A Walter-Lieth climate diagram (climograph) is the canonical visualization of a location's annual climate, plotting monthly mean temperature and monthly precipitation over the twelve months on a single panel. It uses the classic 1:2 axis-scaling convention where 10 °C on the temperature axis aligns with 20 mm on the precipitation axis, so the relationship between the two curves directly reveals water availability: where precipitation exceeds the temperature curve the period is humid (filled blue/hatched), and where the temperature curve rises above precipitation the period is arid (filled red/dotted). A header carries station metadata and annual means, while frost indicators along the baseline mark cold months.
#' anyplot.ai
#' climograph-walter-lieth: Walter-Lieth Climate Diagram
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 92/100 | Created: 2026-06-15
library(ggplot2)
library(ragg)
# 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"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
# Imprint categorical palette (hybrid-v3 sort order)
IMPRINT_PALETTE <- c(
"#009E73", # 1 — brand green (first categorical series)
"#C475FD", # 2 — lavender
"#4467A3", # 3 — blue
"#BD8233", # 4 — ochre
"#AE3030", # 5 — matte red
"#2ABCCD", # 6 — cyan
"#954477", # 7 — rose
"#99B314" # 8 — lime
)
# Domain-convention colors (semantic exception: temperature→red, precipitation→blue/water)
COLOR_TEMP <- IMPRINT_PALETTE[5] # #AE3030 — temperature (hot/warm semantic)
COLOR_PRECIP <- IMPRINT_PALETTE[3] # #4467A3 — precipitation (water semantic)
COLOR_FROST <- IMPRINT_PALETTE[6] # #2ABCCD — frost period (cold/ice semantic)
GRID_COLOR <- adjustcolor(INK, alpha.f = 0.12)
# Station metadata — Ankara, Turkey (continental climate with summer arid period)
station_elev <- 891
annual_mean_temp <- 11.8
annual_precip_total <- 352
# Monthly climate normals (1991-2020 reference period)
climate_df <- data.frame(
month_idx = 1:12,
month_lbl = c("J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"),
temp = c(-0.5, 1.0, 6.0, 11.5, 16.5, 20.0, 23.5, 23.5, 18.5, 12.5, 6.0, 2.0),
precip = c( 38.0, 31.0, 29.0, 33.0, 43.0, 34.0, 16.0, 12.0, 17.0, 27.0, 31.0, 41.0)
)
# Walter-Lieth 1:2 scaling convention: 10°C aligns with 20 mm on the shared axis
climate_df$prec_scaled <- climate_df$precip / 2
# Smooth interpolation between months for continuous fill regions
n_interp <- 600
x_smooth <- seq(1, 12, length.out = n_interp)
t_smooth <- approx(climate_df$month_idx, climate_df$temp, x_smooth)$y
p_smooth <- approx(climate_df$month_idx, climate_df$prec_scaled, x_smooth)$y
p_smooth <- pmin(p_smooth, 50) # cap at 100 mm / 2 = 50 (standard WL scale maximum)
# Ribbon endpoints for thin background tint under pattern fills
fill_df <- data.frame(
x = x_smooth,
temp = t_smooth,
prec_s = p_smooth,
humid_ymax = pmax(t_smooth, p_smooth),
arid_ymin = pmin(t_smooth, p_smooth)
)
# --- Diagonal hatching for humid region (precipitation > temperature curve) ---
# Slope chosen so hatch lines appear at ~45 degrees on 8×4.5-inch canvas
# (x range 11 units at 8/11 in/unit, y range 35 units at ~4.1/35 in/unit → slope ≈ 5.5)
hatch_slope <- 5.5 # dy/dx in data coordinates
hatch_spacing_y <- 2.5 # y-intercept spacing between parallel lines
# Intercepts: must cover y ∈ [-0.5, 21] at x ∈ [1,12]; at x=12: y = 60.5 + b
hatch_intercepts <- seq(-65, 25, by = hatch_spacing_y)
hatch_seg_list <- lapply(hatch_intercepts, function(b) {
y_line <- hatch_slope * (x_smooth - 1) + b
in_humid <- p_smooth > t_smooth & y_line >= t_smooth & y_line <= p_smooth
if (!any(in_humid)) return(NULL)
rle_res <- rle(in_humid)
pos <- 1L
k <- 0L
out_list <- vector("list", sum(rle_res$values))
for (i in seq_along(rle_res$lengths)) {
len <- rle_res$lengths[i]
if (rle_res$values[i]) {
k <- k + 1L
end_pos <- pos + len - 1L
out_list[[k]] <- data.frame(
x = x_smooth[pos], xend = x_smooth[end_pos],
y = y_line[pos], yend = y_line[end_pos]
)
}
pos <- pos + len
}
if (k > 0L) do.call(rbind, out_list[seq_len(k)]) else NULL
})
hatch_seg_df <- do.call(rbind, Filter(Negate(is.null), hatch_seg_list))
# --- Stipple dots for arid region (temperature > precipitation curve) ---
x_dot_seq <- seq(1, 12, by = 0.6)
y_dot_seq <- seq(-5, 30, by = 1.6)
dot_grid <- expand.grid(x = x_dot_seq, y = y_dot_seq)
dot_grid$t_at_x <- approx(x_smooth, t_smooth, dot_grid$x, rule = 2)$y
dot_grid$p_at_x <- approx(x_smooth, p_smooth, dot_grid$x, rule = 2)$y
arid_dots <- dot_grid[
dot_grid$t_at_x > dot_grid$p_at_x &
dot_grid$y >= dot_grid$p_at_x &
dot_grid$y <= dot_grid$t_at_x, ]
# Frost-month rectangles (mean temperature below 0°C)
frost_months <- climate_df$month_idx[climate_df$temp < 0]
frost_df <- if (length(frost_months) > 0) {
data.frame(
xmin = frost_months - 0.45,
xmax = frost_months + 0.45
)
} else {
NULL
}
# Plot title — mandatory anyplot format with descriptive station prefix
plot_title <- paste0(
"Ankara, Turkey · climograph-walter-lieth · r · ggplot2 · anyplot.ai"
)
title_fontsize <- max(8L, round(12L * min(1.0, 67 / nchar(plot_title))))
# Subtitle carries the station header (Walter-Lieth convention)
plot_subtitle <- sprintf(
"%d m a.s.l. · T = %.1f°C · ΣP = %d mm",
station_elev, annual_mean_temp, as.integer(annual_precip_total)
)
# Build plot
p <- ggplot() +
# Humid background tint (blue, very light) under the hatching
geom_ribbon(
data = fill_df,
aes(x = x, ymin = temp, ymax = humid_ymax),
fill = COLOR_PRECIP, alpha = 0.10, inherit.aes = FALSE
) +
# Diagonal hatching lines for humid period (blue/hatched per Walter-Lieth convention)
(if (!is.null(hatch_seg_df) && nrow(hatch_seg_df) > 0)
geom_segment(
data = hatch_seg_df,
aes(x = x, xend = xend, y = y, yend = yend),
color = COLOR_PRECIP, linewidth = 0.35, alpha = 0.65, inherit.aes = FALSE
)
else NULL) +
# Arid background tint (red, very light) under the stipple dots
geom_ribbon(
data = fill_df,
aes(x = x, ymin = arid_ymin, ymax = temp),
fill = COLOR_TEMP, alpha = 0.10, inherit.aes = FALSE
) +
# Stipple dots for arid period (red/dotted per Walter-Lieth convention)
(if (nrow(arid_dots) > 0)
geom_point(
data = arid_dots,
aes(x = x, y = y),
color = COLOR_TEMP, size = 0.55, alpha = 0.70, inherit.aes = FALSE
)
else NULL) +
# Frost-month indicator bands below 0°C
(if (!is.null(frost_df))
geom_rect(
data = frost_df,
aes(xmin = xmin, xmax = xmax),
ymin = -5, ymax = 0,
fill = COLOR_FROST, alpha = 0.40, inherit.aes = FALSE
)
else NULL) +
# Frost threshold reference line
geom_hline(yintercept = 0, color = INK_SOFT, linewidth = 0.4, linetype = "dashed") +
# Precipitation curve (plotted at precip/2 on temp axis; right axis shows actual mm)
geom_line(
data = climate_df,
aes(x = month_idx, y = prec_scaled),
color = COLOR_PRECIP, linewidth = 1.3, lineend = "round"
) +
# Temperature curve
geom_line(
data = climate_df,
aes(x = month_idx, y = temp),
color = COLOR_TEMP, linewidth = 1.3, lineend = "round"
) +
# X axis: month abbreviations
scale_x_continuous(
breaks = 1:12,
labels = c("J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"),
expand = c(0.025, 0)
) +
# Dual y-axis: left = temperature (°C), right = precipitation (mm) at 2× scale
scale_y_continuous(
name = "Temperature (°C)",
limits = c(-5, 30),
breaks = c(0, 10, 20, 30),
sec.axis = sec_axis(
~ . * 2,
name = "Precipitation (mm)",
breaks = c(0, 20, 40, 60),
labels = c("0", "20", "40", "60")
)
) +
labs(
title = plot_title,
subtitle = plot_subtitle,
x = NULL
) +
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.y = element_line(color = GRID_COLOR, linewidth = 0.3),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(color = INK_SOFT, fill = NA, linewidth = 0.5),
axis.title.y.left = element_text(color = COLOR_TEMP, size = 10),
axis.title.y.right = element_text(color = COLOR_PRECIP, size = 10),
axis.text.y.left = element_text(color = INK_SOFT, size = 8),
axis.text.y.right = element_text(color = INK_SOFT, size = 8),
axis.text.x = element_text(color = INK_SOFT, size = 8),
axis.line = element_blank(),
plot.title = element_text(color = INK, size = title_fontsize, face = "bold"),
plot.subtitle = element_text(color = INK_SOFT, size = 9,
margin = margin(t = 2, b = 6)),
plot.margin = margin(12, 16, 10, 12, "pt")
)
# Save (landscape: 8 × 4.5 in @ 400 dpi → 3200 × 1800 px)
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Part of Walter-Lieth Climate Diagram on anyplot.ai.