Overview of Data Visualization
The point of a visualization is to communicate its data in a quick and meaningful way while remaining 100 percent accurate. A visualization should serve a clear purpose and not overwhelm the users with unnecessary details. If possible, a visualization should be designed to encourage the users to compare its various elements so as to give insight into the meaning behind the data.
So, to design a data visualization to get its message through, you need to first understand the data itself. Then use well-known design patterns and use the type of visualization that reveals the data in the best way.
Understand the Data
To design an effective visualization with a clear purpose, you should thoroughly understand your data. The following points will help you find the information you want to convey with your data:
- What kind of data is it? Nominal, ordinal, interval, or ratio data?
- How different parts of the data relate to each other?
- Can you organize the data in a way that makes it easy for you to create your visualizations?
- What do you want to communicate with your data?
It's easy to start thinking about how the visualization should look, but when you have answered these questions it will be easier to decide what kind of visualization you should use and how it will look and communicate its data.
Use Well-known Design Patterns
When you have understood the data, how it is organized, and how its parts relate to each other, you should consider using well-known design patterns to communicate your data. For example, if you want to show how a measure behaves over time, you should use a line chart because its strength is that it tells your users a lot without their having to look at the specific details.
Design Individual Elements to Reveal the Data
Apart from the design pattern you choose, an effective visualization is also about how you design and make individual data elements stand out and reveal the data. In other words, the design of the individual elements of a visualization should reveal the data to your users in a quick and intuitive way. An easy way of achieving this is to use a different color on one of the dots in a group of many dots. The different color makes it much easier for you to find the dot and reduces your load of information. Other examples of intuitive design are:
- Color hue, brightness and saturation
Avoid the Pitfalls of Data Visualization
To experience the benefits of data visualizations you must avoid the pitfalls. Here are some common pitfalls:
Do not overdo colors. Be aware that the wrong color in the wrong place might cause confusion rather than clarity.
Misuse of pie charts
Avoid having pie charts side by side to compare. Try not to squeeze too much information into them.
Too much information defeats the purpose of clarity. Use a maximum of nine KPIs and remove all visual clutter.
A beautiful visualization is not necessarily the most effective. Use design best practices at all times.
Spot and correct issues with your data before you present it. Do not let your visualization take the blame for bad information.
Commonly Used Visualizations
In this course, we will study and understand the most commonly used data visualizations.
- Overview of Data Visualization
- When to Use Bar Chart, Column Chart, and Area Chart
- What is Line Chart and When to Use It
- What are Pie Chart and Donut Chart and When to Use Them
- How to Read Scatter Chart and Bubble Chart
- What is a Box Plot and How to Read It
- Understanding Japanese Candlestick Charts and OHLC Charts
- Understanding Treemap, Heatmap and Other Map Charts
- Visualization in Data Science
- Graphic Systems in R
- Accessing Built-in Datasets in R
- How to Create a Scatter Plot in R
- Create a Scatter Plot in R with Multiple Groups
- Creating a Bar Chart in R
- Creating a Line Chart in R
- Plotting Multiple Datasets on One Chart in R
- Adding Details and Features to R Plots
- Introduction to ggplot2
- Grammar of Graphics in ggplot
- Data Import and Basic Manipulation in R - German Credit Dataset
- Create ggplot Graph with German Credit Data in R
- Splitting Plots with Facets in ggplots
- ggplot2 - Chart Aesthetics and Position Adjustments in R
- Creating a Line Chart in ggplot 2 in R
- Add a Statistical Layer on Line Chart in ggplot2
- stat_summary for Statistical Summary in ggplot2 R
- Facets for ggplot2 Charts in R (Faceting Layer)
- Coordinates in ggplot2 in R
- Changing Themes (Look and Feel) in ggplot2 in R