Intermediate

Data Visualization with ggplot2

Build publication-quality graphics using the grammar of graphics - the most powerful visualization system in any data science language.

The Grammar of Graphics

ggplot2 is built on a layered grammar where every plot is composed of:

  • Data: The dataset to visualize
  • Aesthetics (aes): Mappings from data variables to visual properties (x, y, color, size)
  • Geometries (geom): The visual marks (points, lines, bars)
  • Facets: Subplots for different subsets
  • Statistics: Statistical transformations
  • Coordinates: The coordinate system
  • Themes: Visual styling
R 
library(ggplot2)

# Basic structure: data + aesthetics + geometry
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
  geom_point()

Common Geoms

Scatter Plot - geom_point

R 
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3, alpha = 0.7) +
  labs(title = "MPG vs Weight", color = "Cylinders")

Line Chart - geom_line

R 
ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line(color = "steelblue", linewidth = 1) +
  labs(title = "US Unemployment Over Time")

Bar Chart - geom_bar / geom_col

R 
# geom_bar counts occurrences
ggplot(mtcars, aes(x = factor(cyl), fill = factor(cyl))) +
  geom_bar()

# geom_col uses pre-computed values
df <- mtcars |> group_by(cyl) |> summarise(avg = mean(mpg))
ggplot(df, aes(x = factor(cyl), y = avg, fill = factor(cyl))) +
  geom_col()

Histogram & Boxplot

R 
# Histogram
ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(bins = 15, fill = "steelblue", color = "white")

# Boxplot
ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
  geom_boxplot()

# Violin plot
ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
  geom_violin() +
  geom_jitter(width = 0.1, alpha = 0.5)

Heatmap - geom_tile

R 
cor_data <- as.data.frame(as.table(cor(mtcars)))
ggplot(cor_data, aes(x = Var1, y = Var2, fill = Freq)) +
  geom_tile() +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white")

Faceting

R 
# facet_wrap - wrap into rows/columns
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  facet_wrap(~ cyl)

# facet_grid - 2D grid of panels
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  facet_grid(am ~ cyl)

Themes, Colors, Labels

R 
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  scale_color_brewer(palette = "Set1") +
  labs(
    title = "Fuel Efficiency by Weight",
    subtitle = "Colored by number of cylinders",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon",
    color = "Cylinders",
    caption = "Source: mtcars dataset"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    legend.position = "bottom"
  )

Saving Plots

R 
# Save the last plot
ggsave("my_plot.png", width = 10, height = 6, dpi = 300)
ggsave("my_plot.pdf", width = 10, height = 6)

# Save a specific plot object
p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
ggsave("scatter.png", plot = p)

Interactive with plotly

R 
library(plotly)

p <- ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3)

# Convert any ggplot to interactive
ggplotly(p)
✅
Tip: ggplot2 has over 50 geom_* functions. Start with the basics (point, line, bar, histogram, boxplot) and explore more as needed. The ggplot2 reference is excellent.

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