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#lon.sepalo
boxplot(iris.data.1.2$lon.sepalo~iris.data.1.2$especies)
media <- tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.sepalo
boxplot(iris.data.1.2$ancho.sepalo~iris.data.1.2$especies)
media <- tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#lon.petalo
boxplot(iris.data.1.2$lon.petalo~iris.data.1.2$especies)
media <- tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.petalo
boxplot(iris.data.1.2$ancho.petalo~iris.data.1.2$especies)
media <- tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
dev.off()
summary(iris.data.1.2$lon.sepalo)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,sd)
summary(iris.data.1.2$ancho.sepalo)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,sd)
summary(iris.data.1.2$lon.petalo)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,sd)
summary(iris.data.1.2$ancho.petalo)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,sd)
wine <- read_xlsx("wine.datos.xlsx")
write.csv(wine,"wine.datos.csv")
head(wine)
#ii.Grafique un boxplot de alguna columna (respuesta) en función de la columna factor.
par(mfrow=c(2,7))
boxplot(wine$Alcohol~wine$Cvs)
media <- tapply(wine$Alcohol,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Malic acid`~wine$Cvs)
media <- tapply(wine$`Malic acid`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Ash~wine$Cvs)
media <- tapply(wine$Ash,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Alcalinity of ash`~wine$Cvs)
media <- tapply(wine$`Alcalinity of ash`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Magnesium~wine$Cvs)
media <- tapply(wine$Magnesium,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Total phenols`~wine$Cvs)
media <- tapply(wine$`Total phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Flavanoids~wine$Cvs)
media <- tapply(wine$Flavanoids,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Nonflavanoid phenols`~wine$Cvs)
media <- tapply(wine$`Nonflavanoid phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proanthocyanins~wine$Cvs)
media <- tapply(wine$Proanthocyanins,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Color intensity`~wine$Cvs)
media <- tapply(wine$`Color intensity`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Hue~wine$Cvs)
media <- tapply(wine$Hue,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`OD280/OD315 of diluted wines`~wine$Cvs)
media <- tapply(wine$`OD280/OD315 of diluted wines`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proline~wine$Cvs)
media <- tapply(wine$Proline,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proline~wine$Cvs, main="Proline vs CV",
xlab="CV", ylab="Proline")
media <- tapply(wine$Proline,wine$Cvs,mean)
points(media,pch=16,col="black")
par(mfrow=c(2,7))
boxplot(wine$Alcohol~wine$Cvs,main="Alcohol vd CV",
xlab="CV", ylab="Alcohol")
media <- tapply(wine$Alcohol,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Malic acid`~wine$Cvs,main="Malic acid vs CV",
xlab="CV", ylab="Malic acid")
media <- tapply(wine$`Malic acid`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Ash~wine$Cvs,main="Ash vs CV",
xlab="CV", ylab="Ash")
media <- tapply(wine$Ash,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Alcalinity of ash`~wine$Cvs,main="Alcalinity of Ash vs CV",
xlab="CV", ylab="Alcalinity of ash")
media <- tapply(wine$`Alcalinity of ash`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Magnesium~wine$Cvs,main="Magnesium vs CV",
xlab="CV", ylab="Magnesium")
media <- tapply(wine$Magnesium,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Total phenols`~wine$Cvs,main="Total phenols vs CV",
xlab="CV", ylab="Total phenols")
media <- tapply(wine$`Total phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Flavanoids~wine$Cvs,main="Flavonoids vs CV",
xlab="CV", ylab="Flavonoids")
media <- tapply(wine$Flavanoids,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Nonflavanoid phenols`~wine$Cvs,main="Nonflavanoid phenols vs CV",
ylab="Nonflavanoid phenols", xlab="CV")
media <- tapply(wine$`Nonflavanoid phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proanthocyanins~wine$Cvs,main="Proanthicyanins",
ylab="Proanthicyanins", xlab="CV")
media <- tapply(wine$Proanthocyanins,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Color intensity`~wine$Cvs,main="Color intensity vs CV",
ylab="Color intensity", xlab="CV")
media <- tapply(wine$`Color intensity`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Hue~wine$Cvs,main="Hue vs CV",
xlab="Hue", ylab="CV")
media <- tapply(wine$Hue,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`OD280/OD315 of diluted wines`~wine$Cvs, main="OD280/OD315 vs CV",
xlab="CV", ylab="OD280/OD315")
media <- tapply(wine$`OD280/OD315 of diluted wines`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proline~wine$Cvs, main="Proline vs CV",
xlab="CV", ylab="Proline")
media <- tapply(wine$Proline,wine$Cvs,mean)
points(media,pch=16,col="black")
par(mfrow=c(2,7))
boxplot(wine$Alcohol~wine$Cvs,main="Alcohol vs CV",
xlab="CV", ylab="Alcohol")
media <- tapply(wine$Alcohol,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Malic acid`~wine$Cvs,main="Malic acid vs CV",
xlab="CV", ylab="Malic acid")
media <- tapply(wine$`Malic acid`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Ash~wine$Cvs,main="Ash vs CV",
xlab="CV", ylab="Ash")
media <- tapply(wine$Ash,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Alcalinity of ash`~wine$Cvs,main="Alcalinity of Ash vs CV",
xlab="CV", ylab="Alcalinity of ash")
media <- tapply(wine$`Alcalinity of ash`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Magnesium~wine$Cvs,main="Magnesium vs CV",
xlab="CV", ylab="Magnesium")
media <- tapply(wine$Magnesium,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Total phenols`~wine$Cvs,main="Total phenols vs CV",
xlab="CV", ylab="Total phenols")
media <- tapply(wine$`Total phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Flavanoids~wine$Cvs,main="Flavonoids vs CV",
xlab="CV", ylab="Flavonoids")
media <- tapply(wine$Flavanoids,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Nonflavanoid phenols`~wine$Cvs,main="Nonflavanoid phenols vs CV",
ylab="Nonflavanoid phenols", xlab="CV")
media <- tapply(wine$`Nonflavanoid phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proanthocyanins~wine$Cvs,main="Proanthicyanins vs CV",
ylab="Proanthicyanins", xlab="CV")
media <- tapply(wine$Proanthocyanins,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Color intensity`~wine$Cvs,main="Color intensity vs CV",
ylab="Color intensity", xlab="CV")
media <- tapply(wine$`Color intensity`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Hue~wine$Cvs,main="Hue vs CV",
xlab="Hue", ylab="CV")
media <- tapply(wine$Hue,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`OD280/OD315 of diluted wines`~wine$Cvs, main="OD280/OD315 vs CV",
xlab="CV", ylab="OD280/OD315")
media <- tapply(wine$`OD280/OD315 of diluted wines`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proline~wine$Cvs, main="Proline vs CV",
xlab="CV", ylab="Proline")
media <- tapply(wine$Proline,wine$Cvs,mean)
points(media,pch=16,col="black")
par(mfrow=c(2,7))
boxplot(wine$Alcohol~wine$Cvs,main="Alcohol vs CV",
xlab="CV", ylab="Alcohol")
media <- tapply(wine$Alcohol,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Malic acid`~wine$Cvs,main="Malic acid vs CV",
xlab="CV", ylab="Malic acid")
media <- tapply(wine$`Malic acid`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Ash~wine$Cvs,main="Ash vs CV",
xlab="CV", ylab="Ash")
media <- tapply(wine$Ash,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Alcalinity of ash`~wine$Cvs,main="Alcalinity of Ash vs CV",
xlab="CV", ylab="Alcalinity of ash")
media <- tapply(wine$`Alcalinity of ash`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Magnesium~wine$Cvs,main="Magnesium vs CV",
xlab="CV", ylab="Magnesium")
media <- tapply(wine$Magnesium,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Total phenols`~wine$Cvs,main="Total phenols vs CV",
xlab="CV", ylab="Total phenols")
media <- tapply(wine$`Total phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Flavanoids~wine$Cvs,main="Flavonoids vs CV",
xlab="CV", ylab="Flavonoids")
media <- tapply(wine$Flavanoids,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Nonflavanoid phenols`~wine$Cvs,main="Nonflavanoid phenols vs CV",
ylab="Nonflavanoid phenols", xlab="CV")
media <- tapply(wine$`Nonflavanoid phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proanthocyanins~wine$Cvs,main="Proanthicyanins vs CV",
ylab="Proanthicyanins", xlab="CV")
media <- tapply(wine$Proanthocyanins,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Color intensity`~wine$Cvs,main="Color intensity vs CV",
ylab="Color intensity", xlab="CV")
media <- tapply(wine$`Color intensity`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Hue~wine$Cvs,main="Hue vs CV",
ylab="Hue", xlab="CV")
media <- tapply(wine$Hue,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`OD280/OD315 of diluted wines`~wine$Cvs, main="OD280/OD315 vs CV",
xlab="CV", ylab="OD280/OD315")
media <- tapply(wine$`OD280/OD315 of diluted wines`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proline~wine$Cvs, main="Proline vs CV",
xlab="CV", ylab="Proline")
media <- tapply(wine$Proline,wine$Cvs,mean)
points(media,pch=16,col="black")
cor(wine[,-1])
cor(wine[,c(2,3,4,5,6,7)])
pairs(wine[,c(2,3,4,5,6,7,8)],col="red",pch=20)
dev.off()
library(corrplot)
M = cor(wine[,c(2,3,4,5,6,7,8)])
corrplot(M, method = 'number')
corrplot.mixed(M, lower = 'shade', upper = 'pie', order = 'hclust')
corrplot(M, method = 'color', order = 'alphabet')
rl <- lm(wine$`Total phenols`~wine$Flavanoids)
summary(rl)
plot(wine$`Total phenols`,rl$fitted.values,col="blue", pch=20)
abline(lm(rl$fitted.values~wine$`Total phenols`),col="red")
plot(wine$Flavanoids,rl$fitted.values,col="blue", pch=20)
abline(lm(rl$fitted.values~wine$Flavanoids),col="red")
hist(rl$residuals)
#qqplot
library(car)
qqPlot(rl$residuals)
#Prueba de Shapiro-Wilk
shapiro.test(rl$residuals)
#Prueba de Kolmogorov-Smirnov
ks.test(rl$residuals, "pnorm")
save.image("T6IFQI.RData")
par(mfrow=c(2,2))
#lon.sepalo
boxplot(iris.data.1.2$lon.sepalo~iris.data.1.2$especies,main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon")
media <- tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.sepalo
boxplot(iris.data.1.2$ancho.sepalo~iris.data.1.2$especies,main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon")
media <- tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#lon.petalo
boxplot(iris.data.1.2$lon.petalo~iris.data.1.2$especies,main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon")
media <- tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.petalo
boxplot(iris.data.1.2$ancho.petalo~iris.data.1.2$especies,main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon")
media <- tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
par(mfrow=c(2,2))
#lon.sepalo
boxplot(iris.data.1.2$lon.sepalo~iris.data.1.2$especies,main="Lonngitud de sepalo por Especie",
xlab="Especie", ylab="Longitud de sepalo")
media <- tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.sepalo
boxplot(iris.data.1.2$ancho.sepalo~iris.data.1.2$especies,main="Ancho de sepalo por Especie",
xlab="Especie", ylab="Ancho de sepalo")
media <- tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#lon.petalo
boxplot(iris.data.1.2$lon.petalo~iris.data.1.2$especies,main="Longitud de petalo por Especie",
xlab="Especie", ylab="Longitud de petalo")
media <- tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.petalo
boxplot(iris.data.1.2$ancho.petalo~iris.data.1.2$especies,main="Ancho de petalo por Especie",
xlab="Especie", ylab="Ancho de petalo")
media <- tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
dev.off()
#Resumen lon.sepalo
summary(iris.data.1.2$lon.sepalo)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,sd)
#Resumen ancho.sepalo
summary(iris.data.1.2$ancho.sepalo)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,sd)
#Resumen lon.petalo
summary(iris.data.1.2$lon.petalo)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,sd)
#Resumen ancho.petalo
summary(iris.data.1.2$ancho.petalo)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,mean)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,median)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,var)
tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,sd)
wine <- read_xlsx("wine.datos.xlsx")
write.csv(wine,"wine.datos.csv")
head(wine)
#ii.Grafique un boxplot de alguna columna (respuesta) en función de la columna factor.
par(mfrow=c(2,7))
boxplot(wine$Alcohol~wine$Cvs,main="Alcohol vs CV",
xlab="CV", ylab="Alcohol")
media <- tapply(wine$Alcohol,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Malic acid`~wine$Cvs,main="Malic acid vs CV",
xlab="CV", ylab="Malic acid")
media <- tapply(wine$`Malic acid`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Ash~wine$Cvs,main="Ash vs CV",
xlab="CV", ylab="Ash")
media <- tapply(wine$Ash,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Alcalinity of ash`~wine$Cvs,main="Alcalinity of Ash vs CV",
xlab="CV", ylab="Alcalinity of ash")
media <- tapply(wine$`Alcalinity of ash`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Magnesium~wine$Cvs,main="Magnesium vs CV",
xlab="CV", ylab="Magnesium")
media <- tapply(wine$Magnesium,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Total phenols`~wine$Cvs,main="Total phenols vs CV",
xlab="CV", ylab="Total phenols")
media <- tapply(wine$`Total phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Flavanoids~wine$Cvs,main="Flavonoids vs CV",
xlab="CV", ylab="Flavonoids")
media <- tapply(wine$Flavanoids,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Nonflavanoid phenols`~wine$Cvs,main="Nonflavanoid phenols vs CV",
ylab="Nonflavanoid phenols", xlab="CV")
media <- tapply(wine$`Nonflavanoid phenols`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proanthocyanins~wine$Cvs,main="Proanthicyanins vs CV",
ylab="Proanthicyanins", xlab="CV")
media <- tapply(wine$Proanthocyanins,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`Color intensity`~wine$Cvs,main="Color intensity vs CV",
ylab="Color intensity", xlab="CV")
media <- tapply(wine$`Color intensity`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Hue~wine$Cvs,main="Hue vs CV",
ylab="Hue", xlab="CV")
media <- tapply(wine$Hue,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$`OD280/OD315 of diluted wines`~wine$Cvs, main="OD280/OD315 vs CV",
xlab="CV", ylab="OD280/OD315")
media <- tapply(wine$`OD280/OD315 of diluted wines`,wine$Cvs,mean)
points(media,pch=16,col="black")
boxplot(wine$Proline~wine$Cvs, main="Proline vs CV",
xlab="CV", ylab="Proline")
media <- tapply(wine$Proline,wine$Cvs,mean)
points(media,pch=16,col="black")
dev.off()
cor(wine[,-1])
cor(wine[,c(2,3,4,5,6,7)])
pairs(wine[,c(2,3,4,5,6,7,8)],col="red",pch=20)
dev.off()
library(corrplot)
M = cor(wine[,c(2,3,4,5,6,7,8)])
corrplot(M, method = 'number')
corrplot.mixed(M, lower = 'shade', upper = 'pie', order = 'hclust')
corrplot(M, method = 'color', order = 'alphabet')
rl <- lm(wine$`Total phenols`~wine$Flavanoids)
summary(rl)
plot(wine$`Total phenols`,rl$fitted.values,col="blue", pch=20)
abline(lm(rl$fitted.values~wine$`Total phenols`),col="red")
plot(wine$Flavanoids,rl$fitted.values,col="blue", pch=20)
abline(lm(rl$fitted.values~wine$Flavanoids),col="red")
hist(rl$residuals)
#qqplot
library(car)
qqPlot(rl$residuals)
#Prueba de Shapiro-Wilk
shapiro.test(rl$residuals)
#Prueba de Kolmogorov-Smirnov
ks.test(rl$residuals, "pnorm")
save.image("T6IFQI.RData")
load("C:/Users/ifqi9/OneDrive - COLEGIO DE POSTGRADUADOS/Doctorado CP Entomología/Otoño 2022/Herramientas de cómputo/Tareas/Tarea6/T6COA501IFQI/T6IFQI.RData")
library(PerformanceAnalytics)
chart.Correlation(M, histogram = T, pch = 19)
#Biblioteca corrplot
library(corrplot)
M = cor(wine[,c(2,3,4,5,6,7,8)])
corrplot(M, method = 'number')
corrplot.mixed(M, lower = 'shade', upper = 'pie', order = 'hclust')
corrplot(M, method = 'color', order = 'alphabet')
#Biblioteca PerformanceAnalytics
library(PerformanceAnalytics)
chart.Correlation(M, histogram = T, pch = 19)
chart.Correlation(M, histogram = T, pch = 20)
chart.Correlation(M, histogram = T, pch = 20)
chart.Correlation(M, histogram = T, pch = 20)
plot(wine$`Total phenols`,rl$fitted.values,col="blue", pch=20)
abline(lm(rl$fitted.values~wine$`Total phenols`),col="red")
plot(wine$Flavanoids,rl$fitted.values,col="blue", pch=20)
abline(lm(rl$fitted.values~wine$Flavanoids),col="red")
hist(rl$residuals)
#qqplot
library(car)
qqPlot(rl$residuals)
#Prueba de Shapiro-Wilk
shapiro.test(rl$residuals)
#Prueba de Kolmogorov-Smirnov
ks.test(rl$residuals, "pnorm")
save.image("T6IFQI.RData")
par(mfrow=c(2,2))
#lon.sepalo
boxplot(iris.data.1.2$lon.sepalo~iris.data.1.2$especies,main="Lonngitud de sepalo por Especie",
xlab="Especie", ylab="Longitud de sepalo")
media <- tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.sepalo
boxplot(iris.data.1.2$ancho.sepalo~iris.data.1.2$especies,main="Ancho de sepalo por Especie",
xlab="Especie", ylab="Ancho de sepalo")
media <- tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#lon.petalo
boxplot(iris.data.1.2$lon.petalo~iris.data.1.2$especies,main="Longitud de petalo por Especie",
xlab="Especie", ylab="Longitud de petalo")
media <- tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.petalo
boxplot(iris.data.1.2$ancho.petalo~iris.data.1.2$especies,main="Ancho de petalo por Especie",
xlab="Especie", ylab="Ancho de petalo")
media <- tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
par(mfrow=c(2,2))
#lon.sepalo
boxplot(iris.data.1.2$lon.sepalo~iris.data.1.2$especies,main="Lonngitud de sepalo por Especie",
xlab="Especie", ylab="Longitud de sepalo")
media <- tapply(iris.data.1.2$lon.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.sepalo
boxplot(iris.data.1.2$ancho.sepalo~iris.data.1.2$especies,main="Ancho de sepalo por Especie",
xlab="Especie", ylab="Ancho de sepalo")
media <- tapply(iris.data.1.2$ancho.sepalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#lon.petalo
boxplot(iris.data.1.2$lon.petalo~iris.data.1.2$especies,main="Longitud de petalo por Especie",
xlab="Especie", ylab="Longitud de petalo")
media <- tapply(iris.data.1.2$lon.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#ancho.petalo
boxplot(iris.data.1.2$ancho.petalo~iris.data.1.2$especies,main="Ancho de petalo por Especie",
xlab="Especie", ylab="Ancho de petalo")
media <- tapply(iris.data.1.2$ancho.petalo,iris.data.1.2$especies,mean)
points(media,pch=16,col="black")
#Media para todas las columnas
colMeans(iris.data.1.2[,-c(1,2)],na.rm = T)
iris.data.1.2.sna <- na.omit(iris.data.1.2)
table(iris.data.1.2.sna$especies)
#NA por fila y columna
which(is.na(iris.data.1.2), arr.ind=TRUE)
iris.data.1.2[!complete.cases(iris.data.1.2), ]
newdata <- iris.data1[order(iris.data1$n_obs),-1]
rownames(newdata) <- NULL
newdata2 <- iris.data2[order(iris.data2$n_obs),-1]
rownames(newdata2) <- NULL
iris.data.1.2 <- merge(newdata,newdata2,by=c("n_obs","especies"),sort=F)
head(iris.data.1.2)
#NA por fila y columna
which(is.na(iris.data.1.2), arr.ind=TRUE)
iris.data.1.2[!complete.cases(iris.data.1.2), ]
#NA por fila y columna
which(is.na(iris.data.1.2), arr.ind=TRUE)
iris.data.1.2[35,3] <- 5.008
iris.data.1.2[110,4] <- 2.961
iris.data.1.2[68,5] <- 4.283
iris.data.1.2[58,6] <- 1.339
#NA por fila y columna
which(is.na(iris.data.1.2), arr.ind=TRUE)
iris.data.1.2[!complete.cases(iris.data.1.2), ]