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JAR_Analyses.R
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JAR_Analyses.R
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## JAR Analysis (Consumers)
kids_clustered <- read_csv("FILEPATH")
adults_clustered <- read_csv("FILEPATH")
copy.clipboard = function (x){
clip <- pipe("pbcopy", "w")
write.table(cbind (rownames(x), x) , file=clip, sep = '\t', row.names = FALSE)
close(clip)}
jar.percents <- function (attribute, clustered.data){
jar.attribute = clustered.data[,which( names (clustered.data) == attribute)]
if (length(table(jar.attribute)) == 5){
jar.table <- table (clustered.data$Product, jar.attribute %>% pull(attribute))
table_sums <- cbind ("Too little" = jar.table[,1] + jar.table[,2], JAR =
jar.table[,3], "Too much" = jar.table[,4] + jar.table[,5])
jar.table <- as.data.frame ( t ( round ( table_sums / rowSums (table_sums), 2 ) ) )
jar.table$attribute <- rep ( attribute, 3)
return (jar.table) }
else {
jar.table <- table (clustered.data$Product, jar.attribute %>% pull(attribute))
table_sums <- cbind ("Too little" = jar.table[,1], JAR =
jar.table[,2], "Too much" = jar.table[,3])
jar.table <- as.data.frame ( t ( round ( table_sums / rowSums (table_sums), 2 ) ) )
jar.table$attribute <- rep ( attribute, 3)
return (jar.table)
}
}
jar.percents(colnames(adults_clustered)[9], adults_clustered)
# JAR Percentages
JAR.list <- c("JAR.sweetness", "JAR.sourness", "JAR.size", "JAR.color", "JAR.juiciness")
JAR.list <- colnames(adults_clustered)[c(4,8,9,10,11)]
copy.clipboard ( do.call (rbind, lapply (JAR.list, jar.percents, clustered.data = adults_clustered ) ) )
jar.percents("JAR.sweetness", kids_clustered)
chisq.test ( table ( adults_clustered$Product, adults_clustered$`JAR Size`) )
# JAR attributes by cluster
jar_att_by_cluster <- function (clustered.data, attribute, print.sig = TRUE){
if (print.sig == TRUE ) {print (chisq.test(table (clustered.data$Cluster, clustered.data[,attribute])))}
jar.table <- table (clustered.data$Cluster, clustered.data[,attribute]) / rowSums ( table (clustered.data$Cluster, clustered.data[,attribute] ))
jar.table <- as.data.frame ( cbind ( "Too little" = jar.table[,1] + jar.table[,2], "JAR" = jar.table[,3], "Too much" = jar.table[,4] + jar.table[,5]) )
jar.table2 <- cbind (round (jar.table,2), attribute)
return (jar.table2)
}
do.call ( rbind, lapply (JAR.list, jar_att_by_cluster, clustered.data = clustered.data, print.sig = FALSE) ) %>% copy.clipboard
chisq.test(table (clustered.data$Cluster, clustered.data$`JAR Sourness`))
jar_penalty <- function (df, attribute){
bound <- bind_cols(
df %>% filter(get(attribute) > 3) %>% summarise(mean_high = mean(`Overall Liking`)),
df %>% filter(get(attribute) == 3) %>% summarise(jar= mean(`Overall Liking`)),
df %>% filter(get(attribute) < 3) %>% summarise(meanlow = mean(`Overall Liking`))
)
return (bound %>% mutate (high_diff = mean_high - jar,
low_diff = meanlow-jar, attribute = attribute) ) }
jar_penalty(adults_clustered, "JAR Sweetness")
jar_penalty(adults_clustered, "JAR Sweetness")
sapply ( colnames(adults_clustered)[c(4,8,9,10,11)], jar_penalty, df = adults_clustered) %>% copy.clipboard()
jar_penalty_kids <- function (df, attribute){
bound <- bind_cols(
df %>% filter(get(attribute) > 2) %>% summarise(mean_high = mean(`Overall Liking`)),
df %>% filter(get(attribute) == 2) %>% summarise(jar= mean(`Overall Liking`)),
df %>% filter(get(attribute) < 2) %>% summarise(meanlow = mean(`Overall Liking`))
)
return (bound %>% mutate (high_diff = mean_high - jar,
low_diff = meanlow-jar, attribute = attribute) ) }
sapply ( colnames(kids_clustered)[c(4,8,9,10,11)], jar_penalty_kids, df = kids_clustered) %>% copy.clipboard()
adults_clustered %>% filter (Product == "Sanguinelli_L") %>% pull(`JAR Sourness`) %>% table()
# Extra code from before, unused in these analyses
### JAR Analyses ###
library (readr)
library (reshape2)
library(ggplot2)
# Set appropriate working directory
# Create
adults.file <- Yr1AdultsMandarinsMaster
AdultCons2 <- AdultsYr1MandarinsMaster
{
vali.table <- t ( as.matrix ( ( table (AdultCons2$Cluster, AdultCons2$JAR.Firmness ) ) ) )
vali.table / apply (vali.table, 2, sum)
cluster.usage <- table(AdultCons2$Cluster, AdultCons2[["JAR.Firmness"]])
cluster.usage2 <- t ( cluster.usage / apply ( cluster.usage,1,sum) * 100 )
cluster.split <- split (AdultsYr2MandarinsMaster, AdultsYr2MandarinsMaster$Cluster )
aggregate ( cluster.split[[1]], list (cluster.split[[1]]$Code), mean )
}
# This code needs AdultCons2 which is the master file, rounded JAR data, and cluster
jar.vector <- c("JAR Sweetness", "JAR Sourness", "JAR Firmness", "JAR Juiciness" )
######### Adults ##########
# Labels for adults and children
adult.sweet.labels <- c("Much too sweet", "Somewhat too sweet", "Just about right",
"Not quite sweet enough", "Not at all sweet enough")
adult.sour.labels <- c("Much too sour/tart", "Somewhat too sour/tart", "Just about right",
"Not quite sour/tart enough", "Not at all sour/tart enough")
adult.firm.labels <- c("Much too firm", "Somewhat too firm", "Just about right",
"Somewhat too soft", "Much too soft")
adult.juicy.labels <- c("Much too juicy", "Somewhat too juicy", "Just about right",
"Not quite juicy enough", "Not at all juicy enough")
jar.label.vector <-list ( adult.sweet.labels, adult.sour.labels, adult.firm.labels, adult.juicy.labels )
adult.sweet.labels <- c("Too sweet", "Just about right",
"Not sweet enough")
adult.sour.labels <- c("Too sour", "Just about right",
"Not sour enough")
adult.firm.labels <- c("Too firm", "Just about right",
"Not firm enough")
adult.juicy.labels <- c("Too juicy", "Just about right",
"Not juicy enough")
jar.label.vector <-list ( adult.sweet.labels, adult.sour.labels, adult.firm.labels, adult.juicy.labels )
## Adults JAR by cluster function
## Works with AdultCons2 file
jar.plot.fun <- function (JAR.att, jar.labels) {
cluster.usage <- table(AdultCons2$Cluster, AdultCons2[[JAR.att]])
cluster.usage2 <- t ( cluster.usage / apply ( cluster.usage,1,sum) * 100 )
clust.us3 <- melt ( cluster.usage2)
test.chi <- chisq.test(cluster.usage)
clust.us3$Var1 <- 6-clust.us3$Var1
jarplot <- ggplot() + geom_bar(aes(x = Var2, y = value , fill = as.factor ( Var1 ) ), data = clust.us3,
stat="identity", alpha = 0.75) +
scale_fill_grey(labels = jar.labels,
name = "") +
ylab (label = "% used") + xlab(label = "Cluster") +
geom_text(aes (y = value, label = paste0( round ( value),"%") , x=Var2 ), size = 3, position = position_stack(vjust = 0.5), data=clust.us3) +
#annotate("text", x = Inf, y = Inf, label = sprintf ( "ChiSquare P Value: %s",round ( test.chi$p.value,4 ) ), vjust=1, hjust=1, cex = 3 ) +
theme_minimal() + theme ( panel.grid.major = element_blank(), panel.grid.minor = element_blank() )
print ( chisq.test(cluster.usage))
return ( jarplot )
}
for ( i in 1:4 ){
jarplot <- jar.plot.fun ( jar.vector[i], jar.label.vector[[i]] )
ggsave(sprintf ("Figure___Adults%sClusters.jpg", jar.vector[i]), plot = jarplot, device = "jpeg", width = 15)
}
jar.plot.fun('JAR Sweetness', adult.sweet.labels)
## Adults JAR all products function
adult.jar.all.func <- function (AdultCons2, jar.att, adult.jar.labels) {
table.prods <- table (AdultCons2[[jar.att]], AdultCons2$Code)
table.prods2 <- table.prods / apply ( table.prods,2, sum) * 100
melted2 <- melt (table.prods2)
test.chi <- chisq.test(table.prods2)
jarplot <- ggplot() + geom_bar(aes(y = value, x = Var2, fill = as.factor ( -Var1 )), data = melted2,
stat="identity", alpha = 0.75) +
scale_fill_grey(labels = adult.jar.labels,
name = "JAR Rating") +
ylab (label = "% used") + xlab(label = "Product") +
geom_text(aes (y = value, label = paste0( round ( value),"%") , x=Var2 ), size = 3, position = position_stack(vjust = 0.5), data=melted2) +
#annotate("text", x = Inf, y = Inf, label = sprintf ( "ChiSquare P Value: %s",round ( test.chi$p.value,4 ) ), vjust=1, hjust=1, cex = 3 ) +
theme_minimal() + theme ( panel.grid.major = element_blank(), panel.grid.minor = element_blank() )
return (jarplot)
}
for ( i in 1:4 ){
jarplot <- adult.jar.all.func (AdultCons2, jar.vector[i], jar.label.vector[[i]] )
ggsave(sprintf ("Figure___Adults%s.jpg", jar.vector[i]), plot = jarplot, device = "jpeg", width = 15)
}
adult.jar.all.func(AdultCons2, "JAR Sweetness", adult.sweet.labels)
adult.jar.all.func(AdultCons2, "JAR Sourness", adult.sour.labels)
adult.jar.all.func(AdultCons2, "JAR.Firmness", adult.firm.labels)
adult.jar.all.func(AdultCons2, "JAR.Juiciness", adult.juicy.labels)
##### Kids #######
kids.file <- KidsYr1MandarinsMaster
KidsCons2 <- kids.file
#KidsCons2$JAR.Juiciness[which ( KidsCons2$JAR.Juiciness == 0 ) ] <- 1
jar.vector <- c("JAR Sweetness", "JAR Sourness", "JAR Firmness", "JAR Juiciness" )
# Kids labels in order
kids.sweet.labels <- c("Too sweet", "Just right",
"Not sweet enough")
kids.sour.labels <-c("Too sour", "Just right",
"Not sour enough")
kids.firm.labels <-c("Too firm", "Just right",
"Too soft")
kids.juicy.labels <-c("Too juicy", "Just right",
"Not juicy enough")
kids.jar.label.vector <- list ( kids.sweet.labels, kids.sour.labels, kids.firm.labels, kids.juicy.labels )
## Kids JAR by cluster function
jar.plot.fun.kids <- function (JAR.att, jar.labels) {
cluster.usage <- table(KidsCons2$Cluster, KidsCons2[[JAR.att]])
cluster.usage2 <- t ( cluster.usage / apply ( cluster.usage,1,sum) * 100 )
clust.us3 <- melt ( cluster.usage2)
test.chi <- chisq.test(cluster.usage)
clust.us3$Var1 <- 3-clust.us3$Var1
jarplot <- ggplot() + geom_bar(aes(x = as.factor ( Var2 ), y = value , fill = as.factor ( Var1 ) ), data = clust.us3,
stat="identity", alpha = 0.75) +
scale_fill_hue(labels = jar.labels,
name = "") +
ylab (label = "% used") + xlab(label = "Cluster") +
geom_text(aes (y = value, label = paste0( round ( value),"%") , x=Var2 ), size = 3, position = position_stack(vjust = 0.5), data=clust.us3) +
annotate("text", x = Inf, y = Inf, label = sprintf ( "ChiSquare P Value: %s",round ( test.chi$p.value,4 ) ), vjust=1, hjust=1, cex = 3 ) +
theme_minimal() + theme ( panel.grid.major = element_blank(), panel.grid.minor = element_blank() )
return ( jarplot )
}
jar.plot.fun.kids (jar.vector[1], kids.jar.label.vector[[1]] )
for ( i in 1:4 ){
jarplot <- jar.plot.fun.kids (jar.vector[i], kids.jar.label.vector[[i]] )
ggsave(sprintf ("Figure___Kids%sClusters.jpg", jar.vector[i]), plot = jarplot, device = "jpeg", width = 8)
}
## Kids JAR by product function
kids.jar.all.func <- function (KidsCons2, jar.att, kids.jar.label) {
table.prods <- table (KidsCons2[[jar.att]], KidsCons2$Code)
table.prods2 <- table.prods / apply ( table.prods,2, sum) * 100
melted2 <- melt (table.prods2)
test.chi <- chisq.test(table.prods2)
jarplot <- ggplot() + geom_bar(aes(y = value, x = as.factor ( Var2) , fill = as.factor ( -Var1 )), data = melted2,
stat="identity", alpha = 0.75) +
scale_fill_hue(labels = kids.jar.label,
name = "JAR Rating") +
ylab (label = "% used") + xlab(label = "Product") +
geom_text(aes (y = value, label = paste0( round ( value),"%") , x=Var2 ), size = 3, position = position_stack(vjust = 0.5), data=melted2) +
#annotate("text", x = Inf, y = Inf, label = sprintf ( "ChiSquare P Value: %s",round ( test.chi$p.value,4 ) ), vjust=1, hjust=1, cex = 3 ) +
theme_minimal() + theme ( panel.grid.major = element_blank(), panel.grid.minor = element_blank() )
jarplot
return (jarplot)
}
for ( i in 1:4 ){
jarplot <- kids.jar.all.func (KidsCons2, jar.vector[i], kids.jar.label.vector[[i]] )
ggsave(sprintf ("Figure___Kids%sProducts.jpg", jar.vector[i]), plot = jarplot, device = "jpeg", width = 10)
}
kids.jar.all.func (KidsCons2, jar.vector[1], kids.jar.label.vector[[1]])
##### Penalty Analysis #####
## Penalty Analysis ##
# Round the data files to do penalty analysis cleanly
# Now starting with cluster.merged.full
master.file <- AdultsYr1MandarinsMaster
overall.liking.column <- 6
jar.start <- 9
jar.end <- jar.start + 3
adults.jar.round <- cbind ( master.file[c(1:2, overall.liking.column)],
round(master.file[jar.start:jar.end], 0 ), master.file["Cluster"])
write.csv ( adults.jar.round, file = "adults.jar.round.csv", row.names = FALSE)
kids.jar.round <- cbind ( master.file[c(1:2,overall.liking.column)], round(master.file[jar.start:jar.end], 0 ))
# Fix the 0 in the row 60
kids.jar.round[60,7] <- 1
write.csv ( kids.jar.round, file = "kids.jar.round.csv", row.names = FALSE)
# Take mean by JAR rating
# JAR Sweetness is JAR Attribute
# 7 is the overall liking column
#file <- AdultsYr2MandarinsMaster
#attribute <- "JAR Sweetness"
#liking.col <- 6
## Adults Penalty Analysis
# jar.col.num is the column number that is the first jar
# liking.col is the column used to compare for penalty analysis
# file is the masters file
{
one.att.penalty <- function ( jar.col.num, file, liking.col){
attribute = names(file)[jar.col.num]
too.little <- file[which (( file [,jar.col.num] ) == 1 | file [,jar.col.num] == 2 ),]
just.right <- file[which ( file [,jar.col.num] == 3),]
too.much <- file[which (( file [,jar.col.num] ) == 4 | file [,jar.col.num] == 5 ),]
tl.percent <- nrow ( too.little) / nrow(file) * 100
jr.percent <- nrow ( just.right) / nrow(file) * 100
tm.percent <- nrow ( too.much) / nrow(file) * 100
jr.score <- mean ( just.right[,liking.col] )
tl.penalty <- mean ( just.right[,liking.col] ) - mean ( too.little[,liking.col])
tm.penalty <- mean ( just.right[,liking.col] ) - mean ( too.much[,liking.col])
jar.pen.mat <- matrix (data = c( tl.percent, tm.percent, tl.penalty, tm.penalty, attribute, attribute), nrow = 2, ncol = 3,
dimnames = list ( c("Too little", "Too much"), c("% Used", "Penalty", "Attribute") ) )
jar.pen.mat <- as.data.frame ( jar.pen.mat)
overall.by.jar <- aggregate ( file, list (file[[jar.col.num]]), mean) [,4]
# Extract the penalties
penalty <- overall.by.jar [3] - overall.by.jar
# Remove JAR
no.no.pen <- penalty [!penalty == 0]
# Get the proportions
props <- ( table (file[attribute]) / length(file[,3]) ) * 100
# Extract the JAR name
attribute.name <- tail(strsplit(attribute,split=" ")[[1]],1)
# Add the proportions
penalty.matrix <- rbind ( "props" = props [c(1,2,4,5)], no.no.pen )
one.attribute <- data.frame ( t(penalty.matrix), "Attribute" = rep (attribute.name, 4) )
# Create the JAR Levels
jar.levels <- c("LL", "L", "H", "HH")
penalty.matrix <- cbind (one.attribute, jar.levels )
colnames ( penalty.matrix )[1:2] <- c("% Used", "Mean Drop")
return (penalty.matrix)
}
one.att.penalty (9, AdultsYr1MandarinsMaster, 6)
clust.jar.split <- split ( adults.jar.round, adults.jar.round$Cluster )
jar.list.adults <- lapply (4:7, one.att.penalty, file = clust.jar.split[[3]], liking.col = 3 )
pen.table.adults <- do.call ( rbind, jar.list.adults)
pen.table.adults$category <- paste0 ( pen.table.adults[,4], pen.table.adults[,3] )
shapes = c( 5:8 )
adults.penalty.plot <- ggplot () +
geom_point(data = pen.table.adults, aes (x = pen.table.adults$`% Used`, y = pen.table.adults$`Mean Drop`, shape = Attribute)) +
geom_text_repel (aes(label = pen.table.adults$category, x = pen.table.adults$`% Used`, y = pen.table.adults$`Mean Drop`)) +
scale_shape_manual(values = shapes) +
labs (x = "% Used", y = "Mean Drop" ) +
theme_bw()
ggsave("Figure___AdultsPenaltyAnalysis.jpg", plot =adults.penalty.plot, device = "jpeg", width = 10)
}
one.att.penalty.kids <- function ( jar.col.num, file, liking.col){
#jar.col.num <- 7
#file <- as.data.frame ( kids.jar.round )
#liking.col <- 3
attribute <- names(file)[jar.col.num]
overall.by.jar <- aggregate ( file, list (file[[jar.col.num]]), mean) [,4]
# Extract the penalties
penalty <- overall.by.jar [2] - overall.by.jar
# Remove JAR
no.no.pen <- penalty [!penalty == 0]
# Get the proportions
props<- ( table (file[attribute]) / length(file[,3]) ) * 100
# Extract the JAR name
attribute.name <- tail(strsplit(attribute,split=" ")[[1]],1)
# Add the proportions
penalty.matrix <- rbind ( "props" = props [c(1,3)], no.no.pen )
one.attribute <- data.frame ( t(penalty.matrix), "Attribute" = rep (attribute.name, 2) )
# Create the JAR Levels
jar.levels <- c("L", "H")
penalty.matrix <- cbind (one.attribute, jar.levels )
colnames ( penalty.matrix )[1:2] <- c("% Used", "Mean Drop")
return (penalty.matrix)
}
one.att.penalty.kids(4, kids_jar_round, 3)
jar.list <- lapply (4:7, one.att.penalty.kids,file = kids_jar_round, liking.col = 3 )
pen.table <- do.call ( "rbind", jar.list)
pen.table$`% Used` <- pen.table$`% Used`/1000
pen.table$category <- paste0 ( pen.table[,4], pen.table[,3] )
shapes = c( 5:8 )
kids.penalty.plot <- ggplot () +
geom_point(data = pen.table, aes (x = pen.table$`% Used`, y = pen.table$`Mean Drop`, shape = Attribute)) +
geom_text_repel (aes(label = pen.table$category, x = pen.table$`% Used`, y = pen.table$`Mean Drop`), cex = 8) +
scale_shape_manual(values = shapes) +
labs (x = "% Used", y = "Mean Drop") +
theme_bw() +
xlim(c(0,30)) +
ylim(c(0,2.4)) +
theme(axis.title.x = element_text(size = 50 ),
axis.title.y = element_text(size = 50 ),
axis.text.x = element_text(size = 25 ),
axis.text.y = element_text(size = 25 ))
kids.penalty.plot
ggsave("Figure___KidsPenaltyAnalysis.jpg", plot = kids.penalty.plot, device = "jpeg", width = 10)