Kontrak kuliah
Pengenalan R, RStudio, ggplot, PCA, Git, sogosurvey.com code
Pengenalan R, RStudio, ggplot, PCA, Git, sogosurvey.com ppt
Tugas sebelum kuliah 24 April 2020
0. Baca file di atas
1. Instal R https://www.r-project.org/
2. Instal R Studio https://rstudio.com/
2.5. Baca https://git-scm.com/book/en/v2 bagian 1. Getting Started
3. Instal Git https://git-scm.com/
4. Buat akun di https://github.com/
5. upgrade akun git di https://education.github.com/
6. buat akun di https://www.sogosurvey.com
7. upgrade akun di https://www.sogosurvey.com/free-survey-for-students/
16 April 2020
Pengenalan R, RStudio, ggplot, PCA, Git, sogosurvey.com
Pengenalan R, RStudio, ggplot, PCA, Git, sogosurvey.com
Fitriyono Ayustaningwarno
4/12/2020
knitr::opts_chunk$set(echo = TRUE)
library(knitr) # untuk R markdown
library(Rmisc) # untuk fungsi summarySE## Loading required package: lattice## Loading required package: plyrlibrary(agricolae)# untuk fungsi HSD.test
library(ggplot2) #untuk fungsi grafik dengna ggplot
library(cowplot) #untuk membuat grafik grid##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************library(rstatix ) #untuk fungsi uji normalitas shapiro wilk##
## Attaching package: 'rstatix'## The following object is masked from 'package:plyr':
##
## mutate## The following object is masked from 'package:stats':
##
## filterlibrary(ggpubr) #untuk membuat density plot uji normalitas## Loading required package: magrittr##
## Attaching package: 'ggpubr'## The following object is masked from 'package:cowplot':
##
## get_legend## The following object is masked from 'package:plyr':
##
## mutatelibrary(dplyr) #fungsi kalkulasi untuk membuat boxplot##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr) #fungsi kalkulasi untuk membuat boxplot##
## Attaching package: 'tidyr'## The following object is masked from 'package:magrittr':
##
## extractlibrary(ggfortify) #untuk membuat pca dengan ggplotdata(ToothGrowth)
ToothGrowth## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5
## 7 11.2 VC 0.5
## 8 11.2 VC 0.5
## 9 5.2 VC 0.5
## 10 7.0 VC 0.5
## 11 16.5 VC 1.0
## 12 16.5 VC 1.0
## 13 15.2 VC 1.0
## 14 17.3 VC 1.0
## 15 22.5 VC 1.0
## 16 17.3 VC 1.0
## 17 13.6 VC 1.0
## 18 14.5 VC 1.0
## 19 18.8 VC 1.0
## 20 15.5 VC 1.0
## 21 23.6 VC 2.0
## 22 18.5 VC 2.0
## 23 33.9 VC 2.0
## 24 25.5 VC 2.0
## 25 26.4 VC 2.0
## 26 32.5 VC 2.0
## 27 26.7 VC 2.0
## 28 21.5 VC 2.0
## 29 23.3 VC 2.0
## 30 29.5 VC 2.0
## 31 15.2 OJ 0.5
## 32 21.5 OJ 0.5
## 33 17.6 OJ 0.5
## 34 9.7 OJ 0.5
## 35 14.5 OJ 0.5
## 36 10.0 OJ 0.5
## 37 8.2 OJ 0.5
## 38 9.4 OJ 0.5
## 39 16.5 OJ 0.5
## 40 9.7 OJ 0.5
## 41 19.7 OJ 1.0
## 42 23.3 OJ 1.0
## 43 23.6 OJ 1.0
## 44 26.4 OJ 1.0
## 45 20.0 OJ 1.0
## 46 25.2 OJ 1.0
## 47 25.8 OJ 1.0
## 48 21.2 OJ 1.0
## 49 14.5 OJ 1.0
## 50 27.3 OJ 1.0
## 51 25.5 OJ 2.0
## 52 26.4 OJ 2.0
## 53 22.4 OJ 2.0
## 54 24.5 OJ 2.0
## 55 24.8 OJ 2.0
## 56 30.9 OJ 2.0
## 57 26.4 OJ 2.0
## 58 27.3 OJ 2.0
## 59 29.4 OJ 2.0
## 60 23.0 OJ 2.0summarySE
ToothGrowth_sum<-summarySE(data = ToothGrowth, "len", groupvars = c("supp", "dose"), na.rm = FALSE,
conf.interval = 0.95, .drop = TRUE)
ToothGrowth_sum## supp dose N len sd se ci
## 1 OJ 0.5 10 13.23 4.459709 1.4102837 3.190283
## 2 OJ 1.0 10 22.70 3.910953 1.2367520 2.797727
## 3 OJ 2.0 10 26.06 2.655058 0.8396031 1.899314
## 4 VC 0.5 10 7.98 2.746634 0.8685620 1.964824
## 5 VC 1.0 10 16.77 2.515309 0.7954104 1.799343
## 6 VC 2.0 10 26.14 4.797731 1.5171757 3.432090str(ToothGrowth)## 'data.frame': 60 obs. of 3 variables:
## $ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
## $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
## $ dose: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...statistik
##linier modeltx_supp_dose <- with(ToothGrowth, interaction(supp, dose))
lm_supp_dose <- lm(len~tx_supp_dose, data = ToothGrowth)
summary(lm_supp_dose)##
## Call:
## lm(formula = len ~ tx_supp_dose, data = ToothGrowth)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.20 -2.72 -0.27 2.65 8.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.230 1.148 11.521 3.60e-16 ***
## tx_supp_doseVC.0.5 -5.250 1.624 -3.233 0.00209 **
## tx_supp_doseOJ.1 9.470 1.624 5.831 3.18e-07 ***
## tx_supp_doseVC.1 3.540 1.624 2.180 0.03365 *
## tx_supp_doseOJ.2 12.830 1.624 7.900 1.43e-10 ***
## tx_supp_doseVC.2 12.910 1.624 7.949 1.19e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.631 on 54 degrees of freedom
## Multiple R-squared: 0.7937, Adjusted R-squared: 0.7746
## F-statistic: 41.56 on 5 and 54 DF, p-value: < 2.2e-16anova(lm_supp_dose)## Analysis of Variance Table
##
## Response: len
## Df Sum Sq Mean Sq F value Pr(>F)
## tx_supp_dose 5 2740.10 548.02 41.557 < 2.2e-16 ***
## Residuals 54 712.11 13.19
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1HSD_supp_dose<-HSD.test(lm_supp_dose, trt = "tx_supp_dose", group = TRUE, console=TRUE)##
## Study: lm_supp_dose ~ "tx_supp_dose"
##
## HSD Test for len
##
## Mean Square Error: 13.18715
##
## tx_supp_dose, means
##
## len std r Min Max
## OJ.0.5 13.23 4.459709 10 8.2 21.5
## OJ.1 22.70 3.910953 10 14.5 27.3
## OJ.2 26.06 2.655058 10 22.4 30.9
## VC.0.5 7.98 2.746634 10 4.2 11.5
## VC.1 16.77 2.515309 10 13.6 22.5
## VC.2 26.14 4.797731 10 18.5 33.9
##
## Alpha: 0.05 ; DF Error: 54
## Critical Value of Studentized Range: 4.178265
##
## Minimun Significant Difference: 4.798124
##
## Treatments with the same letter are not significantly different.
##
## len groups
## VC.2 26.14 a
## OJ.2 26.06 a
## OJ.1 22.70 a
## VC.1 16.77 b
## OJ.0.5 13.23 b
## VC.0.5 7.98 cHSD test group p value
HSD_supp_doseP<-HSD.test(lm_supp_dose, trt = "tx_supp_dose", group = FALSE, console=TRUE)##
## Study: lm_supp_dose ~ "tx_supp_dose"
##
## HSD Test for len
##
## Mean Square Error: 13.18715
##
## tx_supp_dose, means
##
## len std r Min Max
## OJ.0.5 13.23 4.459709 10 8.2 21.5
## OJ.1 22.70 3.910953 10 14.5 27.3
## OJ.2 26.06 2.655058 10 22.4 30.9
## VC.0.5 7.98 2.746634 10 4.2 11.5
## VC.1 16.77 2.515309 10 13.6 22.5
## VC.2 26.14 4.797731 10 18.5 33.9
##
## Alpha: 0.05 ; DF Error: 54
## Critical Value of Studentized Range: 4.178265
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## OJ.0.5 - OJ.1 -9.47 0.0000 *** -14.2681238 -4.671876
## OJ.0.5 - OJ.2 -12.83 0.0000 *** -17.6281238 -8.031876
## OJ.0.5 - VC.0.5 5.25 0.0243 * 0.4518762 10.048124
## OJ.0.5 - VC.1 -3.54 0.2640 -8.3381238 1.258124
## OJ.0.5 - VC.2 -12.91 0.0000 *** -17.7081238 -8.111876
## OJ.1 - OJ.2 -3.36 0.3187 -8.1581238 1.438124
## OJ.1 - VC.0.5 14.72 0.0000 *** 9.9218762 19.518124
## OJ.1 - VC.1 5.93 0.0074 ** 1.1318762 10.728124
## OJ.1 - VC.2 -3.44 0.2936 -8.2381238 1.358124
## OJ.2 - VC.0.5 18.08 0.0000 *** 13.2818762 22.878124
## OJ.2 - VC.1 9.29 0.0000 *** 4.4918762 14.088124
## OJ.2 - VC.2 -0.08 1.0000 -4.8781238 4.718124
## VC.0.5 - VC.1 -8.79 0.0000 *** -13.5881238 -3.991876
## VC.0.5 - VC.2 -18.16 0.0000 *** -22.9581238 -13.361876
## VC.1 - VC.2 -9.37 0.0000 *** -14.1681238 -4.571876grafik
base
plot(ToothGrowth_sum$dose,ToothGrowth_sum$len)
#fungsi plot dasar pada R tidak dapat melakukan gruping, sehingga jenis suplemen tidak dapat diamatig.ToothGrowth<-
ggplot(data = ToothGrowth_sum,aes(x = dose,y=len), na.rm = FALSE) +
geom_point(data = ToothGrowth_sum, aes(color=supp), size=4)+
theme_classic(base_size = 14)+
xlab("Dose (mg)") +
ylab("Length (mm)")
g.ToothGrowth
###grafik ggplot dalam 2 plot bersusun
g.ToothGrowth_grid<-
ggplot(data = ToothGrowth_sum,aes(x = dose,y=len), na.rm = FALSE) +
geom_point()+
theme_classic(base_size = 14)+
xlab("Dose (mg)") +
ylab("Length (mm)")+
facet_grid(cols = vars(supp))
g.ToothGrowth_grid
###grafik ggplot dalam 2 plot manual
g.ToothGrowth_OJ<-
ggplot(data = ToothGrowth_sum[ToothGrowth_sum$supp=="OJ",],aes(x = dose,y=len), na.rm = FALSE) +
geom_point()+
theme_classic(base_size = 14)+
xlab("Dose (mg)") +
ylab("Length (mm)")
g.ToothGrowth_OJ
g.ToothGrowth_VC<-
ggplot(data = ToothGrowth_sum[ToothGrowth_sum$supp=="VC",],aes(x = dose,y=len), na.rm = FALSE) +
geom_line()+
geom_point()+
theme_classic(base_size = 14)+
xlab("Dose (mg)") +
ylab("Length (mm)")
g.ToothGrowth_VC
g.ToothGrowth_grid<-plot_grid(g.ToothGrowth_OJ, g.ToothGrowth_VC, ncol=2, align = 'v', rel_heights = c(1/5, 1/5),
labels = c('A', 'B'))
g.ToothGrowth_grid
ggsave("g.ToothGrowth_grid.pdf", plot= g.ToothGrowth_grid, width = 200, height = 130, units = "mm")
#untuk menyimpan dalam bentuk pdf
ggsave("g.ToothGrowth_grid.png", plot= g.ToothGrowth_grid, width = 200, height = 130, units = "mm")
#untuk menyimpan dalam bentuk png
rm(g.ToothGrowth_OV)## Warning in rm(g.ToothGrowth_OV): object 'g.ToothGrowth_OV' not found#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 virginicadata 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)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_scaleg.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## NULLg.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
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