Stacked Bar Chart

A stacked bar chart extends the standard bar chart by dividing each bar into colored segments, one per sub-category. The full height of the bar shows the category total while the segments reveal how each sub-category contributes to that total.

Stacked bar charts excel at comparing totals and understanding composition across categories simultaneously.

An example of an embedded stacked bar chart

Creating an Effective Stacked Bar Chart

Recommended data types for each axis:

X-Axis Dates or categorical data
Y-Axis Numerical values
Breakdown Axis Categorical data (each unique value becomes a stacked segment)

Description

Bars - each bar represents one category on the X-axis; total bar height equals the sum of all segments
Segments - colored divisions within a bar, each representing a sub-category’s contribution
X-Axis - lists the categories being compared
Y-Axis - represents the cumulative value; starts at zero
Colors - each sub-category is assigned a distinct color, consistent across all bars

When to Use a Stacked Bar Chart

Compare totals and composition together - see both how large each category is and how it is made up
Show part-to-whole proportions - segments immediately convey each sub-category’s relative share
Identify dominant components - spot which sub-category contributes most within each bar
Track how composition changes over time - when the X-axis is time-based, shifting proportions are visible across bars

When to Avoid a Stacked Bar Chart

Many sub-categories - more than 5–6 segments makes colors hard to distinguish; consider grouping smaller values into “Other”
Comparing non-baseline segments - only the bottom segment shares a common baseline; comparing middle segments is difficult; use a grouped bar chart instead
Negative values - stacked bars assume all values are positive; mixed positive/negative data can be misleading

Variants

Normalized Stacked Bar Chart

A normalized (100%) stacked bar chart rescales every bar to the same height so each segment shows its percentage of the total rather than an absolute value. Use this when the relative composition matters more than the actual totals - for example, comparing the market-share breakdown across years regardless of total market size.