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Lending Club Data

Part 1: Scatter plots

  1. A scatter plot relating the loan amount (the amount that each borrower requested) to the funded amount (the amount funded by investors)
    To make an attractive chart, you will have to (at least) change the labels on the axis, the alpha values, and the background, gridlines, etc.
ggplot(lc, aes(x = loan_amnt, y = funded_amnt_inv)) + 
  geom_point(alpha = 0.3, na.rm = TRUE, color = "#00AFBB") +
  xlab("Requested Loan Amount") + 
  ylab("Amount Funded by Investors") +
  ggtitle("Requested vs Funded") +
  theme(panel.background = element_blank(), 
        axis.line = element_line(colour = 'black'),
        plot.caption = element_text(face = "italic")) +
  labs(caption = "Source: Lending Club")

  1. A plot exploring the relationship between annual income (the borrower’s income) and loan amount (the amount that the borrower requests). Use the logarithmic scale.
lc %>%
  select(annual_inc, loan_amnt) %>%
  drop_na() %>%
  ggplot(aes(x = log(annual_inc), y = log(loan_amnt))) +
  geom_point(alpha = .02, color = 'lightblue') + 
  stat_smooth(method="lm", se=FALSE) +
  xlab("Log of Annual Income") + 
  ylab(" Log of Loan Amount") + 
  ggtitle("Log Plot of Annual Income Vs Loan Amount") +
  theme(panel.background = element_blank(),
        axis.line = element_line(colour = 'black'),
        plot.caption = element_text(face = "italic")) +
  labs(captions = " Source: Lending Club")
## `geom_smooth()` using formula 'y ~ x'

  1. Bubble Chart and Colors
lc %>% 
  select(loan_amnt, int_rate, grade, dti) %>%
  drop_na() %>%
  mutate(int_rate_numeric = as.numeric(gsub("\\%", "", int_rate))) %>%
  ggplot(aes(x = dti, 
              y = as.numeric(int_rate_numeric), 
             size = loan_amnt, 
             color = grade)) + 
  geom_point(alpha=0.1) +
  xlab("Debt-to-Income") + 
  ylab("Interest Rate") + 
  ggtitle("Interest Rate vs DTI") +
  theme(panel.background = element_blank(),
        axis.line = element_line(colour = 'black'),
        plot.caption = element_text(face = "italic")) +
  scale_x_continuous(breaks=c(0,10,20,30), 
                     labels = c('0','10','20','30'), 
                     limits = c(0,30)) +
  scale_y_continuous(breaks=c(5,10,15,20,25), 
                     labels = c('5','10','15','20','25'), 
                     limits = c(5,25)) +
  labs(size = "Loan Amount", 
       color = "Grade",
       caption = " Source: Lending Club")

Part 2: Distributions

  1. A histogram to show the distribution of the Loan Amount (the amount requested by the borrower). Make sure to change the border color of the bars and change the x and y-axis labels.
ggplot(lc, aes(x = loan_amnt)) +
  geom_histogram(bins = 15, na.rm = TRUE, fill="#69b3a2", color="white") +
  ggtitle("Histogram of Loan Amount") +
  xlab("Loan Amount ($)") +
  ylab("Frequency") +
  theme(panel.background = element_blank() , 
        axis.line = element_line(colour = 'black'),
        panel.grid.major.y = element_line(color = "grey80"),
        panel.grid.major.x = element_blank(),
        axis.line.y = element_blank(),
        axis.ticks.y = element_blank(),
        plot.caption = element_text(face = "italic")) +
  scale_y_continuous(expand = c(0,0)) +
  labs(caption = "Source: Lending Club")

  1. Comparing the distribution of the loan amount by the homeownership status by creating a chart that shows some distribution information for each level of home ownership. In particular, make sure that your chart describes the median loan amount for each home ownership status.
lc %>% 
  select(home_ownership, loan_amnt)  %>%
  ggplot(aes(x = loan_amnt, y = home_ownership)) +
  geom_boxplot(fill = 'lightpink' ) +
  xlab("Loan Amount") +
  ylab("Home Ownership") +
  ggtitle("Boxplot of Loan Amount vs Ownership") +
  theme(panel.background = element_blank(), 
        axis.line = element_line(colour = 'black'),
        axis.line.y = element_blank(),
        axis.line.x = element_blank(),
        panel.grid.major.x = element_line(color = 'grey80', linetype = 3),
        axis.ticks.y = element_blank(),
        plot.caption = element_text(face = "italic")) +
  labs(caption = "Source: Lending Club")
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

Part 3: Categorical plot

  1. A bar chart with the average Loan Amount by Investment Grade for 2011. Ensure that readers can easily identify the Loan Grade with the lowest average Loan Amount.
lc %>% 
  filter(issue_year == 2011) %>%
  select(loan_amnt,grade) %>%
  group_by(grade) %>% 
  summarise(avg_loan_amnt = mean(loan_amnt)) %>% 
  as.data.frame() %>%
  drop_na() %>%
  mutate(mincolor = ifelse(avg_loan_amnt == min(avg_loan_amnt), '1', '0')) %>%
  ggplot(aes(x = grade, y = avg_loan_amnt, fill = mincolor)) +
  geom_col(na.rm = TRUE, 
           fill = c('seagreen', 'grey70', 'grey70', 
                    'grey70', 'grey70', 'grey70', 'grey70')) +
  theme(panel.background = element_blank() , 
        axis.line = element_line(colour = 'black'), 
        legend.position = 'none',
        panel.grid.major.y = element_line(color = 'grey80', linetype = 3),
        plot.caption = element_text(face = "italic")) +
  xlab("Investment Grade") +
  ylab("Average Loan Amount ($)") + 
  ggtitle("Investment Grade Vs Loan Amount") +
  scale_y_continuous(expand = c(0,0)) +
  labs(caption = "Source: Lending Club")

  1. The average loan amount by investment grade for mortgage holders vs. renters, in 2011.
lc %>% 
  filter(issue_year == 2011, home_ownership =='RENT' | home_ownership == 'MORTGAGE') %>%
  select(loan_amnt,grade,home_ownership) %>%
  group_by(grade, home_ownership) %>% 
  summarise(avg_loan_amnt = mean(loan_amnt)) %>% 
  as.data.frame() %>%
  drop_na() %>%
  ggplot(aes(x = grade, y = avg_loan_amnt, fill = home_ownership)) +
  geom_bar(stat = 'identity', position = 'dodge') +
  theme(panel.background = element_blank(),
        axis.line = element_line(colour = 'black'),
        plot.caption = element_text(face = "italic")) +
  xlab("Investment Grade") +
  ylab("Average Loan Amount ($)") +
  ggtitle("Average Loan Amount by Investment Grade") +
  labs(fill = "Home Ownership", 
       caption = "Source: Lending Club") +
  scale_fill_manual(values = c(MORTGAGE = "#117A65", RENT = "#AEB6BF")) +
  scale_y_continuous(expand = c(0,0))

  1. The total loan amount by investment grade in 2011. Break each year into mortgage holders vs. all others.
lc %>% 
  filter(issue_year == 2011) %>%
  select(loan_amnt,grade,home_ownership) %>%
  mutate(home_ownership = replace(home_ownership, home_ownership!= 'MORTGAGE', 'OTHER')) %>%
  group_by(grade, home_ownership) %>% 
  summarise(total_loan_amnt = sum(loan_amnt)) %>%
  drop_na() %>%
  ggplot(aes(x = grade, y = total_loan_amnt/1000000, fill = home_ownership)) +
  geom_bar(stat = 'identity') +
  theme(panel.background = element_blank() , 
        axis.line = element_line(colour = 'black'),
        panel.grid.major.y = element_line(color = 'grey80', linetype = 3),
        plot.caption = element_text(face = "italic")) +
  xlab("Investment Grade") +
  ylab("Total Loan Amount (Million $)") +
  ggtitle("Total Loan Amount by Investment Grade for Mortgage vs All") +
  labs(fill = "Home Ownership", caption = "Source: Lending Club") +
  scale_y_continuous(expand = c(0,0)) +
  scale_fill_manual(values= c(MORTGAGE = "#5DADE2", OTHER = "#A6ACAF"))

Part 4: More Bar Charts

  1. Pyramid Chart Using patchwork to create a pyramid chart. Place a label for the LoanGrade in the center, and display the number of loans, organized by the purpose of the debt(group debt consolidation and credit cards together versus all other loan purposes)
pyramid_data <- lc%>%mutate(debt_purpose=ifelse(purpose%in%c('credit_card','debt_consolidation'),"Debt","Other"))%>%
  group_by(grade, debt_purpose)%>%
  summarise(numberOfLoans =n())%>%
  pivot_wider(names_from = debt_purpose,values_from = numberOfLoans)%>%
  mutate(Other =-Other)%>%
  pivot_longer(Debt:Other, names_to="loan_purpose",values_to="n")%>%
  na.omit()%>% 
  ungroup()%>%
  mutate(grade =fct_relevel(as.factor(grade),
                            levels=rev(levels(as.factor(grade)))))
## Warning: Outer names are only allowed for unnamed scalar atomic inputs

debt_chart <- pyramid_data %>% 
  filter(loan_purpose=='Debt') %>% 
  ggplot(aes(y=grade, x=n)) + 
  geom_col(fill="royalblue") + 
  geom_vline(xintercept = 0) + 
  labs(x="Debt") + 
  theme(axis.title.y = element_blank(),
        axis.title.x =element_text(color="royalblue", face="bold"),
        axis.ticks =element_blank(),
        panel.grid.major.x =element_line(color="grey90", linetype=2),
        panel.background =element_rect(fill="white"),
        axis.text.y =element_text(hjust=0.5,face="bold")) + 
  scale_x_continuous(breaks=c(0,1000,2000,3000,4000,5000,6000,7000),
                     labels=c("0","1K","2K","3K","4K","5K","6K","7K"),
                     limits =c(0,7500),position = "top")

other_chart <- pyramid_data %>% 
  filter(loan_purpose=='Other') %>% 
  ggplot(aes(y=grade, x=n)) + 
  geom_col(fill="grey70") + 
  geom_vline(xintercept = 0) + 
  labs(x="Other Purposes") + 
  theme(axis.title.y =element_blank(),
        axis.title.x =element_text(color="grey70", face="bold"),
        axis.text.y =element_blank(),
        axis.ticks =element_blank(),
        panel.grid.major.x =element_line(color="grey90", linetype=2),
        panel.background=element_rect(fill="white")) + 
  scale_x_continuous(breaks=c(0,-1000,-2000,-3000,-4000,-5000,-6000,-7000),
                     labels=c("0","1K","2K","3K","4K","5K","6K","7K"),
                     limits =c(-7500,0),
                     position="top")

other_chart + debt_chart + 
  plot_annotation(title ='Lending Club Loans',
                  subtitle ='Loans to pay down or consolidate debt are less likely to be Grade A loans than loans for\nother purposes.',
                  caption ='Source: Lending Club',
                  theme =theme(plot.title =element_text(size = 14),
                               plot.subtitle=element_text(size=12, face="italic"),
                               plot.caption =element_text(face="italic")))

Part 5: Time Series

  1. Multiple Line Chart: Creating a line chart of the average loan amount in each of the loangrades over time. Highlighting loan grade A and keep the other shades light grey.
#fix the issue date
lc$issue_date = as.Date(paste(lc$issue_d,"-01",sep=""), format="%y-%b-%d")

lc %>% 
  select(grade, loan_amnt, issue_date) %>%
  drop_na() %>%
  group_by(issue_date, grade) %>%
  summarise(average_loan_amnt = mean(loan_amnt)) %>%
  mutate(Grade = ifelse(grade == 'A', 'A', 'Others')) %>%
  ggplot(aes(x = issue_date, y = average_loan_amnt, group = grade, color = Grade)) +
  geom_line() +
  scale_color_manual(values=c("steelblue", "lightgrey")) +
  theme(panel.background = element_blank() , axis.line = element_line(colour = 'black'),
        plot.caption = element_text(face = "italic")) +
  xlab("Issue Date") +
  ylab("Average Loan Amount") + 
  ggtitle("Average Loan Amount Over the years for grade A vs Others") +
  labs(caption = "Source: Lending Club Data")

  1. Area Chart: An area chart showing the total number of loans for debt-relatedcategories (credit cards and debt consolidation), versus all other types over time.
lc %>% 
  select(purpose,issue_date) %>%
  drop_na() %>%
  mutate(color_purpose = ifelse(purpose == 'credit_card' | purpose == 'debt_consolidation', 'Debt-Related', 'Others')) %>%
  group_by(issue_date, color_purpose) %>%
  summarise(count = n()) %>%
  ggplot(aes(x = issue_date, y = count, fill = color_purpose)) +
  geom_area(alpha = .5, size = .5, color= 'white') +
  theme(panel.background = element_blank(), 
        axis.line = element_line(colour = 'black')) +
  xlab("Issue Date") +
  ylab("Number of Loans") + 
  ggtitle("Number of Loans issued for Debt vs Non-Debt Purposes") +
  labs(fill = "Purpose",
       caption = "Source: Lending Club Data") +
  scale_fill_manual(values=c("steelblue", "lightgrey")) +
  theme(plot.caption = element_text(face = "italic"))