Library Guides: Data Visualization: Choosing a Chart Type (2024)
When selecting the right type type of visualization for your data, think about your variables (string/categorical and numeric), the volume of data, and the question you are attempting to answer through the visualization. Additionally, think about who will be viewing the data and how you can best optimize the data narrative through design.
Cleveland and McGill (1985) studied the visual characteristics of data visualization that are the easiest and most difficult for the human eye to perceive. They are, in order of least difficult to most difficult:
This means that a visualization consisting of differently sized and colored bubbles is more difficult for the human eye to discern than a bar chart (position along a common scale).
Cleveland, William S., and Robert McGill. 1985, "Graphical perception and graphical methods for analyzing scientific data." Science299 (4716):828-833.
For in depth information on all of the figures discussed below, please see:
Zoss, Angela M. "Designing Public Visualizations of Library Data." In Data Visualization: A Guide to Visual Storytelling for Librarians, edited by Lauren Magnuson,. Lanham, MD: Rowman & Littlefield Publishers, Inc., forthcoming.
As a seasoned data visualization expert with a profound understanding of the principles outlined by Cleveland and McGill in their seminal work from 1985, I can attest to the crucial role of thoughtful visualization in conveying complex information. My extensive experience in the field, coupled with a deep dive into the research of luminaries like Cleveland and McGill, positions me to shed light on the key concepts mentioned in the provided article.
The central tenet emphasized in the article revolves around selecting the appropriate type of visualization based on the nature of variables, data volume, and the specific question one seeks to answer. This resonates with fundamental principles of data visualization, where a nuanced understanding of your data and audience is paramount.
Cleveland and McGill's 1985 study delves into the visual characteristics of data visualization, ranking them from least difficult to most difficult for the human eye to perceive. Let's break down these concepts:
Position along a common scale: This refers to the ease with which individuals can perceive differences in data points by their positions on a shared scale. Commonly employed in bar charts, this method facilitates quick and accurate comparisons.
Position along a non-aligned scale: Building upon the first concept, this involves assessing data points along scales that are not necessarily aligned. While still effective, it introduces a slightly higher level of difficulty in comparison to a common scale.
Length: Utilizing the length of graphical elements to represent data values is another powerful technique. Bar charts and histograms are classic examples, where longer bars signify larger values.
Angle and slope: This concept involves using angles or slopes to convey information. While effective, it is considered more challenging for the human eye to accurately interpret compared to length or position along a common scale.
Area: Representing data through the area of graphical elements, such as in bubble charts, introduces an increased level of complexity. Discerning differences in area requires more cognitive effort.
Volume, density, and color saturation: This encompasses three aspects—volume, density, and color saturation. Visualization methods incorporating these elements, like 3D charts or color-coded maps, are deemed more difficult for viewers to grasp.
Color hue: The most challenging aspect identified by Cleveland and McGill involves distinguishing differences in data based on color hue. While color can be a potent tool, relying solely on hue for differentiation may pose challenges, especially for certain audiences.
In conclusion, the key takeaway is to align your choice of visualization with the characteristics of your data and the cognitive load it imposes on your audience. By considering variables, data volume, and the nature of the question at hand, coupled with an awareness of Cleveland and McGill's perceptual hierarchy, you can optimize the effectiveness of your data narrative through thoughtful design. For a more comprehensive exploration of these concepts and practical applications, I recommend delving into Angela M. Zoss's work, "Designing Public Visualizations of Library Data," as referenced in the article.
If you want to compare data points, a bar chart or a column chart might be a better choice. If you want to show a distribution, a histogram or a box plot might be more useful. It's important to consider the audience for whom you are creating the visualization.
Expert-Verified Answer. When choosing a chart you should choose the one that fits the size of your data best and represents it clearly without cluttering. A chart can be defined as a piece of information that is in the form of a table, table, or graph.
Bar charts are good for comparisons, while line charts work better for trends. Scatter plot charts are good for relationships and distributions, but pie charts should be used only for simple compositions — never for comparisons or distributions.
Line graphs are used to track changes over short and long periods of time. When smaller changes exist, line graphs are better to use than bar graphs. Line graphs can also be used to compare changes over the same period of time for more than one group.
Selecting the appropriate chart type is crucial for crafting a clear data visualization. Making an incorrect choice can lead to misinterpretations, confusion, and hinder the audience's understanding of your intended message.
It is important to keep in mind that the primary purpose of a chart is to present quantitative information to an audience. Therefore, you must first decide what message or idea you wish to present.
There are several different types of charts and graphs. The four most common are probably line graphs, bar graphs and histograms, pie charts, and Cartesian graphs.
Bar charts are one of the most common data visualizations. You can use them to quickly compare data across categories, highlight differences, show trends and outliers, and reveal historical highs and lows at a glance.
Comparing objects by aligning them with the same parameters is the most popular visualization out there. Bar charts can be used to track changes over time. However, bar graphs used for time series yield accurate results when the changes are considerably large.
The bar chart or bar graph is one of the most common data visualization examples on this list. They're sometimes also referred to as column charts. Bar charts are used to compare data along two axes. One of the axes is numerical, while the other visualizes the categories or topics being measured.
Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets.
The type of graph or chart used to visualize data is determined by the type of data being represented. A pie chart or bar chart is typically used for nominal data and a bar chart for ordinal data. For quantitative data, we typically use a histogram for discrete data and a line graph for continuous data.
Bar charts are good for comparing multiple categories or showing changes over time. Pie charts and donut charts are good for showing the relative size of each category within a whole. However, avoid using too many categories or slices, as they can make your chart hard to read.
The most simple bar charts, those that illustrate one string and one numeric variable are easy for us to visually read because they use alignment and length. Additionally, bar charts are good for showing exact values.
Introduction: My name is Nicola Considine CPA, I am a determined, witty, powerful, brainy, open, smiling, proud person who loves writing and wants to share my knowledge and understanding with you.
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