Bar charts are a popular type of data visualization that uses rectangular bars to represent and compare data values. They are a fundamental tool in statistics and data analysis, commonly used to display categorical data and make it easier to understand and interpret.
Some key characteristics of bar charts include:
- Orientation – While column charts are vertically aligned, bar charts are typically aligned horizontally with the categories on the y-axis and the values on the x-axis. The choice of orientation depends on the data and the presentation style.
- Discrete data – Bar charts are well-suited for displaying discrete or categorical data, where data points fall into distinct categories or groups. Each bar represents a specific category, with the bar’s length or height corresponding to that category’s value or frequency.
- Comparison – The primary purpose of a bar chart is to facilitate the comparison of data values across different categories. Bar charts make it easy to identify patterns, trends, and variations in the data, helping viewers understand the presentation’s significance.
- Part-to-whole relationships: Stacked bar charts and 100% stacked bar charts are variations that show not only the individual values within categories but also the relationships between categories as parts of a whole. These charts are handy for illustrating how different components contribute to a total.
Common types of bar charts include vertical bar charts, stacked bar charts and 100% stacked bar charts.
List of recommended resources #
For a broad overview #
A Complete Guide to Bar Charts
This data tutorial post by Chartio gives a brief into one of the most commonly used chart types, where it is used, some common practices for using bar charts, and some common bar chart types.
This Google Cloud Guide gives a broad overview of the graphical tool of bar charts and how to customize them.
Bar chart and column chart reference
This article by Google’s Looker Studio gives a guide on how to use and configure column and bar charts in Looker Studio.
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.
Storytelling with Data: A Data Visualization Guide for Business Professionals
This book by Cole Nussbaumer Knaflic teaches readers the fundamentals of data visualization and how to communicate effectively with data. Knaflic’s text is grounded in theory, but made accessible through various real-world examples demonstrating how data can be visualized and used to tell an engaging and informative story.
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.
References #
https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c4.htm
https://labwrite.ncsu.edu//res/gt/gt-bar-home.html
