Variables

• Numerical variables can be classified as continuous or discrete based on whether or not the variable can take on an infinite number of values or only non-negative whole numbers, respectively.
• If the variable is categorical, we can determine if it is ordinal based on whether or not the levels have a natural ordering.

Data

library(openintro)
loans <- loans_full_schema %>%
  select(loan_amount, interest_rate, term, grade, 
         state, annual_income, homeownership, debt_to_income)
glimpse(loans)

## Rows: 10,000
## Columns: 8
## $ loan_amount    <int> 28000, 5000, 2000, 21600, 23000, 5000, 2…
## $ interest_rate  <dbl> 14.07, 12.61, 17.09, 6.72, 14.07, 6.72, …
## $ term           <dbl> 60, 36, 36, 36, 36, 36, 60, 60, 36, 36, …
## $ grade          <ord> C, C, D, A, C, A, C, B, C, A, C, B, C, B…
## $ state          <fct> NJ, HI, WI, PA, CA, KY, MI, AZ, NV, IL, …
## $ annual_income  <dbl> 90000, 40000, 40000, 30000, 35000, 34000…
## $ homeownership  <fct> MORTGAGE, RENT, RENT, RENT, RENT, OWN, M…
## $ debt_to_income <dbl> 18.01, 5.04, 21.15, 10.16, 57.96, 6.46, …

Bar plot

ggplot(loans, aes(x = homeownership)) +
  geom_bar()

Segmented bar plot

ggplot(loans, aes(x = homeownership, 
                  fill = grade)) +
  geom_bar()

Segmented bar plot

ggplot(loans, aes(x = homeownership, fill = grade)) +
  geom_bar(position = "fill")

Customizing bar plots

ggplot(loans, aes(y = homeownership,
                  fill = grade)) +
  geom_bar(position = "fill") +
  labs(
    x = "Proportion",
    y = "Homeownership",
    fill = "Grade",
    title = "Grades of Lending Club loans",
    subtitle = "and homeownership of lendee"
  )

Already talked about...

• Colouring and faceting histograms and density plots
• Side-by-side box plots

Violin plots

ggplot(loans, aes(x = homeownership, y = loan_amount)) +
  geom_violin()

Ridge plots

library(ggridges)
ggplot(loans, aes(x = loan_amount, y = grade, fill = grade, color = grade)) + 
  geom_density_ridges(alpha = 0.5)