Which charts should i use

If this means manipulating your data by removing points, grouping points, or by looking at shorter spans of time , take time to consider the tradeoff between readability and data accuracy.

Try to reduce your dataset down to its most essential pieces without introducing inaccuracy or skewing your data. Contrasting a single bright color with muted greys can help your readers make sense of even hundreds of data points:. A good way to make sure that your chart is simple enough? Have a friend look it over, and resist the urge to explain it to them. Alternatively, do a squint test. If not, simplify your charts a little more. The power of well-presented data should not be underestimated, and the right chart can make all the difference.

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Product Development. Project Management. Real Estate. Infographic Templates. Timeline Templates. Report Templates. Diagram Templates. Presentation Templates. See All Templates. I would not recommend using stacked donut charts at all! I mean, like, never! You might think that you could use a stacked donut to present composition, while allowing some comparison with an emphasis on composition , but it would perform badly for both.

Use stacked column charts instead. For those of you who still feel sentimental about the old PowerPoint Pie charts, and want to keep using them, there are some things to keep in mind.

Scatter charts are primarily used for correlation and distribution analysis. Scatter charts can also show the data distribution or clustering trends and help you spot anomalies or outliers. 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.

If the bubbles are very similar in size, use labels. A good example of a bubble chart would be a graph showing marketing expenditures vs. A standard scatter plot might show a positive correlation for marketing costs and revenue obviously , when a bubble chart could reveal that an increase in marketing costs is chewing on profits. Map charts are good for giving your numbers a geographical context to quickly spot best and worst performing areas, trends, and outliers.

If you have any kind of location data like coordinates, country names, state names or abbreviations, or addresses, you can plot related data on a map. A good example would be website visitors by country, state, or city, or product sales by state, region or city.

Gantt charts were adapted by Karol Adamiecki in But the name comes from Henry Gantt who independently adapted this bar chart type much later, in the s.

Gantt charts are good for planning and scheduling projects. Gantt charts are essentially project maps, illustrating what needs to be done, in what order, and by what deadline. You can visualize the total time a project should take, the resources involved, as well as the order and dependencies of tasks. But project planning is not the only application for a Gantt chart.

It can also be used in rental businesses, displaying a list of items for rent cars, rooms, apartments and their rental periods. To display a Gantt chart, you would typically need, at least, a start date and an end date. A Dashboard would be the most obvious place to use Gauge charts. The bad side of gauge charts is that they take up a lot of space and typically only show a single point of data.

If there are many gauge charts compared against a single performance scale, a column chart with threshold indicators would be a more effective and compact option. There are times when a simple chart just cannot tell the whole story. If you want to show relationships and compare variables on vastly different scales, the best option might be to have multiple axes.

A multi-axes chart will let you plot data using two or more y-axes and one shared x-axis. When the third variable is numeric in nature, that is where the bubble chart comes in. The density curve, or kernel density estimate, is an alternative way of showing distributions of data instead of the histogram. Rather than collecting data points into frequency bins, each data point contributes a small volume of data whose collected whole becomes the density curve.

While density curves may imply some data values that do not exist, they can be a good way to smooth out noise in the data to get an understanding of the distribution signal. In a violin plot, each set of box and whiskers is replaced with a density curve built around a central baseline. This can provide a better comparison of data shapes between groups, though this does lose out on comparisons of precise statistical values. A frequent variation for violin plots is to include box-style markings on top of the violin plot to get the best of both worlds.

The heatmap presents a grid of values based on two variables of interest. The axis variables can be numeric or categorical; the grid is created by dividing each variable into ranges or levels like a histogram or bar chart.

Grid cells are colored based on value, often with darker colors corresponding with higher values. A heatmap can be an interesting alternative to a scatter plot when there are a lot of data points to plot, but the point density makes it difficult to see the true relationship between variables. There are plenty of additional charts out there that encode data in other ways for particular use cases.

Xenographics includes a collection of some fanciful charts that have been driven by very particular purposes. Still, some of these charts have use cases that are common enough that they can be considered essential to know. However, pie charts use an uncommon encoding, depicting values as areas sliced from a circular form.

Since a pie chart typically lacks value markings around its perimeter, it is usually difficult to get a good idea of exact slice sizes. However, the pie chart and its cousin the donut plot excel at telling the reader that the part-to-whole comparison should be the main takeaway from the visualization.

A funnel chart is often seen in business contexts where visitors or users need to be tracked in a pipeline flow. The chart shows how many users make it to each stage of the tracked process from the width of the funnel at each stage division. The tapering of the funnel helps to sell the analogy, but can muddle what the true conversion rates are. A bar chart can often fulfill the same purpose as a funnel chart, but with a cleaner representation of data.

This usually means a perpendicular line showing a target value, but also background shading to provide additional performance benchmarks. Bullet charts are usually used for multiple metrics, and are more compact to render than other types of more fanciful gauges. When values in a dataset correspond to actual geographic locations, it can be valuable to actually plot them with some kind of map. A common example of this type of map is the choropleth like the one above.

This takes a heat map approach to depicting value through the use of color, but instead of values being plotted in a grid, they are filled into regions on a map. For a handy reference guide for more chart types and when they should be used, check out our free eBook, How to Choose the Right Data Visualization.

Funnel charts are specialized charts for showing the flow of users through a process. Learn how to best use this chart type by reading this article. Violin plots are used to compare the distribution of data between groups.

Learn how violin plots are constructed and how to use them in this article. Color is a major factor in creating effective data visualizations. Read this article to learn how color is used to depict data and tools to create color palettes.