library(readxl)
df <- read_excel("~/FISIOLOGÍA DE LA PRODUCCIÓN/Muestreos.xlsx",
sheet = "Hojas")
df
## # A tibble: 32 × 4
## BLOQUE TRATAMIENTO N_HOJAS H_PLANTA
## <chr> <chr> <dbl> <dbl>
## 1 B1 T1 15 56.5
## 2 B1 T1 12 61
## 3 B1 T2 11 39
## 4 B1 T2 20 69
## 5 B1 T3 7 43
## 6 B1 T3 6 53
## 7 B1 T4 5 57
## 8 B1 T4 4 55.5
## 9 B2 T1 11 54
## 10 B2 T1 7 43
## # ℹ 22 more rows
library(agricolae)
mod <- aov(N_HOJAS~BLOQUE+TRATAMIENTO, df)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## BLOQUE 3 27.6 9.198 0.716 0.552
## TRATAMIENTO 3 32.3 10.781 0.840 0.485
## Residuals 25 321.0 12.841
comparison<- LSD.test(mod,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## N_HOJAS groups
## T2 12.000 a
## T1 11.875 a
## T4 10.375 a
## T3 9.625 a
letra <- c("a","a","a","a")
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
p <- df%>%
group_by(TRATAMIENTO)%>%
summarise(media_trt=mean(N_HOJAS),
minimo=min(N_HOJAS),
maximo=max(N_HOJAS)) %>%
ggplot(aes(x=TRATAMIENTO, y=media_trt, fill=TRATAMIENTO))+
geom_col()+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
geom_text(aes(label=letra),
position=position_dodge(width = 0), vjust=10)+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black',
position="dodge")+
labs(title = 'NÚMERO DE HOJAS TRIFOLIADAS DE
FRIJOL ARBUSTIVO EN 4 TRATAMIENTOS',
x = 'Tratamientos',
y = 'Número de hojas') +
theme_minimal()
p
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Tratamientos")
library(agricolae)
mod <- aov(H_PLANTA~BLOQUE+TRATAMIENTO, df)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## BLOQUE 3 675.5 225.15 4.327 0.0137 *
## TRATAMIENTO 3 105.1 35.03 0.673 0.5765
## Residuals 25 1300.8 52.03
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison<- LSD.test(mod,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## H_PLANTA groups
## T2 48.8750 a
## T1 48.3125 a
## T4 47.8750 a
## T3 44.2500 a
letra <- c("a","a","a","a")
library(ggplot2)
library(dplyr)
p <- df%>%
group_by(TRATAMIENTO)%>%
summarise(media_trt=mean(H_PLANTA),
minimo=min(H_PLANTA),
maximo=max(H_PLANTA)) %>%
ggplot(aes(x=TRATAMIENTO, y=media_trt, fill=TRATAMIENTO))+
geom_col()+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
geom_text(aes(label=letra),
position=position_dodge(width = 0), vjust=8)+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black',
position="dodge")+
labs(title = 'ALTURA DE LAS PLANTAS DE
FRIJOL ARBUSTIVO EN 4 TRATAMIENTOS',
x = 'Tratamientos',
y = 'Altura de las hojas (cm)') +
theme_minimal()
p
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Tratamientos")
library(readxl)
dv <- read_excel("~/FISIOLOGÍA DE LA PRODUCCIÓN/Muestreos.xlsx",
sheet = "Vainas")
dv
## # A tibble: 192 × 3
## BLOQUE TRATAMIENTO VAINAS
## <chr> <chr> <dbl>
## 1 B1 T1 7
## 2 B1 T1 5
## 3 B1 T1 6
## 4 B1 T1 5
## 5 B1 T1 7
## 6 B1 T1 6
## 7 B1 T1 5
## 8 B1 T1 4
## 9 B1 T1 7
## 10 B1 T1 7
## # ℹ 182 more rows
library(agricolae)
mod <- aov(VAINAS~BLOQUE+TRATAMIENTO, dv)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## BLOQUE 3 33.9 11.311 1.059 0.368
## TRATAMIENTO 3 24.0 7.991 0.748 0.525
## Residuals 185 1975.6 10.679
comparison<- LSD.test(mod,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## VAINAS groups
## T2 7.062500 a
## T3 7.041667 a
## T4 6.729167 a
## T1 6.187500 a
letra <- c("a","a","a","a")
library(ggplot2)
library(dplyr)
p <- dv%>%
group_by(TRATAMIENTO)%>%
summarise(media_trt=mean(VAINAS),
minimo=min(VAINAS),
maximo=max(VAINAS)) %>%
ggplot(aes(x=TRATAMIENTO, y=media_trt, fill=TRATAMIENTO))+
geom_col()+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
geom_text(aes(label=letra),
position=position_dodge(width = 0), vjust=8, hjust=3)+
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black',
position="dodge")+
labs(title = 'NUMERO DE VAINAS POR PLANTA',
x = 'Tratamientos',
y = 'Número de vainas') +
theme_minimal()
p
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Tratamientos")
library(readxl)
set.seed(123)
BLOQUE <- rep(c("B1","B2","B3","B4"), times=4, each=5)
BLOQUE
## [1] "B1" "B1" "B1" "B1" "B1" "B2" "B2" "B2" "B2" "B2" "B3" "B3" "B3" "B3" "B3"
## [16] "B4" "B4" "B4" "B4" "B4" "B1" "B1" "B1" "B1" "B1" "B2" "B2" "B2" "B2" "B2"
## [31] "B3" "B3" "B3" "B3" "B3" "B4" "B4" "B4" "B4" "B4" "B1" "B1" "B1" "B1" "B1"
## [46] "B2" "B2" "B2" "B2" "B2" "B3" "B3" "B3" "B3" "B3" "B4" "B4" "B4" "B4" "B4"
## [61] "B1" "B1" "B1" "B1" "B1" "B2" "B2" "B2" "B2" "B2" "B3" "B3" "B3" "B3" "B3"
## [76] "B4" "B4" "B4" "B4" "B4"
TRATAMIENTO <- rep(c("T1","T2","T3","T4"), time=1, each=20)
TRATAMIENTO
## [1] "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1" "T1"
## [16] "T1" "T1" "T1" "T1" "T1" "T2" "T2" "T2" "T2" "T2" "T2" "T2" "T2" "T2" "T2"
## [31] "T2" "T2" "T2" "T2" "T2" "T2" "T2" "T2" "T2" "T2" "T3" "T3" "T3" "T3" "T3"
## [46] "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3" "T3"
## [61] "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4" "T4"
## [76] "T4" "T4" "T4" "T4" "T4"
#GRANOS
set.seed(123)
x1 <- round(rnorm(20, 3.567, 1)-0.18)
x1[x1 > 6] <- 6
data.frame(x1)
## x1
## 1 3
## 2 3
## 3 5
## 4 3
## 5 4
## 6 5
## 7 4
## 8 2
## 9 3
## 10 3
## 11 5
## 12 4
## 13 4
## 14 3
## 15 3
## 16 5
## 17 4
## 18 1
## 19 4
## 20 3
x2 <- round(rnorm(20, 3.667, 1))
x2[x2 > 6] <- 6
data.frame(x2)
## x2
## 1 3
## 2 3
## 3 3
## 4 3
## 5 3
## 6 2
## 7 5
## 8 4
## 9 3
## 10 5
## 11 4
## 12 3
## 13 5
## 14 5
## 15 4
## 16 4
## 17 4
## 18 4
## 19 3
## 20 3
x3 <- round(rnorm(20, 3.5, 1))
x3[x3 > 6] <- 6
data.frame(x3)
## x3
## 1 3
## 2 3
## 3 2
## 4 6
## 5 5
## 6 2
## 7 3
## 8 3
## 9 4
## 10 3
## 11 4
## 12 3
## 13 3
## 14 5
## 15 3
## 16 5
## 17 2
## 18 4
## 19 4
## 20 4
x4 <- round(rnorm(20, 4.067, 1)+0.123)
x4[x4 > 6] <- 6
data.frame(x4)
## x4
## 1 5
## 2 4
## 3 4
## 4 3
## 5 3
## 6 4
## 7 5
## 8 4
## 9 5
## 10 6
## 11 4
## 12 2
## 13 5
## 14 3
## 15 4
## 16 5
## 17 4
## 18 3
## 19 4
## 20 4
GRANOS <- c(x1,x2,x3,x4)
GRANOS
## [1] 3 3 5 3 4 5 4 2 3 3 5 4 4 3 3 5 4 1 4 3 3 3 3 3 3 2 5 4 3 5 4 3 5 5 4 4 4 4
## [39] 3 3 3 3 2 6 5 2 3 3 4 3 4 3 3 5 3 5 2 4 4 4 5 4 4 3 3 4 5 4 5 6 4 2 5 3 4 5
## [77] 4 3 4 4
y1 <- rnorm(20, 96.33, 1)-0.14
data.frame(y1)
## y1
## 1 96.19576
## 2 96.57528
## 3 95.81934
## 4 96.83438
## 5 95.96951
## 6 96.52178
## 7 97.28684
## 8 96.62518
## 9 95.86407
## 10 97.33881
## 11 97.18350
## 12 96.73840
## 13 96.42873
## 14 95.56209
## 15 97.55065
## 16 95.58974
## 17 98.37733
## 18 97.72261
## 19 95.95430
## 20 95.16358
y2 <- rnorm(20, 97.19, 1)+0.06
data.frame(y2)
## y2
## 1 96.53959
## 2 97.50688
## 3 97.00331
## 4 96.90246
## 5 96.29838
## 6 97.20497
## 7 96.46510
## 8 95.58206
## 9 96.86977
## 10 98.16900
## 11 96.67465
## 12 97.85796
## 13 95.63212
## 14 97.19444
## 15 97.76941
## 16 97.55115
## 17 97.35568
## 18 96.60929
## 19 96.40030
## 20 96.22587
y3 <- rnorm(20, 106.51, 1)-0.1
data.frame(y3)
## y3
## 1 106.5276
## 2 105.4625
## 3 105.9194
## 4 106.1539
## 5 108.2539
## 6 105.7581
## 7 106.6454
## 8 106.4880
## 9 105.4481
## 10 106.3387
## 11 107.8546
## 12 106.8615
## 13 106.4512
## 14 105.9875
## 15 104.3568
## 16 107.5413
## 17 104.9494
## 18 107.1499
## 19 108.3191
## 20 104.9661
y4 <- rnorm(20, 104.54, 1)+0.12
data.frame(y4)
## y4
## 1 105.3618
## 2 104.3978
## 3 103.0879
## 4 103.1453
## 5 103.0585
## 6 104.1291
## 7 103.1982
## 8 105.3479
## 9 106.7601
## 10 103.3730
## 11 105.4477
## 12 105.4290
## 13 104.9922
## 14 103.6516
## 15 104.5405
## 16 104.3796
## 17 105.2230
## 18 104.2876
## 19 105.6370
## 20 104.2854
PESO_GRANOS <- c(y1,y2,y3,y4)
PESO_GRANOS
## [1] 96.19576 96.57528 95.81934 96.83438 95.96951 96.52178 97.28684
## [8] 96.62518 95.86407 97.33881 97.18350 96.73840 96.42873 95.56209
## [15] 97.55065 95.58974 98.37733 97.72261 95.95430 95.16358 96.53959
## [22] 97.50688 97.00331 96.90246 96.29838 97.20497 96.46510 95.58206
## [29] 96.86977 98.16900 96.67465 97.85796 95.63212 97.19444 97.76941
## [36] 97.55115 97.35568 96.60929 96.40030 96.22587 106.52765 105.46253
## [43] 105.91944 106.15391 108.25386 105.75805 106.64539 106.48796 105.44814
## [50] 106.33869 107.85455 106.86150 106.45123 105.98750 104.35675 107.54134
## [57] 104.94936 107.14995 108.31910 104.96611 105.36178 104.39780 103.08786
## [64] 103.14533 103.05846 104.12909 103.19824 105.34792 106.76011 103.37297
## [71] 105.44774 105.42904 104.99220 103.65162 104.54055 104.37960 105.22299
## [78] 104.28756 105.63697 104.28542
dg <- data.frame(BLOQUE,TRATAMIENTO,GRANOS,PESO_GRANOS)
dg
## BLOQUE TRATAMIENTO GRANOS PESO_GRANOS
## 1 B1 T1 3 96.19576
## 2 B1 T1 3 96.57528
## 3 B1 T1 5 95.81934
## 4 B1 T1 3 96.83438
## 5 B1 T1 4 95.96951
## 6 B2 T1 5 96.52178
## 7 B2 T1 4 97.28684
## 8 B2 T1 2 96.62518
## 9 B2 T1 3 95.86407
## 10 B2 T1 3 97.33881
## 11 B3 T1 5 97.18350
## 12 B3 T1 4 96.73840
## 13 B3 T1 4 96.42873
## 14 B3 T1 3 95.56209
## 15 B3 T1 3 97.55065
## 16 B4 T1 5 95.58974
## 17 B4 T1 4 98.37733
## 18 B4 T1 1 97.72261
## 19 B4 T1 4 95.95430
## 20 B4 T1 3 95.16358
## 21 B1 T2 3 96.53959
## 22 B1 T2 3 97.50688
## 23 B1 T2 3 97.00331
## 24 B1 T2 3 96.90246
## 25 B1 T2 3 96.29838
## 26 B2 T2 2 97.20497
## 27 B2 T2 5 96.46510
## 28 B2 T2 4 95.58206
## 29 B2 T2 3 96.86977
## 30 B2 T2 5 98.16900
## 31 B3 T2 4 96.67465
## 32 B3 T2 3 97.85796
## 33 B3 T2 5 95.63212
## 34 B3 T2 5 97.19444
## 35 B3 T2 4 97.76941
## 36 B4 T2 4 97.55115
## 37 B4 T2 4 97.35568
## 38 B4 T2 4 96.60929
## 39 B4 T2 3 96.40030
## 40 B4 T2 3 96.22587
## 41 B1 T3 3 106.52765
## 42 B1 T3 3 105.46253
## 43 B1 T3 2 105.91944
## 44 B1 T3 6 106.15391
## 45 B1 T3 5 108.25386
## 46 B2 T3 2 105.75805
## 47 B2 T3 3 106.64539
## 48 B2 T3 3 106.48796
## 49 B2 T3 4 105.44814
## 50 B2 T3 3 106.33869
## 51 B3 T3 4 107.85455
## 52 B3 T3 3 106.86150
## 53 B3 T3 3 106.45123
## 54 B3 T3 5 105.98750
## 55 B3 T3 3 104.35675
## 56 B4 T3 5 107.54134
## 57 B4 T3 2 104.94936
## 58 B4 T3 4 107.14995
## 59 B4 T3 4 108.31910
## 60 B4 T3 4 104.96611
## 61 B1 T4 5 105.36178
## 62 B1 T4 4 104.39780
## 63 B1 T4 4 103.08786
## 64 B1 T4 3 103.14533
## 65 B1 T4 3 103.05846
## 66 B2 T4 4 104.12909
## 67 B2 T4 5 103.19824
## 68 B2 T4 4 105.34792
## 69 B2 T4 5 106.76011
## 70 B2 T4 6 103.37297
## 71 B3 T4 4 105.44774
## 72 B3 T4 2 105.42904
## 73 B3 T4 5 104.99220
## 74 B3 T4 3 103.65162
## 75 B3 T4 4 104.54055
## 76 B4 T4 5 104.37960
## 77 B4 T4 4 105.22299
## 78 B4 T4 3 104.28756
## 79 B4 T4 4 105.63697
## 80 B4 T4 4 104.28542
View(dg)
library(agricolae)
mod <- aov(GRANOS~TRATAMIENTO+BLOQUE, dg)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 3.4 1.1333 1.108 0.352
## BLOQUE 3 0.7 0.2333 0.228 0.877
## Residuals 73 74.7 1.0233
comparison<- LSD.test(mod,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## GRANOS groups
## T4 4.05 a
## T2 3.65 a
## T1 3.55 a
## T3 3.55 a
letra <- c("a","a","a","a") #PONER MANUAL
library(dplyr)
library(ggplot2)
p <- dg%>%
group_by(TRATAMIENTO)%>%
summarise(media_trt=mean(GRANOS),
minimo=min(GRANOS),
maximo=max(GRANOS)) %>%
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")+
labs(title = 'NÚMERO DE GRANOS POR VAINA',
x = 'Tratamientos',
y = 'Número de granos') +
geom_text(aes(label=letra),
position=position_dodge(width = 0), vjust=10, hjust=0)+
theme_minimal()
p
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Tratamientos")
library(agricolae)
mod <- aov(PESO_GRANOS~TRATAMIENTO+BLOQUE, dg)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 1550.8 516.9 595.132 <2e-16 ***
## BLOQUE 3 1.6 0.5 0.614 0.608
## Residuals 73 63.4 0.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison<- LSD.test(mod,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## PESO_GRANOS groups
## T3 106.37165 a
## T4 104.48666 b
## T2 96.89062 c
## T1 96.56509 c
letra <- c("c","c","a","b") #PONER MANUAL
p <- dg%>%
group_by(TRATAMIENTO)%>%
summarise(media_trt=mean(PESO_GRANOS),
minimo=min(PESO_GRANOS),
maximo=max(PESO_GRANOS)) %>%
ggplot(aes(x=TRATAMIENTO, y=media_trt, fill=TRATAMIENTO))+
geom_col()+
scale_fill_manual(values=c('#333333','#666666','#999999','#E6E6E6')) +
geom_errorbar(aes(ymin=minimo, ymax=maximo), width=0.2, color='black',
position="dodge")+
labs(title = 'PESO DE 100 GRANOS',
x = 'Tratamientos',
y = 'Peso de granos (g)') +
geom_text(aes(label=letra),
position=position_dodge(width = 0), vjust=5)
theme_minimal()
## List of 97
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## ..$ colour : chr "black"
## ..$ linewidth : num 0.5
## ..$ linetype : num 1
## ..$ lineend : chr "butt"
## ..$ arrow : logi FALSE
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_line" "element"
## $ rect :List of 5
## ..$ fill : chr "white"
## ..$ colour : chr "black"
## ..$ linewidth : num 0.5
## ..$ linetype : num 1
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_rect" "element"
## $ text :List of 11
## ..$ family : chr ""
## ..$ face : chr "plain"
## ..$ colour : chr "black"
## ..$ size : num 11
## ..$ hjust : num 0.5
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## ..$ lineheight : num 0.9
## ..$ margin : 'margin' num [1:4] 0points 0points 0points 0points
## .. ..- attr(*, "unit")= int 8
## ..$ debug : logi FALSE
## ..$ inherit.blank: logi TRUE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ title : NULL
## $ aspect.ratio : NULL
## $ axis.title : NULL
## $ axis.title.x :List of 11
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## $ axis.title.x.top :List of 11
## ..$ family : NULL
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p
p+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())+
xlab("Tratamientos")+
coord_cartesian(ylim = c(90, 110))
library(readxl)
ds <- read_excel("~/FISIOLOGÍA DE LA PRODUCCIÓN/Muestreos.xlsx",
sheet = "Savia")
ds
## # A tibble: 8 × 6
## TRATAMIENTO pH `CE(mS/cm)` `Ca2+` `NO3-` `K+`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 T1 6.68 2.5 300 560 610
## 2 T1 6.78 2.77 220 630 1100
## 3 T1 5.91 4.02 160 380 1300
## 4 T1 5.71 3.62 280 550 1000
## 5 T4 5.9 19.1 250 360 710
## 6 T4 5.79 3.31 310 420 1100
## 7 T4 5.66 3.61 320 430 1100
## 8 T4 5.8 3.7 380 470 940
library(agricolae)
mod <- aov(pH~TRATAMIENTO, ds)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 1 0.4656 0.4656 3.102 0.129
## Residuals 6 0.9005 0.1501
comparison<- LSD.test(mod,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## pH groups
## T1 6.2700 a
## T4 5.7875 a
library(agricolae)
mod1 <- aov(`CE(mS/cm)`~TRATAMIENTO, ds)
summary(mod1)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 1 35.49 35.49 1.157 0.324
## Residuals 6 184.12 30.69
comparison<- LSD.test(mod1,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## CE(mS/cm) groups
## T4 7.4400 a
## T1 3.2275 a
library(agricolae)
mod2 <- aov(`Ca2+`~TRATAMIENTO, ds)
summary(mod2)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 1 11250 11250 3.293 0.12
## Residuals 6 20500 3417
comparison<- LSD.test(mod2,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## Ca2+ groups
## T4 315 a
## T1 240 a
library(agricolae)
mod3 <- aov(`NO3-`~TRATAMIENTO, ds)
summary(mod3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 1 24200 24200 3.63 0.105
## Residuals 6 40000 6667
comparison<- LSD.test(mod3,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## NO3- groups
## T1 530 a
## T4 420 a
library(agricolae)
mod4 <- aov(`K+`~TRATAMIENTO, ds)
summary(mod4)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 1 3200 3200 0.054 0.824
## Residuals 6 354150 59025
comparison<- LSD.test(mod4,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## K+ groups
## T1 1002.5 a
## T4 962.5 a
library(readxl)
Altura <- read_excel("~/FISIOLOGÍA DE LA PRODUCCIÓN/Muestreos.xlsx",
sheet = "Altura")
names(Altura)
## [1] "BLOQUE" "TRATAMIENTO" "MUESTREO" "ALTURA" "NO_HOJAS"
Altura
## # A tibble: 160 × 5
## BLOQUE TRATAMIENTO MUESTREO ALTURA NO_HOJAS
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 B4 T4 48 30 4
## 2 B4 T4 48 26 5
## 3 B3 T3 48 12 3
## 4 B3 T3 48 10 2
## 5 B2 T2 48 5 4
## 6 B2 T2 48 18 4
## 7 B1 T1 48 20 3
## 8 B1 T1 48 13 3
## 9 B1 T4 48 14 3
## 10 B1 T4 48 23 4
## # ℹ 150 more rows
library(collapsibleTree)
collapsibleTree(Altura, c("MUESTREO","BLOQUE", "TRATAMIENTO", "ALTURA", "NO_HOJAS"))
library(agricolae)
mod1 <- aov(NO_HOJAS~TRATAMIENTO+BLOQUE+MUESTREO, Altura)
summary(mod1)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 10.2 3.4 0.862 0.462
## BLOQUE 3 407.5 135.8 34.538 <2e-16 ***
## MUESTREO 1 1428.0 1428.0 363.131 <2e-16 ***
## Residuals 152 597.8 3.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison<- LSD.test(mod1,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## NO_HOJAS groups
## T2 8.500 a
## T3 8.425 a
## T4 8.300 a
## T1 7.850 a
a <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 11.8 7 19
b <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 12.1 9 14
c <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 12.6 7 16
d <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 11.5 7 19
a <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 11.1 9 13
b <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 10.9 8 16
c <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 11.4 8 16
d <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 11 7 16
a <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 7.5 4 12
b <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 8.38 3 14
c <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 8 5 13
d <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 8.12 5 14
a <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 5.62 3 10
b <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 7.12 3 13
c <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 6.25 4 11
d <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 7.12 4 13
a <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 3.25 2 5
b <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 4 2 8
c <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 3.88 2 7
d <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(NO_HOJAS),
minimo=min(NO_HOJAS),
maximo=max(NO_HOJAS))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 3.75 3 5
library(readxl)
Hoja <- read_excel("~/FISIOLOGÍA DE LA PRODUCCIÓN/Muestreos.xlsx",
sheet = "Hoja")
Hoja
## # A tibble: 20 × 5
## Tratamiento Muestreo media_trt minimo maximo
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 T1 48 3.25 2 5
## 2 T1 55 5.62 3 10
## 3 T1 62 7.5 4 12
## 4 T1 69 11.1 9 13
## 5 T1 76 11.8 7 19
## 6 T2 48 4 2 8
## 7 T2 55 7.12 3 13
## 8 T2 62 8.38 3 14
## 9 T2 69 10.9 8 16
## 10 T2 76 12.1 9 14
## 11 T3 48 3.87 2 7
## 12 T3 55 6.25 4 11
## 13 T3 62 8 5 13
## 14 T3 69 11.4 8 16
## 15 T3 76 12.6 7 16
## 16 T4 48 3.75 3 5
## 17 T4 55 7.12 4 13
## 18 T4 62 8.12 5 14
## 19 T4 69 11 7 16
## 20 T4 76 11.5 7 19
ggplot(Hoja,aes(x=Muestreo, y=media_trt, group=Tratamiento, color=Tratamiento))+
geom_line(size=1)+
geom_point(fill="white") +
labs(title = 'Número de las hojas la planta a través del tiempo',
y = 'Numero de hojas', x="Días después de siembra") +
theme_minimal()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
library(agricolae)
mod1 <- aov(ALTURA~TRATAMIENTO+BLOQUE+MUESTREO, Altura)
summary(mod1)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 646 215 4.396 0.00536 **
## BLOQUE 3 3747 1249 25.509 2.01e-13 ***
## MUESTREO 1 15172 15172 309.884 < 2e-16 ***
## Residuals 152 7442 49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
comparison<- LSD.test(mod1,c("TRATAMIENTO"),alpha=0.01,group=TRUE)
print(comparison$groups)
## ALTURA groups
## T4 38.1125 a
## T2 35.0050 ab
## T3 34.4250 ab
## T1 32.5250 b
a <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 15.1 5 28
b <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 19.8 5 38
c <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 18.8 10 36
d <- Altura %>%
filter(MUESTREO=="48",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 22 14 30
a <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 28.6 12 41
b <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 30.5 10 50
c <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 30.4 20 44
d <- Altura %>%
filter(MUESTREO=="55",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 34.5 20 48
a <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 32.6 17 43
b <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 35.8 17 55
c <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 35.1 26 47
d <- Altura %>%
filter(MUESTREO=="62",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 38.8 25 51
a <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 39 26 46
b <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 41.9 23 57
c <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 41.2 28 54
d <- Altura %>%
filter(MUESTREO=="69",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 44.1 36 53
a4 <- data.frame(a,b,c,d)
a4
## media_trt minimo maximo media_trt.1 minimo.1 maximo.1 media_trt.2 minimo.2
## 1 39 26 46 41.875 23 57 41.25 28
## maximo.2 media_trt.3 minimo.3 maximo.3
## 1 54 44.125 36 53
a <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T1") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
a
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 47.2 34 55
b <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T2") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
b
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 47.2 30 57
c <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T3") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
c
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 46.6 38 58
d <- Altura %>%
filter(MUESTREO=="76",TRATAMIENTO=="T4") %>%
summarise(media_trt=mean(ALTURA),
minimo=min(ALTURA),
maximo=max(ALTURA))
d
## # A tibble: 1 × 3
## media_trt minimo maximo
## <dbl> <dbl> <dbl>
## 1 51.2 41 61
library(readxl)
Altura <- read_excel("~/FISIOLOGÍA DE LA PRODUCCIÓN/Muestreos.xlsx",
sheet = "alturas")
Altura
## # A tibble: 20 × 5
## Tratamiento Muestreo media_trt minimo maximo
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 T1 48 15.1 5 28
## 2 T1 55 28.6 12 41
## 3 T1 62 32.6 17 43
## 4 T1 69 39 26 46
## 5 T1 76 47.2 34 55
## 6 T2 48 19.8 5 38
## 7 T2 55 30.5 10 50
## 8 T2 62 35.8 17 55
## 9 T2 69 41.9 23 57
## 10 T2 76 47.2 30 57
## 11 T3 48 18.8 10 36
## 12 T3 55 30.4 20 44
## 13 T3 62 35.1 26 47
## 14 T3 69 41.2 28 54
## 15 T3 76 46.6 38 58
## 16 T4 48 22 14 30
## 17 T4 55 34.5 20 48
## 18 T4 62 38.8 25 51
## 19 T4 69 44.1 36 53
## 20 T4 76 51.2 41 61
letra <- c("a","ab","ab","b")
ggplot(Altura,aes(x=Muestreo, y=media_trt, group=Tratamiento, color=Tratamiento))+
geom_line(size=1)+
geom_point(fill="white") +
labs(title = 'Altura de la planta a través del tiempo',
y = 'Altura de la planta (cm)', x="Días después de siembra") +
theme_minimal()
