331 lines
9.9 KiB
Text
331 lines
9.9 KiB
Text
install.packages(c("corrplot", "ggplot2", "forcats", "reshape2", "gridExtra", "BioStatR", "FactoMineR", "factoextra", "mclust", "cluster", "ppclust", "circlize", "ggalluvial"))
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install.packages(c("lme4", "quantreg"))
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install.packages("FactoMineR")
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install.packages("FactoMineR")
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install.packages("FactoMineR")
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install.packages("nloptr")
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install.packages("nloptr")
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install.packages("FactoMineR")
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install.packages(c("corrplot", "ggplot2", "forcats", "reshape2", "gridExtra", "BioStatR", "FactoMineR", "factoextra", "mclust", "cluster", "ppclust", "circlize", "ggalluvial"))
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clear
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knitr::opts_chunk$set(echo = TRUE)
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library(ggplot2)
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library(gridExtra)
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library(reshape2)
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library(corrplot)
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ggplot(melt(T[1:18]),aes(x=variable,y=value))+
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geom_boxplot()+ theme(axis.text.x = element_text(angle=90, vjust = 0.5, hjust = 1))
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knitr::opts_chunk$set(echo = TRUE)
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library(ggplot2)
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library(gridExtra)
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library(reshape2)
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library(corrplot)
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T = read.table("DataProjet3MIC-2425.txt",header=TRUE,sep=";")
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T$ExpT1 = as.factor(T$ExpT1)
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T$ExpT2 = as.factor(T$ExpT2)
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T$ExpT3 = as.factor(T$ExpT3)
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head(T)
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summary(T)
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str(T)
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levels(T$ExpT1)
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g1<-ggplot(T, aes(x=T$ExpT1))+
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geom_bar()+
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ylab("Effectifs")+ggtitle("Effectifs")
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g2<-ggplot(T, aes(x = T$ExpT1)) +
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geom_bar(aes(y = (..count..)/sum(..count..)))+ylab("")+ggtitle("Frequences")
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df <- data.frame(group = levels(T$ExpT1),
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value = as.vector(table(T$ExpT1))/nrow(T))
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g3<-ggplot(df, aes(x="", y=value, fill=group))+
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geom_bar(width = 1, stat = "identity")+
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coord_polar("y", start=0)+
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theme(legend.position="bottom")
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grid.arrange(g3,g1,g2,ncol=3)
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g1<-ggplot(T, aes(x=T$ExpT2))+
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geom_bar()+
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ylab("Effectifs")+ggtitle("Effectifs")
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g2<-ggplot(T, aes(x = T$ExpT2)) +
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geom_bar(aes(y = (..count..)/sum(..count..)))+ylab("")+ggtitle("Frequences")
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df <- data.frame(group = levels(T$ExpT2),
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value = as.vector(table(T$ExpT2))/nrow(T))
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g3<-ggplot(df, aes(x="", y=value, fill=group))+
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geom_bar(width = 1, stat = "identity")+
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coord_polar("y", start=0)+
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theme(legend.position="bottom")
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grid.arrange(g3,g1,g2,ncol=3)
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g1<-ggplot(T, aes(x=T$ExpT3))+
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geom_bar()+
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ylab("Effectifs")+ggtitle("Effectifs")
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g2<-ggplot(T, aes(x = T$ExpT3)) +
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geom_bar(aes(y = (..count..)/sum(..count..)))+ylab("")+ggtitle("Frequences")
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df <- data.frame(group = levels(T$ExpT3),
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value = as.vector(table(T$ExpT3))/nrow(T))
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g3<-ggplot(df, aes(x="", y=value, fill=group))+
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geom_bar(width = 1, stat = "identity")+
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coord_polar("y", start=0)+
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theme(legend.position="bottom")
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grid.arrange(g3,g1,g2,ncol=3)
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#apply(T[-c(37:39)],2,function(col){
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# which(T == col)
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#hist(col, main = paste("Histogram of", colnames(T)[which(T == col)[2]]),
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# xlab = "Values", col = "lightblue", border = "black")
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#})
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T_long = melt(T[-c(37:39)])
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ggplot(T_long, aes(x = value)) +
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geom_histogram(binwidth = 1, fill = "blue", color = "black", alpha = 0.7) +
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facet_wrap(~variable,scales = "free",ncol=6) +
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labs(title = "Histograms for Each Column", x = "Values", y = "Frequency")
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ggplot(melt(T[1:18]),aes(x=variable,y=value))+
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geom_boxplot()+ theme(axis.text.x = element_text(angle=90, vjust = 0.5, hjust = 1))
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ggplot(melt(T[19:36]),aes(x=variable,y=value))+
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geom_boxplot() + theme(axis.text.x = element_text(angle=90, vjust = 0.5, hjust = 1))
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cr = cor(T[-c(37:39)])
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corrplot(cr,method="ellipse", type="lower", bg = "lightgrey")
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cr = cor(T[-c(37:39)])
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corrplot(cr,method="ellipse", type="lower", bg = "lightgrey")
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library(knitr)
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## Global options
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options(max.print="75")
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opts_chunk$set(echo=TRUE,
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cache=FALSE,
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prompt=FALSE,
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tidy=TRUE,
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comment=NA,
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message=FALSE,
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warning=FALSE,
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class.source="badCode")
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opts_knit$set(width=75)
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library(corrplot)
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library(ggplot2)
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library(gridExtra)
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library(forcats)
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library(reshape2)
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library(BioStatR)
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wine <- read.table("wine.txt",header=TRUE)
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head(wine)
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# A COMPLETER
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dim(wine)
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nrow(wine)
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ncol(wine)
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# A COMPLETER
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is.data.frame(wine)
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attributes(wine)
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# A COMPLETER
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str(wine)
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wine$Qualite<-as.factor(wine$Qualite)
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wine$Type<-factor(wine$Type,labels=c("blanc","rouge"))
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head(wine)
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summary(wine)
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gEx <- ggplot(data=wine)
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summary(gEx)
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names(gEx)
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gEx$layers
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ggplot(data=wine,aes(x=Densite,y=Alcool))+
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geom_point()
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ggplot(data=wine)+
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geom_point(aes(x=Densite,y=Alcool,color=Type))
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ggplot(data=wine)+
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geom_point(aes(x=Densite,y=Alcool),color="blue")
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ggplot(data=wine)+
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geom_violin(aes(x=Qualite,y=Alcool))+
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geom_point(aes(x=Qualite,y=Alcool), col = "blue", alpha = 0.2,position="jitter")
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ggplot(data=wine)+
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geom_point(aes(x=Alcool,y=Densite,size=AcidVol,color=Type))+
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scale_size("Acide vol.", range = c(0,1.5),breaks=seq(0,1.5,0.2)) +
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scale_x_continuous("Alcool",limits=c(8,16)) +
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scale_y_continuous("Densité",limits=c(0.985,1.01))
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# A COMPLETER
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t <- table(wine$Type)
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summary(wine$Type)
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levels(wine$Type)
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# A COMPLETER
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mean(wine$Alcool)
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median(wine$Alcool)
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var(wine$Alcool)
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sd(wine$Alcool)
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e <- range(wine$Alcool)
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e
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e[2]-e[1]
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# A COMPLETER²²
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summary(wine$Alcool)
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q1 = quantile(wine$Alcool,0.25)
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q2 = quantile(wine$Alcool,0.75)
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IQR(wine$Alcool)
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q2-q1
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Lp = q2+1.5*(q2-q1)
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Lm = q1-1.5*(q2-q1)
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print("valeurs adjacentes :")
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Lp
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Lm
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# A COMPLETER
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H <- hist(wine$Alcool)
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H
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ggplot(wine,aes(y=Alcool))+geom_boxplot()
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# A COMPLETER
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B<-boxplot(wine$Alcool)
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quantile(wine$Alcool)
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B
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# A COMPLETER
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cr = cor(wine[c("Alcool","AcidVol","AcidCitr","SO2lbr","SO2tot","Densite")])
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corrplot(cr,method="number", type="lower", bg = "lightgrey",)
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# A COMPLETER
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ggplot(wine,aes(x=wine$Alcool,y=wine$Densite))+geom_point()+geom_smooth(method="lm")
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# A COMPLETER
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ggplot(wine,aes(x=wine$Type,y=wine$Alcool))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Qualite,y=wine$Alcool))+
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geom_boxplot()
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# A COMPLETER
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ggplot(wine,aes(x=wine$Type,y=wine$Densite))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Qualite,y=wine$Densite))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Type,y=wine$AcidVol))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Qualite,y=wine$AcidVol))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Type,y=wine$SO2lbr))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Qualite,y=wine$SO2lbr))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Type,y=wine$AcidCitr))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Qualite,y=wine$AcidCitr))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Type,y=wine$SO2tot))+
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geom_boxplot()
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ggplot(wine,aes(x=wine$Qualite,y=wine$SO2tot))+
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geom_boxplot()
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# A COMPLETER
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eta2(wine$AcidVol,wine$Type)
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eta2(wine$AcidCitr,wine$Type)
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eta2(wine$SO2lbr,wine$Type)
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eta2(wine$SO2tot,wine$Type)
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eta2(wine$Densite,wine$Type)
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eta2(wine$Alcool,wine$Type)
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# A COMPLETER
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t= table(wine$Type,wine$Qualite)
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addmargins(t)
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# A COMPLETER
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mosaicplot(prop.table(t))
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g1<-ggplot(wine, aes(x=Type))+
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geom_bar()+
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ylab("Effectifs")+ggtitle("Effectifs")
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g2<-ggplot(wine, aes(x = Type)) +
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geom_bar(aes(y = (..count..)/sum(..count..)))+ylab("")+ggtitle("Frequences")
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df <- data.frame(group = levels(wine$Type),
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value = as.vector(table(wine$Type))/nrow(wine))
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g3<-ggplot(df, aes(x="", y=value, fill=group))+
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geom_bar(width = 1, stat = "identity")+
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coord_polar("y", start=0)+
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theme(legend.position="bottom")
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grid.arrange(g3,g1,g2,ncol=3)
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# A COMPLETER
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t <- table(wine$Type)
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summary(wine$Type)
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levels(wine$Type)
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ggplot(wine,aes(x=wine$Type,y=wine$Alcool))+
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geom_boxplot()
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T$T2_6H_R2
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T$T3_6H_R1
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# traitement 1 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(wine,aes(x=T$T1_6H_R1,y=T$ExpT1))+
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geom_boxplot()
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T$T2_6H_R2
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T$T3_6H_R1
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# traitement 1 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(wine,aes(y=T$T1_6H_R1,x=T$ExpT1))+
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geom_boxplot()
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T$T2_6H_R2
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T$T3_6H_R1
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# traitement 1 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(wine,aes(y=T$T1_6H_R1,x=levels(T$ExpT1)))+
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geom_boxplot()
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T$T2_6H_R2
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T$T3_6H_R1
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# traitement 1 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(T,aes(y=T$T1_6H_R1,x=T$ExpT1))+
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geom_boxplot()
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ggplot(wine,aes(x=T$T1_6H_R2,y=T$ExpT1))+
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geom_boxplot()
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T$T2_6H_R2
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T$T3_6H_R1
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# traitement 1 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(T,aes(y=T$T1_6H_R1,x=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(x=T$T1_6H_R2,y=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(x=T$T1_6H_R1,y=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(x=T$T1_6H_R2,y=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(x=T$T1_6H_R1,y=T$ExpT3))+
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geom_boxplot()
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ggplot(T,aes(x=T$T1_6H_R2,y=T$ExpT3))+
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geom_boxplot()
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# traitement 2 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(T,aes(x=T$T2_6H_R1,y=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(x=T$T2_6H_R2,y=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(x=T$T2_6H_R1,y=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(x=T$T2_6H_R2,y=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(x=T$T2_6H_R1,y=T$ExpT3))+
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geom_boxplot()
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ggplot(T,aes(x=T$T2_6H_R2,y=T$ExpT3))+
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geom_boxplot()
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# traitement 2 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(T,aes(x=T$T3_6H_R1,y=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(x=T$T3_6H_R2,y=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(x=T$T3_6H_R1,y=T$ExpT3))+
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geom_boxplot()
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ggplot(T,aes(x=T$T3_6H_R2,y=T$ExpT3))+
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geom_boxplot()
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ggplot(T,aes(x=T$T3_6H_R1,y=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(x=T$T3_6H_R2,y=T$ExpT2))+
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geom_boxplot()
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T$T2_6H_R2
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T$T3_6H_R1
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# traitement 1 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(T,aes(y=T$T1_6H_R1,x=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(y=T$T1_6H_R2,x=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(y=T$T1_6H_R1,x=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(y=T$T1_6H_R2,x=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(y=T$T1_6H_R1,x=T$ExpT3))+
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geom_boxplot()
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ggplot(T,aes(y=T$T1_6H_R2,x=T$ExpT3))+
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geom_boxplot()
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# traitement 2 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(T,aes(y=T$T2_6H_R1,x=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(y=T$T2_6H_R2,x=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(y=T$T2_6H_R1,x=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(y=T$T2_6H_R2,x=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(y=T$T2_6H_R1,x=T$ExpT3))+
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geom_boxplot()
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ggplot(T,aes(y=T$T2_6H_R2,x=T$ExpT3))+
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geom_boxplot()
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# traitement 2 corrélation avec l'expression des genes du T1 T2 et T3
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ggplot(T,aes(y=T$T3_6H_R1,x=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(y=T$T3_6H_R2,x=T$ExpT1))+
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geom_boxplot()
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ggplot(T,aes(y=T$T3_6H_R1,x=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(y=T$T3_6H_R2,x=T$ExpT2))+
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geom_boxplot()
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ggplot(T,aes(y=T$T3_6H_R1,x=T$ExpT3))+
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geom_boxplot()
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ggplot(T,aes(y=T$T3_6H_R2,x=T$ExpT3))+
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geom_boxplot()
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