An area chart is similar to a line chart, with the difference that the regions under the lines are filled with a color or shading. Again, one may think this is not a consistent difference, but it has a meaningful effect on how people perceive data in the chart.
People understand data series by how they perceive the total area of the colored region and less by how it changes over a while.
In other words, the area chart not just displays changes over time but also shows volume and quantity.
Even if line and area charts may be similar, or sometimes you may be tempted by using the area chart because it is colorful and eye-catching, they are not interchangeable because of the different perceptions of data.
As discussed in the line chart article, even in the area chart, the x-axis represents the time, while the y-axis corresponds to the value of another variable. A significant exception is that the x-axis must start at zero.
When to use the area chart
There are some cases where this chart is effective for:
- displaying the ups and down of data over time
- highlighting a part-to-whole relationship when one part is vast, or changes from very large to very small
- showing how quantities have changed over time across numerous related data series
- displaying the evolution of numeric variable or several variables
When not to use
It’s not wise choosing this kind of chart in the following cases:
- data sets on scales don’t have a meaningful relationship to zero
- display several volatile data sets over time
- showing fine differences in values
In some cases, the area chart has some limitations because it will likely hide some information, and interpreting the areas may be difficult. Furthermore, there is also a high possibility of overlap between the data, which inevitably creates more confusion.
How to create an area chart on BStreams
- Add a new project
- Drag the area chart and drop it on the canvas
- Select the sample dataset
- Choose one dimension and n. of metrics
- Click apply to show the result
Example of the area chart
The dataset keyword analysis visualizes the search keys of the four companies (2018-2021). Choosing the date field as the dimension and four metrics associated with the total search key for the companies, the area chart will demonstrate the trends in the specific period.
BStreams provides a variety of features for data visualization and customization. For fastening the process of following and understanding data the lines had been smoothed with the smooth feature, the data points had been visualized, and the horizontal grid has been enabled.
Stacked Area chart
The stacked area chart works in the same way as the previous one. It shows the evolution of a numeric variable for several groups of a dataset.
In this chart, each group is displayed on top of each other, making it easier to keep track of the evolution of the numeric variable. However, we must admit that it is hard for the audience to interpret each group accurately.
When to use the stacked area chart
This kind of chart is an efficient data visualization to analyze the development of both the whole and the relative proportion of each group. In addition, comparing the heights of each segment of the curve allows one to visualize how each subgroup compares to the other in their contributions to the total.
The stacked area chart is not advisable for displaying the evolution of every single group. For example, it would be pretty challenging to subtract the height of the other group at each time point.
Creating this chart on Bstreams follows the same steps as the area chart: for that reason, we won’t write it again. Instead, substitute the area chart with the stacked area chart when choosing your chart.
Example of stacked area chart
In the following example, the dataset regarding the emission of Co2 from different countries and continents has been visualized with the help of the stacked area chart.
Want to learn more about scatterplots? Email us at support@bstreams.io
If you haven’t checked our blog yet, give it a look and let us know what you think about it.
Read more here