To read the Intro and download the R packages, see the first part in this article.
After loading the dataset and data management, it is time to make the credit modelling visualizations in R:
Chart on customers
ggplot(data = loan,aes(x = grade)) + geom_bar(color = “blue”,fill =”green”) +geom_text(stat=’count’, aes(label=..count..))+ theme_solarized()
ggplotly(p = ggplot2::last_plot())
The above coding gives us the following graph:
Now lets look at histogram based upon loan amount and interest rate:
#Histogram on loan amount
ggplot(data = loan,aes (x = loan_amnt,fill= grade))+ geom_histogram()
ggplotly(p = ggplot2::last_plot())
#Histogram on interest rate
ggplot(data = loan,aes (x = int_rate,fill= grade))+ geom_histogram()
ggplotly(p = ggplot2::last_plot())
This gives us the following two histograms:
Next, it is time to look at the density plot on loan- and interest rate based grade type.
#density on loan based on grade type
ggplot(data = loan,aes(x = loan_amnt,fill = grade)) + geom_density()
ggplotly(p = ggplot2::last_plot())
#density on interest rate based on grade type
ggplot(data = loan,aes(x = int_rate,fill = grade)) + geom_density()
ggplotly(p = ggplot2::last_plot())
This gives us the following plots:
Visit DataScience+ to download Kristian Larsen’s code and to see the rest of the graphs, including box plots for interest rate based on purpose and grade, histograms and density plot based on interest rate and loan amount.
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