graficas cosecha fisiopro
Luisa Perea
2024-08-01
Todo nuevo desde aca
install.packages("readxl") # Para leer archivos Excel## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("ggplot2") # Para visualización de datos (opcional)## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(readxl)
library(ggplot2)
library(outliers)
library(gridExtra)lectura de datos
datos = read_excel("//cloud//project//pesogranopla.xlsx", sheet = 1)
print(datos)## # A tibble: 48 × 3
## tratamiento surco peso
## <chr> <dbl> <dbl>
## 1 Control 1 10.2
## 2 Control 1 23.8
## 3 Control 1 30.5
## 4 Control 2 21.0
## 5 Control 2 11.9
## 6 Control 2 24.4
## 7 Control 3 8.49
## 8 Control 3 9.6
## 9 Control 3 9.75
## 10 Control 4 30.0
## # ℹ 38 more rowsstr(datos)## tibble [48 × 3] (S3: tbl_df/tbl/data.frame)
## $ tratamiento: chr [1:48] "Control" "Control" "Control" "Control" ...
## $ surco : num [1:48] 1 1 1 2 2 2 3 3 3 4 ...
## $ peso : num [1:48] 10.2 23.8 30.5 21 11.9 ...summary(datos)## tratamiento surco peso
## Length:48 Min. :1.00 Min. : 6.24
## Class :character 1st Qu.:1.75 1st Qu.:16.82
## Mode :character Median :2.50 Median :22.64
## Mean :2.50 Mean :21.52
## 3rd Qu.:3.25 3rd Qu.:25.37
## Max. :4.00 Max. :35.74datos$surco <- factor(datos$surco)
datos$tratamiento <- factor(datos$tratamiento, levels = c("Control", "65mg/1LH2O", "130mg/1LH2O", "260mg/1LH2O"))
anovapeso = aov(peso ~ tratamiento + surco, data = datos)
summary(anovapeso)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 350.2 116.74 3.067 0.0384 *
## surco 3 247.8 82.58 2.170 0.1062
## Residuals 41 1560.6 38.06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anovapeso, "tratamiento")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = peso ~ tratamiento + surco, data = datos)
##
## $tratamiento
## diff lwr upr p adj
## 65mg/1LH2O-Control 3.7191667 -3.0250481 10.463381 0.4605493
## 130mg/1LH2O-Control 7.6075000 0.8632852 14.351715 0.0216488
## 260mg/1LH2O-Control 4.3450000 -2.3992148 11.089215 0.3241307
## 130mg/1LH2O-65mg/1LH2O 3.8883333 -2.8558815 10.632548 0.4214778
## 260mg/1LH2O-65mg/1LH2O 0.6258333 -6.1183815 7.370048 0.9945274
## 260mg/1LH2O-130mg/1LH2O -3.2625000 -10.0067148 3.481715 0.5711207install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(agricolae)
duncan.test(anovapeso, 'tratamiento', console = T)##
## Study: anovapeso ~ "tratamiento"
##
## Duncan's new multiple range test
## for peso
##
## Mean Square Error: 38.06416
##
## tratamiento, means
##
## peso std r se Min Max Q25 Q50 Q75
## 130mg/1LH2O 25.21417 5.982334 12 1.781015 16.40 35.74 21.6800 24.990 29.7075
## 260mg/1LH2O 21.95167 5.368055 12 1.781015 11.02 30.14 19.6575 22.965 24.8850
## 65mg/1LH2O 21.32583 4.229761 12 1.781015 15.78 30.72 19.3875 19.910 23.5975
## Control 17.60667 9.050050 12 1.781015 6.24 30.53 9.7125 16.455 24.6050
##
## Alpha: 0.05 ; DF Error: 41
##
## Critical Range
## 2 3 4
## 5.086689 5.348624 5.520074
##
## Means with the same letter are not significantly different.
##
## peso groups
## 130mg/1LH2O 25.21417 a
## 260mg/1LH2O 21.95167 ab
## 65mg/1LH2O 21.32583 ab
## Control 17.60667 b#Normalidad de residuos
shapiro.test(anovapeso$residuals)##
## Shapiro-Wilk normality test
##
## data: anovapeso$residuals
## W = 0.98594, p-value = 0.8287#Igualdad de varianzas
bartlett.test(anovapeso$residuals, datos$tratamiento)##
## Bartlett test of homogeneity of variances
##
## data: anovapeso$residuals and datos$tratamiento
## Bartlett's K-squared = 6.5607, df = 3, p-value = 0.0873### instalar los paquetes para las graficas
# Instalar los paquetes necesarios
install.packages("multcomp")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Cargar los paquetes
library(multcomp)## Loading required package: mvtnorm## Loading required package: survival## Loading required package: TH.data## Loading required package: MASS##
## Attaching package: 'TH.data'## The following object is masked from 'package:MASS':
##
## geyserlibrary(agricolae)# Posthoc (letras)
posthoc1 <- glht(anovapeso, linfct = mcp(tratamiento = "Tukey"))
letras1 <- cld(posthoc1)$mcletters$Letters
print(letras1)## Control 65mg/1LH2O 130mg/1LH2O 260mg/1LH2O
## "a" "ab" "b" "ab"# Calcular las medias y los errores estándar para los tratamientos
install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
##
## select## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionmedias1<- datos %>%
group_by(tratamiento) %>%
summarise(Media1 = mean(peso), SE1 = sd(peso)/sqrt(n()))# Gráfica de barras
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(ggplot2)# Agregar las letras a las medias
medias1$Letras <- letras1
# Crear el gráfico de barras
#COLORES DE TRATAMIENTOS
colores <- c("Control" = "brown2", "65mg/1LH2O" = "#97FFFF", "130mg/1LH2O" = "#90EE90", "260mg/1LH2O" = "#9F79EE")
ggplot(medias1, aes(x = tratamiento, y = Media1, fill = tratamiento)) +
geom_bar(stat = "identity", color = "black") +
geom_errorbar(aes(ymin = Media1 - SE1, ymax = Media1 + SE1), width = 0.2) +
geom_text(aes(label = letras1, y = Media1 + SE1 + 0.5), vjust = 0) +
labs(title = "Peso de granos x planta",x = "Tratamientos", y = "peso (gr)") +
theme_minimal() +
scale_fill_manual(values = colores)+
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)
)
### Numero de vainas por planta
install.packages("readxl") # Para leer archivos Excel## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("ggplot2") # Para visualización de datos (opcional)## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(readxl)
library(ggplot2)
library(outliers)
library(gridExtra)lectura de datos
datos = read_excel("//cloud//project//pesogranopla.xlsx", sheet = 2)
print(datos)## # A tibble: 48 × 3
## tratamiento surco vainas
## <chr> <dbl> <dbl>
## 1 Control 1 7
## 2 Control 1 6
## 3 Control 1 8
## 4 Control 2 6
## 5 Control 2 5
## 6 Control 2 12
## 7 Control 3 5
## 8 Control 3 5
## 9 Control 3 8
## 10 Control 4 6
## # ℹ 38 more rowsstr(datos)## tibble [48 × 3] (S3: tbl_df/tbl/data.frame)
## $ tratamiento: chr [1:48] "Control" "Control" "Control" "Control" ...
## $ surco : num [1:48] 1 1 1 2 2 2 3 3 3 4 ...
## $ vainas : num [1:48] 7 6 8 6 5 12 5 5 8 6 ...summary(datos)## tratamiento surco vainas
## Length:48 Min. :1.00 Min. : 4.000
## Class :character 1st Qu.:1.75 1st Qu.: 6.000
## Mode :character Median :2.50 Median : 6.000
## Mean :2.50 Mean : 7.167
## 3rd Qu.:3.25 3rd Qu.: 8.250
## Max. :4.00 Max. :12.000datos$surco <- factor(datos$surco)
datos$tratamiento <- factor(datos$tratamiento, levels = c("Control", "65mg/1LH2O", "130mg/1LH2O", "260mg/1LH2O"))
anovavainas = aov(vainas ~ tratamiento + surco, data = datos)
summary(anovavainas)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 4.67 1.556 0.366 0.7780
## surco 3 33.67 11.222 2.639 0.0622 .
## Residuals 41 174.33 4.252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anovavainas, "tratamiento")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = vainas ~ tratamiento + surco, data = datos)
##
## $tratamiento
## diff lwr upr p adj
## 65mg/1LH2O-Control 0.1666667 -2.087425 2.420758 0.9972064
## 130mg/1LH2O-Control 0.8333333 -1.420758 3.087425 0.7559469
## 260mg/1LH2O-Control 0.3333333 -1.920758 2.587425 0.9786735
## 130mg/1LH2O-65mg/1LH2O 0.6666667 -1.587425 2.920758 0.8576913
## 260mg/1LH2O-65mg/1LH2O 0.1666667 -2.087425 2.420758 0.9972064
## 260mg/1LH2O-130mg/1LH2O -0.5000000 -2.754092 1.754092 0.9333382install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(agricolae)
duncan.test(anovavainas, 'tratamiento', console = T)##
## Study: anovavainas ~ "tratamiento"
##
## Duncan's new multiple range test
## for vainas
##
## Mean Square Error: 4.252033
##
## tratamiento, means
##
## vainas std r se Min Max Q25 Q50 Q75
## 130mg/1LH2O 7.666667 2.348436 12 0.5952613 5 12 6.00 7.0 9.25
## 260mg/1LH2O 7.166667 2.081666 12 0.5952613 4 12 6.00 6.5 8.25
## 65mg/1LH2O 7.000000 2.256304 12 0.5952613 5 11 5.00 6.0 9.00
## Control 6.833333 1.992410 12 0.5952613 5 12 5.75 6.0 8.00
##
## Alpha: 0.05 ; DF Error: 41
##
## Critical Range
## 2 3 4
## 1.700103 1.787649 1.844952
##
## Means with the same letter are not significantly different.
##
## vainas groups
## 130mg/1LH2O 7.666667 a
## 260mg/1LH2O 7.166667 a
## 65mg/1LH2O 7.000000 a
## Control 6.833333 a#Normalidad de residuos
shapiro.test(anovavainas$residuals)##
## Shapiro-Wilk normality test
##
## data: anovavainas$residuals
## W = 0.97127, p-value = 0.2837#Igualdad de varianzas
bartlett.test(anovavainas$residuals, datos$tratamiento)##
## Bartlett test of homogeneity of variances
##
## data: anovavainas$residuals and datos$tratamiento
## Bartlett's K-squared = 0.63164, df = 3, p-value = 0.8892### instalar los paquetes para las graficas
# Instalar los paquetes necesarios
install.packages("multcomp")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Cargar los paquetes
library(multcomp)
library(agricolae)# Posthoc (letras)
posthoc2 <- glht(anovavainas, linfct = mcp(tratamiento = "Tukey"))
letras2 <- cld(posthoc1)$mcletters$Letters
print(letras2)## Control 65mg/1LH2O 130mg/1LH2O 260mg/1LH2O
## "a" "ab" "b" "ab"# Calcular las medias y los errores estándar para los tratamientos
install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)medias2<- datos %>%
group_by(tratamiento) %>%
summarise(Media2 = mean(vainas), SE2 = sd(vainas)/sqrt(n()))# Gráfica de barras
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(ggplot2)# Agregar las letras a las medias
medias2$Letras <- letras2
# Crear el gráfico de barras
#COLORES DE TRATAMIENTOS
colores <- c("Control" = "brown2", "65mg/1LH2O" = "#97FFFF", "130mg/1LH2O" = "#90EE90", "260mg/1LH2O" = "#9F79EE")
ggplot(medias2, aes(x = tratamiento, y = Media2, fill = tratamiento)) +
geom_bar(stat = "identity", color = "black") +
geom_errorbar(aes(ymin = Media2 - SE2, ymax = Media2 + SE2), width = 0.2) +
geom_text(aes(label = letras2, y = Media2 + SE2 + 0.5), vjust = 0) +
labs(title = "Numero de vainas x planta",x = "Tratamientos", y = "Numero de vainas") +
theme_minimal() +
scale_fill_manual(values = colores)+
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)
)
Numero de granos por planta
install.packages("readxl") # Para leer archivos Excel## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("ggplot2") # Para visualización de datos (opcional)## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(readxl)
library(ggplot2)
library(outliers)
library(gridExtra)lectura de datos
datos = read_excel("//cloud//project//pesogranopla.xlsx", sheet = 3)
print(datos)## # A tibble: 48 × 3
## tratamiento surco granos
## <chr> <dbl> <dbl>
## 1 Control 1 19
## 2 Control 1 41
## 3 Control 1 49
## 4 Control 2 31
## 5 Control 2 31
## 6 Control 2 42
## 7 Control 3 30
## 8 Control 3 31
## 9 Control 3 25
## 10 Control 4 21
## # ℹ 38 more rowsstr(datos)## tibble [48 × 3] (S3: tbl_df/tbl/data.frame)
## $ tratamiento: chr [1:48] "Control" "Control" "Control" "Control" ...
## $ surco : num [1:48] 1 1 1 2 2 2 3 3 3 4 ...
## $ granos : num [1:48] 19 41 49 31 31 42 30 31 25 21 ...summary(datos)## tratamiento surco granos
## Length:48 Min. :1.00 Min. :18.00
## Class :character 1st Qu.:1.75 1st Qu.:26.75
## Mode :character Median :2.50 Median :31.00
## Mean :2.50 Mean :33.10
## 3rd Qu.:3.25 3rd Qu.:39.00
## Max. :4.00 Max. :53.00datos$surco <- factor(datos$surco)
datos$tratamiento <- factor(datos$tratamiento, levels = c("Control", "65mg/1LH2O", "130mg/1LH2O", "260mg/1LH2O"))
anovagranos = aov(granos ~ tratamiento + surco, data = datos)
summary(anovagranos)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 87.6 29.2 0.422 0.73825
## surco 3 1004.7 334.9 4.841 0.00564 **
## Residuals 41 2836.2 69.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anovagranos, "tratamiento")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = granos ~ tratamiento + surco, data = datos)
##
## $tratamiento
## diff lwr upr p adj
## 65mg/1LH2O-Control 2.0000000 -7.091778 11.091778 0.9348363
## 130mg/1LH2O-Control 2.7500000 -6.341778 11.841778 0.8494156
## 260mg/1LH2O-Control 3.6666667 -5.425111 12.758444 0.7036036
## 130mg/1LH2O-65mg/1LH2O 0.7500000 -8.341778 9.841778 0.9961354
## 260mg/1LH2O-65mg/1LH2O 1.6666667 -7.425111 10.758444 0.9606987
## 260mg/1LH2O-130mg/1LH2O 0.9166667 -8.175111 10.008444 0.9930117install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(agricolae)
duncan.test(anovagranos, 'tratamiento', console = T)##
## Study: anovagranos ~ "tratamiento"
##
## Duncan's new multiple range test
## for granos
##
## Mean Square Error: 69.1753
##
## tratamiento, means
##
## granos std r se Min Max Q25 Q50 Q75
## 130mg/1LH2O 33.75000 9.743296 12 2.40096 21 53 27.25 32 39.25
## 260mg/1LH2O 34.66667 9.948351 12 2.40096 21 53 26.25 35 40.00
## 65mg/1LH2O 33.00000 8.000000 12 2.40096 25 52 27.75 30 38.25
## Control 31.00000 9.553676 12 2.40096 18 49 24.00 31 35.75
##
## Alpha: 0.05 ; DF Error: 41
##
## Critical Range
## 2 3 4
## 6.857291 7.210402 7.441531
##
## Means with the same letter are not significantly different.
##
## granos groups
## 260mg/1LH2O 34.66667 a
## 130mg/1LH2O 33.75000 a
## 65mg/1LH2O 33.00000 a
## Control 31.00000 a#Normalidad de residuos
shapiro.test(anovagranos$residuals)##
## Shapiro-Wilk normality test
##
## data: anovagranos$residuals
## W = 0.97775, p-value = 0.4888#Igualdad de varianzas
bartlett.test(anovagranos$residuals, datos$tratamiento)##
## Bartlett test of homogeneity of variances
##
## data: anovagranos$residuals and datos$tratamiento
## Bartlett's K-squared = 2.5022, df = 3, p-value = 0.4749### instalar los paquetes para las graficas
# Instalar los paquetes necesarios
install.packages("multcomp")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Cargar los paquetes
library(multcomp)
library(agricolae)# Posthoc (letras)
posthoc3 <- glht(anovagranos, linfct = mcp(tratamiento = "Tukey"))
letras3 <- cld(posthoc3)$mcletters$Letters
print(letras3)## Control 65mg/1LH2O 130mg/1LH2O 260mg/1LH2O
## "a" "a" "a" "a"# Calcular las medias y los errores estándar para los tratamientos
install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)medias3<- datos %>%
group_by(tratamiento) %>%
summarise(Media3 = mean(granos), SE3 = sd(granos)/sqrt(n()))# Gráfica de barras
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(ggplot2)# Agregar las letras a las medias
medias3$Letras <- letras3
# Crear el gráfico de barras
#COLORES DE TRATAMIENTOS
colores <- c("Control" = "brown2", "65mg/1LH2O" = "#97FFFF", "130mg/1LH2O" = "#90EE90", "260mg/1LH2O" = "#9F79EE")
ggplot(medias3, aes(x = tratamiento, y = Media3, fill = tratamiento)) +
geom_bar(stat = "identity", color = "black") +
geom_errorbar(aes(ymin = Media3 - SE3, ymax = Media3 + SE3), width = 0.2) +
geom_text(aes(label = letras3, y = Media3 + SE3 + 0.5), vjust = 0) +
labs(title = "Numero de granos x planta",x = "Tratamientos", y = "Numero de granos") +
theme_minimal() +
scale_fill_manual(values = colores)+
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)
)
#Peso de vainas con granos
install.packages("readxl") # Para leer archivos Excel## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("ggplot2") # Para visualización de datos (opcional)## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(readxl)
library(ggplot2)
library(outliers)
library(gridExtra)lectura de datos
datos = read_excel("//cloud//project//pesogranopla.xlsx", sheet = 4)
print(datos)## # A tibble: 48 × 3
## tratamiento surco pesovainas
## <chr> <dbl> <dbl>
## 1 Control 1 4.5
## 2 Control 1 5.62
## 3 Control 1 6.16
## 4 Control 2 5.33
## 5 Control 2 6.4
## 6 Control 2 4.93
## 7 Control 3 4.27
## 8 Control 3 4.54
## 9 Control 3 4.35
## 10 Control 4 4.88
## # ℹ 38 more rowsstr(datos)## tibble [48 × 3] (S3: tbl_df/tbl/data.frame)
## $ tratamiento: chr [1:48] "Control" "Control" "Control" "Control" ...
## $ surco : num [1:48] 1 1 1 2 2 2 3 3 3 4 ...
## $ pesovainas : num [1:48] 4.5 5.62 6.16 5.33 6.4 4.93 4.27 4.54 4.35 4.88 ...summary(datos)## tratamiento surco pesovainas
## Length:48 Min. :1.00 Min. : 3.450
## Class :character 1st Qu.:1.75 1st Qu.: 4.815
## Mode :character Median :2.50 Median : 5.390
## Mean :2.50 Mean : 5.517
## 3rd Qu.:3.25 3rd Qu.: 5.930
## Max. :4.00 Max. :12.600datos$surco <- factor(datos$surco)
datos$tratamiento <- factor(datos$tratamiento, levels = c("Control", "65mg/1LH2O", "130mg/1LH2O", "260mg/1LH2O"))
anovapesovainas = aov(pesovainas ~ tratamiento + surco, data = datos)
summary(anovapesovainas)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 5.03 1.678 0.899 0.450
## surco 3 6.97 2.324 1.245 0.306
## Residuals 41 76.52 1.866TukeyHSD(anovapesovainas, "tratamiento")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = pesovainas ~ tratamiento + surco, data = datos)
##
## $tratamiento
## diff lwr upr p adj
## 65mg/1LH2O-Control 0.4141667 -1.0791655 1.907499 0.8792528
## 130mg/1LH2O-Control 0.8900000 -0.6033322 2.383332 0.3922170
## 260mg/1LH2O-Control 0.6091667 -0.8841655 2.102499 0.6961856
## 130mg/1LH2O-65mg/1LH2O 0.4758333 -1.0174988 1.969166 0.8286217
## 260mg/1LH2O-65mg/1LH2O 0.1950000 -1.2983322 1.688332 0.9851140
## 260mg/1LH2O-130mg/1LH2O -0.2808333 -1.7741655 1.212499 0.9577783install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(agricolae)
duncan.test(anovapesovainas, 'tratamiento', console = T)##
## Study: anovapesovainas ~ "tratamiento"
##
## Duncan's new multiple range test
## for pesovainas
##
## Mean Square Error: 1.866235
##
## tratamiento, means
##
## pesovainas std r se Min Max Q25 Q50 Q75
## 130mg/1LH2O 5.928333 2.3772705 12 0.3943597 4.11 12.60 4.4025 5.380 6.0125
## 260mg/1LH2O 5.647500 0.9883791 12 0.3943597 3.45 7.27 5.3650 5.445 6.0225
## 65mg/1LH2O 5.452500 0.6868919 12 0.3943597 4.22 6.80 5.0450 5.555 5.9000
## Control 5.038333 0.6997770 12 0.3943597 4.27 6.40 4.5300 4.875 5.4025
##
## Alpha: 0.05 ; DF Error: 41
##
## Critical Range
## 2 3 4
## 1.126316 1.184315 1.222278
##
## Means with the same letter are not significantly different.
##
## pesovainas groups
## 130mg/1LH2O 5.928333 a
## 260mg/1LH2O 5.647500 a
## 65mg/1LH2O 5.452500 a
## Control 5.038333 a#Normalidad de residuos
shapiro.test(anovapesovainas$residuals)##
## Shapiro-Wilk normality test
##
## data: anovapesovainas$residuals
## W = 0.82455, p-value = 4.899e-06#Igualdad de varianzas
bartlett.test(anovapesovainas$residuals, datos$tratamiento)##
## Bartlett test of homogeneity of variances
##
## data: anovapesovainas$residuals and datos$tratamiento
## Bartlett's K-squared = 23.831, df = 3, p-value = 2.709e-05### instalar los paquetes para las graficas
# Instalar los paquetes necesarios
install.packages("multcomp")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Cargar los paquetes
library(multcomp)
library(agricolae)# Posthoc (letras)
posthoc4 <- glht(anovapesovainas, linfct = mcp(tratamiento = "Tukey"))
letras4 <- cld(posthoc4)$mcletters$Letters
print(letras4)## Control 65mg/1LH2O 130mg/1LH2O 260mg/1LH2O
## "a" "a" "a" "a"# Calcular las medias y los errores estándar para los tratamientos
install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)medias4<- datos %>%
group_by(tratamiento) %>%
summarise(Media4 = mean(pesovainas), SE4 = sd(pesovainas)/sqrt(n()))# Gráfica de barras
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(ggplot2)# Agregar las letras a las medias
medias4$Letras <- letras4
# Crear el gráfico de barras
#COLORES DE TRATAMIENTOS
colores <- c("Control" = "brown2", "65mg/1LH2O" = "#97FFFF", "130mg/1LH2O" = "#90EE90", "260mg/1LH2O" = "#9F79EE")
ggplot(medias4, aes(x = tratamiento, y = Media4, fill = tratamiento)) +
geom_bar(stat = "identity", color = "black") +
geom_errorbar(aes(ymin = Media4 - SE4, ymax = Media4 + SE4), width = 0.2) +
geom_text(aes(label = letras4, y = Media4 + SE4 + 0.5), vjust = 0) +
labs(title = "Peso de vainas con granos",x = "Tratamientos", y = "Peso de vainas") +
theme_minimal() +
scale_fill_manual(values = colores)+
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)
)
Longitud de vainas
install.packages("readxl") # Para leer archivos Excel## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("ggplot2") # Para visualización de datos (opcional)## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(readxl)
library(ggplot2)
library(outliers)
library(gridExtra)lectura de datos
datos = read_excel("//cloud//project//pesogranopla.xlsx", sheet = 5)
print(datos)## # A tibble: 48 × 3
## tratamiento surco longitud
## <chr> <dbl> <dbl>
## 1 Control 1 6.73
## 2 Control 1 6.99
## 3 Control 1 6.58
## 4 Control 2 6.57
## 5 Control 2 7.4
## 6 Control 2 6.85
## 7 Control 3 7.78
## 8 Control 3 8.25
## 9 Control 3 8.27
## 10 Control 4 7
## # ℹ 38 more rowsstr(datos)## tibble [48 × 3] (S3: tbl_df/tbl/data.frame)
## $ tratamiento: chr [1:48] "Control" "Control" "Control" "Control" ...
## $ surco : num [1:48] 1 1 1 2 2 2 3 3 3 4 ...
## $ longitud : num [1:48] 6.73 6.99 6.58 6.57 7.4 6.85 7.78 8.25 8.27 7 ...summary(datos)## tratamiento surco longitud
## Length:48 Min. :1.00 Min. :6.150
## Class :character 1st Qu.:1.75 1st Qu.:6.725
## Mode :character Median :2.50 Median :7.400
## Mean :2.50 Mean :7.338
## 3rd Qu.:3.25 3rd Qu.:7.985
## Max. :4.00 Max. :8.640datos$surco <- factor(datos$surco)
datos$tratamiento <- factor(datos$tratamiento, levels = c("Control", "65mg/1LH2O", "130mg/1LH2O", "260mg/1LH2O"))
anovalongitud = aov(longitud ~ tratamiento + surco, data = datos)
summary(anovalongitud)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 0.051 0.017 0.066 0.978
## surco 3 13.656 4.552 17.699 1.6e-07 ***
## Residuals 41 10.544 0.257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anovalongitud, "tratamiento")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = longitud ~ tratamiento + surco, data = datos)
##
## $tratamiento
## diff lwr upr p adj
## 65mg/1LH2O-Control 0.05833333 -0.4960280 0.6126947 0.9920763
## 130mg/1LH2O-Control 0.05083333 -0.5035280 0.6051947 0.9947163
## 260mg/1LH2O-Control 0.09083333 -0.4635280 0.6451947 0.9713955
## 130mg/1LH2O-65mg/1LH2O -0.00750000 -0.5618614 0.5468614 0.9999826
## 260mg/1LH2O-65mg/1LH2O 0.03250000 -0.5218614 0.5868614 0.9985993
## 260mg/1LH2O-130mg/1LH2O 0.04000000 -0.5143614 0.5943614 0.9974019install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(agricolae)
duncan.test(anovalongitud, 'tratamiento', console = T)##
## Study: anovalongitud ~ "tratamiento"
##
## Duncan's new multiple range test
## for longitud
##
## Mean Square Error: 0.2571813
##
## tratamiento, means
##
## longitud std r se Min Max Q25 Q50 Q75
## 130mg/1LH2O 7.339167 0.7547963 12 0.146396 6.31 8.50 6.6925 7.355 7.995
## 260mg/1LH2O 7.379167 0.6419071 12 0.146396 6.15 8.03 7.1900 7.470 7.985
## 65mg/1LH2O 7.346667 0.9062243 12 0.146396 6.20 8.64 6.6675 7.040 8.345
## Control 7.288333 0.6300770 12 0.146396 6.57 8.27 6.8200 7.030 7.830
##
## Alpha: 0.05 ; DF Error: 41
##
## Critical Range
## 2 3 4
## 0.4181160 0.4396465 0.4537394
##
## Means with the same letter are not significantly different.
##
## longitud groups
## 260mg/1LH2O 7.379167 a
## 65mg/1LH2O 7.346667 a
## 130mg/1LH2O 7.339167 a
## Control 7.288333 a#Normalidad de residuos
shapiro.test(anovalongitud$residuals)##
## Shapiro-Wilk normality test
##
## data: anovalongitud$residuals
## W = 0.9765, p-value = 0.4426#Igualdad de varianzas
bartlett.test(anovalongitud$residuals, datos$tratamiento)##
## Bartlett test of homogeneity of variances
##
## data: anovalongitud$residuals and datos$tratamiento
## Bartlett's K-squared = 2.7782, df = 3, p-value = 0.4271### instalar los paquetes para las graficas
# Instalar los paquetes necesarios
install.packages("multcomp")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Cargar los paquetes
library(multcomp)
library(agricolae)# Posthoc (letras)
posthoc5 <- glht(anovalongitud, linfct = mcp(tratamiento = "Tukey"))
letras5 <- cld(posthoc5)$mcletters$Letters
print(letras5)## Control 65mg/1LH2O 130mg/1LH2O 260mg/1LH2O
## "a" "a" "a" "a"# Calcular las medias y los errores estándar para los tratamientos
install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)medias5<- datos %>%
group_by(tratamiento) %>%
summarise(Media5 = mean(longitud), SE5 = sd(longitud)/sqrt(n()))# Agregar las letras a las medias
medias5$Letras <- letras5
# Crear el gráfico de barras
#COLORES DE TRATAMIENTOS
colores <- c("Control" = "brown2", "65mg/1LH2O" = "#97FFFF", "130mg/1LH2O" = "#90EE90", "260mg/1LH2O" = "#9F79EE")
ggplot(medias5, aes(x = tratamiento, y = Media5, fill = tratamiento)) +
geom_bar(stat = "identity", color = "black") +
geom_errorbar(aes(ymin = Media5 - SE5, ymax = Media5 + SE5), width = 0.2) +
geom_text(aes(label = letras5, y = Media5 + SE5 + 0.5), vjust = 0) +
labs(title = "Longitud de las vainas",x = "Tratamientos", y = "Longitud (cm)") +
theme_minimal() +
scale_fill_manual(values = colores)+
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)
)
peso de 100 granos x tratamiento
datos = read_excel("//cloud//project//pesogranopla.xlsx", sheet = 7)
print(datos)## # A tibble: 16 × 3
## tratamiento surco peso100
## <chr> <dbl> <dbl>
## 1 Control 1 57.4
## 2 Control 2 53.4
## 3 Control 3 27.6
## 4 Control 4 41.8
## 5 65mg/1LH2O 1 63.6
## 6 65mg/1LH2O 2 62.1
## 7 65mg/1LH2O 3 53.7
## 8 65mg/1LH2O 4 56.1
## 9 130mg/1LH2O 1 72.3
## 10 130mg/1LH2O 2 64.7
## 11 130mg/1LH2O 3 60.8
## 12 130mg/1LH2O 4 60.3
## 13 260mg/1LH2O 1 75.3
## 14 260mg/1LH2O 2 63.9
## 15 260mg/1LH2O 3 61.9
## 16 260mg/1LH2O 4 48.1str(datos)## tibble [16 × 3] (S3: tbl_df/tbl/data.frame)
## $ tratamiento: chr [1:16] "Control" "Control" "Control" "Control" ...
## $ surco : num [1:16] 1 2 3 4 1 2 3 4 1 2 ...
## $ peso100 : num [1:16] 57.4 53.4 27.6 41.9 63.6 ...summary(datos)## tratamiento surco peso100
## Length:16 Min. :1.00 Min. :27.56
## Class :character 1st Qu.:1.75 1st Qu.:53.63
## Mode :character Median :2.50 Median :60.55
## Mean :2.50 Mean :57.69
## 3rd Qu.:3.25 3rd Qu.:63.67
## Max. :4.00 Max. :75.28datos$surco <- factor(datos$surco)
datos$tratamiento <- factor(datos$tratamiento, levels = c("Control", "65mg/1LH2O", "130mg/1LH2O", "260mg/1LH2O"))
anovapeso100 = aov(peso100 ~ tratamiento + surco, data = datos)
summary(anovapeso100)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 917.3 305.78 8.108 0.00631 **
## surco 3 729.2 243.05 6.445 0.01276 *
## Residuals 9 339.4 37.71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anovapeso100, "tratamiento")## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = peso100 ~ tratamiento + surco, data = datos)
##
## $tratamiento
## diff lwr upr p adj
## 65mg/1LH2O-Control 13.8225 0.2664332 27.37857 0.0455900
## 130mg/1LH2O-Control 19.4825 5.9264332 33.03857 0.0067842
## 260mg/1LH2O-Control 17.2600 3.7039332 30.81607 0.0140771
## 130mg/1LH2O-65mg/1LH2O 5.6600 -7.8960668 19.21607 0.5832962
## 260mg/1LH2O-65mg/1LH2O 3.4375 -10.1185668 16.99357 0.8565292
## 260mg/1LH2O-130mg/1LH2O -2.2225 -15.7785668 11.33357 0.9541872install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(agricolae)
duncan.test(anovapeso100, 'tratamiento', console = T)##
## Study: anovapeso100 ~ "tratamiento"
##
## Duncan's new multiple range test
## for peso100
##
## Mean Square Error: 37.7127
##
## tratamiento, means
##
## peso100 std r se Min Max Q25 Q50 Q75
## 130mg/1LH2O 64.5300 5.535419 4 3.070533 60.29 72.28 60.6800 62.775 66.625
## 260mg/1LH2O 62.3075 11.129826 4 3.070533 48.14 75.28 58.4825 62.905 66.730
## 65mg/1LH2O 58.8700 4.725237 4 3.070533 53.72 63.60 55.4900 59.080 62.460
## Control 45.0475 13.392180 4 3.070533 27.56 57.40 38.2775 47.615 54.385
##
## Alpha: 0.05 ; DF Error: 9
##
## Critical Range
## 2 3 4
## 9.823168 10.252933 10.500502
##
## Means with the same letter are not significantly different.
##
## peso100 groups
## 130mg/1LH2O 64.5300 a
## 260mg/1LH2O 62.3075 a
## 65mg/1LH2O 58.8700 a
## Control 45.0475 b#Normalidad de residuos
shapiro.test(anovapeso100$residuals)##
## Shapiro-Wilk normality test
##
## data: anovapeso100$residuals
## W = 0.91357, p-value = 0.1329#Igualdad de varianzas
bartlett.test(anovapeso100$residuals, datos$tratamiento)##
## Bartlett test of homogeneity of variances
##
## data: anovapeso100$residuals and datos$tratamiento
## Bartlett's K-squared = 2.9519, df = 3, p-value = 0.3991### instalar los paquetes para las graficas
# Instalar los paquetes necesarios
install.packages("multcomp")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("agricolae")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# Cargar los paquetes
library(multcomp)
library(agricolae)# Posthoc (letras)
posthoc7 <- glht(anovapeso100, linfct = mcp(tratamiento = "Tukey"))
letras7 <- cld(posthoc7)$mcletters$Letters
print(letras7)## Control 65mg/1LH2O 130mg/1LH2O 260mg/1LH2O
## "a" "b" "b" "b"# Calcular las medias y los errores estándar para los tratamientos
install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(dplyr)medias7<- datos %>%
group_by(tratamiento) %>%
summarise(Media7 = mean(peso100), SE7 = sd(peso100)/sqrt(n()))# Gráfica de barras
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(ggplot2)# Agregar las letras a las medias
medias7$Letras <- letras7
# Crear el gráfico de barras
#COLORES DE TRATAMIENTOS
colores <- c("Control" = "brown2", "65mg/1LH2O" = "#97FFFF", "130mg/1LH2O" = "#90EE90", "260mg/1LH2O" = "#9F79EE")
ggplot(medias7, aes(x = tratamiento, y = Media7, fill = tratamiento)) +
geom_bar(stat = "identity", color = "black") +
geom_errorbar(aes(ymin = Media7 - SE7, ymax = Media7 + SE7), width = 0.2) +
geom_text(aes(label = letras7, y = Media7 + SE7 + 0.5), vjust = 0) +
labs(title = "Peso de 100 granos",x = "Tratamientos", y = "peso (gr)") +
theme_minimal() +
scale_fill_manual(values = colores)+
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)
)
# eficiencia agronomica
datos = read_excel("//cloud//project//pesogranopla.xlsx", sheet = 6)
print(datos)## # A tibble: 3 × 2
## tratamiento efiagro
## <chr> <dbl>
## 1 65mg/1LH2O 69.8
## 2 130mg/1LH2O 87.1
## 3 260mg/1LH2O 84.6str(datos)## tibble [3 × 2] (S3: tbl_df/tbl/data.frame)
## $ tratamiento: chr [1:3] "65mg/1LH2O" "130mg/1LH2O" "260mg/1LH2O"
## $ efiagro : num [1:3] 69.8 87.1 84.6summary(datos)## tratamiento efiagro
## Length:3 Min. :69.83
## Class :character 1st Qu.:77.20
## Mode :character Median :84.58
## Mean :80.49
## 3rd Qu.:85.82
## Max. :87.06datos$tratamiento <- factor(datos$tratamiento, levels = c("65mg/1LH2O", "130mg/1LH2O", "260mg/1LH2O"))
print(datos)## # A tibble: 3 × 2
## tratamiento efiagro
## <fct> <dbl>
## 1 65mg/1LH2O 69.8
## 2 130mg/1LH2O 87.1
## 3 260mg/1LH2O 84.6# Crear el gráfico de barras con colores personalizados
ggplot(datos, aes(x = tratamiento, y = efiagro, fill = tratamiento)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("65mg/1LH2O" = "#97FFFF", "130mg/1LH2O" = "#90EE90", "260mg/1LH2O" = "#9F79EE")) +
theme_minimal() +
labs(title = "Eficiencia agronomica",
x = "Tratamiento",
y = "% de eficiencia") +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)
)