Types of Charts: A Complete Guide to Data Visualization

Choosing the right chart can be the difference between a presentation that lands and one that confuses. The wrong chart type makes accurate data look misleading. The right one makes patterns obvious in seconds.

This guide covers every major chart type, what it is, how it works, when to use it, and just as importantly, when not to. Whether you're building a dashboard, preparing a business report, or visualizing data for a school project, this is the reference you need.

Bar Chart

A bar chart represents data using rectangular bars. Each bar corresponds to a category, and its length or height reflects the value. Bars can be vertical (also called a column chart) or horizontal — the orientation is a stylistic choice, though horizontal bars work better when category names are long.

How it works: Categories are plotted on one axis, values on the other. The height or length of each bar makes comparison immediate — the eye naturally registers which bars are taller or longer without needing to read the numbers.

Grouped vs. stacked bars: A standard bar chart compares individual values. A grouped bar chart places multiple bars side by side for each category (useful when comparing two time periods, for example). A stacked bar chart stacks the bars on top of each other, letting you see both the total and its composition.

Best for:

• Comparing values across categories (sales by product, revenue by region)
• Showing rankings (top 10 countries by population)
• Displaying changes over time when the number of time periods is small

When not to use it: Avoid bar charts when you have too many categories — more than 15 or 20 bars becomes hard to read. Also avoid using them when the relationship between values matters more than the values themselves.

Example: A marketing team uses a grouped bar chart to compare monthly ad spend vs. revenue generated across four channels — SEO, paid search, social, and email.

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Line Chart

A line chart connects individual data points with a line, making it the go-to chart for showing how something changes over time. The slope of the line immediately communicates whether values are rising, falling, or staying flat.

How it works: Time (or another ordered variable) is plotted on the X axis, and the measured value on the Y axis. Multiple lines on the same chart let you compare trends between different groups simultaneously.

Best for:

• Tracking trends over time (stock prices, temperature, website traffic)
• Comparing how multiple groups change over the same period
• Highlighting acceleration or deceleration in change

When not to use it: Line charts imply continuity — that the values between data points follow the drawn line. If your data points are independent categories with no meaningful order between them, use a bar chart instead.

Tips:

• Start your Y axis at zero to avoid exaggerating small changes
• Use fewer than 5–6 lines on a single chart to avoid clutter
• Label your lines directly rather than relying on a distant legend

Example: A SaaS company tracks monthly active users across three subscription tiers over a two-year period, using three lines on a single chart to show how each tier grew (or declined) relative to the others.

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Pie Chart

A pie chart divides a circle into slices, where each slice's angle is proportional to its share of the total. It's one of the most recognizable chart types — and one of the most frequently misused.

How it works: The full circle represents 100% of a whole. Each slice represents one part of that whole. The larger the slice, the larger the proportion.

Best for:

• Showing how a total breaks down into a small number of parts
• Emphasizing the dominance of one category (e.g. one product line representing 70% of revenue)
• Presenting data to a general audience that expects this format

When not to use it:

• When you have more than 5–6 categories (too many slices become impossible to compare)
• When values are similar in size (the human eye struggles to distinguish slice angles that are close)
• When precise comparisons matter — bar charts are always better for that

A common mistake: Exploding slices (pulling one slice away from the circle) can make that slice appear larger than it is. Use this effect sparingly and only to highlight, not distort.

Example: A nonprofit shows donors how their contributions are allocated: 60% programs, 25% operations, 15% fundraising. Three slices, one clear message.

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Area Chart

An area chart is a line chart with the space below the line filled in with color. The fill makes it easier to perceive the magnitude of values — not just the direction of change, but how much of something exists at each point in time.

How it works: Like a line chart, time is on the X axis and value on the Y axis. The filled area emphasizes volume and helps the viewer understand cumulative weight.

Stacked area charts: Multiple datasets can be stacked on top of each other, showing both the total and how each component contributes to it over time. This is especially useful for showing how the composition of a total shifts.

Best for:

• Showing cumulative totals over time (total revenue, total users)
• Visualizing how the composition of a total changes (revenue by product category over the years)
• Emphasizing the size or volume of a trend, not just its direction

When not to use it: When you have many overlapping datasets that make the chart hard to read. If datasets frequently cross each other, individual line charts are cleaner.

Example: An e-commerce platform uses a stacked area chart to show how total orders each month break down by product category — electronics, clothing, home goods — and how that mix has shifted over three years.

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Doughnut Chart

A doughnut chart is a pie chart with a hollow center. Functionally it works the same way, but the open center can display a summary metric — a total, a percentage, or a key label — making the chart both visually lighter and more information-dense.

How it works: Each arc segment represents a proportion of the whole, just like a pie chart. The removed center reduces visual clutter and gives you space to add context.

Best for:

• Part-to-whole comparisons (same use case as a pie chart)
• Dashboards where you want to show a headline number alongside the breakdown
• Modern-looking reports where pie charts feel dated

When not to use it: Same caveats as pie charts — keep the number of segments small and avoid using it when precise comparisons are needed.

Doughnut vs. pie: Research suggests doughnut charts can actually be slightly easier to read than pie charts because the viewer focuses on the arc length rather than the area of the slice. But the practical difference is small — choose based on your design needs.

Example: A product dashboard shows overall customer satisfaction (87%) in the center of a doughnut, with the arc showing how that breaks down by rating: Excellent, Good, Neutral, Poor.

Try the Free Doughnut Chart Generator →

Scatter Chart

A scatter chart plots individual data points as dots on a two-dimensional grid. Each dot represents one observation, positioned by its values on the X and Y axes. The resulting pattern of dots reveals relationships between the two variables.

How it works: You choose two variables — one for each axis. If dots cluster in a diagonal pattern, the variables are correlated. A random scatter suggests no relationship. Outliers appear immediately as isolated dots far from the cluster.

Types of correlation a scatter chart reveals:

• Positive correlation: As X increases, Y tends to increase (dots trend up-right)
• Negative correlation: As X increases, Y tends to decrease (dots trend down-right)
• No correlation: Dots are scattered randomly with no pattern
• Non-linear relationships: Dots form a curve rather than a straight line

Best for:

• Exploring relationships between two continuous variables
• Finding clusters within a dataset
• Identifying outliers
• Testing hypotheses (does advertising spend predict sales?)

When not to use it: When your data is categorical rather than continuous — use a bar chart instead. Also avoid scatter charts in presentations to non-technical audiences who may find them unfamiliar.

Example: A data analyst plots employee tenure (X axis) against performance score (Y axis) for 500 employees, revealing that performance peaks at 2–4 years and declines slightly after 8 years.

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Scatter Bubble Chart

A bubble chart extends the scatter chart by adding a third variable, encoded in the size of each bubble. This lets you visualize three dimensions of data in a two-dimensional space.

How it works: X axis = variable 1, Y axis = variable 2, bubble size = variable 3. The position of each bubble is determined by its X and Y values; the size tells you the third piece of information.

Best for:

• Comparing entities across three variables simultaneously (e.g. countries by GDP, population, and life expectancy)
• Financial analysis (companies by revenue, profit margin, and market cap)
• Product portfolio analysis (products by market share, growth rate, and profit)

When not to use it: When the differences in your third variable are subtle — small size differences between bubbles are hard to perceive accurately. Also avoid it when you have many overlapping bubbles, which makes the chart unreadable.

Tips:

• Size your bubbles by area, not radius (radius-based sizing exaggerates differences)
• Use color to add a fourth dimension if needed (e.g. color by region or category)
• Keep the number of bubbles manageable — 20 or fewer works best

Example: A consultant uses a bubble chart to present a client's product portfolio: X axis = market growth rate, Y axis = market share, bubble size = annual revenue. This immediately shows which products deserve investment and which are cash cows.

Try the Free Scatter Bubble Chart Generator →

Radar Chart

A radar chart (also called a spider chart or web chart) plots multiple variables on axes that radiate from a central point. The data points on each axis are connected by lines, forming a polygon. The shape and size of that polygon tells the story.

How it works: Each axis represents one variable, measured from the center (minimum) to the outer edge (maximum). Plotting multiple subjects on the same radar chart produces overlapping polygons — making it easy to see where one outperforms the other.

Best for:

• Comparing performance across multiple attributes (e.g. a product rated on quality, price, speed, reliability, support)
• Athlete or employee performance reviews
• Comparing two or three options across the same set of criteria

When not to use it:

• When comparing more than 3–4 subjects (the chart becomes a confusing nest of polygons)
• When precise values matter — it's hard to read exact numbers off a radar chart
• When your variables have very different scales (normalization is needed)

A key consideration: Radar charts can create false impressions of overall performance because the area of the polygon depends heavily on the order of the axes. Reordering the axes changes the shape, even with identical data. Use radar charts for general impressions, not precise analysis.

Example: A sports analytics team plots five attributes (speed, strength, stamina, agility, technique) for three athletes on the same radar chart to quickly identify each player's strengths and weaknesses.

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Polar Chart

A polar chart (also called a polar area chart or nightingale chart) is similar to a pie chart in structure — segments radiate from the center — but instead of showing proportions through angle, it shows them through the radius of each segment. All segments have the same angle; their size reflects their value.

How it works: The chart is divided into equal angular segments (like a pie chart), but the area of each segment varies based on the data value. Longer segments = larger values.

Best for:

• Cyclical or periodic data (monthly sales over a year, activity by day of the week)
• Comparing values distributed across a fixed number of categories that have a natural circular order
• Creating visually distinctive charts for reports and presentations

Historical note: The polar chart was famously used by Florence Nightingale in 1858 to visualize causes of soldier deaths in the Crimean War — demonstrating that more soldiers were dying from preventable disease than from combat wounds.

When not to use it: When the circular arrangement of categories doesn't reflect a meaningful structure in the data — a bar chart will communicate the same information more clearly.

Example: A retail business uses a polar chart to show sales volume by month, arranged in a circle from January to December, making seasonal patterns immediately visible.

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Funnel Chart

A funnel chart shows how a quantity reduces as it passes through sequential stages. It's shaped like a funnel — wide at the top, narrow at the bottom — because values typically decrease at each stage.

How it works: Each stage is represented by a horizontal bar or trapezoid. The width of each bar reflects the value at that stage. The drop between stages makes the conversion rate and drop-off points visible.

Best for:

• Sales pipelines (leads → qualified leads → proposals → closed deals)
• Website conversion flows (visitors → sign-ups → activated users → paying customers)
• Any multi-step process where you want to track what percentage makes it through each stage

When not to use it: When your data doesn't naturally flow through sequential stages, or when values don't consistently decrease at each step (a bar chart is more appropriate).

Key metrics funnel charts reveal:

• Stage conversion rate: What percentage passes from one stage to the next
• Overall conversion rate: What percentage makes it from top to bottom
• Biggest drop-off: Where you're losing the most volume

Example: An e-commerce company tracks its checkout funnel: 10,000 product page views → 3,200 add to cart (32%) → 1,800 checkout started (56%) → 1,200 payment entered (67%) → 980 orders completed (82%). The funnel makes it immediately clear that the cart-to-checkout step is where the most shoppers abandon.

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Sankey Chart

A Sankey diagram uses flowing bands to show how quantities move between different nodes. The width of each band is proportional to the flow it represents — thicker bands mean more volume. The result is a rich, visual picture of distribution and transformation.

How it works: Nodes (categories or states) are arranged on the left and right (or top and bottom). Bands flow between them, branching and merging. The eye naturally follows the flows, understanding both the source, the path, and the destination.

Best for:

• Energy or material flows (where energy comes from and how it's used)
• Budget and cost allocation (how money flows from departments to expenses)
• User journey analysis (how users move between sections of a website)
• Supply chain visualization

When not to use it: When your data doesn't have a meaningful flow or transfer between categories. Sankey charts are complex — if a simpler chart can tell the same story, use it.

Example: A government agency uses a Sankey diagram to show national energy flows: coal, oil, gas, nuclear, and renewables enter on the left; residential, commercial, industrial, and transportation use exit on the right. Bands show how each energy source contributes to each usage category.

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Tree Map Chart

A tree map displays hierarchical data as a set of nested rectangles. Each rectangle's area is proportional to its value. The nesting shows the hierarchy — parent categories contain child categories inside them.

How it works: The total space represents 100% of the data. It's divided into large rectangles for the top-level categories, then each large rectangle is subdivided into smaller ones for its sub-categories. Color can encode a second variable (like growth rate or performance).

Best for:

• Hierarchical data where both the structure and the relative size of each part matters
• File system storage analysis
• Budget breakdowns by department and sub-category
• Product revenue by category and sub-category

When not to use it: When values are similar — rectangles that are close in size are hard to compare. Also avoid it for data with more than two or three levels of hierarchy, which becomes difficult to navigate visually.

Tips:

• Use color to encode a meaningful second variable, not just for decoration
• Label only the largest rectangles — small ones get overcrowded with text
• Consider interactive tree maps for complex hierarchies that need drill-down

Example: A tech company visualizes its AWS cloud spend as a tree map: top-level rectangles for each service (EC2, S3, RDS, Lambda), subdivided by region. Color shows which areas are over budget. The finance team can immediately see where the money is going without reading a single spreadsheet row.

Try the Free Tree Map Chart Generator →

World Map Chart

A world map chart (also called a choropleth map) colors geographic regions — countries, states, or territories — based on a data value. The darker or more saturated the color, the higher the value.

How it works: Each region is filled with a color drawn from a gradient scale. The viewer reads the map by comparing color intensities, with the legend translating colors back into values.

Best for:

• Any dataset where the geographic distribution is the point
• Country-level metrics (GDP, population, infection rates, sales)
• Regional performance comparisons
• Showing which markets to prioritize or where opportunity exists

When not to use it: When geography isn't meaningful to your data — don't use a map just because your data happens to have country labels. Also be aware that large countries dominate the visual even if they have small values; small countries with high values can be invisible.

Color scale considerations:

• Use a sequential scale (light to dark) for data that ranges from low to high
• Use a diverging scale (two colors from a center point) for data that has a meaningful midpoint (e.g. growth vs. decline, positive vs. negative)
• Avoid rainbow color scales — they're hard to interpret and inaccessible to colorblind viewers

Example: A global SaaS company maps its monthly active users by country. The map immediately reveals that Western Europe and North America are well-penetrated markets, while Southeast Asia — shown in light colors despite large populations — represents untapped growth potential.

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Box and Whisker Chart

A box and whisker chart (also called a box plot) summarizes the statistical distribution of a dataset in a single compact visual. Rather than showing individual data points or just the average, it shows the full spread — median, quartiles, and outliers.

How it works: The "box" spans from the 25th percentile (Q1) to the 75th percentile (Q3) — this is called the interquartile range (IQR) and contains the middle 50% of your data. A line inside the box marks the median. "Whiskers" extend from the box to the minimum and maximum values within 1.5× the IQR. Points beyond the whiskers are plotted individually as outliers.

What each element tells you:

• Box width (IQR): How spread out the middle 50% of values are
• Median line position: Whether the data is skewed (if the median is closer to Q1 or Q3)
• Whisker length: How much the data varies overall
• Outlier dots: Which individual values are unusually high or low

Best for:

• Comparing distributions across multiple groups side by side
• Identifying skew and outliers in a dataset
• Statistical analysis and data quality checks
• When averages are misleading (a dataset can have the same average but very different distributions)

When not to use it: For general audiences unfamiliar with statistical concepts — box plots require explanation. For small datasets (fewer than ~20 data points), a dot plot or scatter chart shows individual points more clearly.

Example: An HR team compares salaries across five departments using box plots. Even though two departments have similar median salaries, one has a much wider box — meaning salaries in that department vary dramatically, with some employees earning nearly triple what others do.

Try the Free Box and Whisker Chart Generator →

How to Pick the Right Chart

The right chart depends on what question you're trying to answer. Ask yourself: what relationship do I want my audience to see?

A few universal rules

Don't use 3D charts. Three-dimensional effects distort the data and make accurate comparisons harder. A 3D pie chart makes the front slices look larger than the back ones. Stick to flat, clean charts.

Start bar and column charts at zero. Truncating the Y axis makes differences look larger than they are. If the difference between values is genuinely small, use a line chart or annotate the difference explicitly.

Use color purposefully. Every color in a chart should encode information. Avoid using multiple colors just for decoration — it adds visual noise and can imply false categories.

Label your data clearly. A chart that requires the viewer to cross-reference a distant legend is doing extra work. Where space allows, label lines, bars, and segments directly.

Less is more. A single chart with one clear message is more persuasive than a complex chart trying to show everything at once. If your chart needs more than a few seconds of explanation, consider whether it can be simplified.

Ready to build your next chart? MakeChart gives you a free, fast chart generator for every type listed above — paste your data, customize the style, and export in seconds.

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Frequently Asked Questions

What is the most commonly used chart type?

The bar chart is the most commonly used chart type across business, academia, and media. It is straightforward to read, works for a wide range of data, and requires no statistical knowledge to interpret. Line charts are a close second, especially for time-series data.

What is the difference between a bar chart and a column chart?

They are the same chart type. A column chart has vertical bars; a bar chart has horizontal bars. The terms are often used interchangeably. Horizontal bars work better when category labels are long, since they have more room to display text without overlapping.

When should I use a pie chart vs. a bar chart?

Use a pie chart when you want to show how parts make up a whole and you have five or fewer categories. Use a bar chart when you need to compare individual values precisely or when you have more categories. Bar charts are almost always easier to read accurately — pie charts are better for showing a dominant majority at a glance.

What is the best chart for showing data over time?

A line chart is the best choice for showing how a single value changes over time. If you want to emphasize the volume or magnitude of the data (not just the direction of change), an area chart works well. For short time ranges with only a few periods, a bar chart can also work.

What chart type is best for showing relationships between variables?

A scatter chart is best for showing whether two continuous variables are related. If you need to add a third variable, use a bubble chart where the size of each point encodes the third value.

What is the hardest chart type to read?

Sankey charts and radar charts are generally the most difficult for general audiences to interpret without explanation. Box and whisker charts are also challenging for people without a statistics background. For public-facing communications, simpler chart types like bar and line charts are usually more effective.

Can I use multiple chart types together?

Yes. Combining chart types — called a combo chart — is a common technique. For example, a bar chart showing monthly revenue alongside a line chart showing the cumulative total on the same graph. This works well when two datasets have different scales or represent different types of information.

What chart should I use for survey results?

A bar chart is usually the best choice for survey results, especially for single-choice questions where you want to compare how often each option was selected. For Likert scale questions (strongly agree to strongly disagree), a stacked bar chart works well to show the distribution of responses.