Data Visualization
Data visualization is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data.
To communicate information clearly and efficiently, data visualization uses statistical graphics, plots, information graphics and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable.
Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.
There is a load of data in the sea of noise. To turn your numbers into knowledge, your job is not only to separate noise from the data, but also to present it the right way.
How to Pick the Right Chart Type?
There are four basic presentation types that you can use to present your data:- Comparison
- Composition
- Distribution
- Relationship
Selecting the Right Chart
To determine which chart is best suited for each of those presentation types, first you must answer a few questions:- How many variables do you want to show in a single chart? One, two, three, many?
- How many items (data points) will you display for each variable? Only a few or many?
- Will you display values over a period of time, or among items or groups?
Let’s dig in and review the most commonly used chart types, some example, and the dos and don’ts for each chart type.
Tables
Tables are essentially the source for all the charts. They are best used for:- You need to compare or look up individual values.
- You require precise values.
- Values involve multiple units of measure.
- The data has to communicate quantitative information, but not trends.
Column Charts
This chart is best used to compare different values when specific values are important, and it is expected that users will look up and compare individual values between each column.
This chart is best used to present distribution and relationships of a single variable over a set of categories
Stacked Column Charts
Use stacked column charts to show a composition. Do not use too many composition items (not more than three or four) and make sure the composing parts are relatively similar in size.Do not use too many composition items (not more than three or four) and make sure the composing parts are relatively similar in size. It can get messy very quickly.
Bar Charts
Bar charts are essentially horizontal column charts.
If you have long category names, it is best to use bar charts because they give more space for long text. You should also use bar charts, instead of column charts, when the number of categories is greater than seven (but not more than fifteen) or for displaying a set with negative numbers.
Line Charts
Line charts are among the most frequently used chart types. Use lines when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.A line chart is also a good alternative to column charts when the chart is small.
Timeline Charts
The timeline chart is a variation of line charts. Obviously, any line chart that shows values over a period of time is a timeline chart. The most common examples of a time-line chart might be:
- Stock market price changes over time
- Website visitors per day
- Sales numbers by days
Pie Charts
A pie chart typically represents numbers in percentages, used to visualize a part to whole relationship or a composition.Pie charts are not meant to compare individual sections to each other or to represent exact values (you should use a bar chart for that).
Scatter Charts
Scatter charts are primarily used for correlation and distribution analysis. Good for showing the relationship between two different variables where one correlates to another (or doesn’t).Scatter charts can also show the data distribution or clustering trends and help you spot anomalies or outliers.
Bubble Charts
A bubble chart is a great option if you need to add another dimension to a scatter plot chart. Scatter plots compare two values, but you can add bubble size as the third variable and thus enable comparison.
Data Visualization Tips & Tricks
- Time axis: When using time in charts, set it on the horizontal axis. Time should run from left to right. Do not skip values (time periods), even if there are no values.
- Proportional values: The numbers in a chart (displayed as bar, area, bubble, or other physically measured element in the chart) should be directly proportional to the numerical quantities presented.
- Data-Ink Ratio: Remove any excess information, lines, colors, and text from a chart that does not add value.
- Legend: You don’t need a legend if you have only one data category.
- Labels: Use labels directly on the line, column, bar, pie, etc., whenever possible, to avoid indirect look-up.
- Data Complexity: Don’t add too much information to a single chart. If necessary, split data in two charts, use highlighting, simplify colors, or change chart type.
- Colors:
- In any chart, don’t use more than six colors.
- For comparing the same value at different time periods, use the same color in a different intensity (from light to dark).
- For different categories, use different colors. The most widely used colors are black, white, red, green, blue, and yellow.
- Keep the same color palette or style for all charts in the series, and same axes and labels for similar charts to make your charts consistent and easy to compare.
- Check how your charts would look when printed out in grayscale. If you cannot distinguish color differences, you should change hue and saturation of colors.
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