Graficas de muestreos
Andres Felipe Cortes Caucali
2022-06-18
#Librerias
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(lattice)
library(tigerstats)## Loading required package: abd## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
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## collapse## Loading required package: grid## Loading required package: mosaic## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.##
## Attaching package: 'mosaic'## The following object is masked from 'package:Matrix':
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## mean## The following object is masked from 'package:ggplot2':
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## stat## The following objects are masked from 'package:dplyr':
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## count, do, tally## The following objects are masked from 'package:stats':
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## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var## The following objects are masked from 'package:base':
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## max, mean, min, prod, range, sample, sum## Welcome to tigerstats!
## To learn more about this package, consult its website:
## http://homerhanumat.github.io/tigerstatslibrary(multcompView)
library(readxl)Severidad <- read_excel("C:\\Users\\USER\\Documents\\UNIVERSIDAD F\\2022-1\\Fitopatologia\\Fotos fito 2.xlsx",sheet = "Hoja1")
df1=data.frame(Severidad)#Analisis de Varianza de dos factores
#Organizacion de datos
head(df1)str(df1)## 'data.frame': 840 obs. of 5 variables:
## $ Muestreos : num 1 1 1 1 1 2 2 2 2 2 ...
## $ Variedad : chr "Pastusa" "Pastusa" "Pastusa" "Pastusa" ...
## $ Repeticion : num 1 2 3 4 5 1 2 3 4 5 ...
## $ Tratamiento: chr "T1" "T1" "T1" "T1" ...
## $ Severidad : num 0 0 0 0 0 0 0 0 0 0 ...Los Factores Muestreo y tratamientos que estan como caracteres , se transforman en Factores para el analisis de varianzas
# creating a variable as factor for the ANOVA
df1$Muestreos <- as.factor(df1$Muestreos)
df1$Tratamiento <- as.factor(df1$Tratamiento)
df1$Variedad <- as.factor(df1$Variedad)
str(df1)## 'data.frame': 840 obs. of 5 variables:
## $ Muestreos : Factor w/ 14 levels "1","2","3","4",..: 1 1 1 1 1 2 2 2 2 2 ...
## $ Variedad : Factor w/ 4 levels "Criolla","Pastusa",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Repeticion : num 1 2 3 4 5 1 2 3 4 5 ...
## $ Tratamiento: Factor w/ 3 levels "T1","T2","T3": 1 1 1 1 1 1 1 1 1 1 ...
## $ Severidad : num 0 0 0 0 0 0 0 0 0 0 ...# analysis of variance
anova <- aov(df1$Severidad ~ df1$Tratamiento*df1$Variedad, data = df1)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## df1$Tratamiento 2 33787 16894 42.45 <2e-16 ***
## df1$Variedad 3 73703 24568 61.74 <2e-16 ***
## df1$Tratamiento:df1$Variedad 6 70955 11826 29.72 <2e-16 ***
## Residuals 828 329494 398
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# table with factors, means and standard deviation
data_summary <- group_by(df1, Variedad, Tratamiento) %>%
summarise(mean=mean(Severidad), sd=sd(Severidad)) %>%
arrange(desc(mean))## `summarise()` has grouped output by 'Variedad'. You can override using the
## `.groups` argument.print(data_summary)## # A tibble: 12 × 4
## # Groups: Variedad [4]
## Variedad Tratamiento mean sd
## <fct> <fct> <dbl> <dbl>
## 1 Criolla T2 41.6 40.9
## 2 Pastusa T3 33.4 38.2
## 3 Criolla T3 24.4 35.9
## 4 Pastusa T2 6.99 19.0
## 5 Criolla T1 0 0
## 6 Pastusa T1 0 0
## 7 Remolacha T1 0 0
## 8 Remolacha T2 0 0
## 9 Remolacha T3 0 0
## 10 Zanahoria T1 0 0
## 11 Zanahoria T2 0 0
## 12 Zanahoria T3 0 0# Tukey's test
tukey <- TukeyHSD(anova)
print(tukey)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = df1$Severidad ~ df1$Tratamiento * df1$Variedad, data = df1)
##
## $`df1$Tratamiento`
## diff lwr upr p adj
## T2-T1 12.146429 8.187937 16.104920 0.0000000
## T3-T1 14.460714 10.502223 18.419206 0.0000000
## T3-T2 2.314286 -1.644206 6.272777 0.3557498
##
## $`df1$Variedad`
## diff lwr upr p adj
## Pastusa-Criolla -8.523810e+00 -13.535284 -3.512335 7.94e-05
## Remolacha-Criolla -2.200000e+01 -27.011475 -16.988525 0.00e+00
## Zanahoria-Criolla -2.200000e+01 -27.011475 -16.988525 0.00e+00
## Remolacha-Pastusa -1.347619e+01 -18.487665 -8.464716 0.00e+00
## Zanahoria-Pastusa -1.347619e+01 -18.487665 -8.464716 0.00e+00
## Zanahoria-Remolacha 1.551352e-13 -5.011475 5.011475 1.00e+00
##
## $`df1$Tratamiento:df1$Variedad`
## diff lwr upr p adj
## T2:Criolla-T1:Criolla 4.160000e+01 30.548772 52.651228 0.0000000
## T3:Criolla-T1:Criolla 2.440000e+01 13.348772 35.451228 0.0000000
## T1:Pastusa-T1:Criolla -1.158692e-13 -11.051228 11.051228 1.0000000
## T2:Pastusa-T1:Criolla 6.985714e+00 -4.065513 18.036942 0.6432300
## T3:Pastusa-T1:Criolla 3.344286e+01 22.391629 44.494085 0.0000000
## T1:Remolacha-T1:Criolla -2.202682e-13 -11.051228 11.051228 1.0000000
## T2:Remolacha-T1:Criolla -1.487699e-13 -11.051228 11.051228 1.0000000
## T3:Remolacha-T1:Criolla -2.993161e-13 -11.051228 11.051228 1.0000000
## T1:Zanahoria-T1:Criolla -9.769963e-14 -11.051228 11.051228 1.0000000
## T2:Zanahoria-T1:Criolla 1.079137e-13 -11.051228 11.051228 1.0000000
## T3:Zanahoria-T1:Criolla -2.620126e-13 -11.051228 11.051228 1.0000000
## T3:Criolla-T2:Criolla -1.720000e+01 -28.251228 -6.148772 0.0000269
## T1:Pastusa-T2:Criolla -4.160000e+01 -52.651228 -30.548772 0.0000000
## T2:Pastusa-T2:Criolla -3.461429e+01 -45.665513 -23.563058 0.0000000
## T3:Pastusa-T2:Criolla -8.157143e+00 -19.208371 2.894085 0.3941168
## T1:Remolacha-T2:Criolla -4.160000e+01 -52.651228 -30.548772 0.0000000
## T2:Remolacha-T2:Criolla -4.160000e+01 -52.651228 -30.548772 0.0000000
## T3:Remolacha-T2:Criolla -4.160000e+01 -52.651228 -30.548772 0.0000000
## T1:Zanahoria-T2:Criolla -4.160000e+01 -52.651228 -30.548772 0.0000000
## T2:Zanahoria-T2:Criolla -4.160000e+01 -52.651228 -30.548772 0.0000000
## T3:Zanahoria-T2:Criolla -4.160000e+01 -52.651228 -30.548772 0.0000000
## T1:Pastusa-T3:Criolla -2.440000e+01 -35.451228 -13.348772 0.0000000
## T2:Pastusa-T3:Criolla -1.741429e+01 -28.465513 -6.363058 0.0000195
## T3:Pastusa-T3:Criolla 9.042857e+00 -2.008371 20.094085 0.2371296
## T1:Remolacha-T3:Criolla -2.440000e+01 -35.451228 -13.348772 0.0000000
## T2:Remolacha-T3:Criolla -2.440000e+01 -35.451228 -13.348772 0.0000000
## T3:Remolacha-T3:Criolla -2.440000e+01 -35.451228 -13.348772 0.0000000
## T1:Zanahoria-T3:Criolla -2.440000e+01 -35.451228 -13.348772 0.0000000
## T2:Zanahoria-T3:Criolla -2.440000e+01 -35.451228 -13.348772 0.0000000
## T3:Zanahoria-T3:Criolla -2.440000e+01 -35.451228 -13.348772 0.0000000
## T2:Pastusa-T1:Pastusa 6.985714e+00 -4.065513 18.036942 0.6432300
## T3:Pastusa-T1:Pastusa 3.344286e+01 22.391629 44.494085 0.0000000
## T1:Remolacha-T1:Pastusa -1.043990e-13 -11.051228 11.051228 1.0000000
## T2:Remolacha-T1:Pastusa -3.290067e-14 -11.051228 11.051228 1.0000000
## T3:Remolacha-T1:Pastusa -1.834469e-13 -11.051228 11.051228 1.0000000
## T1:Zanahoria-T1:Pastusa 1.816959e-14 -11.051228 11.051228 1.0000000
## T2:Zanahoria-T1:Pastusa 2.237829e-13 -11.051228 11.051228 1.0000000
## T3:Zanahoria-T1:Pastusa -1.461434e-13 -11.051228 11.051228 1.0000000
## T3:Pastusa-T2:Pastusa 2.645714e+01 15.405915 37.508371 0.0000000
## T1:Remolacha-T2:Pastusa -6.985714e+00 -18.036942 4.065513 0.6432300
## T2:Remolacha-T2:Pastusa -6.985714e+00 -18.036942 4.065513 0.6432300
## T3:Remolacha-T2:Pastusa -6.985714e+00 -18.036942 4.065513 0.6432300
## T1:Zanahoria-T2:Pastusa -6.985714e+00 -18.036942 4.065513 0.6432300
## T2:Zanahoria-T2:Pastusa -6.985714e+00 -18.036942 4.065513 0.6432300
## T3:Zanahoria-T2:Pastusa -6.985714e+00 -18.036942 4.065513 0.6432300
## T1:Remolacha-T3:Pastusa -3.344286e+01 -44.494085 -22.391629 0.0000000
## T2:Remolacha-T3:Pastusa -3.344286e+01 -44.494085 -22.391629 0.0000000
## T3:Remolacha-T3:Pastusa -3.344286e+01 -44.494085 -22.391629 0.0000000
## T1:Zanahoria-T3:Pastusa -3.344286e+01 -44.494085 -22.391629 0.0000000
## T2:Zanahoria-T3:Pastusa -3.344286e+01 -44.494085 -22.391629 0.0000000
## T3:Zanahoria-T3:Pastusa -3.344286e+01 -44.494085 -22.391629 0.0000000
## T2:Remolacha-T1:Remolacha 7.149836e-14 -11.051228 11.051228 1.0000000
## T3:Remolacha-T1:Remolacha -7.904788e-14 -11.051228 11.051228 1.0000000
## T1:Zanahoria-T1:Remolacha 1.225686e-13 -11.051228 11.051228 1.0000000
## T2:Zanahoria-T1:Remolacha 3.281819e-13 -11.051228 11.051228 1.0000000
## T3:Zanahoria-T1:Remolacha -4.174439e-14 -11.051228 11.051228 1.0000000
## T3:Remolacha-T2:Remolacha -1.505462e-13 -11.051228 11.051228 1.0000000
## T1:Zanahoria-T2:Remolacha 5.107026e-14 -11.051228 11.051228 1.0000000
## T2:Zanahoria-T2:Remolacha 2.566836e-13 -11.051228 11.051228 1.0000000
## T3:Zanahoria-T2:Remolacha -1.132427e-13 -11.051228 11.051228 1.0000000
## T1:Zanahoria-T3:Remolacha 2.016165e-13 -11.051228 11.051228 1.0000000
## T2:Zanahoria-T3:Remolacha 4.072298e-13 -11.051228 11.051228 1.0000000
## T3:Zanahoria-T3:Remolacha 3.730349e-14 -11.051228 11.051228 1.0000000
## T2:Zanahoria-T1:Zanahoria 2.056133e-13 -11.051228 11.051228 1.0000000
## T3:Zanahoria-T1:Zanahoria -1.643130e-13 -11.051228 11.051228 1.0000000
## T3:Zanahoria-T2:Zanahoria -3.699263e-13 -11.051228 11.051228 1.0000000# creating the compact letter display
tukey.cld <- multcompLetters4(anova, tukey)
print(tukey.cld)## $`df1$Tratamiento`
## T3 T2 T1
## "a" "a" "b"
##
## $`df1$Variedad`
## Criolla Pastusa Remolacha Zanahoria
## "a" "b" "c" "c"
##
## $`df1$Tratamiento:df1$Variedad`
## T2:Criolla T3:Pastusa T3:Criolla T2:Pastusa T1:Criolla T1:Pastusa
## "a" "ab" "b" "c" "c" "c"
## T1:Remolacha T2:Remolacha T3:Remolacha T1:Zanahoria T2:Zanahoria T3:Zanahoria
## "c" "c" "c" "c" "c" "c"# adding the compact letter display to the table with means and sd
cld <- as.data.frame.list(tukey.cld$`df1$Tratamiento:df1$Variedad`)
data_summary$Tukey <- cld$Letters
print(data_summary)## # A tibble: 12 × 5
## # Groups: Variedad [4]
## Variedad Tratamiento mean sd Tukey
## <fct> <fct> <dbl> <dbl> <chr>
## 1 Criolla T2 41.6 40.9 a
## 2 Pastusa T3 33.4 38.2 ab
## 3 Criolla T3 24.4 35.9 b
## 4 Pastusa T2 6.99 19.0 c
## 5 Criolla T1 0 0 c
## 6 Pastusa T1 0 0 c
## 7 Remolacha T1 0 0 c
## 8 Remolacha T2 0 0 c
## 9 Remolacha T3 0 0 c
## 10 Zanahoria T1 0 0 c
## 11 Zanahoria T2 0 0 c
## 12 Zanahoria T3 0 0 c# coloured barplot
ggplot(data_summary, aes(x = factor(Tratamiento), y = mean)) +
geom_bar(stat = "identity")
# coloured barplot
ggplot(data_summary, aes(x = Variedad, y = mean, fill = Tratamiento, colour = Tratamiento)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.5) +
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), position = position_dodge(0.9), width = 0.25,
show.legend = FALSE) +
labs(x="Variedades", y="Severidad (%)") +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(legend.position = c(0.1, 0.75)) +
geom_text(aes(label=Tukey), position = position_dodge(0.90), size = 3,
vjust=-0.8, hjust=-0.5, colour = "gray25") +
ylim(0, 200) +
geom_text(aes(label=Tratamiento, y = 100), position = position_dodge(0.90), show.legend = FALSE) +
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2")
Severidad1 <- read_excel("C:\\Users\\USER\\Documents\\UNIVERSIDAD F\\2022-1\\Fitopatologia\\Fotos fito 2.xlsx",sheet = "Hoja3")
df2=data.frame(Severidad1)##Grafica Sveridad
ggplot(df2)+aes(Tratamiento, Severidad, color=Tratamiento)+geom_point(size = 2)+geom_line(aes(group=Tratamiento))+facet_wrap(.~Muestreos, ncol = 5)
##Incidencia
##Muestreo 2
Severidad2 <- read_excel("C:\\Users\\USER\\Documents\\UNIVERSIDAD F\\2022-1\\Fitopatologia\\Fotos fito 2.xlsx",sheet = "T2")
df3=data.frame(Severidad2)ggplot(df3)+aes(Variedad, Incidencia, color=Variedad)+geom_point(size = 2)+geom_line(aes(group=Variedad))+facet_wrap(.~Muestreos, ncol = 5)## Warning: Removed 220 rows containing missing values (geom_point).## Warning: Removed 164 row(s) containing missing values (geom_path).
##Muestreo 3
Severidad3 <- read_excel("C:\\Users\\USER\\Documents\\UNIVERSIDAD F\\2022-1\\Fitopatologia\\Fotos fito 2.xlsx",sheet = "T3")
df4=data.frame(Severidad3)ggplot(df4)+aes(Variedad, Incidencia, color=Variedad)+geom_point(size = 2)+geom_line(aes(group=Variedad))+facet_wrap(.~Muestreos, ncol = 5)## Warning: Removed 194 rows containing missing values (geom_point).## Warning: Removed 155 row(s) containing missing values (geom_path).