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For Problems $9,10,$ and $11,$ we will be cleaning a data file in preparation for subsequent analysis.

The analysis performed on LendingClub data in the chapter was for the years $20072012.$ We now use LendingClub data for $2013.$

Consider the $2013$ rejected loan data from LendingClub titled $$DAA Chapter $1-2$ Data$.$ Similar to the analysis done in the chapter, let$$s scrub the debt$-$to$-$income data. Because our analysis requires risk scores, debt$-$to$-$income data, and employment length, we need to make sure each of them has valid data.

Sort the file based on debt$-$to$-$income and remove those observations $($the complete row or record$)$ that have a missing score, a score of zero, or a negative score.

Assign each valid debt$-$to$-$income ratio into three buckets $($labeled DTI bucket$)$ by classifying each debt$-$to$-$income ratio into high $(>20\mathrm{.}0$ percent$),$ medium $(10\mathrm{.}020\mathrm{.}0$ percent$),$ and low $(<10\mathrm{.}0$ percent$)$ buckets. Consider using nested if$-$then statements to complete this. Or sort the row and manually input.

Run a PivotTable analysis that shows the number of loans in each DTI bucket.

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Which DTI bucket had the highest and lowest grouping for this rejected Loans dataset?