How to choose the right type of chart

various types of charts

One of the most frequent questions when working with data is how to choose the right type of chart for your data.

Efficient data visualizations will bring your story to the next level and capture the attention of your audience. On the contrary, your best efforts will go down the drain, and you will get the oppostie effect.

We are conscious that there are different types of charts to choose among, which can sometimes be a bit of a struggle. This article will give you some little tips to find the best data visualization that suits your project.

3 ways to choose the best type of chart for your data visualization

Have you ever asked yourself what type of chart or graph works best for your data visualization? We’ll make it simple, giving you just three but very helpful advices to consider when choosing a visualization.

1. Start from the story you want to deliver with your data

Even before thinking about colors in data visualization and other ways to make your chart eye-catching, take a moment and think about the story you want to provide to your audience.

Just remember, data are just another way to tell stories. There’s a story to tell within every data, but the tools you use have no idea of what it is about. That’s why it’s up to you to bring to life, both visually and contextually, that story.

Have you collected your data to tell a story about trends? or to make some comparison? or maybe, to talk about a correlation or connection?

Once you have clear in mind the why of your data, choosing the right type of chart makes the difference between simply showing data and making data storytelling.

2. Think about the size of your data

When deciding among different types of chart and graphs, you have to ask yourself to think about the size of your data. Sure enough, as you may have aldready heard, some types of chart are not the first option for large datasets because the would be challenging to interpret and require extra effort from your audience. On the other hand, other types of data visualization will work perfectly with massive datasets.

3. Establish the type of data you have collected

Figured out the story to deliver and the size of your data, you just have to establish the type of data you have collected. Contrary to what one might think, data are not all the same. They can be continuous, qualitative, quantitative, or descriptive.

Knowing the type of your data will help you to discard some charts in favor of others.

Different types of charts and graphs for presenting your data visualization

There are tons of charts you can choose from. List them all would be even more confusing; that’s why to make your selecting process more accessible, we will give you an overview of the most common type of charts by telling you when to choose or avoid them.

Bar Chart vs Column Chart

bar chart type

They are, no doubt, the most common types of charts when talking about data visualization. The persistent dilemma is bar chart vs column chart. Which one to opt for? And, going deeper, what is a bar chart used for?

Let’s star by saying, the bar chart is the horizontal version, while the column the vertical one.

When to use: They are a powerful visualization for comparing the lenght of the rectangles with categorical data. When labels on the X-axis are too long, the horizontal bar chart’s version is a great option. Bar charts are more comfortable to read for long names since their structure is closer to the western way of reading texts (from left to right).

When to avoid: they are not the best option for comparing trends over time.

We have written an article about how to make bar charts on our user documentation that will tell you more about these two types of charts.

Line Chart

line chart type

As we have already written in a previous article on how to create a line chart, line charts are good for comparing trends over time. That means they work best for analyzing continuous data.

When to use: every time you hear the word “over time”, it’s the turn for a line chart. Thet are also a good option for making forecasts and comparing a significant amount of data all at once.

When to avoid: line charts don’t lend themselves well for making part-to-whole comparisons, managing categorical data, and showing quantities of things.

Area Chart

area chart example

They are similar to lines charts with the difference in the areas under the lines filled with colors or shadings. It may not seems a consistent difference, but in reality, it has a relevant effect on how people perceive data within the chart.

When to use: Area charts are good at displaying part-to-whole relationships where one part is vast or changes from very large to very small. They are also suitable for showing how quantities have changed over time. If you need to analyze times values, it is best to opt for an area or stacked chart; instead, you can choose a treemap with more than one dimensions

When to avoid: displaying several volatile datasets over time and showing nuanced difference in values.

Learn more about this type of chart on by reading our article on how to make area chart.

Scatter Plot Chart VS Bubble Chart

image of a point chart

Scatter plot charts are commonly perceived as challenging-to-understand data visualization, but they are helpful in both scientifict and marketing fields.

When to use: scatter plots are a type of chart used to display the correlation and clustering in vast datasets or to determine the relationship between the two metrics on x-axis. A third metric can be added and it will affect the diameter of the circles.

On the contrary, bubble charts can be an alternative to bar graphs with the difference they encode the data within bubbles not bars. For more information on how to make a bubble chart, read our article on our user documentation.

When to avoid: scatter plots are useless if the values within the dataset are not correlated. Bubble charts sometimes can be perceived much less accurately than bar charts, especially when metric values are similar to each other.

Donut Chart vs Pie Chart

ring chart

Excellent at displaying percentages on the total value or proportional quantities. They work better with small datasets. We have written an article on how to make a donut or pie chart.

When to use: visualizing the part-to-whole relationship and displaying that one slice of the total is relatively small or large.

When to avoid: they are not the best type of chart with vast datasets, precise comparisons, or when percentages do not sum to 100%.

Data Tables

icon of a data table

Data tables are a powerful data visualization for displaying information and data in columns and rows. While columns are known as fields or parameters, rows are called vectors or records. The point of the intersection between a column and a row is called a cell. Data tables are not a type of visualization restricted to a specific type of data; thus, they can be considered versatile visualization.

When to use: there are different scenarios when a data table is the right type of visualization to opt for. An example is when we need to communicate related metrics with multiple units of measure or when we need to keep track of information in terms of numbers, quantities, names, and many other details. Finally, the combination of data types, words, numbers, and images all arranged in grids makes the process of communicating information easier.

When to avoid: opting for a data table when communicating your information live is not good. In fact, when you present a data table in a live presentation, your audience will probably start scanning rows and columns, and you will lose their attention at all. In this case, a chart would be a good alternative because easier and faster to interpret at first glance.

Read more about our article on how to make a data table

Conclusions

To sum up, to choose the best type of chart for your data, you need to keep clean in mind these three main factors:

  1. firstly, the story you want to deliver with your data
  2. secondly, the size of your data
  3. and finally, the type of data you have collected

They will make your selection process much easier and avoid the risk of letting you choose the wrong chart. Remember to don’t overload your data visualization with unnecessary elements and keep your chart as clean as possible.

Ready to make your first chart? Start now here

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