Turning complex datasets into clear and actionable insights is no easy feat. But armed with the right types of data visualization charts, you can unearth patterns, trends, and relationships to tell compelling data stories.
This definitive guide covers the most popular types of data visualization charts, real-world examples of how to use them, and tips for selecting the best visual for your specific data. Let’s dive in!
15 Types of Data Visualization Charts
1. Bar Charts
Bar charts use rectangular bars to compare values across different categories. The height or length of each bar represents the value.
Bar charts work extremely well for displaying rankings, comparing metric values across groups, and showing changes over time when used in a time-series bar chart.
For example, horizontal bar charts effectively rank movie reviews by average user rating. Vertical bar charts help compare the total sales revenue across product categories.
2. Line Charts
To visualize trends and patterns over a time period, line charts are the way to go. They plot data points over time connected by straight lines.
Peaks and valleys in the line allow you to quickly spot increases, decreases, and other trends in the data. Line charts are especially helpful for time-series data.
For instance, a line chart could reveal monthly website visits spiking during the holidays and dropping in the summer months.
3. Pie Charts
When you need to visualize part-to-whole relationships, pie charts have you covered. The circular slices represent the proportional value each category accounts for relative to the total.
Pie charts work best when you just need to display 2-5 categories. They allow you to see how segments stack up as part of a whole, like market share between business competitors.
4. Area Charts
Area charts are basically line charts but with the space under the line filled in. They emphasize the volume or magnitude of changes over time.
For example, an area chart could highlight the total accumulated sales on a website over time. The peaks and valleys show increases and decreases while the filled space shows the total.
5. Scatter Plots
Scatter plots are the chart type you need when assessing relationships between two variable sets. Each data point is shown as a dot on the chart. Patterns in the dots reveal correlations.
For instance, a scatter plot could compare population size vs. average income for cities. Clustering and trends in the dots can suggest positive or negative relationships between the x and y variables.
For large datasets with multiple variables, heat maps are incredibly helpful. Color coding represents values, typically with darker shades for higher numbers.
Marketers often use heatmaps to visualize website click-through rates. Dark spots quickly reveal pages with the most engagement. Heatmaps make patterns easy to spot.
Histograms group numeric data into ranges or bins and then use bar heights to represent the frequency of values in each bin. This allows you to see the overall distribution.
For example, a histogram of student test scores could reveal the grade distribution, showing how many students scored in each grade range. Spot skews and outliers!
8. Box Plots
Box plots visualize statistical summaries of data sets. Boxes show the first, second, and third quartiles. Whiskers extend to the minimum and maximum values, while the line inside the box represents the median.
These plots allow you to quickly compare distributions from multiple groups. You can spot ranges, skewness, and outliers.
9. Radar Charts
Also known as spider charts, radar charts have multiple quantitative scales radiating out from a center point. Data points are plotted on each scale, connected to create a spoke-like visualization.
Radar charts allow you to compare multivariate data sets, like employee skills across different departments. The shapes created reveal insights.
10. Bubble Charts
Bubble charts are like enhanced scatter plots. Each data point is shown as a bubble, with the x and y positions representing two variables. The size of the bubble adds an extra dimension.
A third variable, like sales revenue, could be shown through bubble size. Bubbles reveal correlations and clusters just like scatter plots.
11. Tree Maps
Displaying hierarchical data as nested rectangles, tree maps use size to represent a specific variable, like file size. Colors can also encode additional dimensions.
Treemaps allow you to visualize directory structures and more. And they optimize space since rectangles fill the entire area.
12. Sankey Diagrams
Sankey diagrams visualize the flow of resources, goods, or other data through a system. Arrows of varying thickness connect different stages and represent quantity.
This type of dataviz is perfect for mapping energy transfers, supply chains, budget allocations, and network traffic analyses. The thicker the arrow, the higher the volume!
13. Gantt Charts
Project managers use Gantt charts to plan and schedule tasks over time. Bars represent the duration of tasks positioned along a timeline. Lines show dependencies.
Gantt charts keep projects on track by making it easy to see what needs to happen when, and which tasks block others from starting.
14. Word Clouds
Word clouds visualize textual data by sizing the most frequent words larger. Lower-frequency words appear smaller. Word position and orientation can also encode data.
They provide an at-a-glance summary of key terms and themes within a text source. Word clouds easily highlight the most discussed topics.
15. Pictorial Charts
Unlike most dataviz chart types, pictorial charts use pictograms and icons to represent numeric values. This makes them highly engaging and perfect for infographics.
Pictorial charts work well when you need to simplify complex data. Fun icons attract attention and aid memorability.
The Chart Type Determines the Insight
As you can see, selecting the right data visualization chart type is crucial. It determines how easy or challenging it is to uncover key insights buried within the data.
Now you’re armed with examples of the most popular and powerful chart types for every analysis need. Figure out what you want to learn from your data, then pick the visual representation designed to provide that insight.
With a little practice, you’ll be a data visualization pro, able to create stunning charts that turn raw datasets into decision-making gold. Have any other questions about selecting the perfect dataviz for your project? Let me know in the comments!