In 2024, with data visualization being more important than ever before, choosing the right type of chart to depict time series data can be crucial. When dealing with data over a timeframe, you need to select a chart type that can clearly showcase changes, trends, and patterns across that period.
Line Charts Show Continuous Change
When representing continuous, quantitative data from a timeframe, a line chart is usually the best option. Line charts plot data points and connect them with, you guessed it, a line. This makes them ideal for showing progression over intervals, and demonstrating increases, decreases, trends, acceleration, or inflection points within the data. They are also very helpful for comparing multiple time series side-by-side.
Line charts are particularly useful in business settings analyzing sales figures, stock performance, website traffic metrics, among other examples. Let’s take a look at a sample line chart tracking eCommerce revenue on a daily basis over a one month period:
Notice how we can easily see fluctuations and patterns in revenue from day to day. We can also spot the peak revenue days versus slower days. This granular, continuous view into the data at each time interval would be difficult to capture accurately in another type of chart.
Bar and Column Charts Display Discrete Time Periods
and column charts are the better choice when your time series data represents distinct periods. For example, revenue from different years or sales by month. Instead of a continuous line, bar and column charts use rectangular bars with the height or length corresponding to the data magnitude.
Here is an example column chart comparing yearly revenue for a fictional company:
Since the data intervals are separated into discrete years, viewing them as distinct columns makes more sense than trying to connect them with a continuous line. The columns make it easy to spot which years had the highest or lowest revenue at a glance. The differences between each year also stand out more clearly to the viewer.
Heat Maps Visualize Correlations Over Time
A heat map chart adds another layer of visual insight for timeseries data. It uses color shading to indicate the magnitude or value of the data points across two dimensions usually a timeframe represented vertically or horizontally and another data variable represented on the opposite axis. Darker shading corresponds to higher values, while lighter colors indicate lower values.
Heat maps are exceptional for identifying patterns, trends, clusters, correlations, and outliers that exist between two variables over a time period. Some common examples include:
- Call center call volume by day of week and time of day
- Website visits by geographic location across different months
- Service outages by region over the course of a year
Here is a sample heat map representing website traffic by U.S. state for each month:
The darker shading clearly shows which states generate the most web traffic, how the patterns change month by month, and where there may be seasonality or clusters in the data.
Gantt Charts Plan Scheduling Across Time
Project planning is another case where effectively visualizing information across a timeline is vital. That’s why
have become a standard project management tool for scheduling tasks over a certain time period.
Gantt charts display each task or activity on the vertical axis and designate its timespan along the horizontal timeframe. This offers a birds eye view into what needs to happen when as a project progresses. Here is a sample Gantt chart for a marketing campaign project spanning 6 months with several parallel tasks:
Project stakeholders can immediately recognize task dependencies, resource constraints, the critical path, and when milestones need to be hit from a single Gantt chart view. Keeping everything on schedule is much simpler.
Choose Your Time Series Chart Wisely
As the old saying goes: “a picture is worth 1000 words”. Nowhere is that more relevant than presenting data across a timeframe. Choosing the optimal chart unlocks the stories within the data and conveys them to an audience effectively.
highlight trends and fluctuations through time bound data points.
and column layouts separate timed intervals into discrete segments for easy comparison.
correlate patterns between evolving variables. While
enable optimized scheduling across complex workflows and long time horizons.
Leverage these best practices for selecting timeseries charts:
- Use line charts for continuous data to show flows and patterns
- Use column/bar charts for discrete time periods to compare across intervals
- Use heat maps to reveal correlations between data points over time
- Use Gantt charts to map scheduling and dependencies for project plans
Relying on the wrong chart type can lead to misinterpretation of the datasets. But armed with these tips, you can pick the right visual for every timeframe scenario and present beautiful, insightful pictures of the progressions at play.
Frequently Asked Questions
What is the best chart for showing trends over time?
Line charts are ideal for visualizing trends over time. By plotting data points and connecting them with a continuous line, line charts showcase progression patterns, acceleration, increases/decreases as well as inflection points very effectively for trends across time periods.
When should I use a column or bar chart instead of a line chart?
Choose bar or column charts when your timeframe data represents well defined, discrete periods like sales by year or monthly website visits. The separated columns make it easier to compare one time period against another.
How are heat maps useful for time series data?
Heat maps incorporate color shading to reveal correlations between two variables over time. The color intensity demonstrates how the data dimensions relate across the timeframe selected. This enables heat maps to uncover patterns, associations, and anomalies.
What do Gantt charts convey that other time charts cannot?
Gantt charts are optimized for mapping schedules and task dependencies across complex projects and extended time horizons. The task timing view enables optimization of resource planning in a way not possible in simpler timeseries charts.
Does the type of data I’m presenting dictate what time chart I should use?
Yes, choosing the right time chart depends entirely on the type of data and what insights you aim to uncover. Continuous quantitative data suits line charts, discrete data pairs better with bar/column layouts, multivariate data demands heat maps, and task scheduling data requires Gantt charts for optimal visualization.
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