Introduction
Line charts and area charts are two of the most popular and versatile tools for visualising data, especially when you want to show trends or changes over time. They might seem similar at first glance—after all, both use lines to connect data points—but each chart has its own strengths depending on what story you want your data to tell. In this guide, we’ll break down the key differences between line and area charts, when to use them, and how to get the most out of each. Whether you’re tracking sales, website traffic, or simply comparing numbers over time, we’ve got you covered with all the tips you need to create clear and effective charts.
Line Charts
A line chart is a simple yet powerful way to show how something changes over time. It’s made up of points plotted on a graph and connected by a line, making it easy to spot trends, patterns, or fluctuations. Think of tracking your daily step count or seeing how sales numbers rise and fall each month—that’s exactly what a line chart is perfect for! The line helps you visualise those changes clearly, so you can quickly understand what’s going on with your data at a glance.
Elements of a Line Chart
A line chart is made up of a few key elements that work together to tell the story behind your data. Here’s a quick breakdown of the main components:
- Axes:some text
- The X-axis usually represents time or categories (e.g., days, months, years), while the Y-axis shows the value being measured (e.g., sales, temperature).
- Data Points:some text
- These are the individual values plotted on the chart. Each point represents a specific measurement at a particular moment or category.
- Line:some text
- The line connects the data points and shows the trend or change over time. It helps you see patterns more easily than looking at individual points.
- Labels:some text
- Labels on the X and Y axes tell you what’s being measured, like time on the X-axis and sales figures on the Y-axis. This helps give context to the data.
- Title:some text
- A clear, descriptive title at the top of your chart lets people know what the chart is about at a glance.
- Legend (optional):some text
- If you have multiple lines (for example, comparing sales from different regions), the legend explains which line represents which dataset.
When should you use a Line Chart?
A line chart is your go-to visualisation when you want to track changes or trends over time. However, like any chart, it shines in certain situations and falls short in others. Here are some instances when line charts are the best choice:
Tracking Changes Over Time: If you have data points that are connected over time (like monthly sales, yearly revenue, or daily temperatures), line charts excel at showing trends, increases, or drops clearly.
- Showing Trends in Continuous Data: When your data flows continuously (like stock prices or heart rate monitoring), line charts highlight the smooth progression from one point to the next.
- Comparing Multiple Trends: Line charts can handle multiple lines, making it easy to compare trends for different datasets (e.g., comparing sales for two different products over time).
- Highlighting Patterns: If you're looking for patterns, such as cyclical changes (e.g., seasonal sales fluctuations), a line chart helps you see those recurring trends.
When to Consider Alternatives
- Comparing Categories at a Single Point in Time: If you're comparing distinct categories at a single point in time (e.g., sales of different products in one month), a bar chart might be more effective, as it makes side-by-side comparisons clearer.
- Showing Parts of a Whole: When you want to show how different parts make up a whole (like the percentage of market share for different brands), a pie chart or stacked bar chart might be better suited.
- Displaying Exact Numbers: If precision is key and you're looking to highlight individual data points rather than trends, a table or dot plot might work better.
- Handling Lots of Categories: Line charts can get cluttered if you’re comparing too many lines. If you're tracking more than three or four categories, consider using a bar chart or area chart for a clearer view.
Sparkline Charts
A sparkline chart is a miniature line chart typically embedded in text, tables, or reports to give a quick visual summary of data trends. Unlike full-sized charts, sparklines are small and have no axes, labels, or other detailed chart elements, making them ideal for offering a compact snapshot of a data series.
Ridgeline Plots
A ridgeline plot is a data visualisation that displays the distribution of a numeric variable for several groups or categories, using overlapping density plots. Imagine a series of smooth curves or histograms, each representing the distribution of a dataset, stacked on top of one another with slight separation. This design helps you compare multiple distributions in one visual without them becoming too cluttered.
Showing Uncertainty in a Line Chart
To give your line chart more depth and reflect uncertainty in your data, you can add visual elements that show the variability at each point. When your line represents a statistical summary, like an average or median, it's useful to include error bars at each point, which can indicate the standard deviation or another measure of uncertainty. This helps highlight the spread of the data around the line. Alternatively, you can add confidence intervals by using additional lines or shaded areas above and below the main line. These shaded regions are often used to represent the range within which the data is most likely to fall, giving a clearer sense of data variability or probable outcomes. This method can be especially helpful when you want to visually capture the most common data values alongside your main trend.
Best Practices and Common Mistakes with Line Charts
Plotting too many lines
While you can technically add lots of lines to a single line chart, it's wise to be careful about how much data you display. A good rule of thumb is to stick to five or fewer lines—too many can make your chart look like a tangled mess and hard to read. That said, if your lines are well-separated, you can still include all the values you want to track without overcrowding the chart.
If you need to include more lines than can fit comfortably on one axis, consider breaking them up into a grid of smaller line charts. This way, each chart can highlight its own details more clearly. To make it easier to interpret, you might want to sort these smaller charts by something significant, like their average or final value, to draw attention to key points.
If you're using a tool that allows for interactive plots, you have another option! You can highlight individual lines or grey out others to keep the focus where you want it. This makes it easier for readers to explore the data at their own pace.
The difference in readability between the two charts is clear.
Strictly using a zero-value baseline
While bar charts and histograms require a zero baseline for the vertical axis, you don’t need to stick to that rule for line charts! The main purpose of a line chart is to highlight changes in value over time, rather than focusing on the actual size of those values. If a zero line doesn’t add much value to your data, feel free to adjust the vertical axis range to better showcase the changes that matter most. This way, you can make your chart more informative and easier to understand.
There is one situation where you’ll want to keep that zero baseline in your line chart. If you're using a line chart to display frequency distributions, it serves a similar purpose to bar charts and histograms. In this case, you'll need to include a zero-value baseline to provide a clear anchor for the heights of the line chart.
Curved line chart
In a standard line chart, each point is connected to the next with a straight line segment, moving from the first point to the last. It might be tempting to create a smooth curve that connects all the points, but it’s best to resist that urge! Trying to fit a curve through all the points can distort how trends are perceived. The direction and steepness of the line should reflect actual changes in value, and a curve might suggest there are additional data points in between that simply aren’t there. Stick to straight lines for a clearer and more accurate representation of your data!
Misleading dual axis
So far, we’ve looked at line charts with multiple lines that share the same domain, allowing them to be plotted on the same axis. However, there’s no rule that says each line has to represent values in the same units! When you have two series showing different variables, you end up with a dual-axis plot.
But here’s the catch: dual-axis plots can be easily manipulated and may end up being misleading. Depending on how you scale each axis, the perceived relationship between the two lines can shift dramatically. For instance, in the two plots below, weekly trials and subscriptions are shown in dual-axis plots. The data is exactly the same, but because of how the vertical scales are set, the relationship inferred between the variables can look quite different.
While many visualisation tools can create dual-axis charts, it’s often recommended to avoid them, whether the axes share the same domain or not. Instead, consider faceting the two lines into separate plots. This way, you can still observe the general patterns of change for both variables without the risk of drawing misleading comparisons!
Extra lines
Most lines in a line chart are superfluous, such as borders, grid lines and axis tick marks. The best practice is to stay clear of these lines except for specific situations
One such situation is where the gridlines can act as good reference points
Annotation Lines
When you're adding annotations to your line charts, it’s important to keep a few things in mind to avoid any mix-ups. Try using a thinner line, a dashed line, a softer colour, or even a combination of all three to make sure your annotations don’t get confused with the main lines on your chart.
If you have the option to use a shaded box—like when highlighting a historical period—go for that instead of call-out lines. It helps you steer clear of any visual clutter and keeps everything looking neat!
Choosing Colours
In most cases, different colours for different lines for each variable helps tell the difference between the variables. However there are notable exceptions, such as when you want to highlight certain variables.
Too many data points
Few sayings have been misused as much as Edward Tufte’s famous quote: "Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space." His mantra, "Above all else, show the data," often gets twisted to mean "cram as much data into the chart as possible." The end result? Line charts that end up looking like this.
While the rule of thumb is to show as much data as required, consider whether the intervals truly paint an accurate picture of what you want to convey. The chart below is a much better representation.
Legends
Whenever possible, use direct labelling. It makes the viewer work less to interpret the chart.
Conclusion
And there you have it—the complete guide to line charts, where data meets design in perfect harmony! Whether you’re tracking sales trends or monitoring your cat's increasingly chaotic daily activities, line charts are your trusty sidekick. Remember to keep it simple, label wisely, and don’t overdo it with too many lines, or you might end up with a spaghetti mess rather than a clear visual. With these tips in your toolkit, you’ll be well on your way to creating line charts that not only look great but also communicate your data effectively. So grab that data, plot those points, and let your line charts tell the story like a pro! Happy charting!
For an overview of many other visualizations, check out out the Most Common Visualizations And How To Use Them
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