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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