Oddly enough ggplot2 has no support for a stacked and grouped (position="dodge") bar plot. The seaborn python package, although excellent, also does not provide an alternative. However, I knew it was surely possible to make such a plot in regular matplotlib. Matplotlib, although sometimes clunky, gives you enough flexibility to precisely place plotting elements which is needed for a stacked and grouped bar plot.
Below is a working example of making a stacked and grouped bar plot.
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# make up some fake data
pos_mut_pcts = np.array([20, 10, 5, 7.5, 30, 50])
pos_cna_pcts = np.array([10, 0, 0, 7.5, 10, 0])
pos_both_pcts = np.array([10, 0, 0, 0, 0, 0])
neg_mut_pcts = np.array([10, 30, 5, 0, 10, 25])
neg_cna_pcts = np.array([5, 0, 7.5, 0, 0, 10])
neg_both_pcts = np.array([0, 0, 0, 0, 0, 10])
genes = ['PIK3CA', 'PTEN', 'CDKN2A', 'FBXW7', 'KRAS', 'TP53']
with sns.axes_style("white"):
sns.set_style("ticks")
sns.set_context("talk")
# plot details
bar_width = 0.35
epsilon = .015
line_width = 1
opacity = 0.7
pos_bar_positions = np.arange(len(pos_mut_pcts))
neg_bar_positions = pos_bar_positions + bar_width
# make bar plots
hpv_pos_mut_bar = plt.bar(pos_bar_positions, pos_mut_pcts, bar_width,
color='#ED0020',
label='HPV+ Mutations')
hpv_pos_cna_bar = plt.bar(pos_bar_positions, pos_cna_pcts, bar_width-epsilon,
bottom=pos_mut_pcts,
alpha=opacity,
color='white',
edgecolor='#ED0020',
linewidth=line_width,
hatch='//',
label='HPV+ CNA')
hpv_pos_both_bar = plt.bar(pos_bar_positions, pos_both_pcts, bar_width-epsilon,
bottom=pos_cna_pcts+pos_mut_pcts,
alpha=opacity,
color='white',
edgecolor='#ED0020',
linewidth=line_width,
hatch='0',
label='HPV+ Both')
hpv_neg_mut_bar = plt.bar(neg_bar_positions, neg_mut_pcts, bar_width,
color='#0000DD',
label='HPV- Mutations')
hpv_neg_cna_bar = plt.bar(neg_bar_positions, neg_cna_pcts, bar_width-epsilon,
bottom=neg_mut_pcts,
color="white",
hatch='//',
edgecolor='#0000DD',
ecolor="#0000DD",
linewidth=line_width,
label='HPV- CNA')
hpv_neg_both_bar = plt.bar(neg_bar_positions, neg_both_pcts, bar_width-epsilon,
bottom=neg_cna_pcts+neg_mut_pcts,
color="white",
hatch='0',
edgecolor='#0000DD',
ecolor="#0000DD",
linewidth=line_width,
label='HPV- Both')
plt.xticks(neg_bar_positions, genes, rotation=45)
plt.ylabel('Percentage of Samples')
plt.legend(loc='best')
sns.despine()
@mchun96 Good point, that will make the plotting more robust to the data