##Initial Thoughts An initial question to be answered is whether the answer to this question should be a probabilistic one or a simulation. While a statistician might initially choose the first, the second is better to actually see results.
##Assumptions Our assumptions include:
- Customers and the orders they make are independent of each other
- While this may not be true in the real world, as someone's order may influence the order of the next person, it is easier to pretend that they are not
##First Look
library(ggplot2)
library(reshape2)
details <- read.csv("details.csv", header=T)
sales <- read.csv("sales.csv", header=T)
attach(sales)
demand <- melt(sales[1:4], id.vars="date", variable.name="type", value.name="demand")
supply <- melt(sales[c(1,5,6,7)], id.vars="date", variable.name="type", value.name="supply")
ham <- melt(sales[c(1,2,5)], id.vars="date", variable.name="metric", value.name="amount")
turkey <- melt(sales[c(1,3,6)], id.vars="date", variable.name="metric", value.name="amount")
veggie <- melt(sales[c(1,4,7)], id.vars="date", variable.name="metric", value.name="amount")
plotHam <- ggplot(data=ham, aes(x=date, y=amount, group=metric, color=metric)) + geom_point() + geom_line() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_x_discrete(breaks=c("2014-03-03", "2014-04-01", "2014-05-01", "2014-06-02", "2014-07-01", "2014-08-01", "2014-08-29")) +
ggtitle("Ham")
plotTurkey <- ggplot(data=turkey, aes(x=date, y=amount, group=metric, color=metric)) + geom_point() + geom_line() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_x_discrete(breaks=c("2014-03-03", "2014-04-01", "2014-05-01", "2014-06-02", "2014-07-01", "2014-08-01", "2014-08-29")) +
ggtitle("Turkey")
plotVeggie <- ggplot(data=veggie, aes(x=date, y=amount, group=metric, color=metric)) + geom_point() + geom_line() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_x_discrete(breaks=c("2014-03-03", "2014-04-01", "2014-05-01", "2014-06-02", "2014-07-01", "2014-08-01", "2014-08-29")) +
ggtitle("Veggie")plotHam
plotTurkey
plotVeggie
##Analysis of Historical Data
##Why Poisson distribution
##Simulation
###Assuming there is no storage of sandwiches after each day
###Assuming there is storage of unsold sandwiches after each day
##Interpretations and Recommendations