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Analyse JAR (2).R
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Analyse JAR (2).R
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setwd("C:/Users/aluc/OneDrive - STRATEGIR/Bureau/Conferences Congres/18 - SPISE Vietnam/Workshop/analyse JAR/AL")
######################
library(readxl)
library(ade4)
library(FactoMineR)
library(ggplot2)
library(SensoMineR)
library(stringr)
library(dplyr)
library(gridExtra)
library(cowplot)
######################
## FONCTION DISTRIBUTION DES MODALITES JAR
# distrib_attr <- function(jdd,attr,session=NULL){ #colonne de l'attribut, colonne de la variable session
# if(!is.null(session)){
# nb_session <- length(levels(jdd$Session))
# jdd_attr <- as.data.frame(jdd[,c(attr,session)])
# tab <- as.data.frame(table(jdd_attr[,1],jdd_attr[,2]))
# colnames(tab) <- c("mod","session","count")
# ggplot(tab,aes(x=mod,y=count,fill=session))+
# geom_bar(stat="identity",position=position_dodge())+
# theme_minimal()+
# ggtitle(str_glue("Distribution of modalities for the attribute {colnames(jdd)[attr]}"))+
# xlab("Modalities")+
# ylab("Number of occurrences")+
# scale_fill_manual(values=c("orange","blue"))
#
# }
# else{
# jdd_attr <- as.data.frame(jdd[,attr])
# tab <- as.data.frame(table(jdd_attr))
# colnames(tab) <- c("mod","count")
# ggplot(tab,aes(x=mod,y=count))+
# geom_bar(stat="identity",fill="orange")+
# theme_minimal()+
# ggtitle(str_glue("Distribution of modalities for the attribute {colnames(jdd)[attr]}"))+
# xlab("Modalities")+
# ylab("Number of occurrences")
# }
# }
######################
## FONCTION DISTRIBUTION DES MODALITES JAR PAR PRODUIT
install.packages("gridExtra")
install.packages("cowplot")
library("gridExtra")
library("cowplot")
# distrib_attr_pdt <- function(jdd,attr,pdt,session=NULL){ #colonne de l'attribut, colonne de la variable session
# if(!is.null(session)){
# nb_session <- length(levels(jdd$Session))
# jdd_attr <- as.data.frame(jdd[,c(session,pdt,attr)])
# tab <- as.data.frame(table(jdd_attr[,1],jdd_attr[,2],jdd_attr[,3]))
# colnames(tab) <- c("session","pdt","mod","count")
# list_graph <- list()
# a=1
# for (j in unique(jdd[,pdt])){
# gg <- ggplot(tab[which(tab$pdt==j),-2],aes(x=mod,y=count,fill=session))+
# geom_bar(stat="identity",position=position_dodge())+
# theme_minimal()+
# ggtitle(str_glue("Distribution of modalities for the attribute {colnames(jdd)[attr]}"))+
# xlab("Modalities")+
# ylab("Number of occurrences")+
# scale_fill_manual(values=c("orange","blue"))+
# ylim(0,max(tab$count))
# list_graph[[a]] <- gg
# a=a+1
# }
# if(length(unique(jdd[,pdt]))%%2==0){
# plot_grid(plotlist = list_graph,labels=levels(jdd[,pdt]),ncol = round(length(unique(jdd[,pdt]))/2,0), nrow=2)
# }
# else{
# plot_grid(plotlist = list_graph,labels=levels(jdd[,pdt]),ncol = round(length(unique(jdd[,pdt]))/2,0)+1, nrow=2)
# }
# }
# else{
# jdd_attr <- as.data.frame(jdd[,c(pdt,attr)])
# tab <- as.data.frame(table(jdd_attr[,1],jdd_attr[,2]))
# colnames(tab) <- c("pdt","mod","count")
# list_graph <- list()
# a=1
# for (j in unique(jdd[,pdt])){
# gg <- ggplot(tab[which(tab$pdt==j),-1],aes(x=mod,y=count))+
# geom_bar(stat="identity",fill="orange")+
# theme_minimal()+
# ggtitle(str_glue("Distribution of modalities for the attribute {colnames(jdd)[attr]}"))+
# xlab("Modalities")+
# ylab("Number of occurrences")+
# ylim(0,max(tab$count))
# list_graph[[a]] <- gg
# a=a+1
# }
# if(length(unique(jdd[,pdt]))%%2==0){
# plot_grid(plotlist = list_graph,labels=levels(jdd[,pdt]),ncol = round(length(unique(jdd[,pdt]))/2,0), nrow=2)
# }
# else{
# plot_grid(plotlist = list_graph,labels=levels(jdd[,pdt]),ncol = round(length(unique(jdd[,pdt]))/2,0)+1, nrow=2)
# }
# }
# }
######################
## DPLYR : TABLEAU MULTIPLE PDT x JUGES
multiple_tab <- function(jdd,judge,product,attr,session=NULL){ #numero de colonne des variables juge et pdt, nb attribut = vecteur des colonnes attr, colonne session
if(!is.null(session)){
name_session <- levels(jdd[,session])
res <- list()
b=1
for (k in name_session){
jdd_session <- jdd[which(jdd[,session]==k),]
j = unique(jdd_session[,judge])
p = unique (jdd_session[,product])
x <- as.data.frame(matrix(0,length(p),length(attr)*length(j)))
a=1
for (i in j){
x[1:length(p),a:(a+length(attr)-1)] <- jdd_session[which(jdd_session[,judge]==i),attr]
a=a+length(attr)
}
colnames(x) <- paste(rep(colnames(jdd_session)[attr],length(j)),rep(j,each=length(attr)))
rownames(x) <- unique (jdd_session[,product])
res[[b]] <- x
b=b+1
}
}
else{
j = unique(jdd[,judge])
p = unique (jdd[,product])
res <- as.data.frame(matrix(0,length(p),length(attr)*length(j)))
a=1
for (i in j){
res[1:length(p),a:(a+length(attr)-1)] <- jdd[which(jdd[,judge]==i),attr]
a=a+length(attr)
}
colnames(res) <- paste(rep(colnames(jdd)[attr],length(j)),rep(j,each=length(attr)))
rownames(res) <- unique (jdd[,product])
}
return(res)
}
#########################################################
# Orange example #
# #
#########################################################
orange <- read.csv2("orange.csv")
summary(orange,maxsum = 8)
colnames(orange)
orange <- as.data.frame(orange)
for (j in c(1:2,4:9)) orange[,j] <- as.factor(orange[,j])
summary(orange)
for (j in 4:9) levels(orange[,j]) <- c("ne","ne","JAR","tm","tm")
summary(orange)
orange.disj <- acm.disjonctif(orange[,4:9])
summary(orange.disj)
orange.disj <- cbind(orange[,1:3],orange.disj)
colnames(orange.disj)
num_JAR <- seq(5,21,3)
num_col <- seq(4,21)
num_col <- num_col[-which(num_col%in%num_JAR)]
summary(orange.disj)
#Model 1
eq.anova <- "AovSum(Liking ~ "
for (i in num_col){
eq.anova <- paste(eq.anova,colnames(orange.disj)[i],"+", sep=" ")
}
eq.anova <- paste(eq.anova, "Juice + Consumer, data = orange.disj)")
res.aov <- eval(parse(text = eq.anova))
#Model 2
orange.disj.pdt <- orange.disj[which(orange.disj$Juice=="1JPA"),]
eq.anova <- "AovSum(Liking ~ "
for (i in num_col[1:11]){
eq.anova <- paste(eq.anova,colnames(orange.disj.pdt)[i],"+", sep=" ")
}
eq.anova <- paste(eq.anova, "Pu.tm, data = orange.disj.pdt)")
res.aov <- eval(parse(text = eq.anova))
#Multivariate
summary(orange)
orange$Liking <- as.factor(orange$Liking)
res.mca <- MCA(orange[,-c(1:2)], quali.sup = 1, graph = F)
plot.MCA(res.mca, invisible = c("ind"))
### Facteurs expérimentaux en 3 colonnes
fact_ex <- data.frame(matrix(ncol = 3, nrow = dim(orange)[1]))
colnames(fact_ex) <- c("Brand", "Pulpe", "Temperature")
for (i in 1:dim(orange)[1]){
pdt <- orange$Juice[i]
pdt_split <- str_split(pdt,"")[[1]]
if(pdt_split[2]=="J"){
fact_ex[i,1] <- "Jafaden"
}
else{
fact_ex[i,1] <- "Tropicana"
}
if(pdt_split[3]=="P"){
fact_ex[i,2] <- "With pulpe"
}
else{
fact_ex[i,2] <- "Without pulpe"
}
if(pdt_split[4]=="A"){
fact_ex[i,3] <- "Ambient"
}
else{
fact_ex[i,3] <- "Refrigerated"
}
}
orange_fact <- cbind(orange,fact_ex)
summary(orange_fact)
for (j in c(3,9:12)) orange_fact[,j] <- as.factor(orange_fact[,j])
res.mca <- MCA(orange_fact[,-c(1:2)], quali.sup = c(1,8:10))
## Récupération des modalité correspondant aux chaines de caractère que l'on souhaite représenter dans l'ACM
mod <- "JAR"
all_attr <- rownames(res.mca$var$coord)
mod_select <- all_attr[which(str_detect(all_attr,mod))]
res.mca <- MCA(orange[,-c(1:2)], quali.sup = 1, graph = F)
plot.MCA(res.mca, invisible = c("ind"), selectMod = mod_select)
#SensoMineR
?JAR
summary(orange)
colnames(orange)
for (j in 4:9) levels(orange[,j]) <- c(paste(colnames(orange)[j],levels(orange[,j])[1],sep="_"),"JAR",paste(colnames(orange)[j],levels(orange[,j])[3],sep="_"))
summary(orange)
res.jar.orange <- JAR(orange,col.p = 2, col.j = 1, col.pref = 3, jarlevel = "JAR")
names(res.jar.orange)
res.jar.orange$Frequency
res.jar.orange$penalty2
# orange.1JPA <- orange[orange$Juice=="1JPA",]
# summary(orange.1JPA)
# 16/(21+69+16)*100
plot.JAR(res.jar.orange, name.prod = "1JPA", model = 2)
## distribution modalités JAR
attr = 5
orange_attr <- as.data.frame(orange[,attr])
tab <- as.data.frame(table(orange_attr))
colnames(tab) <- c("mod","count")
ggplot(tab,aes(x=mod,y=count))+
geom_bar(stat="identity",fill="orange")+
theme_minimal()+
ggtitle(str_glue("Distribution of modalities for the attribute {colnames(orange)[attr]}"))+
xlab("Modalities")+
ylab("Number of occurrences")
## distribution modalité JAR par produit
attr = 7
pdt = 2
orange_attr <- as.data.frame(orange[,c(pdt,attr)])
tab <- as.data.frame(table(orange_attr[,1],orange_attr[,2]))
colnames(tab) <- c("pdt","mod","count")
list_graph <- list()
a=1
for (j in unique(orange[,pdt])){
gg <- ggplot(tab[which(tab$pdt==j),-1],aes(x=mod,y=count))+
geom_bar(stat="identity",fill="orange")+
theme_minimal()+
ggtitle(str_glue("Distribution of attribute {colnames(orange)[attr]} for product {j}"))+
xlab("Modalities")+
ylab("Number of occurrences")+
ylim(0,max(tab$count))
list_graph[[a]] <- gg
a=a+1
}
if(length(unique(orange[,pdt]))%%2==0){
plot_grid(plotlist = list_graph,ncol = round(length(unique(orange[,pdt]))/2,0), nrow=2)
} else{
plot_grid(plotlist = list_graph,labels=levels(orange[,pdt]),ncol = round(length(unique(orange[,pdt]))/2,0)+1, nrow=2)
}
## tableau multiple
tab_mul <- multiple_tab(orange,1,2,c(4:9))
#########################################################
# Goji example #
# #
#########################################################
#Analyse du jeu de données
goji <- read.csv2("goji.csv")
summary(goji)
colnames(goji)
goji <- as.data.frame(goji)
for (j in 1:10) goji[,j] <- as.factor(goji[,j])
summary(goji)
goji_jar <- goji[,-c(2,3,12)]
levels(goji_jar$Light_dark) <- c("JAR","t.dark","t.light","t.dark","t.light")
levels(goji_jar$Intensity) <- c("JAR","n.e.intense","n.e.intense","t.intense","t.intense")
levels(goji_jar$Sweet) <- c("JAR","n.e.sweet","n.e.sweet","t.sweet","t.sweet")
levels(goji_jar$Acidity) <- c("JAR","n.e.acid","n.e.acid","t.acid","t.acid")
levels(goji_jar$Bitterness) <- c("JAR","n.e.bitter","n.e.bitter","t.bitter","t.bitter")
levels(goji_jar$Fluid_thick) <- c("JAR","t.fluid","t.thick","t.fluid","t.thick")
colnames(goji_jar)
summary(goji_jar)
res.jar.goji <- JAR(goji_jar,col.p = 2, col.j = 1, col.pref = 9, jarlevel = "JAR")
res.jar.goji$Frequency
res.jar.goji$penalty2
##distribution modalité JAR
attr = 8
session = 2
nb_session <- length(levels(goji$Session))
goji_attr <- as.data.frame(goji[,c(attr,session)])
tab <- as.data.frame(table(goji_attr[,1],goji_attr[,2]))
colnames(tab) <- c("mod","session","count")
ggplot(tab,aes(x=mod,y=count,fill=session))+
geom_bar(stat="identity",position=position_dodge())+
theme_minimal()+
ggtitle(str_glue("Distribution of modalities for the attribute {colnames(goji)[attr]}"))+
xlab("Modalities")+
ylab("Number of occurrences")+
scale_fill_manual(values=c("orange","blue"))
## distribution modalité JAR par produit
attr = 8
session = 2
pdt = 4
nb_session <- length(levels(goji$Session))
goji_attr <- as.data.frame(goji[,c(session,pdt,attr)])
tab <- as.data.frame(table(goji_attr[,1],goji_attr[,2],goji_attr[,3]))
colnames(tab) <- c("session","pdt","mod","count")
list_graph <- list()
a=1
for (j in unique(goji[,pdt])){
gg <- ggplot(tab[which(tab$pdt==j),-2],aes(x=mod,y=count,fill=session))+
geom_bar(stat="identity",position=position_dodge())+
theme_minimal()+
ggtitle(str_glue("Distribution of attribute {colnames(goji)[attr]} for product {j}"))+
xlab("Modalities")+
ylab("Number of occurrences")+
scale_fill_manual(values=c("orange","blue"))+
ylim(0,max(tab$count))
list_graph[[a]] <- gg
a=a+1
}
if(length(unique(goji[,pdt]))%%2==0){
plot_grid(plotlist = list_graph,ncol = round(length(unique(goji[,pdt]))/2,0), nrow=2)
}else{
plot_grid(plotlist = list_graph,ncol = round(length(unique(goji[,pdt]))/2,0)+1, nrow=2)
}
## tableau multiple
tab_mul <- multiple_tab(goji,1,4,c(5:10),session = 2)
#########################################################
# Sennheiser example #
# #
#########################################################
sennheiser <- read.csv2("sennheiser.csv")
colnames(sennheiser)
colnames(sennheiser)[30] <- "Serious"
summary(sennheiser)
for (j in c(1:30,33:35)) sennheiser[,j] <- as.factor(sennheiser[,j])
for (j in 4:22) levels(sennheiser[,j]) <- c("No","Yes")
for (j in 23:30) sennheiser[,j] <- factor(sennheiser[,j], levels = c('r_ne', 'ne', 'JAR', 't', 'r_t'))
#CATA, from 4 to 22
j <- 4
contingency.CATA <- table(sennheiser$Sound,sennheiser[,j])
colnames(contingency.CATA) <- paste(colnames(sennheiser)[j], levels(sennheiser[,j]), sep = "_")
for (j in 5:22){
inter <- table(sennheiser$Sound,sennheiser[,j])
colnames(inter ) <- paste(colnames(sennheiser)[j], levels(sennheiser[,j]), sep = "_")
contingency.CATA <- cbind(contingency.CATA, inter)
}
A <- apply(contingency.CATA[grep("A_",rownames(contingency.CATA)),], FUN = sum, 2)
P <- apply(contingency.CATA[grep("P_",rownames(contingency.CATA)),], FUN = sum, 2)
V <- apply(contingency.CATA[grep("V_",rownames(contingency.CATA)),], FUN = sum, 2)
T120 <- apply(contingency.CATA[grep("_120",rownames(contingency.CATA)),], FUN = sum, 2)
T160 <- apply(contingency.CATA[grep("_160_",rownames(contingency.CATA)),], FUN = sum, 2)
T80 <- apply(contingency.CATA[grep("_80",rownames(contingency.CATA)),], FUN = sum, 2)
P1 <- apply(contingency.CATA[grep("0_1",rownames(contingency.CATA)),], FUN = sum, 2)
P3 <- apply(contingency.CATA[grep("0_3",rownames(contingency.CATA)),], FUN = sum, 2)
P5 <- apply(contingency.CATA[grep("0_5",rownames(contingency.CATA)),], FUN = sum, 2)
row.sup <- rbind(A,P,V,T120,T160,T80,P1,P3,P5)
row.sup <- as.data.frame(row.sup)
contingency.CATA <- rbind(contingency.CATA, row.sup)
contingency.CATA <- contingency.CATA[,seq(2,38,2)]
colnames(contingency.CATA) <- str_replace(colnames(contingency.CATA),"_Yes","")
#JAR, from 23 to 30
j <- 23
contingency.JAR <- table(sennheiser$Sound,sennheiser[,j])
colnames(contingency.JAR) <- paste(colnames(sennheiser)[j], levels(sennheiser[,j]), sep = "_")
for (j in 24:30){
inter <- table(sennheiser$Sound,sennheiser[,j])
colnames(inter) <- paste(colnames(sennheiser)[j], levels(sennheiser[,j]), sep = "_")
contingency.JAR <- cbind(contingency.JAR, inter)
}
A <- apply(contingency.JAR[grep("A_",rownames(contingency.JAR)),], FUN = sum, 2)
P <- apply(contingency.JAR[grep("P_",rownames(contingency.JAR)),], FUN = sum, 2)
V <- apply(contingency.JAR[grep("V_",rownames(contingency.JAR)),], FUN = sum, 2)
T120 <- apply(contingency.JAR[grep("_120",rownames(contingency.JAR)),], FUN = sum, 2)
T160 <- apply(contingency.JAR[grep("_160_",rownames(contingency.JAR)),], FUN = sum, 2)
T80 <- apply(contingency.JAR[grep("_80",rownames(contingency.JAR)),], FUN = sum, 2)
P1 <- apply(contingency.JAR[grep("0_1",rownames(contingency.JAR)),], FUN = sum, 2)
P3 <- apply(contingency.JAR[grep("0_3",rownames(contingency.JAR)),], FUN = sum, 2)
P5 <- apply(contingency.JAR[grep("0_5",rownames(contingency.JAR)),], FUN = sum, 2)
row.sup <- rbind(A,P,V,T120,T160,T80,P1,P3,P5)
row.sup <- as.data.frame(row.sup)
i <- 1
exp.design <- strsplit(rownames(contingency.JAR)[i], split = "_")[[1]]
for (i in 2:27){
exp.design <- rbind(exp.design, strsplit(rownames(contingency.JAR)[i], split = "_")[[1]])
}
contingency.JAR <- cbind(contingency.JAR, as.data.frame(exp.design))
contingency.JAR <- bind_rows(contingency.JAR, row.sup)
contingency.sennheiser <- cbind(contingency.CATA, contingency.JAR)
colnames(contingency.sennheiser)[60:62] <- c("Instrument","Tempo","Pitch")
contingency.sennheiser