A calibration curve plotting absorbance versus concentration following Beer-Lambert law (A = εlc). Measured calibration standards are shown as scatter points with a linear regression fit line. The regression equation (y = mx + b) and R² value are displayed on the plot. An example unknown sample is marked with dashed lines extending to both axes, demonstrating how the curve is used to determine concentration from a measured absorbance. This plot is fundamental in analytical chemistry for quantitative spectrophotometric analysis.
#' anyplot.ai
#' calibration-beer-lambert: Beer-Lambert Calibration Curve
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 87/100 | Created: 2026-06-03
library(ggplot2)
library(ragg)
set.seed(42)
# Theme tokens (Imprint palette — theme-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"
GRID_COLOR <- adjustcolor(INK, alpha.f = 0.15)
# Imprint categorical palette (hybrid-v3 sort)
IMPRINT_PALETTE <- c(
"#009E73", # 1 — brand green (first series, calibration standards)
"#C475FD", # 2 — lavender
"#4467A3", # 3 — blue
"#BD8233", # 4 — ochre
"#AE3030", # 5 — matte red (semantic: unknown sample highlight)
"#2ABCCD", # 6 — cyan
"#954477", # 7 — rose
"#99B314" # 8 — lime
)
# Data: iron (II) calibration by ferrozine colorimetric method (562 nm)
# Beer-Lambert law: A = epsilon * l * C, epsilon*l = 0.245 L/(mg·cm)
conc_standards <- c(0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0)
eps_l <- 0.245
abs_noise <- rnorm(length(conc_standards), mean = 0, sd = 0.006)
abs_standards <- pmax(eps_l * conc_standards + abs_noise, 0)
df <- data.frame(
concentration = conc_standards,
absorbance = abs_standards
)
# Linear regression
lm_fit <- lm(absorbance ~ concentration, data = df)
slope <- coef(lm_fit)[["concentration"]]
intercept <- coef(lm_fit)[["(Intercept)"]]
r_sq <- summary(lm_fit)$r.squared
# Prediction interval across the fit range
x_pred <- data.frame(concentration = seq(0, 4.4, length.out = 200))
pred_ci <- predict(lm_fit, newdata = x_pred, interval = "prediction", level = 0.95)
pred_df <- data.frame(
concentration = x_pred$concentration,
fit = pred_ci[, "fit"],
lwr = pred_ci[, "lwr"],
upr = pred_ci[, "upr"]
)
# Unknown sample: measured absorbance → derived concentration
unknown_abs <- 0.648
unknown_conc <- (unknown_abs - intercept) / slope
unknown_df <- data.frame(concentration = unknown_conc, absorbance = unknown_abs)
# Regression equation annotation text
eq_text <- sprintf(
"A = %.4f × C + %.4f\nR² = %.5f",
slope, intercept, r_sq
)
# Title — font size scaled linearly if longer than the 67-char baseline
plot_title <- "calibration-beer-lambert · r · ggplot2 · anyplot.ai"
title_n <- nchar(plot_title)
title_size <- if (title_n > 67) round(12 * 67 / title_n) else 12
# Plot
p <- ggplot(df, aes(x = concentration, y = absorbance)) +
# 95% prediction interval band
geom_ribbon(
data = pred_df,
aes(x = concentration, ymin = lwr, ymax = upr),
inherit.aes = FALSE,
fill = IMPRINT_PALETTE[1],
alpha = 0.20
) +
# Regression fit line
geom_line(
data = pred_df,
aes(x = concentration, y = fit),
inherit.aes = FALSE,
color = IMPRINT_PALETTE[1],
linewidth = 1.2
) +
# Unknown sample dashed guide: horizontal (absorbance → y-axis)
annotate(
"segment",
x = 0, xend = unknown_conc,
y = unknown_abs, yend = unknown_abs,
linetype = "dashed",
color = IMPRINT_PALETTE[5],
linewidth = 0.7
) +
# Unknown sample dashed guide: vertical (concentration → x-axis)
annotate(
"segment",
x = unknown_conc, xend = unknown_conc,
y = 0, yend = unknown_abs,
linetype = "dashed",
color = IMPRINT_PALETTE[5],
linewidth = 0.7
) +
# Calibration standard points
geom_point(
color = IMPRINT_PALETTE[1],
size = 3.5,
shape = 19
) +
# Unknown sample point (diamond)
geom_point(
data = unknown_df,
color = IMPRINT_PALETTE[5],
size = 4.5,
shape = 18
) +
# Regression equation + R² annotation box
annotate(
"label",
x = 0.08,
y = 0.94,
label = eq_text,
hjust = 0,
vjust = 1,
color = INK,
fill = ELEVATED_BG,
size = 2.8,
label.size = 0.25,
label.padding = unit(0.35, "lines"),
label.r = unit(0.1, "lines")
) +
# Unknown sample label
annotate(
"text",
x = unknown_conc + 0.20,
y = unknown_abs + 0.03,
label = sprintf("Unknown\nC = %.2f mg/L", unknown_conc),
hjust = 0,
vjust = 0,
color = IMPRINT_PALETTE[5],
size = 2.5,
lineheight = 1.1
) +
labs(
title = plot_title,
x = "Concentration (mg/L)",
y = "Absorbance"
) +
scale_x_continuous(
breaks = seq(0, 4, by = 0.5),
expand = expansion(mult = c(0.02, 0.04))
) +
scale_y_continuous(
limits = c(0, 1.15),
breaks = seq(0, 1.0, by = 0.2),
expand = expansion(mult = c(0.01, 0.03))
) +
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 = 0.4),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = INK_SOFT, linewidth = 0.5),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
plot.title = element_text(color = INK, size = title_size,
hjust = 0, face = "plain"),
plot.margin = margin(20, 30, 20, 20, "pt")
)
# Save
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/calibration-beer-lambert/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": "calibration-beer-lambert",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/calibration-beer-lambert/r/ggplot2",
"hub": "https://anyplot.ai/calibration-beer-lambert",
"code_json": "https://api.anyplot.ai/specs/calibration-beer-lambert/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/calibration-beer-lambert",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/calibration-beer-lambert/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/calibration-beer-lambert/r/ggplot2/plot-dark.png",
"quality_score": 87.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Beer-Lambert Calibration Curve on anyplot.ai.