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There are 24 available charts in Tableau’s Show Me feature. Choosing the right chart for your data is crucial for effective data visualization. The right chart can highlight trends, patterns, and insights that might be missed with an inappropriate choice.
Let’s discuss how to choose the most appropriate chart based on your data and aim you what to achieve with your visualization.
1. Understand Your Data and Objectives
Before selecting a chart, consider:
Type of Data: Categorical, numerical, temporal, or geographical.
Objective: Comparison, distribution, relationship, or composition.
Audience: Tailor your visualization to their needs and understanding.
2. Common Types of Charts and Their Uses
Text Table (Crosstab): Similar to an Excel table, a text table allows you to see your data in rows and columns. This is not a great visual chart, however, sometimes it helps to see what data you are pulling in. You can dress up the text table by using colors.
When to Use:
Use this if you want to see your data in rows and columns without any extra visual cues.
Minimum Requirements: 1 or more dimensions, 1 or more measures.
Bar Charts: It is a graph that displays categorical data with rectangular bars with heights that are equivalent to the amounts they depict. The bars can be displayed vertically or horizontally. A vertical bar map is often referred to as the column map.
Use when:
Comparing quantities across different categories.
· Display a variable function (sum, average, standard deviation) by categories.
· Understand relationships between categorical variables.
· Showing changes over time (with vertical bar charts).
Best for:
Comparing sales figures across regions.
Displaying product performance over different time periods.
Line Charts: Line charts connect individual data points in a view. They provide a simple way to visualize a sequence of values.
Use when:
Showing trends over time.
Highlighting changes in data at regular intervals.
Comparing different categories within a field
to forecast future values.
Best for:
Displaying monthly revenue growth.
Tracking stock prices.
Pie Charts: Tableau pie charts are circular visualizations that represent data in sectors proportional to their values. Pie charts are easy to read and can quickly convey complex information.
Use when:
Displaying the distribution of data across different categories
Showing proportions or percentages of a whole.
Comparing parts of a whole at a single point in time.
Best for:
Illustrating market share.
Showing survey results distribution.
Scatter Plots: A scatter plot in Tableau is a visual representation that helps you explore the relationship between two numerical variables. Use when: Best for:
Displaying the relationship between two numerical variables.
Identifying correlations, patterns, or outliers.
Bubble Charts: A Bubble chart is a visualization that can be useful in showing high-level comparisons between members of a field. Use when: Best for:
Adding a third dimension to a scatter plot (using bubble size).
Comparing relationships between three variables.
Heat Maps: A heat Map is a visualization that represents values for a main variable of interest across two axis variables as a grid of colored squares.
Use when:
Showing data density or intensity.
Highlighting variations in data across a matrix.
Best for:
Displaying website click data.
Visualizing sales performance across regions and product lines.
Area Charts: An area chart is a line chart where the area between the line and the axis are shaded with a color.
Use when:
Showing cumulative data trends over time.
Emphasizing the magnitude of change.
Best for:
Displaying the cumulative revenue over several years.
Illustrating population growth over time.
Tree Maps : Use treemaps to display data in nested rectangles. You use dimensions to define the structure of the treemap, and measures to define the size or color of the individual rectangles. Treemaps are a relatively simple data visualization that can provide insight in a visually attractive format.
When to use:
Displaying hierarchical data.
Comparing proportions within a whole.
Best for:
Showing the composition of a company’s revenue by product line.
Visualizing the breakdown of expenses.
Histograms: A histogram is a graphical representation used in statistics to show the distribution of numerical data. It looks somewhat like a bar chart, but with key differences that make it suitable for showing how data is distributed across continuous intervals or specific categories that are considered “bins”.
Use when:
Showing the distribution of a single numerical variable.
Identifying the frequency of data points within certain ranges.
Best for:
Displaying the distribution of test scores.
Analyzing the age distribution of a customer base.
Box Plots: A box and whisker plot, also known as a box plot, is a graphical method for displaying variation in a set of data. Here’s how it works:
Five-Number Summary: A box and whisker plot summarizes data using five key values:
Minimum value (Q0): The smallest data point.
First quartile (Q1): The median of the lower half of the data.
Median (Q2): The middle value when the data is sorted.
Third quartile (Q3): The median of the upper half of the data.
Maximum value (Q4): The largest data point
Use when:
Showing the distribution of data based on quartiles.
Identifying outliers and variability.
Best for:
Comparing the performance of different groups.
Analyzing the spread of salaries within a company.
3. Incorporating Additional Dimensions
Adding Time Dimension
When you want to include a time dimension (like hours, days, months):
Line Charts: Best for showing trends over time.
Gantt Charts: Useful for project timelines and scheduling.
Heat Maps: Effective for showing activity over time (e.g., hourly website visits).
Adding Multiple Variables
When dealing with multiple variables:
Scatter Plots with Colors and Sizes: To show relationships and differentiate groups.
Bubble Charts: To add an extra dimension (e.g., size of bubbles representing another variable).
Dual-Axis Charts: To compare two different measures over the same axis (e.g., sales and profit over time).
4. Advanced Charts in Tableau
Dual-Axis and Combined Charts : A dual axis chart creates two independent axes (which you can synchronise) that you can plot two separate measures on in the same chart.
This has one big positive that it creates two separate mark cards for each of the measures. This enables you to choose a different mark for each of the measures, letting you create different charts.
However, the negatives are that you can’t dual axis more than once, meaning that you can’t dual axis three measures together. It can also take a few more steps to get what you want, reflexing the fact that you have more freedom with the axis’.
A combined axis merges two or more measures into a single axis so you can plot as many measures as you like in the same chart.
The biggest advantage of this is that you have the option of adding an additional dual axis to this chart later if you need another mark type to reflect another measure.
Use when:
Comparing two measures with different scales.
Combining different chart types for a more comprehensive view.
Best for:
Showing sales and profit trends together.
Comparing temperature and precipitation data.
Bullet Charts : A bullet graph is a bar marked with extra encodings to show progress towards a goal or performance against a reference line. Each bar focuses the user on one measure, bringing in more visual elements to provide additional detail.
Use when:
Comparing performance against a target.
Showing progress towards a goal.
Best for:
Visualizing key performance indicators (KPIs).
Comparing actual sales against targets.
Highlight Tables: A highlight table in Tableau is a visual representation that allows you to compare categorical data using color.
Use when:
Showing detailed information with color coding for easy comparison.
Highlighting variations across a table.
Best for:
Displaying sales performance by region and product.
Analyzing customer satisfaction scores.
5. Tips for Effective Visualization
· Keep it Simple: Avoid clutter.
· Use Colors Wisely: Enhance understanding without confusion.
· Label Clearly: Use concise labels.
· Interactivity: Use filters and tooltips for deeper exploration.
Conclusion:
Choosing the right chart in Tableau involves understanding your data and visualization goals. The right chart type can effectively communicate insights and facilitate data-driven decisions. Experiment with different charts to find the best representation for your data.