ggplot2 Assignment

STAA 566 ggplot2 Assignment

Loan delinquency is the idea that individuals are overdue on loan payments by at least 30 days. The prime rate is the minimum rate that a bank would be willing to lend money to an individual with good credit.

We begin by reading in the data

# Read in data
library(zoo)

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

library(viridis)

Loading required package: viridisLite

BD <- read.csv.zoo("BusinessDelinquency.csv")
CD <- read.csv.zoo("CreditDelinquency.csv")
RD <- read.csv.zoo("ResidentialDelinquency.csv")
PR <- read.csv.zoo("PrimeRate.csv")
library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.6     ✓ dplyr   1.0.8
✓ tidyr   1.2.0     ✓ stringr 1.4.0
✓ readr   2.1.2     ✓ forcats 0.5.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(ggplot2)
X <- merge(PR, BD, CD, RD)
X <- data.frame(index(X), as.data.frame(X))
colnames(X)[1] <- "x"
X <- X %>% fill(names(X)[2:5]) %>% filter(x >= "1996-01-01")
X_long <- gather(X, Market, Rate, PR:RD)

Now that data has been loaded and properly formatted, we can go on to creating the graphic

p_loans <- ggplot(data = X_long,
                  mapping = aes(x = x,
                                y = Rate,
                                colour = Market))
#add annotation rectangles to denote recession
p_loans <- p_loans + annotate("rect", xmin = as.Date("2001-01-01"), xmax = as.Date("2001-10-01"), ymin = 0, ymax = 12,
                   alpha = .1, fill = "blue") +
  annotate("rect", xmin = as.Date("2007-10-01"), xmax = as.Date("2009-07-01"), ymin = 0, ymax = 12,
                   alpha = .1, fill = "blue") +
  annotate("rect", xmin = as.Date("2020-01-01"), xmax = as.Date("2020-07-01"), ymin = 0, ymax = 12,
                   alpha = .1, fill = "blue") +
  # Add the line plots
  geom_line()
p_loans <- p_loans + guides(color = F)

Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.

line_ends <- ggplot_build(p_loans)$data[[4]] %>% 
  group_by() %>% 
  filter(x==max(x))
#add market label
line_ends$Market <- X_long %>% pull(Market) %>% 
  unique() %>% 
  as.character() %>% 
  sort()
# add direct labels to graph
p_loans <- p_loans +  ggrepel::geom_label_repel(data = line_ends, 
                         aes(x = as.Date(line_ends$x), y = line_ends$y, 
                             label = c("Business","Credit","Prime","Resident")), 
                         nudge_x = 1000000,
                         label.size=NA,
                         fill = alpha(c("white"),0))
p_loans <- p_loans + expand_limits(x = as.Date("2028-01-01"))
# Add color palette and adjust theme
p_loans + scale_color_viridis(option = "turbo", discrete = T) + theme_bw() +
  # Adjust some details
  ggtitle("Loan Delinqencies & Prime Rate") + xlab("Date")

In the chart above, the blue areas represent times of economic recession while each line is either the Prime lending rate or some form of loan delinquency rate.

All data is from the United States Federal Reserve of St. Louis accessed through the FRED economic data portal <a href=“https://fred.stlouisfed.org/#”>here</a>. The datasets used can be found under:

Prime Rate: PRIME

Residential: DRSFRMACBS

Consumer: DRCLACBS

Business: DRBLACBS

All data was accessed on September 16th, 2022

https://fred.stlouisfed.org/#