ACTIVIDAD 1-TÉCNICAS ESTADÍSTICAS MULTIVARIANTES Y APLICACIONES
INMACULADA ÁLVAREZ DONAIRE
2022-10-02
Uso de la base de datos “Iris”
getwd()## [1] "C:/Users/alvar/OneDrive/Escritorio"data("iris")
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosastr(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...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
##
##
## Species<-c("setosa","versicolor","virginica")
Species=factor(Species, levels = c(0, 1, 2), labels = c("setosa","versicolor","virginica"))
irisnumerico<-iris[,c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")]Estadísticos descriptivos de cada variable
library(modeest)
c(mean(iris$Sepal.Length),sd(iris$Sepal.Length),IQR(iris$Sepal.Length),mfv(iris$Sepal.Length),quantile(iris$Sepal.Length,c(0.25,0.5,0.75),na.rm = TRUE))## 25% 50% 75%
## 5.8433333 0.8280661 1.3000000 5.0000000 5.1000000 5.8000000 6.4000000c(mean(iris$Sepal.Width),sd(iris$Sepal.Width),IQR(iris$Sepal.Width),mfv(iris$Sepal.Width),quantile(iris$Sepal.Width,c(0.25,0.5,0.75),na.rm = TRUE))## 25% 50% 75%
## 3.0573333 0.4358663 0.5000000 3.0000000 2.8000000 3.0000000 3.3000000c(mean(iris$Petal.Length),sd(iris$Petal.Length),IQR(iris$Petal.Length),mfv(iris$Petal.Length),quantile(iris$Petal.Length,c(0.25,0.5,0.75),na.rm = TRUE))## 25% 50% 75%
## 3.758000 1.765298 3.500000 1.400000 1.500000 1.600000 4.350000 5.100000c(mean(iris$Petal.Width),sd(iris$Petal.Width),IQR(iris$Petal.Width),mfv(iris$Petal.Width),quantile(iris$Petal.Width,c(0.25,0.5,0.75),na.rm = TRUE))## 25% 50% 75%
## 1.1993333 0.7622377 1.5000000 0.2000000 0.3000000 1.3000000 1.8000000c(range(iris$Sepal.Length),min(iris$Sepal.Length),max(iris$Sepal.Length),var(iris$Sepal.Length))## [1] 4.3000000 7.9000000 4.3000000 7.9000000 0.6856935c(range(iris$Sepal.Width),min(iris$Sepal.Width),max(iris$Sepal.Width),var(iris$Sepal.Width))## [1] 2.0000000 4.4000000 2.0000000 4.4000000 0.1899794c(range(iris$Petal.Length),min(iris$Petal.Length),max(iris$Petal.Length),var(iris$Petal.Length))## [1] 1.000000 6.900000 1.000000 6.900000 3.116278c(range(iris$Petal.Width),min(iris$Petal.Width),max(iris$Petal.Width),var(iris$Petal.Width))## [1] 0.1000000 2.5000000 0.1000000 2.5000000 0.5810063table(iris$Sepal.Length)##
## 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2
## 1 3 1 4 2 5 6 10 9 4 1 6 7 6 8 7 3 6 6 4
## 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.6 7.7 7.9
## 9 7 5 2 8 3 4 1 1 3 1 1 1 4 1prop.table(table(iris$Sepal.Length))##
## 4.3 4.4 4.5 4.6 4.7 4.8
## 0.006666667 0.020000000 0.006666667 0.026666667 0.013333333 0.033333333
## 4.9 5 5.1 5.2 5.3 5.4
## 0.040000000 0.066666667 0.060000000 0.026666667 0.006666667 0.040000000
## 5.5 5.6 5.7 5.8 5.9 6
## 0.046666667 0.040000000 0.053333333 0.046666667 0.020000000 0.040000000
## 6.1 6.2 6.3 6.4 6.5 6.6
## 0.040000000 0.026666667 0.060000000 0.046666667 0.033333333 0.013333333
## 6.7 6.8 6.9 7 7.1 7.2
## 0.053333333 0.020000000 0.026666667 0.006666667 0.006666667 0.020000000
## 7.3 7.4 7.6 7.7 7.9
## 0.006666667 0.006666667 0.006666667 0.026666667 0.006666667prop.table(table(iris$Sepal.Length))*100##
## 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5
## 0.6666667 2.0000000 0.6666667 2.6666667 1.3333333 3.3333333 4.0000000 6.6666667
## 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8
## 6.0000000 2.6666667 0.6666667 4.0000000 4.6666667 4.0000000 5.3333333 4.6666667
## 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6
## 2.0000000 4.0000000 4.0000000 2.6666667 6.0000000 4.6666667 3.3333333 1.3333333
## 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4
## 5.3333333 2.0000000 2.6666667 0.6666667 0.6666667 2.0000000 0.6666667 0.6666667
## 7.6 7.7 7.9
## 0.6666667 2.6666667 0.6666667cumsum(table(iris$Sepal.Length))## 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2
## 1 4 5 9 11 16 22 32 41 45 46 52 59 65 73 80 83 89 95 99
## 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.6 7.7 7.9
## 108 115 120 122 130 133 137 138 139 142 143 144 145 149 150cumsum(prop.table(table(iris$Sepal.Length)))## 4.3 4.4 4.5 4.6 4.7 4.8
## 0.006666667 0.026666667 0.033333333 0.060000000 0.073333333 0.106666667
## 4.9 5 5.1 5.2 5.3 5.4
## 0.146666667 0.213333333 0.273333333 0.300000000 0.306666667 0.346666667
## 5.5 5.6 5.7 5.8 5.9 6
## 0.393333333 0.433333333 0.486666667 0.533333333 0.553333333 0.593333333
## 6.1 6.2 6.3 6.4 6.5 6.6
## 0.633333333 0.660000000 0.720000000 0.766666667 0.800000000 0.813333333
## 6.7 6.8 6.9 7 7.1 7.2
## 0.866666667 0.886666667 0.913333333 0.920000000 0.926666667 0.946666667
## 7.3 7.4 7.6 7.7 7.9
## 0.953333333 0.960000000 0.966666667 0.993333333 1.000000000table(iris$Sepal.Width)##
## 2 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4
## 1 3 4 3 8 5 9 14 10 26 11 13 6 12 6 4 3 6 2 1
## 4.1 4.2 4.4
## 1 1 1prop.table(table(iris$Sepal.Width))##
## 2 2.2 2.3 2.4 2.5 2.6
## 0.006666667 0.020000000 0.026666667 0.020000000 0.053333333 0.033333333
## 2.7 2.8 2.9 3 3.1 3.2
## 0.060000000 0.093333333 0.066666667 0.173333333 0.073333333 0.086666667
## 3.3 3.4 3.5 3.6 3.7 3.8
## 0.040000000 0.080000000 0.040000000 0.026666667 0.020000000 0.040000000
## 3.9 4 4.1 4.2 4.4
## 0.013333333 0.006666667 0.006666667 0.006666667 0.006666667prop.table(table(iris$Sepal.Width))*100##
## 2 2.2 2.3 2.4 2.5 2.6 2.7
## 0.6666667 2.0000000 2.6666667 2.0000000 5.3333333 3.3333333 6.0000000
## 2.8 2.9 3 3.1 3.2 3.3 3.4
## 9.3333333 6.6666667 17.3333333 7.3333333 8.6666667 4.0000000 8.0000000
## 3.5 3.6 3.7 3.8 3.9 4 4.1
## 4.0000000 2.6666667 2.0000000 4.0000000 1.3333333 0.6666667 0.6666667
## 4.2 4.4
## 0.6666667 0.6666667cumsum(table(iris$Sepal.Width))## 2 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4
## 1 4 8 11 19 24 33 47 57 83 94 107 113 125 131 135 138 144 146 147
## 4.1 4.2 4.4
## 148 149 150cumsum(prop.table(table(iris$Sepal.Width)))## 2 2.2 2.3 2.4 2.5 2.6
## 0.006666667 0.026666667 0.053333333 0.073333333 0.126666667 0.160000000
## 2.7 2.8 2.9 3 3.1 3.2
## 0.220000000 0.313333333 0.380000000 0.553333333 0.626666667 0.713333333
## 3.3 3.4 3.5 3.6 3.7 3.8
## 0.753333333 0.833333333 0.873333333 0.900000000 0.920000000 0.960000000
## 3.9 4 4.1 4.2 4.4
## 0.973333333 0.980000000 0.986666667 0.993333333 1.000000000table(iris$Petal.Length)##
## 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.9 3 3.3 3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3
## 1 1 2 7 13 13 7 4 2 1 2 2 1 1 1 3 5 3 4 2
## 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.3 6.4
## 4 8 3 5 4 5 4 8 2 2 2 3 6 3 3 2 2 3 1 1
## 6.6 6.7 6.9
## 1 2 1prop.table(table(iris$Petal.Length))##
## 1 1.1 1.2 1.3 1.4 1.5
## 0.006666667 0.006666667 0.013333333 0.046666667 0.086666667 0.086666667
## 1.6 1.7 1.9 3 3.3 3.5
## 0.046666667 0.026666667 0.013333333 0.006666667 0.013333333 0.013333333
## 3.6 3.7 3.8 3.9 4 4.1
## 0.006666667 0.006666667 0.006666667 0.020000000 0.033333333 0.020000000
## 4.2 4.3 4.4 4.5 4.6 4.7
## 0.026666667 0.013333333 0.026666667 0.053333333 0.020000000 0.033333333
## 4.8 4.9 5 5.1 5.2 5.3
## 0.026666667 0.033333333 0.026666667 0.053333333 0.013333333 0.013333333
## 5.4 5.5 5.6 5.7 5.8 5.9
## 0.013333333 0.020000000 0.040000000 0.020000000 0.020000000 0.013333333
## 6 6.1 6.3 6.4 6.6 6.7
## 0.013333333 0.020000000 0.006666667 0.006666667 0.006666667 0.013333333
## 6.9
## 0.006666667prop.table(table(iris$Petal.Length))*100##
## 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7
## 0.6666667 0.6666667 1.3333333 4.6666667 8.6666667 8.6666667 4.6666667 2.6666667
## 1.9 3 3.3 3.5 3.6 3.7 3.8 3.9
## 1.3333333 0.6666667 1.3333333 1.3333333 0.6666667 0.6666667 0.6666667 2.0000000
## 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7
## 3.3333333 2.0000000 2.6666667 1.3333333 2.6666667 5.3333333 2.0000000 3.3333333
## 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5
## 2.6666667 3.3333333 2.6666667 5.3333333 1.3333333 1.3333333 1.3333333 2.0000000
## 5.6 5.7 5.8 5.9 6 6.1 6.3 6.4
## 4.0000000 2.0000000 2.0000000 1.3333333 1.3333333 2.0000000 0.6666667 0.6666667
## 6.6 6.7 6.9
## 0.6666667 1.3333333 0.6666667cumsum(table(iris$Petal.Length))## 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.9 3 3.3 3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3
## 1 2 4 11 24 37 44 48 50 51 53 55 56 57 58 61 66 69 73 75
## 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.3 6.4
## 79 87 90 95 99 104 108 116 118 120 122 125 131 134 137 139 141 144 145 146
## 6.6 6.7 6.9
## 147 149 150cumsum(prop.table(table(iris$Petal.Length)))## 1 1.1 1.2 1.3 1.4 1.5
## 0.006666667 0.013333333 0.026666667 0.073333333 0.160000000 0.246666667
## 1.6 1.7 1.9 3 3.3 3.5
## 0.293333333 0.320000000 0.333333333 0.340000000 0.353333333 0.366666667
## 3.6 3.7 3.8 3.9 4 4.1
## 0.373333333 0.380000000 0.386666667 0.406666667 0.440000000 0.460000000
## 4.2 4.3 4.4 4.5 4.6 4.7
## 0.486666667 0.500000000 0.526666667 0.580000000 0.600000000 0.633333333
## 4.8 4.9 5 5.1 5.2 5.3
## 0.660000000 0.693333333 0.720000000 0.773333333 0.786666667 0.800000000
## 5.4 5.5 5.6 5.7 5.8 5.9
## 0.813333333 0.833333333 0.873333333 0.893333333 0.913333333 0.926666667
## 6 6.1 6.3 6.4 6.6 6.7
## 0.940000000 0.960000000 0.966666667 0.973333333 0.980000000 0.993333333
## 6.9
## 1.000000000table(iris$Petal.Width)##
## 0.1 0.2 0.3 0.4 0.5 0.6 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3
## 5 29 7 7 1 1 7 3 5 13 8 12 4 2 12 5 6 6 3 8
## 2.4 2.5
## 3 3prop.table(table(iris$Petal.Width))##
## 0.1 0.2 0.3 0.4 0.5 0.6
## 0.033333333 0.193333333 0.046666667 0.046666667 0.006666667 0.006666667
## 1 1.1 1.2 1.3 1.4 1.5
## 0.046666667 0.020000000 0.033333333 0.086666667 0.053333333 0.080000000
## 1.6 1.7 1.8 1.9 2 2.1
## 0.026666667 0.013333333 0.080000000 0.033333333 0.040000000 0.040000000
## 2.2 2.3 2.4 2.5
## 0.020000000 0.053333333 0.020000000 0.020000000prop.table(table(iris$Petal.Width))*100##
## 0.1 0.2 0.3 0.4 0.5 0.6 1
## 3.3333333 19.3333333 4.6666667 4.6666667 0.6666667 0.6666667 4.6666667
## 1.1 1.2 1.3 1.4 1.5 1.6 1.7
## 2.0000000 3.3333333 8.6666667 5.3333333 8.0000000 2.6666667 1.3333333
## 1.8 1.9 2 2.1 2.2 2.3 2.4
## 8.0000000 3.3333333 4.0000000 4.0000000 2.0000000 5.3333333 2.0000000
## 2.5
## 2.0000000cumsum(table(iris$Petal.Width))## 0.1 0.2 0.3 0.4 0.5 0.6 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3
## 5 34 41 48 49 50 57 60 65 78 86 98 102 104 116 121 127 133 136 144
## 2.4 2.5
## 147 150cumsum(prop.table(table(iris$Petal.Width)))## 0.1 0.2 0.3 0.4 0.5 0.6 1
## 0.03333333 0.22666667 0.27333333 0.32000000 0.32666667 0.33333333 0.38000000
## 1.1 1.2 1.3 1.4 1.5 1.6 1.7
## 0.40000000 0.43333333 0.52000000 0.57333333 0.65333333 0.68000000 0.69333333
## 1.8 1.9 2 2.1 2.2 2.3 2.4
## 0.77333333 0.80666667 0.84666667 0.88666667 0.90666667 0.96000000 0.98000000
## 2.5
## 1.00000000Estadísticos de cada variable según la especie
aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = summary, drop = TRUE)## Group Sepal.Length.Min. Sepal.Length.1st Qu. Sepal.Length.Median
## 1 setosa 4.300 4.800 5.000
## 2 versicolor 4.900 5.600 5.900
## 3 virginica 4.900 6.225 6.500
## Sepal.Length.Mean Sepal.Length.3rd Qu. Sepal.Length.Max. Sepal.Width.Min.
## 1 5.006 5.200 5.800 2.300
## 2 5.936 6.300 7.000 2.000
## 3 6.588 6.900 7.900 2.200
## Sepal.Width.1st Qu. Sepal.Width.Median Sepal.Width.Mean Sepal.Width.3rd Qu.
## 1 3.200 3.400 3.428 3.675
## 2 2.525 2.800 2.770 3.000
## 3 2.800 3.000 2.974 3.175
## Sepal.Width.Max. Petal.Length.Min. Petal.Length.1st Qu. Petal.Length.Median
## 1 4.400 1.000 1.400 1.500
## 2 3.400 3.000 4.000 4.350
## 3 3.800 4.500 5.100 5.550
## Petal.Length.Mean Petal.Length.3rd Qu. Petal.Length.Max. Petal.Width.Min.
## 1 1.462 1.575 1.900 0.100
## 2 4.260 4.600 5.100 1.000
## 3 5.552 5.875 6.900 1.400
## Petal.Width.1st Qu. Petal.Width.Median Petal.Width.Mean Petal.Width.3rd Qu.
## 1 0.200 0.200 0.246 0.300
## 2 1.200 1.300 1.326 1.500
## 3 1.800 2.000 2.026 2.300
## Petal.Width.Max.
## 1 0.600
## 2 1.800
## 3 2.500aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = mean, drop = TRUE)## Group Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.006 3.428 1.462 0.246
## 2 versicolor 5.936 2.770 4.260 1.326
## 3 virginica 6.588 2.974 5.552 2.026aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = sd, drop = TRUE)## Group Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 0.3524897 0.3790644 0.1736640 0.1053856
## 2 versicolor 0.5161711 0.3137983 0.4699110 0.1977527
## 3 virginica 0.6358796 0.3224966 0.5518947 0.2746501aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = IQR, drop = TRUE)## Group Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 0.400 0.475 0.175 0.1
## 2 versicolor 0.700 0.475 0.600 0.3
## 3 virginica 0.675 0.375 0.775 0.5aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = mfv, drop = TRUE)## Group Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.0, 5.1 3.4 1.4, 1.5 0.2
## 2 versicolor 5.5, 5.6, 5.7 3.0 4.5 1.3
## 3 virginica 6.3 3.0 5.1 1.8aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = quantile, drop = TRUE)## Group Sepal.Length.0% Sepal.Length.25% Sepal.Length.50% Sepal.Length.75%
## 1 setosa 4.300 4.800 5.000 5.200
## 2 versicolor 4.900 5.600 5.900 6.300
## 3 virginica 4.900 6.225 6.500 6.900
## Sepal.Length.100% Sepal.Width.0% Sepal.Width.25% Sepal.Width.50%
## 1 5.800 2.300 3.200 3.400
## 2 7.000 2.000 2.525 2.800
## 3 7.900 2.200 2.800 3.000
## Sepal.Width.75% Sepal.Width.100% Petal.Length.0% Petal.Length.25%
## 1 3.675 4.400 1.000 1.400
## 2 3.000 3.400 3.000 4.000
## 3 3.175 3.800 4.500 5.100
## Petal.Length.50% Petal.Length.75% Petal.Length.100% Petal.Width.0%
## 1 1.500 1.575 1.900 0.1
## 2 4.350 4.600 5.100 1.0
## 3 5.550 5.875 6.900 1.4
## Petal.Width.25% Petal.Width.50% Petal.Width.75% Petal.Width.100%
## 1 0.2 0.2 0.3 0.6
## 2 1.2 1.3 1.5 1.8
## 3 1.8 2.0 2.3 2.5aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = range, drop = TRUE)## Group Sepal.Length.1 Sepal.Length.2 Sepal.Width.1 Sepal.Width.2
## 1 setosa 4.3 5.8 2.3 4.4
## 2 versicolor 4.9 7.0 2.0 3.4
## 3 virginica 4.9 7.9 2.2 3.8
## Petal.Length.1 Petal.Length.2 Petal.Width.1 Petal.Width.2
## 1 1.0 1.9 0.1 0.6
## 2 3.0 5.1 1.0 1.8
## 3 4.5 6.9 1.4 2.5aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = min, drop = TRUE)## Group Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 4.3 2.3 1.0 0.1
## 2 versicolor 4.9 2.0 3.0 1.0
## 3 virginica 4.9 2.2 4.5 1.4aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = max, drop = TRUE)## Group Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.8 4.4 1.9 0.6
## 2 versicolor 7.0 3.4 5.1 1.8
## 3 virginica 7.9 3.8 6.9 2.5aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = var, drop = TRUE)## Group Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 0.1242490 0.14368980 0.03015918 0.01110612
## 2 versicolor 0.2664327 0.09846939 0.22081633 0.03910612
## 3 virginica 0.4043429 0.10400408 0.30458776 0.07543265aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = table, drop = TRUE)## Group Sepal.Length
## 1 setosa 1, 3, 1, 4, 2, 5, 4, 8, 8, 3, 1, 5, 2, 2, 1
## 2 versicolor 1, 2, 1, 1, 1, 5, 5, 5, 3, 2, 4, 4, 2, 3, 2, 1, 2, 3, 1, 1, 1
## 3 virginica 1, 1, 1, 3, 1, 2, 2, 2, 6, 5, 4, 5, 2, 3, 1, 3, 1, 1, 1, 4, 1
## Sepal.Width
## 1 1, 1, 6, 4, 5, 2, 9, 6, 3, 3, 4, 2, 1, 1, 1, 1
## 2 1, 2, 3, 3, 4, 3, 5, 6, 7, 8, 3, 3, 1, 1
## 3 1, 4, 2, 4, 8, 2, 12, 4, 5, 3, 2, 1, 2
## Petal.Length
## 1 1, 1, 2, 7, 13, 13, 7, 4, 2
## 2 1, 2, 2, 1, 1, 1, 3, 5, 3, 4, 2, 4, 7, 3, 5, 2, 2, 1, 1
## 3 1, 2, 3, 3, 7, 2, 2, 2, 3, 6, 3, 3, 2, 2, 3, 1, 1, 1, 2, 1
## Petal.Width
## 1 5, 29, 7, 7, 1, 1
## 2 7, 3, 5, 13, 7, 10, 3, 1, 1
## 3 1, 2, 1, 1, 11, 5, 6, 6, 3, 8, 3, 3aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN = prop.table, drop = TRUE)## Group Sepal.Length.1 Sepal.Length.2 Sepal.Length.3 Sepal.Length.4
## 1 setosa 0.02037555 0.01957651 0.01877747 0.01837795
## 2 versicolor 0.02358491 0.02156334 0.02324798 0.01853100
## 3 virginica 0.01912568 0.01760777 0.02155434 0.01912568
## Sepal.Length.5 Sepal.Length.6 Sepal.Length.7 Sepal.Length.8 Sepal.Length.9
## 1 0.01997603 0.02157411 0.01837795 0.01997603 0.01757891
## 2 0.02190027 0.01920485 0.02122642 0.01650943 0.02223720
## 3 0.01973285 0.02307225 0.01487553 0.02216151 0.02034001
## Sepal.Length.10 Sepal.Length.11 Sepal.Length.12 Sepal.Length.13
## 1 0.01957651 0.02157411 0.01917699 0.01917699
## 2 0.01752022 0.01684636 0.01987871 0.02021563
## 3 0.02185792 0.01973285 0.01942927 0.02064359
## Sepal.Length.14 Sepal.Length.15 Sepal.Length.16 Sepal.Length.17
## 1 0.01717938 0.02317219 0.02277267 0.02157411
## 2 0.02055256 0.01886792 0.02257412 0.01886792
## 3 0.01730419 0.01760777 0.01942927 0.01973285
## Sepal.Length.18 Sepal.Length.19 Sepal.Length.20 Sepal.Length.21
## 1 0.02037555 0.02277267 0.02037555 0.02157411
## 2 0.01954178 0.02088949 0.01886792 0.01987871
## 3 0.02337583 0.02337583 0.01821494 0.02094718
## Sepal.Length.22 Sepal.Length.23 Sepal.Length.24 Sepal.Length.25
## 1 0.02037555 0.01837795 0.02037555 0.01917699
## 2 0.02055256 0.02122642 0.02055256 0.02156334
## 3 0.01700061 0.02337583 0.01912568 0.02034001
## Sepal.Length.26 Sepal.Length.27 Sepal.Length.28 Sepal.Length.29
## 1 0.01997603 0.01997603 0.02077507 0.02077507
## 2 0.02223720 0.02291105 0.02257412 0.02021563
## 3 0.02185792 0.01882210 0.01851852 0.01942927
## Sepal.Length.30 Sepal.Length.31 Sepal.Length.32 Sepal.Length.33
## 1 0.01877747 0.01917699 0.02157411 0.02077507
## 2 0.01920485 0.01853100 0.01853100 0.01954178
## 3 0.02185792 0.02246509 0.02398300 0.01942927
## Sepal.Length.34 Sepal.Length.35 Sepal.Length.36 Sepal.Length.37
## 1 0.02197363 0.01957651 0.01997603 0.02197363
## 2 0.02021563 0.01819407 0.02021563 0.02257412
## 3 0.01912568 0.01851852 0.02337583 0.01912568
## Sepal.Length.38 Sepal.Length.39 Sepal.Length.40 Sepal.Length.41
## 1 0.01957651 0.01757891 0.02037555 0.01997603
## 2 0.02122642 0.01886792 0.01853100 0.01853100
## 3 0.01942927 0.01821494 0.02094718 0.02034001
## Sepal.Length.42 Sepal.Length.43 Sepal.Length.44 Sepal.Length.45
## 1 0.01797843 0.01757891 0.01997603 0.02037555
## 2 0.02055256 0.01954178 0.01684636 0.01886792
## 3 0.02094718 0.01760777 0.02064359 0.02034001
## Sepal.Length.46 Sepal.Length.47 Sepal.Length.48 Sepal.Length.49
## 1 0.01917699 0.02037555 0.01837795 0.02117459
## 2 0.01920485 0.01920485 0.02088949 0.01718329
## 3 0.02034001 0.01912568 0.01973285 0.01882210
## Sepal.Length.50 Sepal.Width.1 Sepal.Width.2 Sepal.Width.3 Sepal.Width.4
## 1 0.01997603 0.02042007 0.01750292 0.01866978 0.01808635
## 2 0.01920485 0.02310469 0.02310469 0.02238267 0.01660650
## 3 0.01791135 0.02219233 0.01815736 0.02017485 0.01950235
## Sepal.Width.5 Sepal.Width.6 Sepal.Width.7 Sepal.Width.8 Sepal.Width.9
## 1 0.02100350 0.02275379 0.01983664 0.01983664 0.01691949
## 2 0.02021661 0.02021661 0.02382671 0.01732852 0.02093863
## 3 0.02017485 0.02017485 0.01681237 0.01950235 0.01681237
## Sepal.Width.10 Sepal.Width.11 Sepal.Width.12 Sepal.Width.13 Sepal.Width.14
## 1 0.01808635 0.02158693 0.01983664 0.01750292 0.01750292
## 2 0.01949458 0.01444043 0.02166065 0.01588448 0.02093863
## 3 0.02420982 0.02151984 0.01815736 0.02017485 0.01681237
## Sepal.Width.15 Sepal.Width.16 Sepal.Width.17 Sepal.Width.18 Sepal.Width.19
## 1 0.02333722 0.02567095 0.02275379 0.02042007 0.02217036
## 2 0.02093863 0.02238267 0.02166065 0.01949458 0.01588448
## 3 0.01882986 0.02151984 0.02017485 0.02555481 0.01748487
## Sepal.Width.20 Sepal.Width.21 Sepal.Width.22 Sepal.Width.23 Sepal.Width.24
## 1 0.02217036 0.01983664 0.02158693 0.02100350 0.01925321
## 2 0.01805054 0.02310469 0.02021661 0.01805054 0.02021661
## 3 0.01479489 0.02151984 0.01882986 0.01882986 0.01815736
## Sepal.Width.25 Sepal.Width.26 Sepal.Width.27 Sepal.Width.28 Sepal.Width.29
## 1 0.01983664 0.01750292 0.01983664 0.02042007 0.01983664
## 2 0.02093863 0.02166065 0.02021661 0.02166065 0.02093863
## 3 0.02219233 0.02151984 0.01882986 0.02017485 0.01882986
## Sepal.Width.30 Sepal.Width.31 Sepal.Width.32 Sepal.Width.33 Sepal.Width.34
## 1 0.01866978 0.01808635 0.01983664 0.02392065 0.02450408
## 2 0.01877256 0.01732852 0.01732852 0.01949458 0.01949458
## 3 0.02017485 0.01882986 0.02555481 0.01882986 0.01882986
## Sepal.Width.35 Sepal.Width.36 Sepal.Width.37 Sepal.Width.38 Sepal.Width.39
## 1 0.01808635 0.01866978 0.02042007 0.02100350 0.01750292
## 2 0.02166065 0.02454874 0.02238267 0.01660650 0.02166065
## 3 0.01748487 0.02017485 0.02286483 0.02084734 0.02017485
## Sepal.Width.40 Sepal.Width.41 Sepal.Width.42 Sepal.Width.43 Sepal.Width.44
## 1 0.01983664 0.02042007 0.01341890 0.01866978 0.02042007
## 2 0.01805054 0.01877256 0.02166065 0.01877256 0.01660650
## 3 0.02084734 0.02084734 0.02084734 0.01815736 0.02151984
## Sepal.Width.45 Sepal.Width.46 Sepal.Width.47 Sepal.Width.48 Sepal.Width.49
## 1 0.02217036 0.01750292 0.02217036 0.01866978 0.02158693
## 2 0.01949458 0.02166065 0.02093863 0.02093863 0.01805054
## 3 0.02219233 0.02017485 0.01681237 0.02017485 0.02286483
## Sepal.Width.50 Petal.Length.1 Petal.Length.2 Petal.Length.3 Petal.Length.4
## 1 0.01925321 0.01915185 0.01915185 0.01778386 0.02051984
## 2 0.02021661 0.02206573 0.02112676 0.02300469 0.01877934
## 3 0.02017485 0.02161383 0.01837176 0.02125360 0.02017291
## Petal.Length.5 Petal.Length.6 Petal.Length.7 Petal.Length.8 Petal.Length.9
## 1 0.01915185 0.02325581 0.01915185 0.02051984 0.01915185
## 2 0.02159624 0.02112676 0.02206573 0.01549296 0.02159624
## 3 0.02089337 0.02377522 0.01621037 0.02269452 0.02089337
## Petal.Length.10 Petal.Length.11 Petal.Length.12 Petal.Length.13
## 1 0.02051984 0.02051984 0.02188782 0.01915185
## 2 0.01830986 0.01643192 0.01971831 0.01877934
## 3 0.02197406 0.01837176 0.01909222 0.01981268
## Petal.Length.14 Petal.Length.15 Petal.Length.16 Petal.Length.17
## 1 0.01504788 0.01641587 0.02051984 0.01778386
## 2 0.02206573 0.01690141 0.02065728 0.02112676
## 3 0.01801153 0.01837176 0.01909222 0.01981268
## Petal.Length.18 Petal.Length.19 Petal.Length.20 Petal.Length.21
## 1 0.01915185 0.02325581 0.02051984 0.02325581
## 2 0.01924883 0.02112676 0.01830986 0.02253521
## 3 0.02413545 0.02485591 0.01801153 0.02053314
## Petal.Length.22 Petal.Length.23 Petal.Length.24 Petal.Length.25
## 1 0.02051984 0.01367989 0.02325581 0.02599179
## 2 0.01877934 0.02300469 0.02206573 0.02018779
## 3 0.01765130 0.02413545 0.01765130 0.02053314
## Petal.Length.26 Petal.Length.27 Petal.Length.28 Petal.Length.29
## 1 0.02188782 0.02188782 0.02051984 0.01915185
## 2 0.02065728 0.02253521 0.02347418 0.02112676
## 3 0.02161383 0.01729107 0.01765130 0.02017291
## Petal.Length.30 Petal.Length.31 Petal.Length.32 Petal.Length.33
## 1 0.02188782 0.02188782 0.02051984 0.02051984
## 2 0.01643192 0.01784038 0.01737089 0.01830986
## 3 0.02089337 0.02197406 0.02305476 0.02017291
## Petal.Length.34 Petal.Length.35 Petal.Length.36 Petal.Length.37
## 1 0.01915185 0.02051984 0.01641587 0.01778386
## 2 0.02394366 0.02112676 0.02112676 0.02206573
## 3 0.01837176 0.02017291 0.02197406 0.02017291
## Petal.Length.38 Petal.Length.39 Petal.Length.40 Petal.Length.41
## 1 0.01915185 0.01778386 0.02051984 0.01778386
## 2 0.02065728 0.01924883 0.01877934 0.02065728
## 3 0.01981268 0.01729107 0.01945245 0.02017291
## Petal.Length.42 Petal.Length.43 Petal.Length.44 Petal.Length.45
## 1 0.01778386 0.01778386 0.02188782 0.02599179
## 2 0.02159624 0.01877934 0.01549296 0.01971831
## 3 0.01837176 0.01837176 0.02125360 0.02053314
## Petal.Length.46 Petal.Length.47 Petal.Length.48 Petal.Length.49
## 1 0.01915185 0.02188782 0.01915185 0.02051984
## 2 0.01971831 0.01971831 0.02018779 0.01408451
## 3 0.01873199 0.01801153 0.01873199 0.01945245
## Petal.Length.50 Petal.Width.1 Petal.Width.2 Petal.Width.3 Petal.Width.4
## 1 0.01915185 0.016260163 0.016260163 0.016260163 0.016260163
## 2 0.01924883 0.021116139 0.022624434 0.022624434 0.019607843
## 3 0.01837176 0.024679171 0.018756170 0.020730503 0.017769003
## Petal.Width.5 Petal.Width.6 Petal.Width.7 Petal.Width.8 Petal.Width.9
## 1 0.016260163 0.032520325 0.024390244 0.016260163 0.016260163
## 2 0.022624434 0.019607843 0.024132730 0.015082956 0.019607843
## 3 0.021717670 0.020730503 0.016781836 0.017769003 0.017769003
## Petal.Width.10 Petal.Width.11 Petal.Width.12 Petal.Width.13 Petal.Width.14
## 1 0.008130081 0.016260163 0.016260163 0.008130081 0.008130081
## 2 0.021116139 0.015082956 0.022624434 0.015082956 0.021116139
## 3 0.024679171 0.019743337 0.018756170 0.020730503 0.019743337
## Petal.Width.15 Petal.Width.16 Petal.Width.17 Petal.Width.18 Petal.Width.19
## 1 0.016260163 0.032520325 0.032520325 0.024390244 0.024390244
## 2 0.019607843 0.021116139 0.022624434 0.015082956 0.022624434
## 3 0.023692004 0.022704837 0.017769003 0.021717670 0.022704837
## Petal.Width.20 Petal.Width.21 Petal.Width.22 Petal.Width.23 Petal.Width.24
## 1 0.024390244 0.016260163 0.032520325 0.016260163 0.040650407
## 2 0.016591252 0.027149321 0.019607843 0.022624434 0.018099548
## 3 0.014807502 0.022704837 0.019743337 0.019743337 0.017769003
## Petal.Width.25 Petal.Width.26 Petal.Width.27 Petal.Width.28 Petal.Width.29
## 1 0.016260163 0.016260163 0.032520325 0.016260163 0.016260163
## 2 0.019607843 0.021116139 0.021116139 0.025641026 0.022624434
## 3 0.020730503 0.017769003 0.017769003 0.017769003 0.020730503
## Petal.Width.30 Petal.Width.31 Petal.Width.32 Petal.Width.33 Petal.Width.34
## 1 0.016260163 0.016260163 0.032520325 0.008130081 0.016260163
## 2 0.015082956 0.016591252 0.015082956 0.018099548 0.024132730
## 3 0.015794669 0.018756170 0.019743337 0.021717670 0.014807502
## Petal.Width.35 Petal.Width.36 Petal.Width.37 Petal.Width.38 Petal.Width.39
## 1 0.016260163 0.016260163 0.016260163 0.008130081 0.016260163
## 2 0.022624434 0.024132730 0.022624434 0.019607843 0.019607843
## 3 0.013820336 0.022704837 0.023692004 0.017769003 0.017769003
## Petal.Width.40 Petal.Width.41 Petal.Width.42 Petal.Width.43 Petal.Width.44
## 1 0.016260163 0.024390244 0.024390244 0.016260163 0.048780488
## 2 0.019607843 0.018099548 0.021116139 0.018099548 0.015082956
## 3 0.020730503 0.023692004 0.022704837 0.018756170 0.022704837
## Petal.Width.45 Petal.Width.46 Petal.Width.47 Petal.Width.48 Petal.Width.49
## 1 0.032520325 0.024390244 0.016260163 0.016260163 0.016260163
## 2 0.019607843 0.018099548 0.019607843 0.019607843 0.016591252
## 3 0.024679171 0.022704837 0.018756170 0.019743337 0.022704837
## Petal.Width.50
## 1 0.016260163
## 2 0.019607843
## 3 0.017769003aggregate.data.frame(iris[,1:4], by=list(Group=iris$Species), FUN =cumsum , drop = TRUE)## Group Sepal.Length.1 Sepal.Length.2 Sepal.Length.3 Sepal.Length.4
## 1 setosa 5.1 10.0 14.7 19.3
## 2 versicolor 7.0 13.4 20.3 25.8
## 3 virginica 6.3 12.1 19.2 25.5
## Sepal.Length.5 Sepal.Length.6 Sepal.Length.7 Sepal.Length.8 Sepal.Length.9
## 1 24.3 29.7 34.3 39.3 43.7
## 2 32.3 38.0 44.3 49.2 55.8
## 3 32.0 39.6 44.5 51.8 58.5
## Sepal.Length.10 Sepal.Length.11 Sepal.Length.12 Sepal.Length.13
## 1 48.6 54.0 58.8 63.6
## 2 61.0 66.0 71.9 77.9
## 3 65.7 72.2 78.6 85.4
## Sepal.Length.14 Sepal.Length.15 Sepal.Length.16 Sepal.Length.17
## 1 67.9 73.7 79.4 84.8
## 2 84.0 89.6 96.3 101.9
## 3 91.1 96.9 103.3 109.8
## Sepal.Length.18 Sepal.Length.19 Sepal.Length.20 Sepal.Length.21
## 1 89.9 95.6 100.7 106.1
## 2 107.7 113.9 119.5 125.4
## 3 117.5 125.2 131.2 138.1
## Sepal.Length.22 Sepal.Length.23 Sepal.Length.24 Sepal.Length.25
## 1 111.2 115.8 120.9 125.7
## 2 131.5 137.8 143.9 150.3
## 3 143.7 151.4 157.7 164.4
## Sepal.Length.26 Sepal.Length.27 Sepal.Length.28 Sepal.Length.29
## 1 130.7 135.7 140.9 146.1
## 2 156.9 163.7 170.4 176.4
## 3 171.6 177.8 183.9 190.3
## Sepal.Length.30 Sepal.Length.31 Sepal.Length.32 Sepal.Length.33
## 1 150.8 155.6 161.0 166.2
## 2 182.1 187.6 193.1 198.9
## 3 197.5 204.9 212.8 219.2
## Sepal.Length.34 Sepal.Length.35 Sepal.Length.36 Sepal.Length.37
## 1 171.7 176.6 181.6 187.1
## 2 204.9 210.3 216.3 223.0
## 3 225.5 231.6 239.3 245.6
## Sepal.Length.38 Sepal.Length.39 Sepal.Length.40 Sepal.Length.41
## 1 192.0 196.4 201.5 206.5
## 2 229.3 234.9 240.4 245.9
## 3 252.0 258.0 264.9 271.6
## Sepal.Length.42 Sepal.Length.43 Sepal.Length.44 Sepal.Length.45
## 1 211.0 215.4 220.4 225.5
## 2 252.0 257.8 262.8 268.4
## 3 278.5 284.3 291.1 297.8
## Sepal.Length.46 Sepal.Length.47 Sepal.Length.48 Sepal.Length.49
## 1 230.3 235.4 240.0 245.3
## 2 274.1 279.8 286.0 291.1
## 3 304.5 310.8 317.3 323.5
## Sepal.Length.50 Sepal.Width.1 Sepal.Width.2 Sepal.Width.3 Sepal.Width.4
## 1 250.3 3.5 6.5 9.7 12.8
## 2 296.8 3.2 6.4 9.5 11.8
## 3 329.4 3.3 6.0 9.0 11.9
## Sepal.Width.5 Sepal.Width.6 Sepal.Width.7 Sepal.Width.8 Sepal.Width.9
## 1 16.4 20.3 23.7 27.1 30.0
## 2 14.6 17.4 20.7 23.1 26.0
## 3 14.9 17.9 20.4 23.3 25.8
## Sepal.Width.10 Sepal.Width.11 Sepal.Width.12 Sepal.Width.13 Sepal.Width.14
## 1 33.1 36.8 40.2 43.2 46.2
## 2 28.7 30.7 33.7 35.9 38.8
## 3 29.4 32.6 35.3 38.3 40.8
## Sepal.Width.15 Sepal.Width.16 Sepal.Width.17 Sepal.Width.18 Sepal.Width.19
## 1 50.2 54.6 58.5 62.0 65.8
## 2 41.7 44.8 47.8 50.5 52.7
## 3 43.6 46.8 49.8 53.6 56.2
## Sepal.Width.20 Sepal.Width.21 Sepal.Width.22 Sepal.Width.23 Sepal.Width.24
## 1 69.6 73.0 76.7 80.3 83.6
## 2 55.2 58.4 61.2 63.7 66.5
## 3 58.4 61.6 64.4 67.2 69.9
## Sepal.Width.25 Sepal.Width.26 Sepal.Width.27 Sepal.Width.28 Sepal.Width.29
## 1 87.0 90.0 93.4 96.9 100.3
## 2 69.4 72.4 75.2 78.2 81.1
## 3 73.2 76.4 79.2 82.2 85.0
## Sepal.Width.30 Sepal.Width.31 Sepal.Width.32 Sepal.Width.33 Sepal.Width.34
## 1 103.5 106.6 110.0 114.1 118.3
## 2 83.7 86.1 88.5 91.2 93.9
## 3 88.0 90.8 94.6 97.4 100.2
## Sepal.Width.35 Sepal.Width.36 Sepal.Width.37 Sepal.Width.38 Sepal.Width.39
## 1 121.4 124.6 128.1 131.7 134.7
## 2 96.9 100.3 103.4 105.7 108.7
## 3 102.8 105.8 109.2 112.3 115.3
## Sepal.Width.40 Sepal.Width.41 Sepal.Width.42 Sepal.Width.43 Sepal.Width.44
## 1 138.1 141.6 143.9 147.1 150.6
## 2 111.2 113.8 116.8 119.4 121.7
## 3 118.4 121.5 124.6 127.3 130.5
## Sepal.Width.45 Sepal.Width.46 Sepal.Width.47 Sepal.Width.48 Sepal.Width.49
## 1 154.4 157.4 161.2 164.4 168.1
## 2 124.4 127.4 130.3 133.2 135.7
## 3 133.8 136.8 139.3 142.3 145.7
## Sepal.Width.50 Petal.Length.1 Petal.Length.2 Petal.Length.3 Petal.Length.4
## 1 171.4 1.4 2.8 4.1 5.6
## 2 138.5 4.7 9.2 14.1 18.1
## 3 148.7 6.0 11.1 17.0 22.6
## Petal.Length.5 Petal.Length.6 Petal.Length.7 Petal.Length.8 Petal.Length.9
## 1 7.0 8.7 10.1 11.6 13.0
## 2 22.7 27.2 31.9 35.2 39.8
## 3 28.4 35.0 39.5 45.8 51.6
## Petal.Length.10 Petal.Length.11 Petal.Length.12 Petal.Length.13
## 1 14.5 16.0 17.6 19.0
## 2 43.7 47.2 51.4 55.4
## 3 57.7 62.8 68.1 73.6
## Petal.Length.14 Petal.Length.15 Petal.Length.16 Petal.Length.17
## 1 20.1 21.3 22.8 24.1
## 2 60.1 63.7 68.1 72.6
## 3 78.6 83.7 89.0 94.5
## Petal.Length.18 Petal.Length.19 Petal.Length.20 Petal.Length.21
## 1 25.5 27.2 28.7 30.4
## 2 76.7 81.2 85.1 89.9
## 3 101.2 108.1 113.1 118.8
## Petal.Length.22 Petal.Length.23 Petal.Length.24 Petal.Length.25
## 1 31.9 32.9 34.6 36.5
## 2 93.9 98.8 103.5 107.8
## 3 123.7 130.4 135.3 141.0
## Petal.Length.26 Petal.Length.27 Petal.Length.28 Petal.Length.29
## 1 38.1 39.7 41.2 42.6
## 2 112.2 117.0 122.0 126.5
## 3 147.0 151.8 156.7 162.3
## Petal.Length.30 Petal.Length.31 Petal.Length.32 Petal.Length.33
## 1 44.2 45.8 47.3 48.8
## 2 130.0 133.8 137.5 141.4
## 3 168.1 174.2 180.6 186.2
## Petal.Length.34 Petal.Length.35 Petal.Length.36 Petal.Length.37
## 1 50.2 51.7 52.9 54.2
## 2 146.5 151.0 155.5 160.2
## 3 191.3 196.9 203.0 208.6
## Petal.Length.38 Petal.Length.39 Petal.Length.40 Petal.Length.41
## 1 55.6 56.9 58.4 59.7
## 2 164.6 168.7 172.7 177.1
## 3 214.1 218.9 224.3 229.9
## Petal.Length.42 Petal.Length.43 Petal.Length.44 Petal.Length.45
## 1 61.0 62.3 63.9 65.8
## 2 181.7 185.7 189.0 193.2
## 3 235.0 240.1 246.0 251.7
## Petal.Length.46 Petal.Length.47 Petal.Length.48 Petal.Length.49
## 1 67.2 68.8 70.2 71.7
## 2 197.4 201.6 205.9 208.9
## 3 256.9 261.9 267.1 272.5
## Petal.Length.50 Petal.Width.1 Petal.Width.2 Petal.Width.3 Petal.Width.4
## 1 73.1 0.2 0.4 0.6 0.8
## 2 213.0 1.4 2.9 4.4 5.7
## 3 277.6 2.5 4.4 6.5 8.3
## Petal.Width.5 Petal.Width.6 Petal.Width.7 Petal.Width.8 Petal.Width.9
## 1 1.0 1.4 1.7 1.9 2.1
## 2 7.2 8.5 10.1 11.1 12.4
## 3 10.5 12.6 14.3 16.1 17.9
## Petal.Width.10 Petal.Width.11 Petal.Width.12 Petal.Width.13 Petal.Width.14
## 1 2.2 2.4 2.6 2.7 2.8
## 2 13.8 14.8 16.3 17.3 18.7
## 3 20.4 22.4 24.3 26.4 28.4
## Petal.Width.15 Petal.Width.16 Petal.Width.17 Petal.Width.18 Petal.Width.19
## 1 3.0 3.4 3.8 4.1 4.4
## 2 20.0 21.4 22.9 23.9 25.4
## 3 30.8 33.1 34.9 37.1 39.4
## Petal.Width.20 Petal.Width.21 Petal.Width.22 Petal.Width.23 Petal.Width.24
## 1 4.7 4.9 5.3 5.5 6.0
## 2 26.5 28.3 29.6 31.1 32.3
## 3 40.9 43.2 45.2 47.2 49.0
## Petal.Width.25 Petal.Width.26 Petal.Width.27 Petal.Width.28 Petal.Width.29
## 1 6.2 6.4 6.8 7.0 7.2
## 2 33.6 35.0 36.4 38.1 39.6
## 3 51.1 52.9 54.7 56.5 58.6
## Petal.Width.30 Petal.Width.31 Petal.Width.32 Petal.Width.33 Petal.Width.34
## 1 7.4 7.6 8.0 8.1 8.3
## 2 40.6 41.7 42.7 43.9 45.5
## 3 60.2 62.1 64.1 66.3 67.8
## Petal.Width.35 Petal.Width.36 Petal.Width.37 Petal.Width.38 Petal.Width.39
## 1 8.5 8.7 8.9 9.0 9.2
## 2 47.0 48.6 50.1 51.4 52.7
## 3 69.2 71.5 73.9 75.7 77.5
## Petal.Width.40 Petal.Width.41 Petal.Width.42 Petal.Width.43 Petal.Width.44
## 1 9.4 9.7 10.0 10.2 10.8
## 2 54.0 55.2 56.6 57.8 58.8
## 3 79.6 82.0 84.3 86.2 88.5
## Petal.Width.45 Petal.Width.46 Petal.Width.47 Petal.Width.48 Petal.Width.49
## 1 11.2 11.5 11.7 11.9 12.1
## 2 60.1 61.3 62.6 63.9 65.0
## 3 91.0 93.3 95.2 97.2 99.5
## Petal.Width.50
## 1 12.3
## 2 66.3
## 3 101.3Representaciones gráficas de cada variable
library(ggplot2)
ggplot(irisnumerico, aes(Petal.Width)) +
geom_histogram(fill="red", alpha=0.7, position="identity")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(irisnumerico, aes(Sepal.Width)) +
geom_histogram(fill="blue", alpha=0.7, position="identity")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(iris$Sepal.Length, xlab = "sepal.length", ylab = "cantidad",main="histograma")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(iris$Petal.Length, xlab = "petal.length", ylab = "cantidad",main="histograma")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Representaciones gráficas de cada variable por tipo de plantas
boxplot(iris$Petal.Length~iris$Species ,main="Diagrama de caja", xlab="",
ylab="Petallength", col=c("orange","blue","red"))
boxplot(iris$Petal.Width~iris$Species ,main="Diagrama de caja", xlab="",
ylab="petalwidth", col=c("orange","blue","red"))
boxplot(iris$Sepal.Length~iris$Species ,main="Diagrama de caja", xlab="",
ylab="sepallength", col=c("orange","blue","red"))
boxplot(iris$Sepal.Width~iris$Species ,main="Diagrama de caja", xlab="",
ylab="sepalwidth", col=c("orange","blue","red"))
Hipótesis para cada variable
library(pastecs)
round(stat.desc(iris,basic=FALSE,norm=TRUE),digits=3)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## median 5.800 3.000 4.350 1.300 NA
## mean 5.843 3.057 3.758 1.199 NA
## SE.mean 0.068 0.036 0.144 0.062 NA
## CI.mean.0.95 0.134 0.070 0.285 0.123 NA
## var 0.686 0.190 3.116 0.581 NA
## std.dev 0.828 0.436 1.765 0.762 NA
## coef.var 0.142 0.143 0.470 0.636 NA
## skewness 0.309 0.313 -0.269 -0.101 NA
## skew.2SE 0.779 0.789 -0.680 -0.255 NA
## kurtosis -0.606 0.139 -1.417 -1.358 NA
## kurt.2SE -0.770 0.176 -1.800 -1.725 NA
## normtest.W 0.976 0.985 0.876 0.902 NA
## normtest.p 0.010 0.101 0.000 0.000 NAlibrary(nortest)
irisnumerico<-iris[,c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")]
lapply(irisnumerico, lillie.test)## $Sepal.Length
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: X[[i]]
## D = 0.088654, p-value = 0.005788
##
##
## $Sepal.Width
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: X[[i]]
## D = 0.10566, p-value = 0.0003142
##
##
## $Petal.Length
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: X[[i]]
## D = 0.19815, p-value = 7.901e-16
##
##
## $Petal.Width
##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: X[[i]]
## D = 0.17283, p-value = 7.33e-12library(MVN)## Registered S3 method overwritten by 'psych':
## method from
## plot.residuals rmutillibrary(MSQC)
mvn(irisnumerico,mvnTest = "mardia",univariateTest = "Lillie")## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 67.430508778062 4.75799820400869e-07 NO
## 2 Mardia Kurtosis -0.230112114481001 0.818004651478012 YES
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Lilliefors (Kolmogorov-Smirnov) Sepal.Length 0.0887 0.0058 NO
## 2 Lilliefors (Kolmogorov-Smirnov) Sepal.Width 0.1057 3e-04 NO
## 3 Lilliefors (Kolmogorov-Smirnov) Petal.Length 0.1982 <0.001 NO
## 4 Lilliefors (Kolmogorov-Smirnov) Petal.Width 0.1728 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew
## Sepal.Length 150 5.843333 0.8280661 5.80 4.3 7.9 5.1 6.4 0.3086407
## Sepal.Width 150 3.057333 0.4358663 3.00 2.0 4.4 2.8 3.3 0.3126147
## Petal.Length 150 3.758000 1.7652982 4.35 1.0 6.9 1.6 5.1 -0.2694109
## Petal.Width 150 1.199333 0.7622377 1.30 0.1 2.5 0.3 1.8 -0.1009166
## Kurtosis
## Sepal.Length -0.6058125
## Sepal.Width 0.1387047
## Petal.Length -1.4168574
## Petal.Width -1.3581792library(car)## Loading required package: carDatalibrary(nortest)
library(PerformanceAnalytics)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:pastecs':
##
## first, last##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:modeest':
##
## skewness## The following object is masked from 'package:graphics':
##
## legendplot(irisnumerico)
chart.Correlation(irisnumerico, histogram=TRUE)## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
Hipótesis según el tipo de especie
lapply(irisnumerico,leveneTest,iris$Species)## $Sepal.Length
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 6.3527 0.002259 **
## 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Sepal.Width
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.5902 0.5555
## 147
##
## $Petal.Length
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 19.48 3.129e-08 ***
## 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Petal.Width
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 19.892 2.261e-08 ***
## 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(irisnumerico,iris$Species)
lapply(irisnumerico,leveneTest,iris$Species)## $Sepal.Length
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 6.3527 0.002259 **
## 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Sepal.Width
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.5902 0.5555
## 147
##
## $Petal.Length
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 19.48 3.129e-08 ***
## 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $Petal.Width
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 19.892 2.261e-08 ***
## 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(iris$Species,iris$Sepal.Length)
plot(iris$Species,iris$Sepal.Width)
plot(iris$Species,iris$Petal.Length)
plot(iris$Species,iris$Petal.Width)