Data visualization is a powerful tool for making data more understandable and insightful. Different chart categories serve distinct purposes in presenting data effectively. The four basic chart categories in data visualization include comparison, composition, distribution, and relationship charts.
Comparison Charts
Comparison charts, such as bar charts and line charts, facilitate comparisons between data points or categories. They are useful for:
- Comparing values: Bar charts excel in comparing data values across categories.
- Showing trends: Line charts are ideal for displaying trends over time.
Comparison charts provide clarity in comparing data and are easy to interpret. However, they can prove to be limiting for showing part-to-whole relationships.
Composition Charts
Composition charts, including pie charts and stacked bar charts, emphasize the makeup of a whole by showcasing how individual parts contribute. They are beneficial for:
- Displaying proportions: Pie charts highlight the distribution of parts within a whole.
- Part-to-whole relationships: Stacked bar charts represent components within categories.
Composition charts are effective in illustrating part-to-whole relationships but they can be challenging to interpret when dealing with complex data.
Distribution Charts
Distribution charts, such as histograms and box plots, depict the distribution and spread of data values. They are valuable for:
- Showing data spread: Histograms display the frequency distribution of continuous data.
- Visualizing outliers: Box plots reveal data distribution and identify outliers.
While distribution charts are helpful for understanding data distribution, they can be limiting when comparing discrete data categories.
Relationship Charts
Relationship charts, including scatter plots and network diagrams, emphasize the connections and relationships between data points. They are essential for:
- Identifying correlations: Scatter plots highlight relationships between two variables.
- Visualizing networks: Network diagrams represent connections within a network.
Relationship charts are effective for exploring relationships and connections. However, they may require more complex analysis for meaningful insights.
List of recommended resources #
For a broad overview #
This brief overview by IBM gives a brief description and illustrations of the different categories of chart types used in data visualization.
Dos and don’ts of data visualization
This guide by the European Environment Agency provides 23 dos and don’t for creating charts for presentations. Each recommendation provides ways for better data visualization along with guidelines for how to choose the kind of chart to be used.
Resource: The Data Visualisation Catalogue
This catalog provides an easily accessible database for different types of charts, maps and plots used in data visualization. The resources can even be grouped according to their functions for easier understanding.
For in depth understanding #
Data Visualization: A Practical Introduction
This accessible primer by Kieran Healy explains how to create effective graphics from data. It explains what makes some graphs succeed while others fail and how to think about data visualization in an honest and effective way.
The Truthful Art: Data, Charts, and Maps for Communication
This textbook by Alberto Cairo provides an introduction to the graphical representation of quantitative thinking using charts, graphs, and maps.
Case study #
Benchmarking Costs of Financial Intermediation around the World
This paper by Pietro Calice and Nan Zhou uses bank-level data for 160 countries during 2005-14 to construct country-level bar charts of relative contributing factors to financial intermediation costs.
Structuring Complex Results using Network Maps and Hierarchical Charts
This case study, by Chloe A. Lanters and Peter Fantke, uses hierarchical column charts for structuring results for four category levels: exposure pathways, population group, exposure environment and age group.
References #
Data Visualization Resources: Types of Charts and Graphs for Data Viz
