A yield curve plots interest rates (yields) of bonds against their maturities, from short-term (e.g., 1 month) to long-term (e.g., 30 years). It is one of the most iconic charts in macroeconomics and finance, used to assess market expectations for future interest rates, economic growth, and inflation. An inverted yield curve (where short-term rates exceed long-term rates) is a widely followed recession indicator. The plot should support displaying multiple curves (e.g., different dates) on the same axes to show how the term structure evolves over time.
#' anyplot.ai
#' line-yield-curve: Yield Curve (Interest Rate Term Structure)
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 87/100 | Created: 2026-06-10
library(ggplot2)
library(scales)
library(ragg)
# Theme tokens (Imprint palette + adaptive chrome)
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
GRID_COLOR <- scales::alpha(INK, 0.15)
# Imprint categorical palette — positions 1, 2, 3 for three yield curves
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3")
# Data: U.S. Treasury yield curves on three historically significant dates
maturities <- c("1M", "3M", "6M", "1Y", "2Y", "3Y", "5Y", "7Y", "10Y", "20Y", "30Y")
maturity_years <- c(1/12, 0.25, 0.5, 1.0, 2.0, 3.0, 5.0, 7.0, 10.0, 20.0, 30.0)
# Jan 2021: Normal steep curve (near-zero short rates, post-pandemic QE)
yields_jan2021 <- c(0.05, 0.06, 0.08, 0.09, 0.12, 0.20, 0.45, 0.80, 1.08, 1.57, 1.83)
# Jul 2023: Deeply inverted (peak of Fed hiking cycle, 5.25-5.50% fed funds)
yields_jul2023 <- c(5.30, 5.43, 5.52, 5.44, 4.87, 4.55, 4.28, 4.18, 3.97, 4.11, 3.96)
# Jan 2024: Still inverted, beginning to normalize
yields_jan2024 <- c(5.52, 5.43, 5.36, 5.12, 4.43, 4.17, 4.00, 4.01, 3.97, 4.35, 4.22)
df <- data.frame(
maturity = rep(maturities, 3),
maturity_years = rep(maturity_years, 3),
yield_pct = c(yields_jan2021, yields_jul2023, yields_jan2024),
curve_date = factor(
rep(c("Jan 2021", "Jul 2023", "Jan 2024"), each = length(maturities)),
levels = c("Jan 2021", "Jul 2023", "Jan 2024")
)
)
y_top <- max(df$yield_pct)
y_annot <- y_top + 0.30
plot_title <- "line-yield-curve · r · ggplot2 · anyplot.ai"
# Data-driven ribbon: front-end region (1M–2Y) between Jan 2021 baseline and
# Jul 2023 peak — shows the full magnitude of the hiking cycle at the short end
front_end_mask <- maturity_years <= 2.0
inversion_df <- data.frame(
maturity_years = maturity_years[front_end_mask],
ymin = yields_jan2021[front_end_mask],
ymax = yields_jul2023[front_end_mask]
)
p <- ggplot(df, aes(x = maturity_years, y = yield_pct, color = curve_date)) +
geom_ribbon(
data = inversion_df,
aes(x = maturity_years, ymin = ymin, ymax = ymax),
fill = INK_MUTED, alpha = 0.10, inherit.aes = FALSE
) +
geom_line(linewidth = 1.2, lineend = "round") +
geom_point(size = 3.5) +
annotate("text",
x = 0.7, y = y_annot,
label = "Inversion zone",
color = INK_MUTED, size = 3.5, hjust = 0.5, vjust = 0
) +
scale_color_manual(values = IMPRINT_PALETTE, name = NULL) +
scale_x_log10(
breaks = maturity_years,
labels = maturities
) +
scale_y_continuous(
labels = function(x) sprintf("%.1f%%", x),
expand = expansion(mult = c(0.05, 0.22))
) +
labs(
title = plot_title,
x = "Maturity",
y = "Yield (%)"
) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid.major = element_line(color = GRID_COLOR, linewidth = 0.3),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.line = element_line(color = INK_SOFT, linewidth = 0.5),
axis.ticks = element_blank(),
plot.title = element_text(color = INK, size = 12, margin = margin(b = 12)),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.3),
legend.text = element_text(color = INK_SOFT, size = 9),
legend.title = element_blank(),
legend.key.width = unit(1.5, "cm"),
legend.position = "right",
plot.margin = margin(20, 20, 20, 20, "pt")
)
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/line-yield-curve/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "line-yield-curve",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/line-yield-curve/r/ggplot2",
"hub": "https://anyplot.ai/line-yield-curve",
"code_json": "https://api.anyplot.ai/specs/line-yield-curve/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/line-yield-curve",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/line-yield-curve/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/line-yield-curve/r/ggplot2/plot-dark.png",
"quality_score": 87.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Yield Curve (Interest Rate Term Structure) on anyplot.ai.