Plot graphs using R programming for the following: (Datasets are attached/provided) ****************Things to do in Delivery Analysis · Over time – variation of time to delivery by month – 2018 (for...

Assignment/plot1.png
Assignment/plot2.png
Assignment/plot3.1.png
Assignment/plot3.2.png
Assignment/plot4.1.png
Assignment/plot4.2.png
Assignment/plot5.1.png
Assignment/plot5.2.png
Assignment/plotting.R
##importing dataset
data=read.csv('C:/Users/vikki/Desktop/Order_Customer.csv')
data1=read.csv('C:/Users/vikki/Desktop/order_items_products.csv')
attach(data)
attach(data1)
##order_items_products data
#-----------------------Things to do in Delivery Analysis-------------------
#plot1
#Over time - variation of time to delivery by month - 2018 (for Sept.17 - Aug. 18)
estimatedate=as.Date(data$order_estimated_delivery_date)
custmerdate=as.Date(data$order_delivered_customer_date)
data_variation=data.frame(estimatedate,custmerdate)
data_variation$differencedate=(data_variation$estimatedate- data_variation$custmerdate)
data_variation$custmerdate <- as.POSIXlt(data_variation$custmerdate)
bymonth <- aggregate(data_variation$differencedate~month(data_variation$custmerdate),
data=data_variation,FUN=sum)
bymonth
bymonth$`data_variation$differencedate`= as.numeric(bymonth$`data_variation$differencedate`)
plot(bymonth$`month(data_variation$custmerdate)`,bymonth$`data_variation$differencedate`)
#Impact of freight value, weight on delivery time?
#plot2
#we have order_delivered_customer_date and order_estimated_delivery_date
#differece between above two delivery...