data(iris)
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
En la siguiente figura se observa el histograma de la variable logintud del sepalo
library(ggplot2)
ggplot(iris, aes(x=Sepal.Length))+
geom_histogram(fill="red")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(iris, aes(x=Petal.Length))+
geom_histogram(fill="pink")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(iris, aes(x=Petal.Width))+
geom_histogram(fill="gold")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(iris, aes(x=Sepal.Width))+
geom_histogram(fill="skyblue")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
diagrama de cajas
HOLA
ggplot(iris, aes(y=Sepal.Length))+
geom_boxplot(fill="gold")
ggplot(iris, aes(y=Sepal.Width))+
geom_boxplot(fill="red")
ggplot(iris, aes(y=Petal.Length))+
geom_boxplot(fill="blue")
ggplot(iris, aes(y=Petal.Width))+
geom_boxplot(fill="green")
ggplot(iris, aes(y=Sepal.Length, x=Species, fill = Species))+
geom_boxplot()+
scale_fill_brewer(palette="Dark2")
ggplot(iris, aes(y=Sepal.Width, x=Species, fill = Species))+
geom_boxplot()+
scale_fill_brewer(palette="Dark2")
ggplot(iris, aes(y=Petal.Length, x=Species, fill = Species))+
geom_boxplot()+
scale_fill_brewer(palette="Dark3")
## Warning: Unknown palette: "Dark3"
ggplot(iris, aes(y=Petal.Width, x=Species, fill = Species))+
geom_boxplot()+
scale_fill_brewer(palette="Dark3")
## Warning: Unknown palette: "Dark3"
Se observa que la especie Virgnica presenta caracteristicas mas grande y la setosa mas pequeña.
La es pecie setos presenta mas daros atipicos