Data visualization is an effective way to communicate insights from data analysis. With various types of charts and graphs to choose from, selecting the right visual display for your data is crucial. The chart type must match the message you want to convey.
This article will explain different data visualization options and when to use each chart type. Follow these best practices to create clear, accurate data visualizations that engage your audience.
Know Your Data and Objective
First, understand your data and analyze it. What is the key message or insight you want to communicate? Identify the relationships and patterns in your dataset.
Consider aspects like
- Number of data variables
- Data types – categorical, numerical, time-series etc.
- Range and distribution of values
- Presence of outliers
This analysis will guide your visual design choices. Always pick chart types that highlight your main point and suit your data characteristics.
Choosing Between Common Chart Types
Some popular charts are:
Bar Charts
Use for: Comparing values across different categories or groups. Displaying rankings.
Bar charts are great for comparisons. Their length makes it easy to compare quantities. Use horizontal bars for long category names and vertical bars otherwise.
![Bar chart example][]
Bar charts effectively compare values across groups
Line Charts
Use for: Visualizing trends and progress over time. Viewing changes.
Lines elegantly highlight trends and trajectories. Use line charts for time series data, not complex comparisons.![Line chart example][]
Line charts display trends effectively
Pie Charts
Use for: Showing part-to-whole relationships or compositions.
Pie charts visualize how components contribute to a total. Use them for simple compositions of 2-5 parts. Avoid 3D effects and clutter.![Pie chart example][]
Pie charts work for basic compositions
Scatter Plots
Use for: Visualizing relationships between two numerical variables.
Scatter plots use dots to indicate values. They highlight correlations, clusters, and outliers. Avoid overplotting by reducing markers or using transparency.![Scatter plot example][]
Scatter plots effectively show numerical relationships
Histogram
Use for: Depicting value distributions and frequencies.
Histograms display distribution shapes using bars for value ranges. Pick suitable bin sizes to prevent information loss or distortion. ![Histogram example][]
Histograms visualize value distributions
Choosing Charts for Specific Data Types
Beyond those basics, choose chart types suited for your exact data characteristics.
Time-Series Data
For data aligned chronologically, use:
- Line charts
- Stacked area charts
- Gantt charts
Parts-to-Whole Data
For compositional data, use:
- Pie charts
- Treemaps
- Waffle charts
Geographical Data
For location-based data, use:
- Maps
- Heatmaps
- Dot density maps
Correlational Data
For relationships between variables, use:
- Scatter plots
- Bubble charts
- Parallel coordinates plots
Ranking/Ordinal Data
For ordered/hierarchical data, use:
- Bar charts
- Slope charts
- Bullet graphs
Statistical Distribution Data
For understanding value distributions, use:
- Histograms
- Box plots
- Violin plots
Best Practices for Chart Design
Follow these tips to create effective, easy-to-interpret visualizations:
- Highlight key points using colors, sizes, text labels etc. But don’t clutter.
- Label properly by giving titles, legends, axes etc. descriptive names.
- Pick suitable scales and axes to accurately plot all data.
- Maintain data integrity – don’t exaggerate with visual effects.
- Use minimal elements that support your message. Remove chartjunk.
- Format consistently using colors, shapes, font sizes etc.
- Avoid misleading designs like improper axes, obscured data etc.
- Consider accessibility with text sizes, color contrasts and alt text.
Tools for Data Visualization
Many excellent software options create charts and dashboards for analytics and presentations:
- Tableau
- Microsoft Power BI
- Google Data Studio
- Highcharts
- D3.js
- Chart.js
- RawGraphs
- Infogram
- Visme
Their drag-and-drop interfaces help select chart types, customize designs, and create dashboards using data connections.
Key Takeaways
- Analyze your data goals and characteristics before choosing a chart type.
- Use the right charts to showcase key data insights effectively.
- Follow best practices for accurate, easy-to-understand visual designs.
- Explore data viz tools to create high-quality, customized charts and dashboards.
Matching chart types to data is crucial for impactful data visualization. By understanding options and using proper designs, you can create engaging charts that communicate insights clearly.
Explain the relationship between metrics with a scatter plot, bubble chart, or combo chart
Explaining a relationship or correlation between two metrics can be very beneficial since it’s one step further towards getting valuable insights from the data. If you ever need to answer a question like the following examples, the scatter plot is the best choice:
- How does the campaign spend relate to its revenue across many different campaigns?
- What is the relationship between the number of orders and total revenue by the product category?
- How many courses each student started and finished?
Each campaign, product category, or student is represented by the dot on the chart, and each metric is then encoded on one of the X and Y axes.
Scatter plot showing the relationship between two metrics.
Adding a third metric to the mix brings an extra level of insight but also an extra level of complexity. This additional metric is typically represented by the size or the color of the dot, which then becomes a bubble, therefore a bubble chart. A few example questions that can be answered with bubble charts are:
- How many courses did each student start and finish (X and Y axis) and what university did they study at (bubble color)?
- What is the relationship of the company’s total revenue in each country (bubble size) to the number of stores in that country (Y-axis) and to the number of employees in that country (X-axis)?
- Or one of the most famous bubble charts by Hans Rosling — how do the country’s GDP per capita (X-axis) and population (bubble size) relate to life expectancy (Y-axis)?
Bubble chart can show even more complicated relationships well.
A relationship or correlation between two metrics can also be shown over time. For example, the number of visitors and the revenue of the e-commerce website. In that case, a combo chart consisting of a column chart and the line chart comes into play. With two independent Y axes, it is possible to show the relationship even between metrics with vastly different scales.
Combo chart is the best option when the two comparable metrics have very different scales.
Show one big number with a KPI
Just because you have numbers, there is not necessarily a reason to create a chart. Showing a KPI (key performance indicator) is a great way to convey headline figure information, whether you build a marketing, project management, or supply chain dashboard. Good examples of KPI-worthy numbers are the following metrics:
- Number of sold items
- Gross revenue, net revenue
- Number of visitors to a website
There are two very important pieces of information that must come along with a KPI.
First is the related date range or time frame — meaning the time span represented by the KPI. Typically in the form of a date filter on the dashboard or indicated in the title of the KPI.
The second is context. Showing the number alone is not enough. You need to give it context. For example, to show the difference from the previous period or compare it with another metric. Without context, the user cannot tell if the number should make them smile or frown.
KPIs are best when showing the metrics as one big number.
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How To Choose The Right Graph (Types of Graphs and When To Use Them)
FAQ
How do I choose the most appropriate chart?
What is the key to selecting the chart type?
How do I decide which Excel chart to use?
How do I choose a chart type?
You need to select a chart type that fits the size of your data best and represents it clearly without cluttering. №4. What is your data type? There are several types of data, describe, continuous, qualitative, or categorial. You can use the kind of data to eliminate some chart types.
What are the different types of charts?
To summarize, here are the top types of charts and their uses: Number Chart – gives an immediate overview of a specific value. Line Chart – shows trends and changes in data over a period of time. Maps – visualizes data by geographical location. Waterfall Chart – demonstrates the static composition of data.
What type of data should I use for a chart?
You can use the kind of data to eliminate some chart types. For example, if you have continuous data, a bar chart may not be the best choice; you may need to go with a line chart instead. Similarly, if you have categorical data, then using a bar chart or a pie chart may be a good idea.
What types of charts are used in data analysis?
Number Chart – gives an immediate overview of a specific value. Line Chart – shows trends and changes in data over a period of time. Maps – visualizes data by geographical location. Waterfall Chart – demonstrates the static composition of data. Bar Graphs – used to compare data of large or more complex items.
