diff --git a/Data.R b/Data.R
index 9e66561..250df58 100644
--- a/Data.R
+++ b/Data.R
@@ -20,35 +20,42 @@ coul <- brewer.pal(5, "Set2")
 #Read spreadsheet file
 grocery_entries <- read.csv(file.choose())
 
+
 #Compare cash and credit totals -Sewelam
 
 cash_credit <- cbind(grocery_entries[3], grocery_entries[8])
 sum_cash <-sum(cash_credit[which(cash_credit$paymentType=='Cash'),1])
 sum_credit <-sum(cash_credit[which(cash_credit$paymentType=='Credit'),1])
 CompCashCredit <- c(sum_cash,sum_credit)
-barplot(CompCashCredit,names.arg = c('Cash','Credit'),horiz = FALSE,col = coul)
+barplot(CompCashCredit,names.arg = c('Cash','Credit'),horiz = TRUE,col = coul)
 
 #City and Total Spent comparison -Jimmy
 city_total <- cbind(grocery_entries[3], grocery_entries[7])
 sum_cities<-aggregate(total ~city ,city_total,sum)
-pie(sum_cities$total
-        ,col = coul
-        ,labels =   sum_cities$city
-        ,main = "Cities and total spent")
+sum_citiesOrdered <- sum_cities[order(sum_cities$total),]
+barplot(height = sum_citiesOrdered$total,names.arg = sum_citiesOrdered$city,col = coul,las=2)
         
 
 
 #Compare between ages and their total spent (Youssri)
 age <- cbind(grocery_entries[6] , grocery_entries[3])
 sum_ages <- aggregate(total ~ age,age,sum)
-plot(sum_ages)
+pie(sum_ages$total,col = coul,labels = sum_ages$age,main = "Ages and total spent")
 
 
 #Distribution of spending - Abdo
 
 plot(grocery_entries$total, col = coul,type = "l", main = "spending")
 
+#All in one dashboard
+pdf(file = "graphs.pdf")
 
+plot(grocery_entries$total, col = coul,type = "l", main = "spending")
+pie(sum_ages$total,col = coul,labels = sum_ages$age,main = "Ages and total spent")
+barplot(height = sum_citiesOrdered$total,names.arg = sum_citiesOrdered$city,col = coul,las=2)
+barplot(CompCashCredit,names.arg = c('Cash','Credit'),horiz = TRUE,col = coul)
+
+dev.off()
 
 #kmeans --Yousri
 name_total_age<-cbind(grocery_entries[5],grocery_entries[3],grocery_entries[6])
@@ -59,7 +66,7 @@ final_result<-cbind(name_total_age,result$cluster)
 
 
 #Association Rules --Sewelam
-clean_data <- grocery_entries[,-5]
+clean_data <- grocery_entries[,-5] #remove names
 minsup <- as.numeric(readline("Enter minimum support: "))
 minconf <- as.numeric(readline("Enter minimum confidence: "))
 asoc_rules <- apriori(clean_data,parameter = list(supp = minsup,conf = minconf))
diff --git a/graphs.pdf b/graphs.pdf
new file mode 100644
index 0000000..914ff50
Binary files /dev/null and b/graphs.pdf differ