pertemuan 1
14 February 2023
14 November 2022
Critical review on the immunomodulatory activities of carrot’s β-carotene and other bioactive compounds
Abstract
Carrots (Daucus carota L.) are vegetables proven to have nutraceutical effects and beneficial for health due to its natural bioactive substances. One of the potential of carrots to maintain optimum health status is by regulating immune response. This literature review summarized the proposed immunomodulatory mechanisms of the antioxidant properties of carrot’s β-carotene and other bioactive compounds such as phenolic acid, flavonoid, polyacetylene and ascorbic acid via the anti-inflammatory, antioxidant and overall (innate and adaptive) immune response modulation. Overall, carrot’s bioactive compounds regulated pro-inflammatory and anti-inflammatory cytokines, reduced oxidative stress by decreasing the reactive oxygen species accumulation and improving antioxidant capacity and the expression of genes in order to prevent more damaging oxidative destruction. Carrots also modulated the immune components by regulating leukocytes, antigens, immunoglobulins, and histamine levels. Thus, the immunomodulatory activity makes carrots as a functional food source that has the potential to prevent and treat various diseases.
free, open access, gratis
https://doi.org/10.1016/j.jff.2022.105303
14 February 2022
IOT 2022
1. pendahuluan
video
tugas di pertemuan 1 ini dipresentasikan di pertemuan 5
bentuk adalah produk, bukan kajian ilmiah
4 software and hardware IOT
video
tugas project dikumpulkan dalam format docx melalui kormat ke email ayustaningwarno@fk.undip.ac.id
1 hari sebelum presentasi
5 big data dan iot
video
9 cyber security
video
--------------------------
UUW00006 Internet
of things (IOT)
Course Descriptions
The internet of things (IoT) course contains
basic material about the use of the internet from the dimensions of society
that can be integrated in objects / devices / objects of science and technology
(science, technology and art) ecosystem. In the final results of the lecture,
students are expected to be skilled in applying the internet of things (IoT) in
a polite, practical and comprehensive manner.
Course coordinator
Fitriyono Ayustaningwarno STP., MSi
Lecturer(s)
Fitriyono Ayustaningwarno STP., MSi
Dewi Marfu’ah Kurniawati, S.Gz, M.Gizi
08 November 2021
hands on PCA dengan R
file code saja
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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