Report 3

Your task this week is to: prepare your own descriptive analysis for the “CreditCard” dataset (AER package). It is a cross-sectional dataframe on the credit history for a sample of applicants for a type of credit card.

Are the yearly incomes (in USD 10,000), credit card expenditures, age, ratio of monthly credit card expenditure to yearly income - significantly different for applicants for customers with different credit risk (“card” variable - factor)?

Prepare a professional data visualizations, descriptive statistics’ tables and interpret them.

Summary:

selected_data <- CreditCard %>%
  select(card, income, expenditure, age) %>%
  mutate(expenditure_income_ratio = expenditure / (income * 10000 / 12))

(stats_by_rooms <- stby(data      = selected_data, INDICES   = selected_data$card, FUN       = descr, stats     = "common", transpose = TRUE))

## Non-numerical variable(s) ignored: card

## Descriptive Statistics  
## selected_data  
## Group: card = no  
## N: 296  
## 
##                                   Mean   Std.Dev    Min   Median     Max   N.Valid   Pct.Valid
## ------------------------------ ------- --------- ------ -------- ------- --------- -----------
##                            age   33.20      9.92   0.75    31.83   80.17    296.00      100.00
##                    expenditure    0.00      0.00   0.00     0.00    0.00    296.00      100.00
##       expenditure_income_ratio    0.00      0.00   0.00     0.00    0.00    296.00      100.00
##                         income    3.07      1.62   0.49     2.59   11.00    296.00      100.00
## 
## Group: card = yes  
## N: 1023  
## 
##                                    Mean   Std.Dev    Min   Median       Max   N.Valid   Pct.Valid
## ------------------------------ -------- --------- ------ -------- --------- --------- -----------
##                            age    33.22     10.21   0.17    31.08     83.50   1023.00      100.00
##                    expenditure   238.60    287.71   0.00   150.18   3099.50   1023.00      100.00
##       expenditure_income_ratio     0.09      0.10   0.00     0.06      0.91   1023.00      100.00
##                         income     3.45      1.71   0.21     3.00     13.50   1023.00      100.00

Yearly incomes:

library(ggplot2)


ggplot(CreditCard, aes(x = card, y = income, fill = card)) +
  geom_boxplot() +
  labs(x = "Card Status", y = "Yearly Income (USD 10,000)",
       title = "Yearly Income by Card Status") +
  theme_minimal()

ggplot(CreditCard, aes(x = income, fill = card)) +
  geom_histogram(position = "dodge", bins = 30) +
  labs(title = "Income Distribution by Card Status:",
       x = "Yearly Income ($10,000)",
       y = "Frequency",
       fill = "Card Status")

Credit cardholders typically exhibit higher average incomes compared to individuals without credit cards.

Expenditures, and ratio:

Credit card expenditures, and ratio of monthly credit card expenditure to yearly income:

ggplot(CreditCard, aes(x = card, y = expenditure, fill = card)) +
  geom_boxplot() +
  labs(x = "Card Status", y = "Credit Card Expenditure",
       title = "Credit Card Expenditure by Card Status") +
  theme_minimal()

ggplot(CreditCard, aes(x = card, y = expenditure / (income * 10000 / 12), fill = card)) +
  geom_boxplot() +
  labs(x = "Card Status", y = "Ratio of Monthly Expenditure to Yearly Income",
       title = "Ratio of Monthly Expenditure to Yearly Income by Card Status") +
  theme_minimal()

ggplot(CreditCard, aes(x = expenditure / (income * 10000 / 12), fill = card)) +
  geom_histogram(position = "dodge", bins = 30) +
  labs(title = "Monthly Expenditure to Yearly Income Ratio by Card Status:",
       x = "Expenditure to Income Ratio",
       y = "Frequency",
       fill = "Card Status")

Expenditures of all people who did not get accepted, are equal to 0, so is the ratio of expenditures to income.

Age:

ggplot(CreditCard, aes(x = card, y = age, fill = card)) +
  geom_boxplot() +
  labs(x = "Card Status", y = "Age",
       title = "Age by Card Status") +
  theme_minimal()

ggplot(CreditCard, aes(x = age, fill = card)) +
  geom_histogram(position = "dodge", bins = 30) +
  labs(title = "Age Distribution by Card Status:",
       x = "Years",
       y = "Frequency",
       fill = "Card Status")

We found no significant difference in mean age between individuals with and without credit cards, suggesting age does not impact credit card ownership.