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Colored plot for Distrib of spending

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Pie for cities (colorised)
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LinlyBoi
2021-12-26 23:18:40 +02:00
parent 2d6fb5ef64
commit 2397446d94
1 changed files with 10 additions and 4 deletions
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14
Data.R
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@@ -11,10 +11,11 @@ library(gridExtra)
library(ggplot2) library(ggplot2)
library(grid) library(grid)
library(arules) library(arules)
library(RColorBrewer)
#library(cluster) #library(cluster)
coul <- brewer.pal(5, "Set2")
#Read spreadsheet file #Read spreadsheet file
grocery_entries <- read.csv(file.choose()) grocery_entries <- read.csv(file.choose())
@@ -25,13 +26,16 @@ cash_credit <- cbind(grocery_entries[3], grocery_entries[8])
sum_cash <-sum(cash_credit[which(cash_credit$paymentType=='Cash'),1]) sum_cash <-sum(cash_credit[which(cash_credit$paymentType=='Cash'),1])
sum_credit <-sum(cash_credit[which(cash_credit$paymentType=='Credit'),1]) sum_credit <-sum(cash_credit[which(cash_credit$paymentType=='Credit'),1])
CompCashCredit <- c(sum_cash,sum_credit) CompCashCredit <- c(sum_cash,sum_credit)
barplot(CompCashCredit,names.arg = c('Cash','Credit'),horiz = FALSE,col = c(rgb(0,1,0),rgb(1,0,0))) barplot(CompCashCredit,names.arg = c('Cash','Credit'),horiz = FALSE,col = coul)
#City and Total Spent comparison -Jimmy #City and Total Spent comparison -Jimmy
city_total <- cbind(grocery_entries[3], grocery_entries[7]) city_total <- cbind(grocery_entries[3], grocery_entries[7])
sum_cities<-aggregate(total ~city ,city_total,sum) sum_cities<-aggregate(total ~city ,city_total,sum)
pie(sum_cities$total
,col = coul
,labels = sum_cities$city
,main = "Cities and total spent")
#Compare between ages and their total spent (Youssri) #Compare between ages and their total spent (Youssri)
@@ -42,6 +46,7 @@ plot(sum_ages)
#Distribution of spending - Abdo #Distribution of spending - Abdo
plot(grocery_entries$total, col = coul,type = "l", main = "spending")
@@ -52,6 +57,7 @@ keameans<-cbind(grocery_entries[3],grocery_entries[6])
result<-kmeans(keameans,centers =n) result<-kmeans(keameans,centers =n)
final_result<-cbind(name_total_age,result$cluster) final_result<-cbind(name_total_age,result$cluster)
#Association Rules --Sewelam #Association Rules --Sewelam
clean_data <- grocery_entries[,-5] clean_data <- grocery_entries[,-5]
minsup <- as.numeric(readline("Enter minimum support: ")) minsup <- as.numeric(readline("Enter minimum support: "))