hc <- hclust(dist(andmed1), method="complete", members=NULL)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
remove(hc)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Andmete laadimine
andmed <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed1 <- andmed[,6:49]
#Centred log-trans
library(compositions)
andmed2 <- clr(andmed1, robust=T)
#Hierachic cluster
hc <- hclust(dist(andmed1), method="complete", members=NULL)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
remove(hc)
#Andmete laadimine
andmed <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed1 <- andmed[,6:49]
#Centred log-trans
library(compositions)
andmed2 <- clr(andmed1, robust=T)
#Hierachic cluster
hc <- hclust(dist(andmed1), method="complete", members=NULL)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
fviz_pca_biplot(res.pca, col.var="lightgray", label="all", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="all", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Andmete laadimine
andmed <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed1 <- andmed[,7:50]
#Centred log-trans
library(compositions)
andmed2 <- clr(andmed1, robust=T)
#Hierachic cluster
hc <- hclust(dist(andmed1), method="complete", members=NULL)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:45], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Aluspohja lademed
andmed_lade1 <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed_lade2 <- andmed_lade1[,7:50]
andmed_lade3 <- clr(andmed_lade2, robust=T)
andmed$Lade<-as.factor(andmed$Lade)
res.pca4 = PCA(andmed_lade3 [,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
scale_fill_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
geom_point(aes(colour=factor(andmed$Lade)))
#Andmete laadimine
andmed <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed1 <- andmed[,7:50]
#Centred log-trans
library(compositions)
andmed2 <- clr(andmed1, robust=T)
#Hierachic cluster
hc <- hclust(dist(andmed1), method="complete", members=NULL)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Aluspohja lademed
andmed_lade1 <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed_lade2 <- andmed_lade1[,7:50]
andmed_lade3 <- clr(andmed_lade2, robust=T)
andmed$Lade<-as.factor(andmed$Lade)
res.pca4 = PCA(andmed_lade3 [,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
scale_fill_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
geom_point(aes(colour=factor(andmed$Lade)))
#Andmete laadimine
andmed <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed1 <- andmed[,7:50]
#Centred log-trans
library(compositions)
andmed2 <- clr(andmed1, robust=T)
#Hierachic cluster
hc <- hclust(dist(andmed1), method="complete", members=NULL)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Aluspohja lademed
andmed_lade1 <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed_lade2 <- andmed_lade1[,7:50]
andmed_lade3 <- clr(andmed_lade2, robust=T)
andmed$Lade<-as.factor(andmed$Lade)
res.pca4 = PCA(andmed_lade3 [,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
scale_fill_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
geom_point(aes(colour=factor(andmed$Lade)))
#Andmete laadimine
andmed <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed1 <- andmed[,7:50]
#Centred log-trans
library(compositions)
andmed2 <- clr(andmed1, robust=T)
#Hierachic cluster
hc <- hclust(dist(andmed1), method="complete", members=NULL)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Aluspohja lademed
andmed_lade1 <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed_lade2 <- andmed_lade1[,7:50]
andmed_lade3 <- clr(andmed_lade2, robust=T)
andmed$Lade<-as.factor(andmed$Lade)
res.pca4 = PCA(andmed_lade3 [,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
scale_fill_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
geom_point(aes(colour=factor(andmed$Lade)))
View(andmed_jalg)
View(andmed_lade2)
remove(hc)
#Andmete laadimine
andmed <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed1 <- andmed[,7:50]
#Centred log-trans
library(compositions)
andmed2 <- clr(andmed1, robust=T)
#FactoMiner
library(FactoMineR)
library(factoextra)
res.pca = PCA(andmed2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
#Asukoht eristajana
andmed$Asukoht<-as.factor(andmed$Asukoht)
#Ainult rauasulatusslakk
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
scale_fill_manual(values = c("green", "orange", "red", "cyan", "magenta", "blue")) +
geom_point(aes(colour=factor(andmed$Asukoht)))
#Aluspõhja järgi
andmed$Aluspohi<-as.factor(andmed$Aluspohi)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red")) +
scale_fill_manual(values = c("green", "blue", "red")) +
geom_point(aes(colour=factor(andmed$Aluspohi)))
#Kogutabel
andmed_kogu <- read.csv("uus_data.csv", sep=";",header=T)
andmed_kogu1 <- andmed_kogu[,6:49]
library(FactoMineR)
library(factoextra)
library(compositions)
andmed_kogu2 <- clr(andmed_kogu1, robust=T)
res.pca2 = PCA(andmed_kogu2[,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca2, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca2, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Ainult jälgelemendid
andmed_jalg <- read.csv("uus_data.csv", sep=";",header=T)
andmed_jalg1 <- andmed_jalg[,22:49]
andmed_jalg2 <- clr(andmed_jalg1, robust=T)
res.pca3 = PCA(andmed_jalg2[,1:28], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca3, axes=c(1, 2), choix="ind", habillage=2)
andmed_kogu$Aluspohi<-as.factor(andmed_kogu$Aluspohi)
fviz_pca_biplot(res.pca3, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
scale_fill_manual(values = c("#808000", "orange", "green","cyan", "magenta", "yellow", "purple", "blue", "brown", "pink", "red", "lightblue", "black")) +
geom_point(aes(colour=factor(andmed_kogu$Aluspohi)))
#Aluspohja lademed
andmed_lade1 <- read.csv("uus_data_rauasulatusslakk.csv", sep=";",header=T)
andmed_lade2 <- andmed_lade1[,7:50]
andmed_lade3 <- clr(andmed_lade2, robust=T)
andmed$Lade<-as.factor(andmed$Lade)
res.pca4 = PCA(andmed_lade3 [,1:44], scale.unit=TRUE, ncp=5, graph=T)
plot.PCA(res.pca, axes=c(1, 2), choix="ind", habillage=2)
fviz_pca_biplot(res.pca, col.var="lightgray", label="var", pointsize=1.5, labelsize=2)  +
theme_minimal()  +
scale_colour_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
scale_fill_manual(values = c("green", "blue", "red", "yellow", "orange", "magenta", "cyan")) +
geom_point(aes(colour=factor(andmed$Lade)))
