ggplot2 Quick Reference

library(ggplot2)

# Every ggplot has 3 parts:
# 1. Data  2. Aesthetics  3. Geom
ggplot(data = mpg, aes(x = displ, y = hwy)) +
  geom_point()

# Store and build incrementally
p <- ggplot(mpg, aes(displ, hwy))
p + geom_point() + geom_smooth()

# Continuous
ggplot(mpg, aes(hwy)) +
  geom_histogram(bins = 20, fill = "#e04a2f")

ggplot(mpg, aes(hwy)) +
  geom_density(fill = "steelblue", alpha = 0.5)

# Discrete
ggplot(mpg, aes(class)) +
  geom_bar(fill = "#e04a2f")

# Scatter
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class), size = 2)

# Line (time series)
ggplot(economics, aes(date, unemploy)) +
  geom_line(color = "#e04a2f", linewidth = 0.8)

# Bar (pre-computed values)
ggplot(df, aes(category, value)) +
  geom_col(fill = "steelblue")

# Boxplot
ggplot(mpg, aes(class, hwy)) +
  geom_boxplot(fill = "#fde8e4")

# Violin
ggplot(mpg, aes(class, hwy)) +
  geom_violin(fill = "#e04a2f", alpha = 0.3) +
  geom_jitter(width = 0.15, size = 0.8)

# Area
ggplot(economics, aes(date, unemploy)) +
  geom_area(fill = "#e04a2f", alpha = 0.4)

# Heatmap / tile
ggplot(df, aes(x_var, y_var, fill = value)) +
  geom_tile()

# Map data to visual properties inside aes()
aes(x, y, color, fill, size, shape,
    alpha, linetype, group)

# Set fixed values OUTSIDE aes()
geom_point(color = "red", size = 3)

# Map variable to aesthetic INSIDE aes()
geom_point(aes(color = class, size = cyl))

# Common shapes: 0-25 (16=filled circle)
# Common linetypes: "solid","dashed",
#   "dotted","dotdash","longdash"

# Wrap by one variable
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~ class, ncol = 3)

# Grid by two variables
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_grid(drv ~ cyl)

# Free scales
facet_wrap(~ class, scales = "free_y")

# Axis limits and breaks
scale_x_continuous(
  limits = c(0, 50),
  breaks = seq(0, 50, 10)
)
scale_y_log10()    # log scale

# Color / fill
scale_color_manual(
  values = c("a"="#e04a2f","b"="steelblue")
)
scale_fill_brewer(palette = "Set2")
scale_color_gradient(low="white", high="red")
scale_color_viridis_d() # colorblind-safe

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  labs(
    title = "Engine Size vs. Fuel Efficiency",
    subtitle = "EPA data for 234 cars",
    x = "Displacement (L)",
    y = "Highway MPG",
    color = "Vehicle Class",
    caption = "Source: fueleconomy.gov"
  )

# Add text annotations
+ annotate("text", x=5, y=40,
    label="Outlier region", color="red")
+ annotate("rect", xmin=4, xmax=6,
    ymin=35, ymax=45, alpha=0.1)

# Built-in themes
+ theme_minimal()     # clean, no boxes
+ theme_bw()          # white bg, grid
+ theme_classic()     # axes only
+ theme_void()        # blank (maps)

# Custom tweaks
+ theme(
    plot.title = element_text(
      face = "bold", size = 14),
    axis.text.x = element_text(
      angle = 45, hjust = 1),
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  )

# Trend line
geom_smooth(method = "lm", se = TRUE)
geom_smooth(method = "loess", span = 0.5)

# Summary statistics
stat_summary(fun = mean,
  geom = "point", size = 3)

# Flip coordinates
+ coord_flip()

# Dodged bars (grouped)
ggplot(df, aes(group, value, fill = cat)) +
  geom_col(position = position_dodge(0.9))

# Stacked bars
geom_col(position = "stack")   # default
geom_col(position = "fill")    # 100%

# Save last plot
ggsave("plot.png", width = 8, height = 5,
       dpi = 300)

# Save a stored plot
ggsave("plot.pdf", plot = p,
       width = 10, height = 6)

# Common formats: png, pdf, svg, jpg

Rtuition Cheat Sheet — ggplot2 Quick Reference — rtuition.com