Understanding what types of charts to use, and especially why is one of the struggles that slows down also my own reporting and analysis. Choosing the wrong visual could cause confusion with the viewer or lead to mistaken data interpretation.

To create charts that clarify and provide the right canvas for analysis, you should first understand the reasons why you might need a chart.

Choosing a type of chart depends first and foremost on what kind of data you have and what you want to express.

When presenting your data, you should think about what you want your viewers to take away from the information and make those points stand out. Different types of charts are best for certain circumstances and not everywhere.

Basically there are four main types of charts (see also picture below) :

• Comparison charts are used to compare one or more data sets. They can compare items or show differences over time.
• Relationship charts are used to show a connection or correlation between two or more variables.
• Composition charts are used to display parts of a whole and change over time.
• Distribution charts are used to show how variables are distributed over time, helping identify outliers and trends.

In this article, we’ll go over some suggestions for the best types of charts:

Best practices for creating Histogram, Bar and Column Charts (also stacked version):

• Start the y-axis at zero – Our eyes are sensitive to the area of bars on a chart. If those bars are truncated, the viewer might draw the wrong conclusions.
• Label the axes – Labelling the axes gives your viewer context. Pay attention if you have too many categories on a stacked chart, can confuse the viewers.
• Put value labels on bars – This helps to preserve the clean lines of the bar lengths.
• Avoid using too many colours. Using a single colour, or varying shades of the same colour, is a much better practice. Otherwise, use colour palette.

Best practices for creating Pie/Donut Charts:

• Make sure your segments add up to 100%
• Keep it clean and consistent. Compare just a few categories to get your point across. If the pie slices have roughly the same size, consider to use a bar or column chart instead.
• If the labels are too long, think about a toolpit with mouse-over.

Best Practices for creating Funnel Charts:

• Scale the size of each section to accurately reflect the size of the data set.
• Use contrasting colours or one colour in gradating hues, from darkest to lightest as the size of the funnel decreases.
• Use labels or Toolpit with the mouse-over to help the viewer understanding the data.

Best Practices for creating Area Charts:

• Use transparent colours (50% more or less) so information isn’t obscured in the background.
• Don’t display more than four categories to avoid clutter.
• Organize highly variable data at the top of the chart to make it easy to read.

Best practices for creating Line Charts:

• Clearly label your axes – Make sure the viewer knows what they are evaluating.
• Remove distracting chart elements – Grids, varying colours, and bulky legends can distract the viewer from quickly seeing the overall trend.
• Zoom in on the y-axis if your data set starts above zero.
• Avoid comparing more than 5-7 lines. Visualize the data you need to tell your story, nothing more., otherwise use some filter (or TOP N) if you have several lines.

Best practices for creating a Treemap:

• Start with clean data and a clear message – Treemaps can often involve a lot of data, so it’s important to know exactly what you want to highlight.
• Use bright, contrasting colours. Choose your colours wisely.
• Label each region appropriately with text or numbers –  is makes it easier for the viewer to evaluate your treemap quickly, without error.
• Avoid clutter your treemap with too many boxes – Treemaps can contain any number of boxes, but space is limited! It would be wise in some cases to use a drill-through option and go to the next level once clicked on a box.

Here below some charts that are not so popular but need to be mentioned:

Word clouds (also known as tag clouds) are a type of weighted list. Word clouds display text in varying font sizes, weight, or colours to show frequencies or categories. They can be arranged alphabetically or at random. They help people identify trends and patterns that might have been difficult to see otherwise.

Heatmaps visualise data through variations in colouring. When applied to a tabular format, Heatmaps are useful for cross-examining multivariate data, through placing variables in the rows and columns and colouring the cells within the table. Heatmaps are good for showing variance across multiple variables, revealing any patterns, displaying whether any variables are similar to each other, and for detecting if any correlations exist in-between them.

Best Practices for creating Heat Map

• Use a basic and clear map outline to avoid distracting from the data.
• Use a single colour in varying shades to show changes in data.
• Avoid using multiple patterns.