library(readxl)
## Warning: package 'readxl' was built under R version 4.1.3
data <- read_excel("proyec_F.xlsx")
names(data)
## [1] "Muestreo"
## [2] "Tratamiento"
## [3] "Repetición"
## [4] "Temperatura de la hoja"
## [5] "CRC (Contenido relativo de clorofila)"
## [6] "Longitud parte aérea"
## [7] "Peso fresco de la raíz tuberosa"
## [8] "Área Foliar específica (AFE)"
## [9] "Tasa de Asimilación Neta (TAN)"
## [10] "Distribución de masa seca foliar"
## [11] "Distribución de masa seca raíz"
## [12] "Área foliar"
View(data)
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(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.3
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`Temperatura de la hoja`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 105.70 35.23 36.15 2.39e-05 ***
## Repetición 3 9.00 3.00 3.08 0.083 .
## Residuals 9 8.77 0.97
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`Temperatura de la hoja` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 1.0275 -1.151687 3.206687 0.4906905
## +POL-PBZ--POL-PBZ -0.0250 -2.204187 2.154187 0.9999822
## +POL+PBZ--POL-PBZ -5.5200 -7.699187 -3.340813 0.0001162
## +POL-PBZ--POL+PBZ -1.0525 -3.231687 1.126687 0.4718208
## +POL+PBZ--POL+PBZ -6.5475 -8.726687 -4.368313 0.0000294
## +POL+PBZ-+POL-PBZ -5.4950 -7.674187 -3.315813 0.0001204
##
## $Repetición
## diff lwr upr p adj
## II-I -1.5575 -3.736687 0.6216875 0.1864899
## III-I -2.0250 -4.204187 0.1541875 0.0697068
## IV-I -1.1350 -3.314187 1.0441875 0.4122517
## III-II -0.4675 -2.646687 1.7116875 0.9058917
## IV-II 0.4225 -1.756687 2.6016875 0.9278563
## IV-III 0.8900 -1.289187 3.0691875 0.5993617
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Temperatura de la hoja`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 73.84 24.614 23.305 0.000141 ***
## Repetición 3 16.52 5.507 5.214 0.023272 *
## Residuals 9 9.51 1.056
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Temperatura de la hoja` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 6.065 3.7964362 8.3335638 0.0000756
## +POL-PBZ--POL-PBZ 2.775 0.5064362 5.0435638 0.0176824
## +POL+PBZ--POL-PBZ 3.125 0.8564362 5.3935638 0.0088181
## +POL-PBZ--POL+PBZ -3.290 -5.5585638 -1.0214362 0.0064088
## +POL+PBZ--POL+PBZ -2.940 -5.2085638 -0.6714362 0.0126998
## +POL+PBZ-+POL-PBZ 0.350 -1.9185638 2.6185638 0.9612741
##
## $Repetición
## diff lwr upr p adj
## II-I -1.5775 -3.846064 0.6910638 0.2030139
## III-I -2.0975 -4.366064 0.1710638 0.0712434
## IV-I -2.7500 -5.018564 -0.4814362 0.0185995
## III-II -0.5200 -2.788564 1.7485638 0.8884678
## IV-II -1.1725 -3.441064 1.0960638 0.4183172
## IV-III -0.6525 -2.921064 1.6160638 0.8063205
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Temperatura de la hoja`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 94.37 31.455 16.631 0.000516 ***
## Repetición 3 11.25 3.750 1.983 0.187185
## Residuals 9 17.02 1.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Temperatura de la hoja` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 5.775 2.739177 8.810823 0.0010182
## +POL-PBZ--POL-PBZ 0.075 -2.960823 3.110823 0.9998225
## +POL+PBZ--POL-PBZ 0.470 -2.565823 3.505823 0.9608996
## +POL-PBZ--POL+PBZ -5.700 -8.735823 -2.664177 0.0011187
## +POL+PBZ--POL+PBZ -5.305 -8.340823 -2.269177 0.0018585
## +POL+PBZ-+POL-PBZ 0.395 -2.640823 3.430823 0.9760129
##
## $Repetición
## diff lwr upr p adj
## II-I -0.87 -3.905823 2.1658233 0.8079512
## III-I -0.93 -3.965823 2.1058233 0.7764662
## IV-I -2.34 -5.375823 0.6958233 0.1451043
## III-II -0.06 -3.095823 2.9758233 0.9999090
## IV-II -1.47 -4.505823 1.5658233 0.4698027
## IV-III -1.41 -4.445823 1.6258233 0.5024559
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Temperatura de la hoja`),
minimo=min(`Temperatura de la hoja`),
maximo=max(`Temperatura de la hoja`)) %>%
ggplot(aes(x=Tratamiento, y=media_trt, fill=Tratamiento))+
geom_col()+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo, switch = "both")+
labs(title = 'Temperatura de la hoja', x = 'Tratamiento', y = 'Temperatura de la hoja (°C)') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`CRC (Contenido relativo de clorofila)`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 179.61 59.87 8.737 0.00496 **
## Repetición 3 30.04 10.01 1.461 0.28921
## Residuals 9 61.68 6.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`CRC (Contenido relativo de clorofila)` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -5.400 -11.178637 0.3786372 0.0680177
## +POL-PBZ--POL-PBZ -9.075 -14.853637 -3.2963628 0.0038345
## +POL+PBZ--POL-PBZ -6.900 -12.678637 -1.1213628 0.0202209
## +POL-PBZ--POL+PBZ -3.675 -9.453637 2.1036372 0.2617522
## +POL+PBZ--POL+PBZ -1.500 -7.278637 4.2786372 0.8481204
## +POL+PBZ-+POL-PBZ 2.175 -3.603637 7.9536372 0.6560884
##
## $Repetición
## diff lwr upr p adj
## II-I -0.900 -6.678637 4.878637 0.9602454
## III-I 0.925 -4.853637 6.703637 0.9571122
## IV-I 2.800 -2.978637 8.578637 0.4692737
## III-II 1.825 -3.953637 7.603637 0.7608577
## IV-II 3.700 -2.078637 9.478637 0.2570506
## IV-III 1.875 -3.903637 7.653637 0.7463606
plot(TukeyHSD (mod, conf.level = .95 ), las = 2 )
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`CRC (Contenido relativo de clorofila)`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 340.2 113.40 2.869 0.0961 .
## Repetición 3 60.2 20.08 0.508 0.6865
## Residuals 9 355.7 39.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`CRC (Contenido relativo de clorofila)` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 2.325 -11.552784 16.202784 0.9513828
## +POL-PBZ--POL-PBZ -9.550 -23.427784 4.327784 0.2095015
## +POL+PBZ--POL-PBZ -5.200 -19.077784 8.677784 0.6590738
## +POL-PBZ--POL+PBZ -11.875 -25.752784 2.002784 0.0982691
## +POL+PBZ--POL+PBZ -7.525 -21.402784 6.352784 0.3806392
## +POL+PBZ-+POL-PBZ 4.350 -9.527784 18.227784 0.7647854
##
## $Repetición
## diff lwr upr p adj
## II-I 4.050 -9.827784 17.92778 0.7997334
## III-I 5.225 -8.652784 19.10278 0.6558853
## IV-I 3.300 -10.577784 17.17778 0.8776479
## III-II 1.175 -12.702784 15.05278 0.9930764
## IV-II -0.750 -14.627784 13.12778 0.9981622
## IV-III -1.925 -15.802784 11.95278 0.9712505
plot(TukeyHSD (mod, conf.level = .95 ), las = 2 )
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`CRC (Contenido relativo de clorofila)`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 617.9 205.95 15.868 0.000615 ***
## Repetición 3 57.4 19.13 1.474 0.285989
## Residuals 9 116.8 12.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`CRC (Contenido relativo de clorofila)` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 10.525 2.5723236 18.477676 0.0112221
## +POL-PBZ--POL-PBZ -6.900 -14.8526764 1.052676 0.0929617
## +POL+PBZ--POL-PBZ 0.425 -7.5276764 8.377676 0.9982224
## +POL-PBZ--POL+PBZ -17.425 -25.3776764 -9.472324 0.0003574
## +POL+PBZ--POL+PBZ -10.100 -18.0526764 -2.147324 0.0142842
## +POL+PBZ-+POL-PBZ 7.325 -0.6276764 15.277676 0.0724283
##
## $Repetición
## diff lwr upr p adj
## II-I -2.825 -10.777676 5.127676 0.6933751
## III-I -3.325 -11.277676 4.627676 0.5822889
## IV-I -5.300 -13.252676 2.652676 0.2300683
## III-II -0.500 -8.452676 7.452676 0.9971208
## IV-II -2.475 -10.427676 5.477676 0.7684756
## IV-III -1.975 -9.927676 5.977676 0.8636939
plot(TukeyHSD (mod, conf.level = .95 ), las = 2 )
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`CRC (Contenido relativo de clorofila)`),
minimo=min(`CRC (Contenido relativo de clorofila)`),
maximo=max(`CRC (Contenido relativo de clorofila)`))%>%
ggplot(aes(x=Tratamiento, y=media_trt, fill=Tratamiento))+
geom_col()+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6'))+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo, switch = "both")+
labs(title = 'Contenido relativo de clorofilas', x = 'Tratamiento', y = ' Contenido Relativo de Clorofilas (SPAD)') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`Longitud parte aérea`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 78.82 26.272 25.493 9.86e-05 ***
## Repetición 3 3.30 1.100 1.067 0.41
## Residuals 9 9.28 1.031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`Longitud parte aérea` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -1.625 -3.8659162 0.6159162 0.1780742
## +POL-PBZ--POL-PBZ 4.400 2.1590838 6.6409162 0.0008091
## +POL+PBZ--POL-PBZ 0.325 -1.9159162 2.5659162 0.9674142
## +POL-PBZ--POL+PBZ 6.025 3.7840838 8.2659162 0.0000723
## +POL+PBZ--POL+PBZ 1.950 -0.2909162 4.1909162 0.0918664
## +POL+PBZ-+POL-PBZ -4.075 -6.3159162 -1.8340838 0.0014053
##
## $Repetición
## diff lwr upr p adj
## II-I -0.60 -2.840916 1.640916 0.8363527
## III-I 0.45 -1.790916 2.690916 0.9208224
## IV-I 0.55 -1.690916 2.790916 0.8676072
## III-II 1.05 -1.190916 3.290916 0.4955935
## IV-II 1.15 -1.090916 3.390916 0.4239336
## IV-III 0.10 -2.140916 2.340916 0.9989607
plot(TukeyHSD (mod, conf.level = .95 ), las = 2 )
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Longitud parte aérea`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 78.82 26.272 25.493 9.86e-05 ***
## Repetición 3 3.30 1.100 1.067 0.41
## Residuals 9 9.28 1.031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Longitud parte aérea` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -1.625 -3.8659162 0.6159162 0.1780742
## +POL-PBZ--POL-PBZ 4.400 2.1590838 6.6409162 0.0008091
## +POL+PBZ--POL-PBZ 0.325 -1.9159162 2.5659162 0.9674142
## +POL-PBZ--POL+PBZ 6.025 3.7840838 8.2659162 0.0000723
## +POL+PBZ--POL+PBZ 1.950 -0.2909162 4.1909162 0.0918664
## +POL+PBZ-+POL-PBZ -4.075 -6.3159162 -1.8340838 0.0014053
##
## $Repetición
## diff lwr upr p adj
## II-I -0.60 -2.840916 1.640916 0.8363527
## III-I 0.45 -1.790916 2.690916 0.9208224
## IV-I 0.55 -1.690916 2.790916 0.8676072
## III-II 1.05 -1.190916 3.290916 0.4955935
## IV-II 1.15 -1.090916 3.390916 0.4239336
## IV-III 0.10 -2.140916 2.340916 0.9989607
plot(TukeyHSD (mod, conf.level = .95 ), las = 2 )
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Longitud parte aérea`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 354.5 118.16 27.286 7.51e-05 ***
## Repetición 3 7.0 2.35 0.542 0.666
## Residuals 9 39.0 4.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Longitud parte aérea` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -5.625 -10.2187271 -1.031273 0.0175809
## +POL-PBZ--POL-PBZ 7.325 2.7312729 11.918727 0.0034661
## +POL+PBZ--POL-PBZ -1.825 -6.4187271 2.768727 0.6190518
## +POL-PBZ--POL+PBZ 12.950 8.3562729 17.543727 0.0000494
## +POL+PBZ--POL+PBZ 3.800 -0.7937271 8.393727 0.1121055
## +POL+PBZ-+POL-PBZ -9.150 -13.7437271 -4.556273 0.0007284
##
## $Repetición
## diff lwr upr p adj
## II-I -0.475 -5.068727 4.118727 0.9875995
## III-I 0.125 -4.468727 4.718727 0.9997629
## IV-I 1.325 -3.268727 5.918727 0.8050581
## III-II 0.600 -3.993727 5.193727 0.9757502
## IV-II 1.800 -2.793727 6.393727 0.6286987
## IV-III 1.200 -3.393727 5.793727 0.8457744
plot(TukeyHSD (mod, conf.level = .95 ), las = 2 )
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Longitud parte aérea`),
minimo=min(`Longitud parte aérea`),
maximo=max(`Longitud parte aérea`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo, switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Longitud de la parte aérea', x = 'Tratamiento', y = 'Longitud de la parte aérea (cm)') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`Peso fresco de la raíz tuberosa`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 63.21 21.070 3.972 0.0468 *
## Repetición 3 12.54 4.179 0.788 0.5304
## Residuals 9 47.74 5.305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`Peso fresco de la raíz tuberosa` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -2.50425 -7.588444 2.5799441 0.4563421
## +POL-PBZ--POL-PBZ -4.67950 -9.763694 0.4046941 0.0726567
## +POL+PBZ--POL-PBZ -4.93375 -10.017944 0.1504441 0.0574587
## +POL-PBZ--POL+PBZ -2.17525 -7.259444 2.9089441 0.5652131
## +POL+PBZ--POL+PBZ -2.42950 -7.513694 2.6546941 0.4802071
## +POL+PBZ-+POL-PBZ -0.25425 -5.338444 4.8299441 0.9985409
##
## $Repetición
## diff lwr upr p adj
## II-I -1.82050 -6.904694 3.263694 0.6883901
## III-I 0.20825 -4.875944 5.292444 0.9991950
## IV-I 0.38875 -4.695444 5.472944 0.9948680
## III-II 2.02875 -3.055444 7.112944 0.6159516
## IV-II 2.20925 -2.874944 7.293444 0.5535709
## IV-III 0.18050 -4.903694 5.264694 0.9994748
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Peso fresco de la raíz tuberosa`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 868.9 289.63 30.667 4.69e-05 ***
## Repetición 3 44.1 14.69 1.555 0.267
## Residuals 9 85.0 9.44
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Peso fresco de la raíz tuberosa` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -10.3025 -17.086393 -3.5186073 0.0047738
## +POL-PBZ--POL-PBZ -17.3275 -24.111393 -10.5436073 0.0001088
## +POL+PBZ--POL-PBZ -18.5420 -25.325893 -11.7581073 0.0000633
## +POL-PBZ--POL+PBZ -7.0250 -13.808893 -0.2411073 0.0423129
## +POL+PBZ--POL+PBZ -8.2395 -15.023393 -1.4556073 0.0184000
## +POL+PBZ-+POL-PBZ -1.2145 -7.998393 5.5693927 0.9417499
##
## $Repetición
## diff lwr upr p adj
## II-I 1.40425 -5.379643 8.188143 0.9142529
## III-I 2.03950 -4.744393 8.823393 0.7857090
## IV-I 4.58025 -2.203643 11.364143 0.2216081
## III-II 0.63525 -6.148643 7.419143 0.9907051
## IV-II 3.17600 -3.607893 9.959893 0.4962448
## IV-III 2.54075 -4.243143 9.324643 0.6593794
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Peso fresco de la raíz tuberosa`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 3772 1257.3 19.220 0.000298 ***
## Repetición 3 59 19.5 0.299 0.825707
## Residuals 9 589 65.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Peso fresco de la raíz tuberosa` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -26.15375 -44.00787 -8.299627 0.0060155
## +POL-PBZ--POL-PBZ -35.86250 -53.71662 -18.008377 0.0006848
## +POL+PBZ--POL-PBZ -39.10575 -56.95987 -21.251627 0.0003584
## +POL-PBZ--POL+PBZ -9.70875 -27.56287 8.145373 0.3784133
## +POL+PBZ--POL+PBZ -12.95200 -30.80612 4.902123 0.1778493
## +POL+PBZ-+POL-PBZ -3.24325 -21.09737 14.610873 0.9394118
##
## $Repetición
## diff lwr upr p adj
## II-I 0.40525 -17.44887 18.25937 0.9998622
## III-I 0.68975 -17.16437 18.54387 0.9993240
## IV-I 4.74800 -13.10612 22.60212 0.8389968
## III-II 0.28450 -17.56962 18.13862 0.9999523
## IV-II 4.34275 -13.51137 22.19687 0.8705335
## IV-III 4.05825 -13.79587 21.91237 0.8908262
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Peso fresco de la raíz tuberosa`),
minimo=min(`Peso fresco de la raíz tuberosa`),
maximo=max(`Peso fresco de la raíz tuberosa`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo, switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Peso fresco de la raíz tuberosa', x = 'Tratamiento', y = 'Peso fresco de la raíz tuberosa (g)') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`Área Foliar específica (AFE)`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 663218 221073 2.205 0.157
## Repetición 3 326564 108855 1.086 0.403
## Residuals 9 902147 100239
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`Área Foliar específica (AFE)` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -501.6803 -1200.5677 197.2072 0.1839448
## +POL-PBZ--POL-PBZ -491.0188 -1189.9062 207.8687 0.1967219
## +POL+PBZ--POL-PBZ -286.0918 -984.9792 412.7957 0.5976948
## +POL-PBZ--POL+PBZ 10.6615 -688.2259 709.5489 0.9999581
## +POL+PBZ--POL+PBZ 215.5885 -483.2989 914.4759 0.7729811
## +POL+PBZ-+POL-PBZ 204.9270 -493.9604 903.8144 0.7975473
##
## $Repetición
## diff lwr upr p adj
## II-I 211.5367 -487.3507 910.4242 0.7824203
## III-I -124.6868 -823.5742 574.2007 0.9422941
## IV-I -149.4158 -848.3032 549.4717 0.9067291
## III-II -336.2235 -1035.1109 362.6639 0.4749124
## IV-II -360.9525 -1059.8399 337.9349 0.4188970
## IV-III -24.7290 -723.6164 674.1584 0.9994800
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Área Foliar específica (AFE)`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 60156 20052 2.737 0.106
## Repetición 3 11206 3735 0.510 0.685
## Residuals 9 65943 7327
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Área Foliar específica (AFE)` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -23.33400 -212.28667 165.6187 0.9793288
## +POL-PBZ--POL-PBZ 44.36250 -144.59017 233.3152 0.8815003
## +POL+PBZ--POL-PBZ 137.00425 -51.94842 325.9569 0.1781359
## +POL-PBZ--POL+PBZ 67.69650 -121.25617 256.6492 0.6880361
## +POL+PBZ--POL+PBZ 160.33825 -28.61442 349.2909 0.1015690
## +POL+PBZ-+POL-PBZ 92.64175 -96.31092 281.5944 0.4599793
##
## $Repetición
## diff lwr upr p adj
## II-I -68.01900 -256.9717 120.9337 0.6850394
## III-I -58.71175 -247.6644 130.2409 0.7692892
## IV-I -52.66750 -241.6202 136.2852 0.8199885
## III-II 9.30725 -179.6454 198.2599 0.9986052
## IV-II 15.35150 -173.6012 204.3042 0.9938658
## IV-III 6.04425 -182.9084 194.9969 0.9996154
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Área Foliar específica (AFE)`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 42357 14119 7.028 0.00984 **
## Repetición 3 1178 393 0.195 0.89685
## Residuals 9 18080 2009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Área Foliar específica (AFE)` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -19.20025 -118.140222 79.73972 0.9276781
## +POL-PBZ--POL-PBZ 6.81975 -92.120222 105.75972 0.9962206
## +POL+PBZ--POL-PBZ 112.63550 13.695528 211.57547 0.0261593
## +POL-PBZ--POL+PBZ 26.02000 -72.919972 124.95997 0.8432514
## +POL+PBZ--POL+PBZ 131.83575 32.895778 230.77572 0.0107770
## +POL+PBZ-+POL-PBZ 105.81575 6.875778 204.75572 0.0360855
##
## $Repetición
## diff lwr upr p adj
## II-I 14.24375 -84.69622 113.18372 0.9680818
## III-I 23.15350 -75.78647 122.09347 0.8824711
## IV-I 17.85275 -81.08722 116.79272 0.9404978
## III-II 8.90975 -90.03022 107.84972 0.9917073
## IV-II 3.60900 -95.33097 102.54897 0.9994305
## IV-III -5.30075 -104.24072 93.63922 0.9982091
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Área Foliar específica (AFE)`),
minimo=min(`Área Foliar específica (AFE)`),
maximo=max(`Área Foliar específica (AFE)`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo,switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Área foliar específica', x = 'Tratamiento', y = 'Área foliar específica (cm^2^)') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Tasa de Asimilación Neta (TAN)`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 0.04972 0.016574 13.028 0.00126 **
## Repetición 3 0.00155 0.000515 0.405 0.75299
## Residuals 9 0.01145 0.001272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Tasa de Asimilación Neta (TAN)` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -0.060069449 -0.13880439 0.018665490 0.1503571
## +POL-PBZ--POL-PBZ -0.133901902 -0.21263684 -0.055166963 0.0022422
## +POL+PBZ--POL-PBZ -0.132325703 -0.21106064 -0.053590764 0.0024317
## +POL-PBZ--POL+PBZ -0.073832453 -0.15256739 0.004902486 0.0669861
## +POL+PBZ--POL+PBZ -0.072256254 -0.15099119 0.006478686 0.0735768
## +POL+PBZ-+POL-PBZ 0.001576199 -0.07715874 0.080311139 0.9999054
##
## $Repetición
## diff lwr upr p adj
## II-I 0.013891546 -0.06484339 0.09262649 0.9440085
## III-I -0.013535563 -0.09227050 0.06519938 0.9478280
## IV-I -0.003614823 -0.08234976 0.07512012 0.9988688
## III-II -0.027427109 -0.10616205 0.05130783 0.7053839
## IV-II -0.017506370 -0.09624131 0.06122857 0.8968015
## IV-III 0.009920740 -0.06881420 0.08865568 0.9781072
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Tasa de Asimilación Neta (TAN)`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 0.03713 0.012377 1.900 0.200
## Repetición 3 0.01052 0.003508 0.539 0.668
## Residuals 9 0.05862 0.006513
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Tasa de Asimilación Neta (TAN)` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -4.847179e-05 -0.1782003 0.17810333 1.0000000
## +POL-PBZ--POL-PBZ -8.772848e-02 -0.2658803 0.09042332 0.4565333
## +POL+PBZ--POL-PBZ -1.036890e-01 -0.2818408 0.07446278 0.3261882
## +POL-PBZ--POL+PBZ -8.768001e-02 -0.2658318 0.09047179 0.4569694
## +POL+PBZ--POL+PBZ -1.036405e-01 -0.2817924 0.07451125 0.3265407
## +POL+PBZ-+POL-PBZ -1.596054e-02 -0.1941123 0.16219126 0.9918311
##
## $Repetición
## diff lwr upr p adj
## II-I 0.03105649 -0.1470953 0.2092083 0.9458085
## III-I 0.06995350 -0.1081983 0.2481053 0.6272384
## IV-I 0.04889787 -0.1292539 0.2270497 0.8264059
## III-II 0.03889701 -0.1392548 0.2170488 0.9015163
## IV-II 0.01784138 -0.1603104 0.1959932 0.9886983
## IV-III -0.02105563 -0.1992074 0.1570962 0.9817684
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`Distribución de masa seca foliar`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 7786 2595.5 17.407 0.000434 ***
## Repetición 3 177 59.1 0.396 0.758899
## Residuals 9 1342 149.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`Distribución de masa seca foliar` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 34.475 7.520552 61.42945 0.0137129
## +POL-PBZ--POL-PBZ 55.925 28.970552 82.87945 0.0005386
## +POL+PBZ--POL-PBZ 51.875 24.920552 78.82945 0.0009361
## +POL-PBZ--POL+PBZ 21.450 -5.504448 48.40445 0.1295077
## +POL+PBZ--POL+PBZ 17.400 -9.554448 44.35445 0.2514429
## +POL+PBZ-+POL-PBZ -4.050 -31.004448 22.90445 0.9640248
##
## $Repetición
## diff lwr upr p adj
## II-I 5.900 -21.05445 32.85445 0.9008742
## III-I -3.325 -30.27945 23.62945 0.9793933
## IV-I 1.800 -25.15445 28.75445 0.9965583
## III-II -9.225 -36.17945 17.72945 0.7159729
## IV-II -4.100 -31.05445 22.85445 0.9627732
## IV-III 5.125 -21.82945 32.07945 0.9315110
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Distribución de masa seca foliar`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 6982 2327.2 68.042 1.66e-06 ***
## Repetición 3 122 40.5 1.185 0.369
## Residuals 9 308 34.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Distribución de masa seca foliar` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -4.525 -17.43474 8.38474 0.7016154
## +POL-PBZ--POL-PBZ 42.325 29.41526 55.23474 0.0000143
## +POL+PBZ--POL-PBZ 35.975 23.06526 48.88474 0.0000542
## +POL-PBZ--POL+PBZ 46.850 33.94026 59.75974 0.0000061
## +POL+PBZ--POL+PBZ 40.500 27.59026 53.40974 0.0000206
## +POL+PBZ-+POL-PBZ -6.350 -19.25974 6.55974 0.4574313
##
## $Repetición
## diff lwr upr p adj
## II-I -1.650 -14.55974 11.25974 0.9772002
## III-I -7.350 -20.25974 5.55974 0.3429212
## IV-I -3.925 -16.83474 8.98474 0.7802208
## III-II -5.700 -18.60974 7.20974 0.5414697
## IV-II -2.275 -15.18474 10.63474 0.9441899
## IV-III 3.425 -9.48474 16.33474 0.8399099
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Distribución de masa seca foliar`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 6180 2060.1 15.368 0.000692 ***
## Repetición 3 134 44.7 0.333 0.801696
## Residuals 9 1206 134.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Distribución de masa seca foliar` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -0.675 -26.232923 24.882923 0.9997832
## +POL-PBZ--POL-PBZ 46.225 20.667077 71.782923 0.0014602
## +POL+PBZ--POL-PBZ 26.700 1.142077 52.257923 0.0405356
## +POL-PBZ--POL+PBZ 46.900 21.342077 72.457923 0.0013176
## +POL+PBZ--POL+PBZ 27.375 1.817077 52.932923 0.0358158
## +POL+PBZ-+POL-PBZ -19.525 -45.082923 6.032923 0.1496672
##
## $Repetición
## diff lwr upr p adj
## II-I 3.725 -21.83292 29.28292 0.9669605
## III-I -4.150 -29.70792 21.40792 0.9553872
## IV-I 1.675 -23.88292 27.23292 0.9967442
## III-II -7.875 -33.43292 17.68292 0.7735547
## IV-II -2.050 -27.60792 23.50792 0.9940926
## IV-III 5.825 -19.73292 31.38292 0.8900754
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Distribución de masa seca foliar`),
minimo=min(`Distribución de masa seca foliar`),
maximo=max(`Distribución de masa seca foliar`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo,switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Distribución de masa seca foliar', x = 'Tratamiento', y = '%') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`Distribución de masa seca raíz`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 7786 2595.5 17.407 0.000434 ***
## Repetición 3 177 59.1 0.396 0.758899
## Residuals 9 1342 149.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`Distribución de masa seca raíz` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -34.475 -61.42945 -7.520552 0.0137129
## +POL-PBZ--POL-PBZ -55.925 -82.87945 -28.970552 0.0005386
## +POL+PBZ--POL-PBZ -51.875 -78.82945 -24.920552 0.0009361
## +POL-PBZ--POL+PBZ -21.450 -48.40445 5.504448 0.1295077
## +POL+PBZ--POL+PBZ -17.400 -44.35445 9.554448 0.2514429
## +POL+PBZ-+POL-PBZ 4.050 -22.90445 31.004448 0.9640248
##
## $Repetición
## diff lwr upr p adj
## II-I -5.900 -32.85445 21.05445 0.9008742
## III-I 3.325 -23.62945 30.27945 0.9793933
## IV-I -1.800 -28.75445 25.15445 0.9965583
## III-II 9.225 -17.72945 36.17945 0.7159729
## IV-II 4.100 -22.85445 31.05445 0.9627732
## IV-III -5.125 -32.07945 21.82945 0.9315110
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Distribución de masa seca raíz`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 6982 2327.2 68.042 1.66e-06 ***
## Repetición 3 122 40.5 1.185 0.369
## Residuals 9 308 34.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Distribución de masa seca raíz` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 4.525 -8.38474 17.43474 0.7016154
## +POL-PBZ--POL-PBZ -42.325 -55.23474 -29.41526 0.0000143
## +POL+PBZ--POL-PBZ -35.975 -48.88474 -23.06526 0.0000542
## +POL-PBZ--POL+PBZ -46.850 -59.75974 -33.94026 0.0000061
## +POL+PBZ--POL+PBZ -40.500 -53.40974 -27.59026 0.0000206
## +POL+PBZ-+POL-PBZ 6.350 -6.55974 19.25974 0.4574313
##
## $Repetición
## diff lwr upr p adj
## II-I 1.650 -11.25974 14.55974 0.9772002
## III-I 7.350 -5.55974 20.25974 0.3429212
## IV-I 3.925 -8.98474 16.83474 0.7802208
## III-II 5.700 -7.20974 18.60974 0.5414697
## IV-II 2.275 -10.63474 15.18474 0.9441899
## IV-III -3.425 -16.33474 9.48474 0.8399099
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Distribución de masa seca raíz`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 6180 2060.1 15.368 0.000692 ***
## Repetición 3 134 44.7 0.333 0.801696
## Residuals 9 1206 134.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Distribución de masa seca raíz` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 0.675 -24.882923 26.232923 0.9997832
## +POL-PBZ--POL-PBZ -46.225 -71.782923 -20.667077 0.0014602
## +POL+PBZ--POL-PBZ -26.700 -52.257923 -1.142077 0.0405356
## +POL-PBZ--POL+PBZ -46.900 -72.457923 -21.342077 0.0013176
## +POL+PBZ--POL+PBZ -27.375 -52.932923 -1.817077 0.0358158
## +POL+PBZ-+POL-PBZ 19.525 -6.032923 45.082923 0.1496672
##
## $Repetición
## diff lwr upr p adj
## II-I -3.725 -29.28292 21.83292 0.9669605
## III-I 4.150 -21.40792 29.70792 0.9553872
## IV-I -1.675 -27.23292 23.88292 0.9967442
## III-II 7.875 -17.68292 33.43292 0.7735547
## IV-II 2.050 -23.50792 27.60792 0.9940926
## IV-III -5.825 -31.38292 19.73292 0.8900754
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Distribución de masa seca raíz`),
minimo=min(`Distribución de masa seca raíz`),
maximo=max(`Distribución de masa seca raíz`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo,switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Distribución de masa seca raíz', x = 'Tratamiento', y = '%') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
dt1 <- data %>%
filter(Muestreo=="28 DDS")
dt1
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28 DDS -POL-PBZ I 14.3 42.1
## 2 28 DDS -POL-PBZ II 14.3 38.5
## 3 28 DDS -POL-PBZ III 14 41.4
## 4 28 DDS -POL-PBZ IV 14.5 43
## 5 28 DDS -POL+PBZ I 16.7 35.6
## 6 28 DDS -POL+PBZ II 15.1 31.3
## 7 28 DDS -POL+PBZ III 14.1 37.2
## 8 28 DDS -POL+PBZ IV 15.3 39.3
## 9 28 DDS +POL-PBZ I 14.5 33
## 10 28 DDS +POL-PBZ II 14.1 30
## 11 28 DDS +POL-PBZ III 14.2 30.5
## 12 28 DDS +POL-PBZ IV 14.2 35.2
## 13 28 DDS +POL+PBZ I 11.8 30.1
## 14 28 DDS +POL+PBZ II 7.57 37.4
## 15 28 DDS +POL+PBZ III 6.9 35.4
## 16 28 DDS +POL+PBZ IV 8.75 34.5
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt1$`Área foliar`~Tratamiento+Repetición, dt1)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 0.3372 0.11241 1.091 0.402
## Repetición 3 0.1081 0.03603 0.350 0.791
## Residuals 9 0.9275 0.10306
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt1$`Área foliar` ~ Tratamiento + Repetición, data = dt1)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 0.0519625 -0.6566905 0.7606155 0.9954638
## +POL-PBZ--POL-PBZ 0.2828000 -0.4258530 0.9914530 0.6158875
## +POL+PBZ--POL-PBZ -0.1164100 -0.8250630 0.5922430 0.9539403
## +POL-PBZ--POL+PBZ 0.2308375 -0.4778155 0.9394905 0.7442144
## +POL+PBZ--POL+PBZ -0.1683725 -0.8770255 0.5402805 0.8778993
## +POL+PBZ-+POL-PBZ -0.3992100 -1.1078630 0.3094430 0.3510424
##
## $Repetición
## diff lwr upr p adj
## II-I 0.1335025 -0.5751505 0.8421555 0.9331809
## III-I 0.2105025 -0.4981505 0.9191555 0.7914684
## IV-I 0.1906775 -0.5179755 0.8993305 0.8344146
## III-II 0.0770000 -0.6316530 0.7856530 0.9856873
## IV-II 0.0571750 -0.6514780 0.7658280 0.9939901
## IV-III -0.0198250 -0.7284780 0.6888280 0.9997424
dt2 <- data %>%
filter(Muestreo=="35 DDS")
dt2
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 35 DDS -POL-PBZ I 10.8 40.3
## 2 35 DDS -POL-PBZ II 10.9 40.3
## 3 35 DDS -POL-PBZ III 11.1 39.2
## 4 35 DDS -POL-PBZ IV 10.8 42.6
## 5 35 DDS -POL+PBZ I 19.4 28.1
## 6 35 DDS -POL+PBZ II 17.2 52.6
## 7 35 DDS -POL+PBZ III 16.4 48.7
## 8 35 DDS -POL+PBZ IV 14.9 42.3
## 9 35 DDS +POL-PBZ I 16.1 35.6
## 10 35 DDS +POL-PBZ II 12.8 26.6
## 11 35 DDS +POL-PBZ III 12.6 31
## 12 35 DDS +POL-PBZ IV 13.2 31
## 13 35 DDS +POL+PBZ I 15.7 33.4
## 14 35 DDS +POL+PBZ II 14.8 34.1
## 15 35 DDS +POL+PBZ III 13.5 39.4
## 16 35 DDS +POL+PBZ IV 12.1 34.7
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt2$`Área foliar`~Tratamiento+Repetición, dt2)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 2.2099 0.7366 6.956 0.0102 *
## Repetición 3 0.1661 0.0554 0.523 0.6773
## Residuals 9 0.9531 0.1059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt2$`Área foliar` ~ Tratamiento + Repetición, data = dt2)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -0.871540 -1.5898925 -0.1531875 0.0185119
## +POL-PBZ--POL-PBZ -0.311625 -1.0299775 0.4067275 0.5548348
## +POL+PBZ--POL-PBZ -0.859730 -1.5780825 -0.1413775 0.0199659
## +POL-PBZ--POL+PBZ 0.559915 -0.1584375 1.2782675 0.1395218
## +POL+PBZ--POL+PBZ 0.011810 -0.7065425 0.7301625 0.9999476
## +POL+PBZ-+POL-PBZ -0.548105 -1.2664575 0.1702475 0.1503094
##
## $Repetición
## diff lwr upr p adj
## II-I -0.0647175 -0.7830700 0.6536350 0.9916966
## III-I -0.0159650 -0.7343175 0.7023875 0.9998707
## IV-I 0.2018875 -0.5164650 0.9202400 0.8164752
## III-II 0.0487525 -0.6696000 0.7671050 0.9963899
## IV-II 0.2666050 -0.4517475 0.9849575 0.6653792
## IV-III 0.2178525 -0.5005000 0.9362050 0.7814645
dt3 <- data %>%
filter(Muestreo=="41 DDS")
dt3
## # A tibble: 16 x 12
## Muestreo Tratamiento Repetición `Temperatura de la hoja` `CRC (Contenido re~`
## <chr> <chr> <chr> <dbl> <dbl>
## 1 41 DDS -POL-PBZ I 14 47.3
## 2 41 DDS -POL-PBZ II 14.2 42.1
## 3 41 DDS -POL-PBZ III 13.8 40.1
## 4 41 DDS -POL-PBZ IV 14 39.6
## 5 41 DDS -POL+PBZ I 21.7 60
## 6 41 DDS -POL+PBZ II 20.6 48.9
## 7 41 DDS -POL+PBZ III 21.1 54.6
## 8 41 DDS -POL+PBZ IV 15.8 47.7
## 9 41 DDS +POL-PBZ I 14.2 31.8
## 10 41 DDS +POL-PBZ II 14.3 38.4
## 11 41 DDS +POL-PBZ III 14.2 35.2
## 12 41 DDS +POL-PBZ IV 13.6 36.1
## 13 41 DDS +POL+PBZ I 16.6 45.5
## 14 41 DDS +POL+PBZ II 13.9 43.9
## 15 41 DDS +POL+PBZ III 13.6 41.4
## 16 41 DDS +POL+PBZ IV 13.7 40
## # ... with 7 more variables: `Longitud parte aérea` <dbl>,
## # `Peso fresco de la raíz tuberosa` <dbl>,
## # `Área Foliar específica (AFE)` <dbl>,
## # `Tasa de Asimilación Neta (TAN)` <dbl>,
## # `Distribución de masa seca foliar` <dbl>,
## # `Distribución de masa seca raíz` <dbl>, `Área foliar` <dbl>
mod <- aov(dt3$`Área foliar`~Tratamiento+Repetición, dt3)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 5.600 1.8665 13.730 0.00105 **
## Repetición 3 0.856 0.2852 2.098 0.17075
## Residuals 9 1.223 0.1359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey <- TukeyHSD(mod, conf.level = 0.95);tukey
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dt3$`Área foliar` ~ Tratamiento + Repetición, data = dt3)
##
## $Tratamiento
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ -1.1882625 -2.0021496 -0.3743754 0.0061438
## +POL-PBZ--POL-PBZ 0.0860200 -0.7278671 0.8999071 0.9867880
## +POL+PBZ--POL-PBZ -1.0843600 -1.8982471 -0.2704729 0.0107847
## +POL-PBZ--POL+PBZ 1.2742825 0.4603954 2.0881696 0.0039120
## +POL+PBZ--POL+PBZ 0.1039025 -0.7099846 0.9177896 0.9772755
## +POL+PBZ-+POL-PBZ -1.1703800 -1.9842671 -0.3564929 0.0067596
##
## $Repetición
## diff lwr upr p adj
## II-I 0.0244400 -0.7894471 0.8383271 0.9996816
## III-I 0.3678500 -0.4460371 1.1817371 0.5235703
## IV-I 0.5459775 -0.2679096 1.3598646 0.2257826
## III-II 0.3434100 -0.4704771 1.1572971 0.5755485
## IV-II 0.5215375 -0.2923496 1.3354246 0.2565020
## IV-III 0.1781275 -0.6357596 0.9920146 0.9009064
library(pairwise)
## Warning: package 'pairwise' was built under R version 4.1.3
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Área foliar`),
minimo=min(`Área foliar`),
maximo=max(`Área foliar`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
facet_grid(~Muestreo,switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Área foliar', x = 'Tratamiento', y = 'Área foliar m^2^') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
Rep <- c("I","II","III","IV")
Rep
## [1] "I" "II" "III" "IV"
Trt <- c("-POL-PBZ","-POL+PBZ","+POL-PBZ","+POL+PBZ")
Trt
## [1] "-POL-PBZ" "-POL+PBZ" "+POL-PBZ" "+POL+PBZ"
PE <- c(7.23,7.96,7.14,10.54)
PE
## [1] 7.23 7.96 7.14 10.54
df <- data.frame(Trt,Rep,PE)
df
## Trt Rep PE
## 1 -POL-PBZ I 7.23
## 2 -POL+PBZ II 7.96
## 3 +POL-PBZ III 7.14
## 4 +POL+PBZ IV 10.54
mod <- aov(df$PE~Trt, df)
summary(mod)
## Df Sum Sq Mean Sq
## Trt 3 7.596 2.532
shapiro.test(PE)
##
## Shapiro-Wilk normality test
##
## data: PE
## W = 0.79542, p-value = 0.09425
pairwise.t.test(x = df$PE, g = df$Trt, p.adjust.method = "holm",
pool.sd = TRUE, paired = FALSE, alternative = "two.sided")
##
## Pairwise comparisons using t tests with pooled SD
##
## data: df$PE and df$Trt
##
## -POL-PBZ -POL+PBZ +POL-PBZ
## -POL+PBZ - - -
## +POL-PBZ - - -
## +POL+PBZ - - -
##
## P value adjustment method: holm
TukeyHSD(mod)
## Warning in qtukey(conf.level, length(means), x$df.residual): NaNs produced
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df$PE ~ Trt, data = df)
##
## $Trt
## diff lwr upr p adj
## -POL+PBZ--POL-PBZ 0.73 NaN NaN NaN
## +POL-PBZ--POL-PBZ -0.09 NaN NaN NaN
## +POL+PBZ--POL-PBZ 3.31 NaN NaN NaN
## +POL-PBZ--POL+PBZ -0.82 NaN NaN NaN
## +POL+PBZ--POL+PBZ 2.58 NaN NaN NaN
## +POL+PBZ-+POL-PBZ 3.40 NaN NaN NaN
p <- df%>%
group_by(Trt)%>%
summarise(media_trt=mean(PE),
maximo=max(PE),
minimo=min(PE))%>%
ggplot(aes(x=Trt, y=media_trt))+
geom_col(aes(fill=Trt), color='black', width = 0.6, position = "dodge")+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black', position="dodge")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Pérdida de electrolitos', x = 'Tratamiento', y = 'Pérdida de electrolitos (%)') +
theme_minimal()
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p1 <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Distribución de masa seca raíz`),
minimo=min(`Distribución de masa seca raíz`),
maximo=max(`Distribución de masa seca raíz`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
facet_grid(~Muestreo,switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Distribución de masa seca raíz', x = 'Tratamiento', y = '%') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p1+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
p2 <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Distribución de masa seca foliar`),
minimo=min(`Distribución de masa seca foliar`),
maximo=max(`Distribución de masa seca foliar`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
facet_grid(~Muestreo,switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Distribución de masa seca foliar', x = 'Tratamiento', y = '%') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
p2+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Días después de siembra")
p <- data%>%
group_by(Tratamiento,Muestreo)%>%
summarise(media_trt=mean(`Distribución de masa seca raíz`),
minimo=min(`Distribución de masa seca raíz`),
maximo=max(`Distribución de masa seca raíz`))%>%
ggplot(aes(x=Tratamiento, y=media_trt))+
geom_col(aes(fill=Tratamiento), color='black', width = 0.6, position = "dodge")+
geom_smooth(data=data, aes(x = Tratamiento,
y = c(100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100))) +
scale_y_discrete(dup_axis(~., name = "Título del eje Z"))+
facet_grid(~Muestreo,switch = "both")+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
labs(title = 'Distribución de masa seca raíz', x = 'Tratamiento', y = '%') +
theme_minimal()
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
Muestreo=data$Muestreo
Tratamiento=data$Tratamiento
Ps_raiz=data$`Distribución de masa seca raíz`
Ps_foliar=c(100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100)
df <- data.frame(Muestreo,Tratamiento,Ps_raiz,Ps_foliar)
df
## Muestreo Tratamiento Ps_raiz Ps_foliar
## 1 28 DDS -POL-PBZ 72.0 100
## 2 28 DDS -POL-PBZ 59.7 100
## 3 28 DDS -POL-PBZ 70.7 100
## 4 28 DDS -POL-PBZ 50.2 100
## 5 28 DDS -POL+PBZ 8.9 100
## 6 28 DDS -POL+PBZ 19.3 100
## 7 28 DDS -POL+PBZ 37.5 100
## 8 28 DDS -POL+PBZ 49.0 100
## 9 28 DDS +POL-PBZ 15.1 100
## 10 28 DDS +POL-PBZ 6.2 100
## 11 28 DDS +POL-PBZ 4.0 100
## 12 28 DDS +POL-PBZ 3.6 100
## 13 28 DDS +POL+PBZ 18.7 100
## 14 28 DDS +POL+PBZ 5.9 100
## 15 28 DDS +POL+PBZ 15.8 100
## 16 28 DDS +POL+PBZ 4.7 100
## 17 35 DDS -POL-PBZ 58.2 100
## 18 35 DDS -POL-PBZ 56.6 100
## 19 35 DDS -POL-PBZ 60.7 100
## 20 35 DDS -POL-PBZ 61.4 100
## 21 35 DDS -POL+PBZ 57.5 100
## 22 35 DDS -POL+PBZ 67.9 100
## 23 35 DDS -POL+PBZ 66.0 100
## 24 35 DDS -POL+PBZ 63.6 100
## 25 35 DDS +POL-PBZ 10.1 100
## 26 35 DDS +POL-PBZ 18.0 100
## 27 35 DDS +POL-PBZ 28.7 100
## 28 35 DDS +POL-PBZ 10.8 100
## 29 35 DDS +POL+PBZ 24.4 100
## 30 35 DDS +POL+PBZ 14.3 100
## 31 35 DDS +POL+PBZ 24.2 100
## 32 35 DDS +POL+PBZ 30.1 100
## 33 41 DDS -POL-PBZ 74.2 100
## 34 41 DDS -POL-PBZ 75.7 100
## 35 41 DDS -POL-PBZ 66.6 100
## 36 41 DDS -POL-PBZ 71.1 100
## 37 41 DDS -POL+PBZ 61.3 100
## 38 41 DDS -POL+PBZ 71.9 100
## 39 41 DDS -POL+PBZ 77.4 100
## 40 41 DDS -POL+PBZ 79.7 100
## 41 41 DDS +POL-PBZ 16.5 100
## 42 41 DDS +POL-PBZ 25.9 100
## 43 41 DDS +POL-PBZ 41.9 100
## 44 41 DDS +POL-PBZ 18.4 100
## 45 41 DDS +POL+PBZ 64.6 100
## 46 41 DDS +POL+PBZ 28.2 100
## 47 41 DDS +POL+PBZ 47.3 100
## 48 41 DDS +POL+PBZ 40.7 100