site mirror RPubs - hands on PCA dengan R
PCA R training
PCA R training
ayusta
11/8/2021
#instalasi
#instal R dari https://www.r-project.org/
#instal R Studio dari https://www.rstudio.com/#library
#instal package dengan klik package/install/nama masing masing package di bawah
library(knitr) # untuk R markdown
library(ggplot2) #untuk fungsi grafik dengna ggplot
library(ggfortify) #untuk membuat pca dengan ggplot
library(cowplot) #untuk membuat grafik grid
library(rstatix ) #untuk fungsi uji normalitas shapiro wilk##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterlibrary(ggpubr) #untuk membuat density plot uji normalitas##
## Attaching package: 'ggpubr'## The following object is masked from 'package:cowplot':
##
## get_legend#PCA ##data
#data
data("iris")
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 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica##data export import
write.csv(iris,"iris.csv") #untuk export data dalam bentuk cvs
##ubah data cvs menjadi xlsx
### new document/data/get data/from cvs
#import data xlsx
##klik import datasetdata profile
iris %>% shapiro_test(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)## # A tibble: 4 x 3
## variable statistic p
## <chr> <dbl> <dbl>
## 1 Petal.Length 0.876 7.41e-10
## 2 Petal.Width 0.902 1.68e- 8
## 3 Sepal.Length 0.976 1.02e- 2
## 4 Sepal.Width 0.985 1.01e- 1# p-value > 0.05 implying that the distribution of the data are not significantly different from normal distribution. In other words, we can assume the normality.
g.density_Sepal.Length<-ggdensity(iris$Sepal.Length,
main = "Density plot of Sepal Length",
xlab = "Sepal Length")
g.density_Sepal.Width<-ggdensity(iris$Sepal.Width,
main = "Density plot of Sepal Width",
xlab = "Sepal Width")
g.density_Petal.Length<-ggdensity(iris$Petal.Length,
main = "Density plot of Petal Length",
xlab = "Petal Length")
g.density_Petal.Width<-ggdensity(iris$Petal.Width,
main = "Density plot of Petal Width",
xlab = "Petal Width")
g.density_iris_grid<-plot_grid(g.density_Petal.Length, g.density_Petal.Width, g.density_Sepal.Length, g.density_Sepal.Width, ncol=2, align = 'v', rel_heights = c(1/5, 1/5, 1/5, 1/5),
labels = c('A', 'B', 'C', 'D'))
g.density_iris_grid
##data transformation ###log transformation
#https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
#menggunakan log transformation, scaling and mean centering transformation
log.ir <- log(iris[, 1:4])
ir.species <- iris[, 5]
log.ir## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 1.629241 1.2527630 0.33647224 -1.60943791
## 2 1.589235 1.0986123 0.33647224 -1.60943791
## 3 1.547563 1.1631508 0.26236426 -1.60943791
## 4 1.526056 1.1314021 0.40546511 -1.60943791
## 5 1.609438 1.2809338 0.33647224 -1.60943791
## 6 1.686399 1.3609766 0.53062825 -0.91629073
## 7 1.526056 1.2237754 0.33647224 -1.20397280
## 8 1.609438 1.2237754 0.40546511 -1.60943791
## 9 1.481605 1.0647107 0.33647224 -1.60943791
## 10 1.589235 1.1314021 0.40546511 -2.30258509
## 11 1.686399 1.3083328 0.40546511 -1.60943791
## 12 1.568616 1.2237754 0.47000363 -1.60943791
## 13 1.568616 1.0986123 0.33647224 -2.30258509
## 14 1.458615 1.0986123 0.09531018 -2.30258509
## 15 1.757858 1.3862944 0.18232156 -1.60943791
## 16 1.740466 1.4816045 0.40546511 -0.91629073
## 17 1.686399 1.3609766 0.26236426 -0.91629073
## 18 1.629241 1.2527630 0.33647224 -1.20397280
## 19 1.740466 1.3350011 0.53062825 -1.20397280
## 20 1.629241 1.3350011 0.40546511 -1.20397280
## 21 1.686399 1.2237754 0.53062825 -1.60943791
## 22 1.629241 1.3083328 0.40546511 -0.91629073
## 23 1.526056 1.2809338 0.00000000 -1.60943791
## 24 1.629241 1.1939225 0.53062825 -0.69314718
## 25 1.568616 1.2237754 0.64185389 -1.60943791
## 26 1.609438 1.0986123 0.47000363 -1.60943791
## 27 1.609438 1.2237754 0.47000363 -0.91629073
## 28 1.648659 1.2527630 0.40546511 -1.60943791
## 29 1.648659 1.2237754 0.33647224 -1.60943791
## 30 1.547563 1.1631508 0.47000363 -1.60943791
## 31 1.568616 1.1314021 0.47000363 -1.60943791
## 32 1.686399 1.2237754 0.40546511 -0.91629073
## 33 1.648659 1.4109870 0.40546511 -2.30258509
## 34 1.704748 1.4350845 0.33647224 -1.60943791
## 35 1.589235 1.1314021 0.40546511 -1.60943791
## 36 1.609438 1.1631508 0.18232156 -1.60943791
## 37 1.704748 1.2527630 0.26236426 -1.60943791
## 38 1.589235 1.2809338 0.33647224 -2.30258509
## 39 1.481605 1.0986123 0.26236426 -1.60943791
## 40 1.629241 1.2237754 0.40546511 -1.60943791
## 41 1.609438 1.2527630 0.26236426 -1.20397280
## 42 1.504077 0.8329091 0.26236426 -1.20397280
## 43 1.481605 1.1631508 0.26236426 -1.60943791
## 44 1.609438 1.2527630 0.47000363 -0.51082562
## 45 1.629241 1.3350011 0.64185389 -0.91629073
## 46 1.568616 1.0986123 0.33647224 -1.20397280
## 47 1.629241 1.3350011 0.47000363 -1.60943791
## 48 1.526056 1.1631508 0.33647224 -1.60943791
## 49 1.667707 1.3083328 0.40546511 -1.60943791
## 50 1.609438 1.1939225 0.33647224 -1.60943791
## 51 1.945910 1.1631508 1.54756251 0.33647224
## 52 1.856298 1.1631508 1.50407740 0.40546511
## 53 1.931521 1.1314021 1.58923521 0.40546511
## 54 1.704748 0.8329091 1.38629436 0.26236426
## 55 1.871802 1.0296194 1.52605630 0.40546511
## 56 1.740466 1.0296194 1.50407740 0.26236426
## 57 1.840550 1.1939225 1.54756251 0.47000363
## 58 1.589235 0.8754687 1.19392247 0.00000000
## 59 1.887070 1.0647107 1.52605630 0.26236426
## 60 1.648659 0.9932518 1.36097655 0.33647224
## 61 1.609438 0.6931472 1.25276297 0.00000000
## 62 1.774952 1.0986123 1.43508453 0.40546511
## 63 1.791759 0.7884574 1.38629436 0.00000000
## 64 1.808289 1.0647107 1.54756251 0.33647224
## 65 1.722767 1.0647107 1.28093385 0.26236426
## 66 1.902108 1.1314021 1.48160454 0.33647224
## 67 1.722767 1.0986123 1.50407740 0.40546511
## 68 1.757858 0.9932518 1.41098697 0.00000000
## 69 1.824549 0.7884574 1.50407740 0.40546511
## 70 1.722767 0.9162907 1.36097655 0.09531018
## 71 1.774952 1.1631508 1.56861592 0.58778666
## 72 1.808289 1.0296194 1.38629436 0.26236426
## 73 1.840550 0.9162907 1.58923521 0.40546511
## 74 1.808289 1.0296194 1.54756251 0.18232156
## 75 1.856298 1.0647107 1.45861502 0.26236426
## 76 1.887070 1.0986123 1.48160454 0.33647224
## 77 1.916923 1.0296194 1.56861592 0.33647224
## 78 1.902108 1.0986123 1.60943791 0.53062825
## 79 1.791759 1.0647107 1.50407740 0.40546511
## 80 1.740466 0.9555114 1.25276297 0.00000000
## 81 1.704748 0.8754687 1.33500107 0.09531018
## 82 1.704748 0.8754687 1.30833282 0.00000000
## 83 1.757858 0.9932518 1.36097655 0.18232156
## 84 1.791759 0.9932518 1.62924054 0.47000363
## 85 1.686399 1.0986123 1.50407740 0.40546511
## 86 1.791759 1.2237754 1.50407740 0.47000363
## 87 1.902108 1.1314021 1.54756251 0.40546511
## 88 1.840550 0.8329091 1.48160454 0.26236426
## 89 1.722767 1.0986123 1.41098697 0.26236426
## 90 1.704748 0.9162907 1.38629436 0.26236426
## 91 1.704748 0.9555114 1.48160454 0.18232156
## 92 1.808289 1.0986123 1.52605630 0.33647224
## 93 1.757858 0.9555114 1.38629436 0.18232156
## 94 1.609438 0.8329091 1.19392247 0.00000000
## 95 1.722767 0.9932518 1.43508453 0.26236426
## 96 1.740466 1.0986123 1.43508453 0.18232156
## 97 1.740466 1.0647107 1.43508453 0.26236426
## 98 1.824549 1.0647107 1.45861502 0.26236426
## 99 1.629241 0.9162907 1.09861229 0.09531018
## 100 1.740466 1.0296194 1.41098697 0.26236426
## 101 1.840550 1.1939225 1.79175947 0.91629073
## 102 1.757858 0.9932518 1.62924054 0.64185389
## 103 1.960095 1.0986123 1.77495235 0.74193734
## 104 1.840550 1.0647107 1.72276660 0.58778666
## 105 1.871802 1.0986123 1.75785792 0.78845736
## 106 2.028148 1.0986123 1.88706965 0.74193734
## 107 1.589235 0.9162907 1.50407740 0.53062825
## 108 1.987874 1.0647107 1.84054963 0.58778666
## 109 1.902108 0.9162907 1.75785792 0.58778666
## 110 1.974081 1.2809338 1.80828877 0.91629073
## 111 1.871802 1.1631508 1.62924054 0.69314718
## 112 1.856298 0.9932518 1.66770682 0.64185389
## 113 1.916923 1.0986123 1.70474809 0.74193734
## 114 1.740466 0.9162907 1.60943791 0.69314718
## 115 1.757858 1.0296194 1.62924054 0.87546874
## 116 1.856298 1.1631508 1.66770682 0.83290912
## 117 1.871802 1.0986123 1.70474809 0.58778666
## 118 2.041220 1.3350011 1.90210753 0.78845736
## 119 2.041220 0.9555114 1.93152141 0.83290912
## 120 1.791759 0.7884574 1.60943791 0.40546511
## 121 1.931521 1.1631508 1.74046617 0.83290912
## 122 1.722767 1.0296194 1.58923521 0.69314718
## 123 2.041220 1.0296194 1.90210753 0.69314718
## 124 1.840550 0.9932518 1.58923521 0.58778666
## 125 1.902108 1.1939225 1.74046617 0.74193734
## 126 1.974081 1.1631508 1.79175947 0.58778666
## 127 1.824549 1.0296194 1.56861592 0.58778666
## 128 1.808289 1.0986123 1.58923521 0.58778666
## 129 1.856298 1.0296194 1.72276660 0.74193734
## 130 1.974081 1.0986123 1.75785792 0.47000363
## 131 2.001480 1.0296194 1.80828877 0.64185389
## 132 2.066863 1.3350011 1.85629799 0.69314718
## 133 1.856298 1.0296194 1.72276660 0.78845736
## 134 1.840550 1.0296194 1.62924054 0.40546511
## 135 1.808289 0.9555114 1.72276660 0.33647224
## 136 2.041220 1.0986123 1.80828877 0.83290912
## 137 1.840550 1.2237754 1.72276660 0.87546874
## 138 1.856298 1.1314021 1.70474809 0.58778666
## 139 1.791759 1.0986123 1.56861592 0.58778666
## 140 1.931521 1.1314021 1.68639895 0.74193734
## 141 1.902108 1.1314021 1.72276660 0.87546874
## 142 1.931521 1.1314021 1.62924054 0.83290912
## 143 1.757858 0.9932518 1.62924054 0.64185389
## 144 1.916923 1.1631508 1.77495235 0.83290912
## 145 1.902108 1.1939225 1.74046617 0.91629073
## 146 1.902108 1.0986123 1.64865863 0.83290912
## 147 1.840550 0.9162907 1.60943791 0.64185389
## 148 1.871802 1.0986123 1.64865863 0.69314718
## 149 1.824549 1.2237754 1.68639895 0.83290912
## 150 1.774952 1.0986123 1.62924054 0.58778666iris_log<-cbind(log.ir,ir.species)
log.ir %>% shapiro_test(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)## # A tibble: 4 x 3
## variable statistic p
## <chr> <dbl> <dbl>
## 1 Petal.Length 0.817 2.04e-12
## 2 Petal.Width 0.821 3.05e-12
## 3 Sepal.Length 0.983 5.39e- 2
## 4 Sepal.Width 0.989 3.01e- 1scaling and mean centering
iris_data<-iris[,1:4]
iris_center_scale_data<-as.data.frame(scale(iris_data, center = TRUE, scale = TRUE))
iris_center_scale<-cbind(iris_center_scale_data, ir.species)
iris_log_center_scale_data<-as.data.frame(scale(log.ir, center = TRUE, scale = TRUE))
iris_log_center_scale<-cbind(iris_log_center_scale_data, ir.species)##boxspot comparison with ggplot
g.boxplot_iris<-iris %>% dplyr::select(Species, everything()) %>% tidyr::gather("id", "value",2:5) %>%
ggplot(., aes(x = id, y = value))+geom_boxplot()
g.boxplot_iris_log<-iris_log %>% dplyr::select(ir.species, everything()) %>% tidyr::gather("id", "value",2:5) %>%
ggplot(., aes(x = id, y = value))+geom_boxplot()
g.boxplot_iris_center_scale<-iris_center_scale %>% dplyr::select(ir.species, everything()) %>% tidyr::gather("id", "value",2:5) %>%
ggplot(., aes(x = id, y = value))+geom_boxplot()
g.boxplot_iris_log_center_scale<-iris_log_center_scale %>% dplyr::select(ir.species, everything()) %>% tidyr::gather("id", "value",2:5) %>%
ggplot(., aes(x = id, y = value))+geom_boxplot()
g.boxplot_iris_comparison<-plot_grid(g.boxplot_iris, g.boxplot_iris_log, g.boxplot_iris_center_scale, g.boxplot_iris_log_center_scale, ncol=2, align = 'v', rel_heights = c(1/5, 1/5, 1/5, 1/5),
labels = c('A', 'B', 'C', 'D'))
g.boxplot_iris_comparison
# A. g.boxplot_iris,
# B. g.boxplot_iris_log,
# C. g.boxplot_iris_center_scale,
# D. g.boxplot_iris_log_center_scale##PCA dengan ggplot profil lengkap
g.pca_iris_compl<-autoplot(prcomp(iris_data), data = iris, colour = 'Species', frame = T, loadings = TRUE, loadings.label = TRUE)
g.pca_iris_compl
g.scree_iris<-plot(prcomp(iris_data))
g.scree_iris## NULL##PCA dengan ggplot perbandingan
g.pca_iris<-autoplot(prcomp(iris_data), data = iris, colour = 'Species', frame = T)
g.pca_iris_log<-autoplot(prcomp(log.ir), data = iris_log, colour = 'ir.species', frame = T)
g.pca_iris_center_scale<-autoplot(prcomp(iris_center_scale_data, center = FALSE), data = iris_center_scale, colour = 'ir.species', frame = T)
g.pca_iris_log_center_scale<-autoplot(prcomp(iris_log_center_scale_data, center = FALSE), data = iris_log_center_scale, colour = 'ir.species', frame = T)
g.pca_iris_comparison<-plot_grid(g.pca_iris, g.pca_iris_log, g.pca_iris_center_scale, g.pca_iris_log_center_scale, ncol=2, align = 'v', rel_heights = c(1/5, 1/5, 1/5, 1/5),
labels = c('A', 'B', 'C', 'D'))
g.pca_iris_comparison
# A. g.pca_iris,
# B. g.pca_iris_log,
# C. g.pca_iris_center_scale,
# D. g.pca_iris_log_center_scale
ggsave("g.pca_iris_comparison.pdf", g.pca_iris_comparison, width = 200, height = 110, units = "mm",
dpi = 600, limitsize = TRUE)#untuk cetak ke dalam pdf
ggsave("g.pca_iris_comparison.jpg", g.pca_iris_comparison, width = 200, height = 110, units = "mm",
dpi = 600, limitsize = TRUE)#untuk cetak ke dalam jpg
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