Education Level by Gender

College educated men and women make up the majority of default payers. Not enough information is presented however college debt could be a contributing factor.

ggplot(data = credit_card)+
  geom_bar(mapping = aes(EDUCATION, fill = SEX))+
  facet_wrap(~SEX)+
  labs(x= "Education Level", y= "Total")+
  theme(axis.text.x = element_text(angle =90))

Marital Status by Gender

Another factor that could contribute to college educated men and women being the highest demographic is marital status. Single men and women are the highest defaulters. Although not enough information is given about household income to draw a clear conclusion it is possible that single men and women have only one source of income and with the potential for student loan debt as previously discussed this could make it harder for them to meet their financial objections.

ggplot(data = credit_card)+
  geom_bar(mapping = aes(MARRIAGE, fill = SEX))+
  facet_wrap(~SEX)+
  labs(x= "Marital Status", y= "Total")

Age by Gender

The avgerage age of defaulters for men and women are 37 and 35 respectively. So the average age of defaulters shows that they weren’t just young and irresponsible. The defaulters are college educated and in their mid to late 30s.

avg_age <- credit_card %>% 
  group_by(SEX) %>% 
  summarise(avg_age = mean (AGE))
print(avg_age)
## # A tibble: 2 × 2
##   SEX    avg_age
##   <chr>    <dbl>
## 1 Female    34.8
## 2 Male      36.5
ggplot(data = credit_card)+
  geom_line(mapping = aes(AGE, fill = SEX,color = SEX), stat ="count", 
           position = "identity")+
  labs(title = "Ages", x="Age", y= "Total")

Balance Limit by Marital Status

On average married men and women have the highest balance limit.

ggplot(data = credit_card)+
  geom_point(mapping = aes(LIMIT_BAL, fill = SEX,color = SEX), stat ="count", 
  position = "jitter")+
  facet_wrap(~MARRIAGE)+
  labs( x = "Limit Balance", y= "Total")

Balance Limit by Gender

Women are borrowing at a higher rate than men.

ggplot(data = credit_card)+
  geom_line(mapping = aes(LIMIT_BAL, fill=SEX, color=SEX), stat ="count", 
            position = "identity")+
  labs(title = "Balance Limit by Gender", x="Limit Balance", y= "Total")
## Warning: Ignoring unknown aesthetics: fill

six_month_bill_amt <- credit_card %>%
  group_by(SEX) %>% 
  summarise(avg_bill_1 = mean(BILL_AMT1),avg_bill_2 = mean(BILL_AMT2),
            avg_bill_3 = mean(BILL_AMT3), avg_bill_4 = mean(BILL_AMT4),
            avg_bill_5 = mean(BILL_AMT5), avg_bill_6 = mean(BILL_AMT6))
print(six_month_bill_amt)
## # A tibble: 2 × 7
##   SEX    avg_bill_1 avg_bill_2 avg_bill_3 avg_bill_4 avg_bill_5 avg_bill_6
##   <chr>       <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
## 1 Female     49216.     47381.     45633.     42123.     39474.     38064.
## 2 Male       54281.     51919.     49116.     45000.     41588.     40102.
six_month_pay_amt<- credit_card %>%
  group_by(SEX) %>% 
  summarise(avg_pay_1 = mean(PAY_AMT1),avg_pay_2 = mean(PAY_AMT2),
            avg_pay_3 = mean(PAY_AMT3), avg_pay_4 = mean(PAY_AMT4),
            avg_pay_5 = mean(PAY_AMT5), avg_pay_6 = mean(PAY_AMT6))
print(six_month_pay_amt)
## # A tibble: 2 × 7
##   SEX    avg_pay_1 avg_pay_2 avg_pay_3 avg_pay_4 avg_pay_5 avg_pay_6
##   <chr>      <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
## 1 Female     5660.     5895.     5103.     4798.     4779.     5176.
## 2 Male       5669.     5961.     5413.     4869.     4831.     5276.