Bio Data Visualization Ggplot2 Fundamentals

Version Compatibility

Reference examples tested with: ggplot2 3.5+, scales 1.3+, ggrepel 0.9.5+, ggtext 0.1.2+, viridis 0.6+, scico 1.5+, patchwork 1.2+ (axes='collect' requires 1.2.0+).

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

ggplot2 Fundamentals

"Build a publication figure in R" -> Express the figure as data + aesthetic mappings + one or more geometries + scales + facets + theme. The grammar of graphics (Wilkinson 2005; Wickham 2010 J Comput Graph Stat 19:3) makes each visual element separately addressable — change scales without rewriting geoms; swap geom_point for geom_violin without touching aesthetics.

• R: ggplot(data, aes(x, y)) + geom_point() + scale_color_manual(...) + theme_classic()
• Programmatic: aes(x = .data[[var]]) for tidy-eval; !!sym(var) for older base R style

The Three Modern Defaults

1. theme_classic() + remove panel grid + Okabe-Ito palette as the publication baseline. theme_minimal adds light gridlines; theme_bw adds a panel border; both work but theme_classic is the cleanest for journals.

2. cairo_pdf for export — ggsave('out.pdf', device = cairo_pdf) embeds TrueType fonts (searchable PDFs); default ggsave('.pdf') uses pdf() which produces journal-incompatible fonts on some systems.

3. Tidy evaluation for programmatic aes — aes(x = .data[[var]]) is the modern idiom (ggplot2 3.0+); the older aes_string(x = var) is deprecated. For dplyr-style symbol evaluation, use !!sym(var) with aes(x = !!sym(var)).

Grammar in Layers

library(ggplot2)

# data + aes + geom is the minimum
ggplot(df, aes(x = condition, y = expression)) +
    geom_boxplot() +
    geom_jitter(width = 0.2, alpha = 0.5) +
    # scales
    scale_y_continuous(trans = 'log10', labels = scales::label_log()) +
    scale_color_manual(values = c('#0072B2', '#D55E00')) +
    # labels
    labs(x = NULL, y = 'Expression (log10)',
         title = 'Gene X across conditions',
         caption = 'Source: ...') +
    # facets
    facet_wrap(~ tissue, ncol = 3, scales = 'free_y') +
    # theme
    theme_classic(base_size = 10) +
    theme(panel.grid = element_blank(),
          strip.background = element_blank(),
          strip.text = element_text(face = 'bold'))

Common Geoms

geom_point(alpha = 0.7, size = 1, rasterize = TRUE)   # rasterize: ggplot2 3.5+ inline OR ggrastr::rasterize()
geom_line(linewidth = 0.5)                             # linewidth replaces size for lines (ggplot2 3.4+)
geom_col()                                              # bar with y values (use this; geom_bar(stat='identity') is older)
geom_bar()                                              # bar with counts
geom_boxplot(outlier.shape = NA)                       # always suppress when overlaying jitter
geom_violin(bw = 'SJ', trim = FALSE)                   # Sheather-Jones bandwidth; show full tails
geom_histogram(bins = 30)                              # bins NOT binwidth for control
geom_density(alpha = 0.5)
geom_tile(aes(fill = z))                               # heatmap building block
geom_text(aes(label = label), check_overlap = TRUE)
geom_text_repel(aes(label = label), max.overlaps = Inf)   # ggrepel; max.overlaps = Inf prevents silent label drops

Aesthetic Mappings

aes(x, y, color, fill, shape, size, alpha, linetype, linewidth, group)

# Color vs fill: color = stroke; fill = interior (boxplot, bar, area, polygon)
# Use both when needed: geom_point(aes(color = group, fill = group), shape = 21)

Constant inside vs mapping inside aes is a common confusion:

geom_point(color = 'red')             # constant: every point red
geom_point(aes(color = group))        # mapping: color varies with group

Scales

# Continuous
scale_x_continuous(limits = c(0, 10), breaks = seq(0, 10, 2),
                    labels = scales::label_number(scale = 1e-6, suffix = 'M'))
scale_y_log10()
scale_y_continuous(trans = 'sqrt')

# Discrete
scale_x_discrete(limits = c('Control', 'Treatment', 'Vehicle'))   # explicit order
scale_color_manual(values = c(Control = '#0072B2', Treatment = '#D55E00'))

# Colormap (sequential, diverging, cyclic) -- see color-palettes
scale_color_viridis_c(option = 'viridis')
scale_color_scico(palette = 'batlow')                              # Crameri
scale_fill_gradient2(low = '#0072B2', mid = 'white', high = '#D55E00', midpoint = 0)

# Date / time
scale_x_date(date_breaks = '1 year', date_labels = '%Y')

Facets

facet_wrap(~ var, ncol = 3, scales = 'free_y')
facet_grid(rows = vars(condition), cols = vars(timepoint), scales = 'free_x')
facet_grid(condition ~ timepoint)                                  # formula syntax

scales = 'free_y' lets each panel have its own y-range — appropriate when biological scales differ across facets. scales = 'fixed' (default) is the right choice when comparing across panels.

Theme

# Publication baseline
theme_pub <- theme_classic(base_size = 10) +
    theme(
        panel.grid = element_blank(),
        axis.text = element_text(color = 'black'),
        axis.ticks = element_line(color = 'black', linewidth = 0.3),
        axis.line = element_line(color = 'black', linewidth = 0.3),
        legend.position = 'right',
        legend.key.size = unit(0.4, 'cm'),
        strip.background = element_blank(),
        strip.text = element_text(face = 'bold', size = 9),
        plot.title = element_text(face = 'bold', size = 11),
        plot.tag = element_text(face = 'bold', size = 11))

# Save as a function for re-use across project

Programmatic Plots (Tidy Evaluation)

# Pass variable name as a string
plot_var <- function(df, x_var, y_var) {
    ggplot(df, aes(x = .data[[x_var]], y = .data[[y_var]])) +
        geom_point()
}
plot_var(df, 'PC1', 'PC2')

# Alternative: bare names via embracing
plot_var2 <- function(df, x_var, y_var) {
    ggplot(df, aes(x = {{ x_var }}, y = {{ y_var }})) +
        geom_point()
}
plot_var2(df, PC1, PC2)

aes_string is deprecated as of ggplot2 3.0. .data[[var]] is the modern programmatic idiom.

Labels with ggtext (rich-text)

library(ggtext)
ggplot(df, aes(x, y)) + geom_point() +
    labs(x = 'log<sub>2</sub> fold change',
         y = '\\u2212log<sub>10</sub>(*p*)') +
    theme(axis.title.x = element_markdown(),
          axis.title.y = element_markdown())

ggtext renders inline HTML / Markdown in titles, captions, axis labels — much better than expression(...) for italics + subscripts + special characters.

Saving — TrueType Embedding

# cairo_pdf for TrueType embedded; portable across systems
ggsave('figure.pdf', plot = p,
       width = 89, height = 70, units = 'mm',
       device = cairo_pdf)

# Vector + raster mix via ggrastr (for large scatter)
library(ggrastr)
ggplot(df, aes(x, y)) +
    rasterise(geom_point(alpha = 0.5), dpi = 300) +
    theme_pub
ggsave('out.pdf', device = cairo_pdf)

# PNG for raster
ggsave('figure.png', p, width = 89, height = 70, units = 'mm', dpi = 300)

# TIFF for some journals
ggsave('figure.tiff', p, width = 89, height = 70, units = 'mm', dpi = 300,
       compression = 'lzw')

Common Failure Modes

Default ggsave fonts not embedded

Trigger: ggsave('out.pdf', p) without device = cairo_pdf.

Mechanism: Default pdf() device on some systems produces non-embedded fonts.

Symptom: Reviewer or coauthor opens PDF; text renders in wrong font; journal rejects.

Fix: Always device = cairo_pdf for PDF saves.

Mapping vs constant aesthetic confusion

Trigger: geom_point(aes(color = 'red')) — string 'red' becomes a categorical mapping.

Mechanism: aes() interprets its arguments as variables; 'red' becomes a 1-level factor and gets mapped to the FIRST default color.

Symptom: Points appear blue (or whatever default) with a legend showing "red" as a category.

Fix: Move outside aes: geom_point(color = 'red') for a constant; keep inside for a mapping.

linewidth vs size for lines

Trigger: geom_line(size = 0.5) in ggplot2 3.4+.

Mechanism: ggplot2 3.4+ renamed line-width control from size to linewidth; size still works for points.

Symptom: Warning "Using size aesthetic for lines was deprecated"; lines render but warning.

Fix: geom_line(linewidth = 0.5). geom_point(size = 1) is correct.

facet_wrap scales = 'free' confuses cross-panel comparison

Trigger: facet_wrap(~ var, scales = 'free') for figures intended to compare across panels.

Mechanism: Each panel has its own scale; visual comparison invalid.

Symptom: Reviewer asks "why are these heights different?"

Fix: Use scales = 'fixed' (default) when cross-panel comparison matters; use 'free_y' only when panels are inherently different scales.

aes_string deprecated

Trigger: aes_string(x = 'PC1', y = 'PC2') for programmatic plotting.

Mechanism: Deprecated since ggplot2 3.0; emits warning.

Symptom: Deprecation warning in script log.

Fix: aes(x = .data[['PC1']], y = .data[['PC2']]) OR aes(x = !!sym(x_var)).

ggrepel max.overlaps default drops labels

Trigger: geom_text_repel(aes(label = label)) with N > 10 labels.

Mechanism: Default max.overlaps = 10; labels exceeding this are silently dropped with a warning.

Symptom: Some labeled genes are silently missing; warning buried in log.

Fix: geom_text_repel(aes(label = label), max.overlaps = Inf) OR options(ggrepel.max.overlaps = Inf) at script top.

Saving with size in inches but intended mm

Trigger: ggsave('out.pdf', p, width = 89, height = 70) thinking mm.

Mechanism: Default units = 'in'.

Symptom: Figure is 89 inches wide — too large to open in Illustrator.

Fix: units = 'mm' explicit. Nature single column = 89mm; double column = 183mm.

References

• Wickham H. 2016. ggplot2: Elegant Graphics for Data Analysis (2nd ed). Springer.
• Wickham H. 2010. A layered grammar of graphics. J Comput Graph Stat 19(1):3-28.
• Wilkinson L. 2005. The Grammar of Graphics (2nd ed). Springer.

Related Skills

• data-visualization/color-palettes - Scale_color/_fill palette selection
• data-visualization/multipanel-figures - patchwork composition
• data-visualization/distribution-plots - Box / violin / raincloud geoms
• data-visualization/volcano-and-ma-plots - ggplot2 volcano with ggrepel
• data-visualization/heatmaps-clustering - ComplexHeatmap and ggplot2 geom_tile