The right charts and maps will ease the point of your visualization for the audience. The BStreams provide a variety of formats for your stories and reports.
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 opposite 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, graph or map works better for your data visualization? Here we are giving you three helpful pieces of advice to consider, when it comes to choosing a visualization.
1.Start from the story you want to deliver with your data
Before going through data visualization, let’s take into consideration one important initial step which is : the Story of your data visualization.
Remember, data is just another way to tell stories. There’s a story to tell within every data, but the tools that you use are quite important. That’s why it’s up to you to bring life, both visually and contextually, to the story.
Consider you have collected your data to represent a story about a trend in sales of your company, or to make comparison and analysis, or to highlight the correlation and connection. Once you have a clear idea about your aim of data visualization, choosing the right type of chart makes a huge difference between simply showing data by some simple graph, or smart choice and combination of graphs, charts and maps.
2.Think about the size of your data
Making a decision about the right chart is also dependent on the size of the data that you wanna represent. BStreams provides the best tools for filtering and connecting your data representation together, but some types of the charts are not the first option for large datasets. Choosing a wrong chart might increase the probability of misleading, wrong interpretation and extra time and effort to understand, for your audience, while other formats of data visualization (i.e. Table and Scatter Plot ) are considered as the best options for large datasets.
3.Type of the data that you have been collected
To ease the process of choosing the best chart, graph or other format of data visualization, you need to have an insight about the type of your dataset. Data as a matter of type can be categorized in these groups: Continuous, Qualitative, Quantitative or Descriptive. Knowing the type of your data will help you to discard some charts and graphs for your data visualization and storytelling.
Different types of charts and graphs for presenting your data visualization
There are a variety of charts, graphs and other formats of data visualization that you can choose. To prevent confusion and also ease the process of selection, in continuation we will give you a short overview of the most common type of charts and hints to when to choose or avoid them.
Bar chart vs Column Chart
Without any doubt they are the most common types of charts in data visualization. The persistent dilemma is bar chart vs column chart.
Let’s start by the definition of the bar chart which is a diagram in which the numerical values of variables are represented by the height or length of lines or rectangles of equal width, while the column chart in reality is the vertical representation of the bar chart.
Recommended for: They are a powerful visualization for comparing the length of the rectangulares with categorical data. When labels on the X-axis are too long, the horizontal bar charts’s version is a great option. Bar chart are more suitable to represent comparison among items over a specific period of value (i.e. Time)
Avoid for: If the threshold of your considered time is large(i.e. Days of one month) using the bar or column chart is not the best option for deriving the trends among that specific period of time, since interpreting and visualizing a vast number of rectangles in horizontal or vertical is not an easy job.
Line chart
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.
Recommended for: 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.
Avoid for: line charts don’t lend themselves well for making part-to-whole comparisons, managing categorical data, and showing quantities of things.
Area Charts
They are similar to line charts with the difference in the areas under the lines filled with colors or shadings. It may not seemas a consistent difference, but in reality, it has a relevant effect on how people perceive data within the chart.
Recommended for: 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.
Avoided for: displaying several volatile datasets over time and showing nuanced differences in values.
Learn more about this type of chart by reading our article on how to make area chart.
Scatter Plot Chart VS Bubble Chart
Scatter plot charts are commonly perceived as challenging-to-understand data visualization, but they are helpful in both scientifict and marketing fields.
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.
Recommended for: 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.
Avoided for: 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.
Conclusion
To sum up, to choose the best type of chart for your data, you need to keep clean in mind these three main factors:
- firstly, the story you want to deliver with your data
- secondly, the size of your data
- 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