4 Choosing the best chart
Ensure you use appropriate charts based on the type of data you are visualizing. The four most common types of charts are bar charts, line charts, scatter charts and pie charts (however, many more are available!).
With so many options available, choosing the right chart that would most clearly potentially be overwhelming. After exploring your data, the first consideration for explanatory data visualization is the ‘story’ you are trying to convey: what message would you like to share with your audience? Secondly, you must consider the relationship between the variables you would like to highlight, and therefore what type of chart is appropriate (more details below).
Charts can be broken into 4 main categories, which should be chosen based on what information you are trying to convey and what type of data you have (as seen below).
First, comparison charts can compare one or more datasets, also potentially showing differences over time. Relationship charts show patterns between two or more variables. Composition charts can show the breakdown of different parts of a whole either over time or at one static moment. Finally, distribution charts are used to show how different variables are distributed, which can assist in recognizing outliers and/or trends.
As stated above, the most commonly used charts are bar charts, line charts, scatter charts and pie charts, so we will go into more specific instructions with each of those below. Generally, line graphs are used to show changes over time, while bar and pie charts show categorical variables. If you are trying to show proportions, bar charts are the easiest way to quickly understand relationships. Relationships between variables with many data points can be seen through scatter plots.
For more information and practical exercises, please review content developed by Terre des Hommes and CartONG, on how to choose the best chart.
This section is divided into 5 sub-sections: