(This article was first published on

**OutLie..R**, and kindly contributed to R-bloggers)With all the cleaning done, the only thing left to do is save the data to be analyzed, for future use, and I hope by others. The data I thought would be simple, but there were a few interesting twist, like the Primary Credit*, and using ifelse() to edit the districts.

I have included the product as well as the R-code in a single file for people to use and learn from. I would like to thanks all those who made comments, I find all of them helpful. Below are the links to the files generated and used in the series, and the r-code used to exporting and reloading the data.

List of files used and their links

- DiscountWindow.R -the code used to download and clean up discount window data
- Districts.csv -the table used in post 5 of 6 to seperate the districts
- DiscountWindow.csv -a csv of the final product
- DiscountWindow.RData -RData of the final cleaned up product

#Export the data, csv and RData

setwd("C:/Users Defined/")

write.csv(dw, file='DiscountWindow.csv')

save(dw, file='DiscountWindow.RData')

#note when loading the data the envir= needs to be defined

#with larger files the RData is definately the way to go

#this file is small enough it does not matter

load('DiscountWindow.RData', envir=.GlobalEnv)

dw<-read.csv(file.choose(), header=T)

To

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