How to Choose the Best Chart for Your Data
The amount of data at our fingers tips today can be overwhelming. How do you know what really matters? Data visualizations can help, but how can you set up your data to best visualize it? What chart will help you analyze and digest the data into actionable insights?
One struggle is that there are so many different chart types. Which one makes the data most meaningful? Are you measuring performance? Do you have 1 or many variables? Is your data time-based? Geospatial? How many data points do you have? Are you comparing different categories of data?
What is a data chart?
Let’s start with the basics – what is a data chart?
A data chart is a visual representation of a set of numerical data points that can be represented in the form of bars, lines, slices of a pie, symbols, and more. The most popular types of data charts are:
- Bar or column charts
- Stacked charts
- Pie charts
- Line graphs
- Area charts
How to choose the right chart for your data
As consumers or businesses contend with the explosion of more complex information, we need to help them understand it faster. This is where the right chart comes in to help you best get your message or data story across. While your data could potentially work with multiple charts, it is up to you as the creator to make sure you are selecting one that makes the data clear and concise for the consumer.
Consider these key questions to help guide you in your choice:
- What is the key point you want to communicate with your chart?
- Do you want to compare variables?
- Do you need to understand the distribution of the data?
- Are there possible trends you need to analyze for?
There are 4 basic chart types when it comes to presenting your data:
What is a comparison chart?
A comparison chart draws a comparison between two or more variables. These can be used to effectively show your end users differences between two or more set of variables over time. So what types of charts do you use to compare data?
The best chart types to use for comparing data are:
- Bar charts
- Column Charts
- Line Charts
- Spline Charts
- Combo Charts
- Radial Graph
- Spline Area Chart
- OCHL Chart
Now that you know what type of chart to use for your data, be sure to follow best practices like using proper color and text, highlighting what’s important and other techniques you can find in our whitepaper.
What is a composition chart?
A composition chart is best for showing how individual variables make up a whole. You can either show the relationship of these variables over time or as a static sum.
The best charts for tracking changes of variables over time are:
- Stacked Column
- Stacked Area
The best charts for measuring static relationships of variables are:
- Pie Chart
- Doughnut Chart
All of these chart types allow you to set up drill-downs to drill into hierarchies in your data and even gain deeper insights.
Some best practices to keep in mind when using these charts are:
- Try to keep your variables for under 6
- Use only positive values
- Don’t use 3D charts
Charts like stacked columns and stacked area, for example, sre great for showing the parts to a whole over time to allow you to compare changes and trends. Charts like pie charts and donut charts are best for comparing values as a sum.
What is a distribution chart?
A distribution chart shows how a set of quantitative values are distributed across an entire range and helps you identify key outliers and trends in your data. Say you have a set of values and you want to know if there is a correlation between them by seeing any intersections. It is best to use any of the following charts:
The best charts for distribution data are:
- Combo Chart
- Line Chart
What is a relationship chart?
A relationship chart will show a correlation between two or more variables through the data you pull together. This can be used to show either a positive or a negative effect that the given variables exert on each other. The below charts are best when you have two or more variables:
Best Practices with Data Visualizations
When it comes to creating data visualizations, choosing the right chart type is just one part of the puzzle!