40167
## dds 1 344603 344603 7.522 0.00849 **
## Nombre:dds 6 230454 38409 0.838 0.54642
## Residuals 49 2244912 45815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## #adultos/foliolo groups
## BUP 299.44444 a
## JP 162.11111 a
## CAP 149.33333 a
## CAP_BUP 148.44444 a
## JP_CAP 138.55556 a
## CTR 127.66667 a
## JP_BUP 53.77778 a
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
df1 <- filter(df, dds=="29")
mod <- aov(`#adultos/foliolo`~Nombre*dds, df1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 584729 97455 1.18 0.371
## Residuals 14 1156225 82587
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## #adultos/foliolo groups
## BUP 629.00000 a
## CTR 302.33333 a
## CAP 287.33333 a
## JP_CAP 273.00000 a
## JP 270.00000 a
## CAP_BUP 120.33333 a
## JP_BUP 63.33333 a
library(dplyr)
df2 <- filter(df, dds=="45")
mod <- aov(`#adultos/foliolo`~Nombre*dds, df2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 165475 27579 0.828 0.567
## Residuals 14 466285 33306
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## #adultos/foliolo groups
## CAP_BUP 287.66667 a
## BUP 118.00000 a
## JP 52.00000 a
## CAP 47.00000 a
## CTR 34.00000 a
## JP_BUP 26.00000 a
## JP_CAP 18.66667 a
library(dplyr)
df3 <- filter(df, dds=="59")
mod <- aov(`#adultos/foliolo`~Nombre*dds, df3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 45239 7540 0.511 0.791
## Residuals 14 206737 14767
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## #adultos/foliolo groups
## JP 164.33333 a
## BUP 151.33333 a
## JP_CAP 124.00000 a
## CAP 113.66667 a
## JP_BUP 72.00000 a
## CTR 46.66667 a
## CAP_BUP 37.33333 a
library(ggplot2)
library(dplyr)
library(ggtext)
## Warning: package 'ggtext' was built under R version 4.3.3
text <- c("Aplicación")
p <- df%>%
group_by(Nombre, dds)%>%
summarise(media_trt=mean(`#adultos/foliolo`),
minimo=min(`#adultos/foliolo`),
maximo=max(`#adultos/foliolo`)) %>%
ggplot(aes(x=dds, y=media_trt, color=Nombre))+
geom_line(size=1, linetype=1)+
geom_point()+
ggtitle( 'NÚMERO DE ADULTOS DE MOSCA BLANCA (_Trialeurodes vaporariorum_)
\nPRESENTES EN UN FOLIOLO EN SIETE TRATAMIENTOS')+
theme(plot.title = element_markdown())+
labs(x = 'Días Después de Siembra',
y = 'Número de adultos por foliolo',
color = "TRATAMIENTOS")
## `summarise()` has grouped output by 'Nombre'. You can override using the
## `.groups` argument.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
p
p+
geom_vline(size=1, linetype=2, xintercept=37, color="red")+
annotate("text",
label = "Aplicación",
x = 40, y = 610, hjust = 0.5, vjust = 0)
mod <- aov(`%ninfas`~Nombre*dds, df1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 1.143 0.1905 0.167 0.982
## Residuals 14 16.000 1.1429
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## %ninfas groups
## JP_CAP 3.000000 a
## BUP 2.333333 a
## CAP 2.333333 a
## CAP_BUP 2.333333 a
## CTR 2.333333 a
## JP 2.333333 a
## JP_BUP 2.333333 a
mod <- aov(`%ninfas`~Nombre*dds, df2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 14.48 2.413 0.905 0.519
## Residuals 14 37.33 2.667
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## %ninfas groups
## CAP 3.666667 a
## JP 3.666667 a
## BUP 3.000000 a
## CTR 3.000000 a
## JP_BUP 3.000000 a
## JP_CAP 3.000000 a
## CAP_BUP 1.000000 a
mod <- aov(`%ninfas`~Nombre*dds, df3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 23.62 3.937 0.504 0.795
## Residuals 14 109.33 7.810
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## %ninfas groups
## CTR 6.333333 a
## BUP 5.666667 a
## CAP 5.000000 a
## CAP_BUP 4.333333 a
## JP_CAP 4.333333 a
## JP 3.666667 a
## JP_BUP 3.000000 a
mod <- aov(`%ninfas`~Nombre*dds, df)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Nombre 6 13.21 2.20 0.590 0.736968
## dds 1 49.05 49.05 13.139 0.000687 ***
## Nombre:dds 6 12.47 2.08 0.557 0.762352
## Residuals 49 182.92 3.73
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison<- LSD.test(mod,c("Nombre"),alpha=0.01,group=TRUE)
print(comparison$groups)
## %ninfas groups
## CTR 3.888889 a
## BUP 3.666667 a
## CAP 3.666667 a
## JP_CAP 3.444444 a
## JP 3.222222 a
## JP_BUP 2.777778 a
## CAP_BUP 2.555556 a
library(nortest)
library(moments)
skewness(rnorm(n=df$`%ninfas`, mean=mean(df$`%ninfas`), sd=sd(df$`%ninfas`)))
## [1] -0.1636103
kurtosis(df$`%ninfas`)
## [1] 0.8061241
shapiro.test(df$`%ninfas`)
##
## Shapiro-Wilk normality test
##
## data: df$`%ninfas`
## W = 0.8016, p-value = 8.706e-08
lillie.test(df$`%ninfas`)
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: df$`%ninfas`
## D = 0.33965, p-value < 2.2e-16
library(ggplot2)
library(dplyr)
library(ggtext)
text <- c("Applicación")
p <- df%>%
group_by(Nombre, dds)%>%
summarise(media_trt=mean(`%ninfas`),
minimo=min(`%ninfas`),
maximo=max(`%ninfas`)) %>%
ggplot(aes(x=dds, y=media_trt, color=Nombre))+
geom_line(size=1, linetype=1)+
geom_point()+
ggtitle('GRADO DE PRESENCIA DE NINFAS DE MOSCA BLANCA
\n(_Trialeurodes vaporariorum_)
\nPRESENTES EN UN FOLIOLO EN SIETE TRATAMIENTOS')+
theme(plot.title = element_markdown())+
labs(x = 'Días Después de Siembra',
y = 'Grado de presencia de ninfas por foliolo',
color="TRATAMIENTOS")
## `summarise()` has grouped output by 'Nombre'. You can override using the
## `.groups` argument.
p
p+
geom_vline(size=1, linetype=2, xintercept=37, color="red")+
annotate("text",
label = "Aplicación",
x = 34, y = 6, hjust = 0.5, vjust = 0)
efi <- read_excel("C:/Users/User/Documents/MANEJOS/MANEJO INTEGRADO DE PLAGAS/datos_m.xlsx",
sheet = "EFICIENCIAS")
efi
## # A tibble: 12 × 3
## TRATAMIENTO METODO `EFICIENCIA (%)`
## <chr> <chr> <dbl>
## 1 JABÓN POTÁSICO ABOTT 8.88e-14
## 2 CAPSIALIL ABOTT -2.22e+ 1
## 3 BUPROFEZIN ABOTT 0
## 4 JP+CAP ABOTT 0
## 5 JP+BUP ABOTT 2.22e+ 1
## 6 CAP+BUP ABOTT 6.67e+ 1
## 7 JABÓN POTÁSICO HENDERSON-TILTON 1.89e-13
## 8 CAPSIALIL HENDERSON-TILTON -2.22e+ 1
## 9 BUPROFEZIN HENDERSON-TILTON 0
## 10 JP+CAP HENDERSON-TILTON -2.86e+ 1
## 11 JP+BUP HENDERSON-TILTON 2.22e+ 1
## 12 CAP+BUP HENDERSON-TILTON 6.67e+ 1
EFICIENCIA <- c(0.00,-22.222, 0.00, 0.00, 22.222, 66.667, 0.00,-22.222,0.00, -28.571, 22.222, 66.667 )
efi$`EFICIENCIA (%)` <- EFICIENCIA
efi$`EFICIENCIA (%)`
## [1] 0.000 -22.222 0.000 0.000 22.222 66.667 0.000 -22.222 0.000
## [10] -28.571 22.222 66.667
efi
## # A tibble: 12 × 3
## TRATAMIENTO METODO `EFICIENCIA (%)`
## <chr> <chr> <dbl>
## 1 JABÓN POTÁSICO ABOTT 0
## 2 CAPSIALIL ABOTT -22.2
## 3 BUPROFEZIN ABOTT 0
## 4 JP+CAP ABOTT 0
## 5 JP+BUP ABOTT 22.2
## 6 CAP+BUP ABOTT 66.7
## 7 JABÓN POTÁSICO HENDERSON-TILTON 0
## 8 CAPSIALIL HENDERSON-TILTON -22.2
## 9 BUPROFEZIN HENDERSON-TILTON 0
## 10 JP+CAP HENDERSON-TILTON -28.6
## 11 JP+BUP HENDERSON-TILTON 22.2
## 12 CAP+BUP HENDERSON-TILTON 66.7
library(agricolae)
mod <- aov(`EFICIENCIA (%)`~TRATAMIENTO+METODO, efi)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 5 10358 2072 30.45 0.000946 ***
## METODO 1 68 68 1.00 0.363217
## Residuals 5 340 68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison<- HSD.test(mod,c("TRATAMIENTO"),group=TRUE)
print(comparison$groups)
## EFICIENCIA (%) groups
## CAP+BUP 66.6670 a
## JP+BUP 22.2220 b
## BUPROFEZIN 0.0000 bc
## JABÓN POTÁSICO 0.0000 bc
## JP+CAP -14.2855 c
## CAPSIALIL -22.2220 c
letra <- c("bc", "a", "c", "bc", "b", "c")
library(ggplot2)
library(dplyr)
p <- efi%>%
group_by(TRATAMIENTO)%>%
summarise(media_trt=mean(`EFICIENCIA (%)`),
minimo=min(`EFICIENCIA (%)`),
maximo=max(`EFICIENCIA (%)`)) %>%
ggplot(aes(x=TRATAMIENTO, y=media_trt, fill=TRATAMIENTO))+
geom_col()+
geom_text(aes(label=letra),vjust = 1, hjust=1)+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black',
position="dodge")+
labs(title = 'Eficiencia de los tratamientos (Método de Abott)',
x = 'Tratamientos',
y = 'Eficiencia (%)') +
theme_minimal()
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Tratamientos")
EFICIENCIA <- c(0,0,0,0,0,0,0.00,-22.222,0.00, -28.571, 22.222, 66.667 )
TRT <- c("JABON POTÁSICO", "CAPSIALIL","BUPROFEZIN", "JP+CAP", "JP+BUP", "CAP+BUP", "JABON POTÁSICO", "CAPSIALIL","BUPROFEZIN", "JP+CAP", "JP+BUP", "CAP+BUP")
efi_cor <- data.frame(TRT,EFICIENCIA)
efi_cor
## TRT EFICIENCIA
## 1 JABON POTÁSICO 0.000
## 2 CAPSIALIL 0.000
## 3 BUPROFEZIN 0.000
## 4 JP+CAP 0.000
## 5 JP+BUP 0.000
## 6 CAP+BUP 0.000
## 7 JABON POTÁSICO 0.000
## 8 CAPSIALIL -22.222
## 9 BUPROFEZIN 0.000
## 10 JP+CAP -28.571
## 11 JP+BUP 22.222
## 12 CAP+BUP 66.667
library(agricolae)
mod <- aov(EFICIENCIA~TRT, efi_cor)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 5 3003 600.7 1.154 0.426
## Residuals 6 3124 520.7
comparison<- HSD.test(mod,c("TRT"),group=TRUE)
print(comparison$groups)
## EFICIENCIA groups
## CAP+BUP 33.3335 a
## JP+BUP 11.1110 a
## BUPROFEZIN 0.0000 a
## JABON POTÁSICO 0.0000 a
## CAPSIALIL -11.1110 a
## JP+CAP -14.2855 a
letra <- c("a", "a", "a", "a", "a", "a")
library(ggplot2)
library(dplyr)
p <- efi_cor%>%
group_by(TRT)%>%
summarise(media_trt=mean(EFICIENCIA),
minimo=min(EFICIENCIA),
maximo=max(EFICIENCIA)) %>%
ggplot(aes(x=TRT, y=media_trt, fill=TRT))+
geom_col()+
geom_text(aes(label=letra),vjust = 1, hjust=1)+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black',
position="dodge")+
labs(title = 'Eficiencia de los tratamientos (Método de Henderson-Tilton)',
x = 'Tratamientos',
y = 'Eficiencia (%)') +
theme_minimal()
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Tratamientos")
