Now that we have one data frame, time to make larger changes to the data. The first is to get the dates into a format that R can understand. The as.Date() function does this by defining the variable, then the pattern for the date. At this point, I had a hard time figuring out what each one meant; basically you are defining what the date looks like now in the data frame, not in the future.

For this data set the ‘%b %d %Y’ or in other words Feb 01 2011, if the date looked like Feb-01-2011, then the code would be ‘%b-%d-%Y’, or if the date was 02-02-2011, then ‘%m-%d-%Y’. For a more comprehensive tutorial, see the post on Quick-R.

#Changing the date variables, then 
#isolating the year variable for alter use
library(stringr)
dw$loan.date<-as.Date(dw$loan.date, '%b %d %Y')
dw$mat.date<-as.Date(dw$mat.date, '%b %d %Y')
dw$repay.date<-as.Date(dw$repay.date, '%b %d %Y')

At this point, I like to have two extra variables so I can aggregate the data later for some nice results, in particular the year and the month. The reason is I want to know if there is a difference in the years.  I know there are only 2 years so far, but every quarter new data will be released so I am setting up the code for it now. The month I want to know if there is any seasonality to it. If I choose to I can isolate the day, but this gets messy because February has 28/29 days, then the rest of the months fluctuate between 30 and 31. The data is scattered and blotchy as is, making the day too small of a unit to be useful.

The code assumes the date has been changed to the R default of YYYY-MM-DD, for the year I selected the first 4 numbers using the str_sub() function, while making it a numerical value- as.numeric(). The year and date variable I made it a factor for easier sorting and categorizing, with a similar process as above except I want both.

#Create a year variable
dw$year<-as.numeric(str_sub(dw$loan.date, start=1, 
   end=4))
 
#Create a year and month variable
dw$year.month<-as.factor(str_sub(dw$loan.date, 
   start=1, end=7))

The next step is to change the credit type to something simpler for tables and graphs. I used the gsub, one of the most interesting and fun functions I never knew existed until I did this. Basically it will take a string then replace it with another. For this data I wanted to replace the "Primary Credit" with "primary" because it make things so much easier for graphs and tables. Then I changed it to a factor.

 
#Changing the type of credit to one word
dw$type.credit<-with(dw, 
   gsub("Primary Credit", 'primary', type.credit))
dw$type.credit<-with(dw, 
   gsub("Seasonal Credit", 'seasonal', type.credit))
dw$type.credit<-with(dw, 
   gsub("Secondary Credit", 'secondary', type.credit))
 
#change to factor
dw$type.credit<-as.factor(dw$type.credit)
summary(dw)