ggplot2 Fundamentals

Basic Structure

library(ggplot2)

Grammar of graphics: data + aesthetics + geometry

ggplot(data, aes(x = var1, y = var2)) + geom_point()

Common Geoms

Scatter plot

ggplot(df, aes(x, y)) + geom_point()

Line plot

ggplot(df, aes(x, y)) + geom_line()

Bar plot

ggplot(df, aes(x, y)) + geom_col() # y values ggplot(df, aes(x)) + geom_bar() # counts

Boxplot

ggplot(df, aes(group, value)) + geom_boxplot()

Violin plot

ggplot(df, aes(group, value)) + geom_violin()

Histogram

ggplot(df, aes(x)) + geom_histogram(bins = 30)

Density

ggplot(df, aes(x, fill = group)) + geom_density(alpha = 0.5)

Heatmap

ggplot(df, aes(x, y, fill = value)) + geom_tile()

Aesthetic Mappings

Color by group

ggplot(df, aes(x, y, color = group)) + geom_point()

Size by value

ggplot(df, aes(x, y, size = value)) + geom_point()

Shape by category

ggplot(df, aes(x, y, shape = category)) + geom_point()

Fill for bars/boxes

ggplot(df, aes(x, y, fill = group)) + geom_boxplot()

Alpha for transparency

ggplot(df, aes(x, y, alpha = value)) + geom_point()

Publication Theme

theme_publication <- function(base_size = 12) { theme_bw(base_size = base_size) + theme( panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.border = element_rect(color = 'black', linewidth = 0.5), axis.text = element_text(color = 'black'), axis.ticks = element_line(color = 'black'), legend.key = element_blank(), strip.background = element_blank(), strip.text = element_text(face = 'bold') ) }

Usage

ggplot(df, aes(x, y)) + geom_point() + theme_publication()

Color Palettes

library(RColorBrewer) library(viridis)

Qualitative (categorical)

scale_color_brewer(palette = 'Set1') scale_fill_brewer(palette = 'Set2')

Sequential (continuous)

scale_fill_viridis_c() scale_color_gradient(low = 'white', high = 'red')

Diverging

scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = 0) scale_fill_distiller(palette = 'RdBu')

Manual colors

scale_color_manual(values = c('Control' = '#1f77b4', 'Treatment' = '#d62728'))

Volcano Plot

volcano_plot <- function(res, fdr = 0.05, lfc = 1) { res <- res %>% mutate( significance = case_when( padj < fdr & log2FoldChange > lfc ~ 'Up', padj < fdr & log2FoldChange < -lfc ~ 'Down', TRUE ~ 'NS' ) )

ggplot(res, aes(log2FoldChange, -log10(pvalue), color = significance)) +
    geom_point(alpha = 0.6, size = 1) +
    scale_color_manual(values = c('Up' = '#d62728', 'Down' = '#1f77b4', 'NS' = 'grey60')) +
    geom_vline(xintercept = c(-lfc, lfc), linetype = 'dashed', color = 'grey40') +
    geom_hline(yintercept = -log10(fdr), linetype = 'dashed', color = 'grey40') +
    labs(x = 'Log2 Fold Change', y = '-Log10 P-value') +
    theme_publication()

}

MA Plot

ma_plot <- function(res, fdr = 0.05) { res <- res %>% mutate(significant = padj < fdr)

ggplot(res, aes(log10(baseMean), log2FoldChange, color = significant)) +
    geom_point(alpha = 0.5, size = 1) +
    scale_color_manual(values = c('TRUE' = 'red', 'FALSE' = 'grey60')) +
    geom_hline(yintercept = 0, color = 'black') +
    labs(x = 'Log10 Mean Expression', y = 'Log2 Fold Change') +
    theme_publication()

}

Boxplot with Points

ggplot(df, aes(group, value, fill = group)) + geom_boxplot(outlier.shape = NA, alpha = 0.7) + geom_jitter(width = 0.2, alpha = 0.5, size = 1) + scale_fill_brewer(palette = 'Set2') + labs(x = NULL, y = 'Expression') + theme_publication() + theme(legend.position = 'none')

Faceting

Wrap by one variable

ggplot(df, aes(x, y)) + geom_point() + facet_wrap(~ group, scales = 'free')

Grid by two variables

ggplot(df, aes(x, y)) + geom_point() + facet_grid(rows = vars(condition), cols = vars(timepoint))

Labels and Text

library(ggrepel)

ggplot(res, aes(log2FoldChange, -log10(pvalue))) + geom_point() + geom_text_repel( data = subset(res, padj < 0.01), aes(label = gene), max.overlaps = 20, size = 3 )

Multi-Panel Figures

library(patchwork)

p1 <- ggplot(df, aes(x, y)) + geom_point() p2 <- ggplot(df, aes(group, value)) + geom_boxplot() p3 <- ggplot(df, aes(x)) + geom_histogram()

Combine horizontally

p1 + p2 + p3

Combine with layout

(p1 | p2) / p3

Add labels

(p1 + p2 + p3) + plot_annotation(tag_levels = 'A')

Shared legend

(p1 + p2) + plot_layout(guides = 'collect')

Saving Figures

For publication (300 DPI)

ggsave('figure.pdf', p, width = 7, height = 5, units = 'in') ggsave('figure.png', p, width = 7, height = 5, units = 'in', dpi = 300) ggsave('figure.tiff', p, width = 7, height = 5, units = 'in', dpi = 300, compression = 'lzw')

For presentations

ggsave('figure.png', p, width = 10, height = 6, dpi = 150)

Axis Formatting

library(scales)

Scientific notation

scale_y_continuous(labels = scientific)

Comma separators

scale_x_continuous(labels = comma)

Log scale

scale_y_log10(labels = trans_format('log10', math_format(10^.x)))

Percent

scale_y_continuous(labels = percent)

Limits

coord_cartesian(xlim = c(0, 10), ylim = c(0, 100))

Breaks

scale_x_continuous(breaks = seq(0, 10, 2))

Legend Customization

Position

theme(legend.position = 'bottom') theme(legend.position = 'none') theme(legend.position = c(0.8, 0.2))

Title

labs(color = 'Condition', fill = 'Group') guides(color = guide_legend(title = 'Condition'))

Order

scale_color_discrete(limits = c('Control', 'Treatment'))

Heatmap with pheatmap

library(pheatmap) library(RColorBrewer)

pheatmap( mat, scale = 'row', color = colorRampPalette(rev(brewer.pal(9, 'RdBu')))(100), cluster_rows = TRUE, cluster_cols = TRUE, show_rownames = TRUE, show_colnames = TRUE, annotation_col = annotation_df, fontsize = 8, filename = 'heatmap.pdf', width = 8, height = 10 )

Related Skills

differential-expression/de-visualization - DE-specific plots
pathway-analysis/enrichment-visualization - Enrichment plots
reporting/rmarkdown-reports - Figures in reports