Excel Charts: Choosing the Right Chart Type

Introduction to Excel Charts

A picture is worth a thousand words, and in Excel, the right chart can convey complex data insights instantly. Excel offers a rich variety of chart types, each designed for specific data visualization needs. Choosing the wrong chart can confuse your audience, while the right one can make patterns, trends, and insights jump off the page.

This guide will help you understand when and how to use each chart type effectively.

Understanding Chart Categories

Comparison Charts

Show differences between items or categories

Trend Charts

Display changes over time

Part-to-Whole Charts

Show composition and proportions

Relationship Charts

Reveal correlations between variables

Column and Bar Charts

Column Charts (Vertical Bars)

Best for: Comparing values across categories

Use when:

• You have 2-7 categories to compare
• Category names are short
• Comparing sales by product or region

Variations:

• Clustered: Compare multiple series side-by-side
• Stacked: Show part-to-whole relationship
• 100% Stacked: Compare proportions

Bar Charts (Horizontal Bars)

Best for: Categories with long names or many items

Use when:

• Category labels are lengthy (e.g., full product names)
• You have many categories (8+)
• Showing ranking or comparison

Line Charts

Basic Line Chart

Best for: Showing trends over time

Use when:

• Time is on the horizontal axis
• Tracking stock prices, temperature, or any time-series data
• Showing continuous data
• Comparing multiple trends simultaneously

Pro Tips:

• Limit to 5-6 lines for clarity
• Use different colors or line styles
• Add data labels for key points

Area Charts

Similar to line charts but with filled areas beneath lines

Best for: Emphasizing magnitude of change over time

Caution: Stacked area charts can hide underlying trends

Pie and Doughnut Charts

Pie Charts

Best for: Showing parts of a whole (percentages)

Use when:

• You have 3-5 categories maximum
• Values add up to 100%
• Showing market share or budget allocation

Best Practices:

• Start largest slice at 12 o'clock
• Use contrasting colors
• Show percentages in labels
• Avoid 3D effects (they distort perception)

When NOT to use pie charts:

• More than 5-6 categories
• Comparing multiple pies
• Precise value comparison needed

Doughnut Charts

Pie chart with a hole in the center

Advantage: Can display multiple data series (concentric rings)

Disadvantage: Harder to compare values than pie charts

Scatter (XY) Charts

Best for: Showing relationships between two numeric variables

Use when:

• Looking for correlations (e.g., price vs. demand)
• Both axes represent continuous values
• Identifying outliers or clusters
• Scientific or statistical analysis

With Trendlines

Add trendlines to show correlation strength:

• Linear: Straight-line relationship
• Exponential: Accelerating growth
• Polynomial: Curved relationships

Pro Tip: Display R² value to quantify correlation strength

Specialized Chart Types

Combo Charts

Combine two chart types (e.g., column + line)

Best for: Comparing data with different scales or units

Example: Sales volume (columns) vs. profit margin (line)

Waterfall Charts

Best for: Showing sequential positive and negative contributions

Use cases:

• Budget variance analysis
• Cash flow changes
• Bridge from opening to closing balance

Funnel Charts

Best for: Showing stages in a linear process

Use cases:

• Sales pipeline stages
• Conversion funnels
• Order fulfillment process

Histogram

Best for: Showing frequency distribution

Use when: Analyzing data distribution (normal, skewed, etc.)

Box and Whisker

Best for: Statistical analysis showing quartiles and outliers

Shows: Minimum, Q1, median, Q3, maximum, and outliers

Creating Your First Chart

Quick Method

1. Select your data (including headers)
2. Press Alt + F1 for default chart in same sheet
3. Or F11 for new chart sheet

Recommended Charts

1. Select your data
2. Insert tab → Recommended Charts
3. Excel suggests best chart types based on your data
4. Preview and select

Manual Selection

1. Select data
2. Insert tab → Choose chart category
3. Select specific chart subtype

Chart Design Best Practices

Essential Elements

• Clear Title: Describe what the chart shows
• Axis Labels: Include units (%, $, items)
• Legend: Only if multiple series; position appropriately
• Data Labels: Use sparingly for key values
• Gridlines: Minimal for clarity

Color Guidelines

• Use contrasting colors for different categories
• Maintain consistent colors across related charts
• Avoid red/green for colorblind accessibility
• Use color to highlight key insights
• Limit to 5-6 distinct colors

Common Mistakes to Avoid

• 3D Effects: Distort perception; avoid unless necessary
• Too Much Information: Keep it simple and focused
• Wrong Chart Type: Pie chart for time series, etc.
• Truncated Y-Axis: Can exaggerate differences
• Misleading Scales: Always start numerical axes at zero (unless good reason)

Customizing Charts

Quick Formatting

Click chart, then use three buttons that appear:

• + (Chart Elements): Add/remove title, legend, data labels, etc.
• Paintbrush (Chart Styles): Apply color schemes and styles
• Funnel (Chart Filters): Show/hide specific data series or categories

Format Pane

Right-click any chart element → Format...

Detailed control over:

• Colors, borders, shadows
• Number formats
• Label positions
• Axis scaling

Chart Types Decision Matrix

Your Goal	Best Chart Type
Compare categories	Column / Bar
Show trend over time	Line / Area
Display part-to-whole	Pie / Doughnut
Show correlation	Scatter
Display distribution	Histogram / Box Plot
Show progress through stages	Funnel
Explain variance	Waterfall

Conclusion

Mastering chart selection is as important as creating them. The right chart makes data insights obvious, while the wrong one obscures them. Always ask: "What story am I trying to tell?" and choose the chart that tells it most clearly.

Practice with different datasets and chart types. Over time, you'll develop an intuition for which charts work best in different situations. Remember: clarity and honesty should always be your guiding principles in data visualization!