The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Yield Curve (Interest Rate Term Structure) in Python, R, Julia and JavaScript.
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 | Updated: 2026-10-09
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"
# Inversion region of the Jul 2023 curve: where its short-term yields sit
# above its own 30Y yield (1M–10Y)
inv_mask <- maturity_years <= 10.0 & yields_jul2023 > yields_jul2023[length(yields_jul2023)]
inversion_df <- data.frame(
maturity_years = maturity_years[inv_mask],
ymin = yields_jul2023[length(yields_jul2023)],
ymax = yields_jul2023[inv_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 = 0.20, 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 = 13, margin = margin(b = 12)),
legend.background = element_rect(fill = ELEVATED_BG, color = NA),
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.