7.4: Best Practices for Choosing Chart Types

Best Practices for Choosing Chart Types

One of the most practical aspects of data visualization is choosing the right chart type for your data and the question at hand. The effectiveness of a visualization often hinges on matching the data’s characteristics to an appropriate graphical form. Here we discuss some of the most common chart types—bar charts, line charts, scatter plots, pie charts, and heatmaps—and best practices for using each.

Bar Chart:

• The bar chart is a staple of categorical data visualization. Use a bar chart when you want to compare values across discrete categories. Each bar’s length (for vertical bars) or height (for horizontal bars) represents the value for that category, making differences easy to see. Bar charts are especially effective for comparisons because our eyes excel at comparing lengths (aligned on a common baseline). For example, a bar chart could show sales revenue by product line, allowing viewers to see immediately which product sold the most. Always start the quantitative axis at zero to keep the visual comparison honest (since bar lengths encode value) – more on this in the mistakes section. Bar charts can also accommodate grouped or stacked bars to show sub-category breakdowns, but be careful: too many components can make them busy. In general, stick to a reasonable number of categories (a bar chart with 3–10 bars is ideal; dozens of bars become hard to read). Label categories clearly on one axis and the numeric scale on the other. Figure 1 below shows an example of a simple bar chart.

Line Chart:

• Use a line chart (or line graph) to display trends over a continuous interval, typically time. If you have data that varies over time (e.g., monthly sales over a year, or sensor readings over seconds), a line chart connects data points to emphasize the change from one point to the next. The x-axis of a line chart is usually a time axis or other ordered sequence, and the y-axis is the quantitative value. Line charts are great for showing patterns such as seasonal fluctuations, upward or downward trends, and cycles. For instance, a line chart could show a company’s stock price each day for a year, revealing overall growth and short-term volatility. Best practices for line charts include using a solid line for continuous data and avoiding too many overlapping lines (if comparing multiple series, differentiate lines with color or line style and include a legend or labels). It’s also helpful to highlight significant points (like a peak or trough) with markers or annotations. Remember that if time (or the independent variable) is not uniformly spaced, a line can be misleading—line charts imply a continuous flow. When comparing two or three lines, ensure the scale is appropriate so that all lines’ variations are visible. If the ranges differ greatly, you might use dual axes sparingly, but be cautious as dual axes can confuse viewers if not clearly labeled.

Scatter Plot:

• Scatter plots display relationships between two numerical variables by plotting data points on an x–y coordinate plane. Use a scatter plot to examine correlation or clustering in data. For example, a scatter plot could show each customer’s purchase amount (x-axis) vs. customer age (y-axis) to see if older customers tend to spend more. Each point represents one observation (e.g., one customer). If a pattern or trend exists (like a positive correlation, where points slope upward), the scatter plot will reveal it. Scatter plots are also invaluable for spotting outliers (points far away from others) and clusters or subgroups in data. Best practices: ensure both axes are quantitative and labeled with their units; avoid over-plotting by using transparency or smaller points if there are many data points overlapping. You can add trend lines or use different colors/shapes for points to encode a third variable (e.g., coloring points by species in the famous Iris dataset, as shown in Figure 2). However, don’t overload a scatter plot with too many different symbols—clarity is key. A variant, the bubble chart, scales point sizes to encode a third numeric variable, but use this with caution as human perception of area is not as precise as length or position. For simple two-variable relationships, a classic scatter with perhaps a regression line is usually best.

Pie Chart:

• A pie chart displays parts of a whole as slices of a circle. While pie charts are common, they should be used cautiously. The strength of a pie chart is in showing a simple composition—how a total breaks down into percentages for a few categories. For example, a pie chart might show the market share of 4 companies summing to 100%. However, pie charts make it hard to accurately compare slices (except for very large differences) because humans are not as good at judging angles or areas as we are at judging lengths. If the slices are close in size, it’s tough to tell them apart, and if there are too many slices (e.g., more than 5 or 6), the chart becomes cluttered and ineffective. Best practice is to use pie charts only for a quick, rough comparison of parts-to-whole, and only when you have a small number of categories. Always label slices with their percentage or value, and consider ordering them (say, from largest to smallest, starting at 12 o’clock). Avoid 3D pie charts or exploding slices which distort perception. In many cases, a bar chart is superior to a pie chart for comparing values. Some experts even advise almost never to use pie charts; but if you do, keep them simple and clear. They can add visual appeal in a presentation, but ensure they communicate effectively. (For instance, a pie chart could effectively show that one category is about half the whole and the others share the remainder; but if you need precise comparisons between slices, a bar might be better.)

Heatmap:

• A heatmap is a grid-based visualization where values in a matrix are represented by colors. Heatmaps are excellent for showing relationships or patterns across two dimensions of data. One axis of the grid represents one variable (or category), and the other axis represents a second variable; each cell in the grid is colored according to the value of some metric for that combination. For example, a heatmap could display a correlation matrix for multiple variables, where each cell’s color intensity indicates the correlation coefficient between a pair of variables. Darker or warmer colors might signify stronger correlation (positive or negative), making it easy to spot which pairs are highly correlated. Another common use is in representing geographic or spatial data (e.g., a heatmap of population density on a map, or a website heatmap showing where users click most). When creating heatmaps, use a color palette that has a logical progression (sequential for values that range low to high, or diverging for values that go above and below a midpoint). Include a legend or color bar to interpret the colors. Be mindful of color choice for accessibility (more on that later) – some viewers may have difficulty if you use a troublesome palette (like red-green without additional cues). Label both axes clearly, and if necessary, annotate important cells. Heatmaps can simplify complex data by encoding it into color patterns, allowing viewers to see hotspots, clusters, or voids at a glance.

In addition to the above, there are many other chart types (area charts, histograms, box plots, tree maps, etc.), but mastering the basics of bars, lines, scatters, pies, and heatmaps covers a large majority of analytic needs. A good rule of thumb is to consider your data’s structure and your message: use position/length (bar, line, scatter) for precise comparison, use area/angle (pie, bubble) sparingly for part-to-whole when precision is less important, and use color intensity (heatmap) for matrix data or patterns across two variables. Always ask what insight do I want to convey? and which chart type will make that easiest to see? When in doubt, simplicity and clarity should guide your choice of visualization.

Figure \(\PageIndex{1}\): Chart Type Selection Guide. This decision flowchart helps analysts select the most appropriate chart type based on their data characteristics and the analytical question being asked -- whether comparing categories, showing trends, examining relationships, displaying composition, or revealing patterns across two dimensions. (CC BY 4.0; Felix Amoruwa) Access to a detailed description of Figure \(\PageIndex{1}\)