A psychrometric chart plots dry-bulb temperature against humidity ratio, overlaid with curves for relative humidity, wet-bulb temperature, enthalpy, and specific volume. It is the fundamental tool for HVAC system design and air conditioning process analysis. The chart reveals the thermodynamic properties of moist air at a glance, enabling engineers to trace heating, cooling, humidification, and dehumidification processes as paths on the diagram.
#' anyplot.ai
#' psychrometric-basic: Psychrometric Chart for HVAC
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 90/100 | Created: 2026-06-16
library(ggplot2)
library(dplyr)
library(ragg)
# --- Theme tokens -----------------------------------------------------------
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 palette — one hue per property family, semantically chosen
GREEN <- "#009E73" # 1 — relative-humidity curves (saturation = hero series)
CYAN <- "#2ABCCD" # 6 — wet-bulb temperature (cool / wet association)
LAV <- "#C475FD" # 2 — constant-enthalpy lines
BLUE <- "#4467A3" # 3 — constant specific-volume lines
RED <- "#AE3030" # 5 — example HVAC process path (emphasis)
# --- Psychrometric model (ASHRAE, sea-level 101.325 kPa) --------------------
P_ATM <- 101.325 # kPa
p_ws <- function(t) 0.61078 * exp(17.27 * t / (t + 237.3)) # sat. vapour pressure, kPa
W_of <- function(t, rh) { # humidity ratio, g/kg dry air
pw <- rh * p_ws(t)
1000 * 0.62198 * pw / (P_ATM - pw)
}
W_sat <- function(t) W_of(t, 1.0) # saturation curve, g/kg
T_MIN <- -10; T_MAX <- 50
W_MIN <- 0; W_MAX <- 30
temps <- seq(T_MIN, T_MAX, by = 0.2)
# keep only the physical region (at or below the saturation curve, inside the box)
clip_region <- function(df) {
df |>
filter(w >= W_MIN, w <= W_MAX, t >= T_MIN, t <= T_MAX,
w <= W_sat(t) + 1e-6)
}
# --- Relative-humidity curves (10 % .. 100 %) -------------------------------
rh_levels <- seq(10, 100, by = 10)
rh_df <- bind_rows(lapply(rh_levels, function(rh) {
data.frame(rh = rh, t = temps, w = W_of(temps, rh / 100))
})) |> clip_region()
sat_df <- filter(rh_df, rh == 100) # 100 % RH — saturation boundary
rh_minor <- filter(rh_df, rh < 100)
# stagger label offsets so converging high-RH curves near the top don't crowd
rh_lab <- rh_minor |> group_by(rh) |> slice_max(t, n = 1) |> ungroup() |>
mutate(vj = ifelse(rh %% 20 == 0, -0.4, 1.3))
# --- Constant wet-bulb temperature lines ------------------------------------
wb_levels <- seq(0, 30, by = 5)
wb_df <- bind_rows(lapply(wb_levels, function(twb) {
ws_wb <- W_of(twb, 1.0) / 1000 # kg/kg at wet-bulb temp
tt <- seq(twb, T_MAX, by = 0.2)
w <- ((2501 - 2.326 * twb) * ws_wb - 1.006 * (tt - twb)) /
(2501 + 1.86 * tt - 4.186 * twb)
data.frame(twb = twb, t = tt, w = w * 1000)
})) |> clip_region()
wb_lab <- wb_df |> group_by(twb) |> slice_min(t, n = 1) |> ungroup()
# --- Constant-enthalpy lines (kJ/kg dry air) --------------------------------
h_levels <- seq(20, 110, by = 15)
h_df <- bind_rows(lapply(h_levels, function(h) {
w <- (h - 1.006 * temps) / (2501 + 1.86 * temps) # kg/kg
data.frame(h = h, t = temps, w = w * 1000)
})) |> clip_region()
h_lab <- h_df |> group_by(h) |> slice_min(t, n = 1) |> ungroup()
# --- Constant specific-volume lines (m3/kg dry air) -------------------------
v_levels <- seq(0.78, 0.94, by = 0.02)
v_df <- bind_rows(lapply(v_levels, function(v) {
w <- (v * P_ATM / (0.287042 * (temps + 273.15)) - 1) / 1.6078 # kg/kg
data.frame(v = v, t = temps, w = w * 1000)
})) |> clip_region()
v_lab <- v_df |> group_by(v) |> slice_max(t, n = 1) |> ungroup()
# --- Thermal comfort zone (~20-26 C, 30-60 % RH) ----------------------------
comfort <- bind_rows(
data.frame(t = seq(20, 26, 0.25), rh = 30),
data.frame(t = 26, rh = seq(30, 60, 2)),
data.frame(t = seq(26, 20, -0.25), rh = 60),
data.frame(t = 20, rh = seq(60, 30, -2))
) |> mutate(w = W_of(t, rh / 100))
# --- Example HVAC process: cooling & dehumidification -----------------------
state_a <- data.frame(t = 30, w = W_of(30, 0.50)) # warm humid return air
state_b <- data.frame(t = 13, w = W_sat(13)) # cooled, near-saturated supply
process <- bind_rows(cbind(state_a, lab = "A"), cbind(state_b, lab = "B"))
# --- Plot -------------------------------------------------------------------
p <- ggplot() +
# comfort zone
geom_polygon(data = comfort, aes(t, w),
fill = INK_MUTED, alpha = 0.16, color = NA) +
# specific-volume lines (steep, dotted)
geom_line(data = v_df, aes(t, w, group = v),
color = BLUE, linewidth = 0.5, linetype = "dotted") +
# enthalpy lines (oblique, dashed)
geom_line(data = h_df, aes(t, w, group = h),
color = LAV, linewidth = 0.5, linetype = "22") +
# wet-bulb lines (diagonal, dashed)
geom_line(data = wb_df, aes(t, w, group = twb),
color = CYAN, linewidth = 0.5, linetype = "longdash") +
# relative-humidity curves
geom_line(data = rh_minor, aes(t, w, group = rh),
color = GREEN, linewidth = 0.55, alpha = 0.65) +
# saturation curve (100 % RH) — the hero boundary
geom_line(data = sat_df, aes(t, w),
color = GREEN, linewidth = 1.5) +
# comfort outline + label
geom_path(data = comfort, aes(t, w),
color = INK_SOFT, linewidth = 0.5, linetype = "dashed") +
annotate("text", x = 23, y = W_of(23, 0.45), label = "Comfort\nzone",
color = INK, size = 2.7, lineheight = 0.9, fontface = "bold") +
# process path A -> B
geom_segment(data = data.frame(x = state_a$t, y = state_a$w,
xe = state_b$t, ye = state_b$w),
aes(x = x, y = y, xend = xe, yend = ye),
color = RED, linewidth = 1.1,
arrow = arrow(length = unit(0.16, "in"), type = "closed")) +
geom_point(data = process, aes(t, w), color = RED, size = 2.6) +
geom_text(data = process, aes(t, w, label = lab),
color = RED, size = 3.1, fontface = "bold",
hjust = -0.5, vjust = -0.3) +
annotate("text", x = 30.5, y = W_of(30, 0.50) + 1.2,
label = "Cooling &\ndehumidification", color = RED,
size = 2.6, hjust = 0, lineheight = 0.9, fontface = "bold")
# --- Direct line labels -----------------------------------------------------
p <- p +
geom_text(data = rh_lab, aes(t, w, label = paste0(rh, "%")),
color = GREEN, size = 2.5, hjust = 1.15, vjust = rh_lab$vj,
fontface = "bold") +
annotate("text", x = 28.5, y = W_sat(28.5), label = "Saturation (100% RH)",
color = GREEN, size = 2.8, hjust = 1.05, vjust = -0.6,
fontface = "bold", angle = 52) +
geom_text(data = wb_lab, aes(t, w, label = twb),
color = CYAN, size = 2.7, hjust = 1.25, vjust = 1.7) +
geom_text(data = h_lab, aes(t, w, label = h),
color = LAV, size = 2.7, hjust = 1.3, vjust = -1.0) +
geom_text(data = v_lab, aes(t, w, label = sprintf("%.2f", v)),
color = BLUE, size = 2.7, hjust = -0.1, vjust = 1.2) +
# family descriptors (direct, colour-keyed)
annotate("text", x = -9, y = 28.3, label = "Relative humidity",
color = GREEN, size = 2.9, hjust = 0, fontface = "bold") +
annotate("text", x = -9, y = 26.6, label = "Wet-bulb temperature (°C)",
color = CYAN, size = 2.9, hjust = 0, fontface = "bold") +
annotate("text", x = -9, y = 24.9, label = "Enthalpy (kJ/kg)",
color = LAV, size = 2.9, hjust = 0, fontface = "bold") +
annotate("text", x = -9, y = 23.2, label = "Specific volume (m³/kg)",
color = BLUE, size = 2.9, hjust = 0, fontface = "bold")
# --- Scales, labels, theme --------------------------------------------------
p <- p +
scale_x_continuous(breaks = seq(-10, 50, 5),
expand = expansion(mult = c(0.01, 0.04))) +
scale_y_continuous(position = "right", breaks = seq(0, 30, 5),
expand = expansion(mult = c(0.0, 0.02))) +
coord_cartesian(xlim = c(T_MIN, T_MAX), ylim = c(W_MIN, W_MAX)) +
labs(
title = "psychrometric-basic · r · ggplot2 · anyplot.ai",
subtitle = "Moist-air properties at sea level (101.325 kPa)",
x = "Dry-bulb temperature (°C)",
y = "Humidity ratio (g water / kg dry air)"
) +
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 = INK, linewidth = 0.18),
panel.grid.minor = element_blank(),
panel.border = element_rect(color = INK_SOFT, fill = NA, linewidth = 0.4),
axis.title = element_text(color = INK, size = 10),
axis.title.x = element_text(margin = margin(t = 4)),
axis.title.y.right = element_text(margin = margin(l = 6), angle = 90),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.ticks = element_line(color = INK_SOFT, linewidth = 0.3),
plot.title = element_text(color = INK, size = 12, face = "bold"),
plot.subtitle = element_text(color = INK_SOFT, size = 9),
plot.margin = margin(10, 12, 8, 12)
)
# --- Save -------------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Part of Psychrometric Chart for HVAC on anyplot.ai.