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What do you think makes a bigger impression: a table comparing GDP growth in a country over the years or a graph with the exact same data?
While both tell the same thing, the graph is much easier to understand - this is the power of data visualization. But how do you choose which data visualization techniques to use? Sometimes, even the best data can be rendered useless if you use the wrong type of chart or visualization.
Today, we’re going to show you the different chart types: how they work and what you should (not) use them for. You don’t have to be an Excel pro to become an expert in showing your data in a more graphical way.
When you want to track and trace the evolution of a specific quantitative value, this chart type is a good choice. It is often used to show trends and analyze data changes over time. The line graph can also be used for comparing data from multiple values by using multiple curved or straight lines.
In a line chart, the y-axis represents the quantitive value, while the x-axis shows the timescale or a sequence of intervals. The direction of the line on the chart tells you a story about where the data is going - an upward or a downward slope shows you the general direction of the values in the visualization.
Best for: trend analysis, time series data, comparing multiple data series.
One of the most commonly used chart types is the bar chart, and for a good reason. If you’re looking for an answer to the question of “how many” for several categories and a specific period of time, this is the chart to use.
The classic bar graph setup uses horizontal bars for comparing numeric values across different categories. The longer the bar, the bigger the value displayed on the horizontal axis. For example, you could compare the sales performance of different products this year to see which product sells best.
Best for: comparing categories, ranking items, frequency distribution.
Simple bar charts are great to compare values of a specific category. But sometimes, you may want to compare numerical values across multiple categories. You can do exactly that with a stacked bar chart, which is a more advanced version of the bar chart.
With a stacked bar chart, you can group data from one bar into multiple subdivisions to show even more data in one visualization. For example, we could break down our sales performance bar chart to see how much sales each product drove quarter over quarter.
That way, we can see the overall best-performing products, but we can also compare shifts in preferences over time.
Best for: comparing categories, ranking items, and frequency distribution - grouped by multiple subdivisions.
If you have two or more variables and want to discover the relationship between them, a scatter plot can be a solid choice. When you have two different variables, they are located on the X and Y axis. The data point is horizontally and vertically located for each variable on the scatter chart.
The closer the data points are together, the more related they are. Moreover, if they form a line or a curve, it signifies a strong relationship. The more spaced out the data points are, the weaker the relationship.
Best for: identifying relationships, correlation analysis, and detecting outliers.
If you’re doing explanatory data analysis, a boxplot chart (or box and whisker plot) is an excellent choice. You can use it to summarize data measured on an interval scale. With a box plot chart, you show the distribution of data points, their central value and their variability.
One major advantage of a box plot over other types of charts is that you can see a variable’s spread and outliers in one place. To do so, box plots use the concept of quartiles, which divides your datasets into equal fourths.
Also, this graph type does not take up much space in your dashboard. Perfect if you want to pack a lot of information, but you’re limited in space.
Best for: comparing distributions, identifying central tendency, understanding data spread.
This is one of the most common chart types that can be used for various sets of data. It’s great for answering the question of “how many” for a specific time period and across different categories. Time indication can be used as a category too so you can track a metric over time.
It uses vertical bars to compare numeric values across different categories or time frames. The longer the bar, the higher the value, which you can see on the vertical axis in the chart.
Best for: comparing categories, ranking items, frequency distribution.
Dual axis chart (also called a multiple axis chart) lets you you compare metrics or different units of measure or scales on a single chart. This way, you can visualize trends that may not be obvious when viewing data points in isolation from each other.
For example, you can have a dual axis chart with products sold on one axis and revenue over time on another axis. This way, you can show the relationship between the two.
Best for: comparing different data types, showing relationships, highlighting trends
Similar to the line chart, an area chart lets you track changes over time for one or more categories. You can just as easily compare data from multiple categories with multiple areas.
You can use it to show trends over time for related categories. The main difference between an area chart and a line chart is that the area below the line is filled with color to show volume.
If the differences between the values in the data are big enough for a clear representation, an area chart may be a better choice than a line chart.
Best for: showing trends over time, parts-to-whole relationships, stacked data comparison
A stacked area chart shows you how a measure changes over time, as seen through multiple category values. Opposed to the regular area chart, it stacks all of the lines on top of each other. In other words, these charts visualize the cumulated sum with numbers or percentages over a certain time period, so you can show the contribution of each category.
Similarly, the stacked column chart measures contributions from different individual values with their total value.
While they have their pros, they are also not ideal for some use cases. For example, when you want to give an accurate representation of fluctuations per category.
Best for: part-to-whole contribution, changes to contribution over time, trend analysis.
It does not get more classic than a pie chart, which was invented in the 18th century. This chart type is ideal for showing different parts of a whole and getting a proportional distribution of your data. The circle (pie) is divided into pieces to show how much space a certain value takes up in proportion to the whole.
The problem with pie charts is that as humans, we are naturally not very good at interpreting the sizes of the different slices. To combat this issue, make sure you always follow common data visualization best practices. That means no more than five different slices in a pie, ideally only two.
Best for: showing relative proportions, highlighting the relationship of a part to a whole, simple data sets.
Pies vs. donuts debate in terms of taste is a tricky one. However, when it comes to chart types, donut charts are a better option than pie charts. There is just one difference: the doughnut chart has a hole in the middle of the slices, hence, making it a donut.
The downside is the same: our brain can't interpret the sizes easily, so keep it under five slices for this chart type as well. However, since there is no center (that we focus on by default), we judge the pieces by their length. This makes them easier to read than pies. If you do have more than five data points, use a bar chart as a good alternative.
Best for: showing relative proportions, highlighting the relationship of a part to a whole, simple data sets.
A bubble chart is a fun way to show how many values you have per category. The higher the value, the bigger the bubble, which gives you a super easy overview of your top categories. To get even more value from this chart type, you can group similar categories by using the same or similar colors.
You can adjust and customize bubble charts to show the type of data you need. For example, you can limit the number of bubbles or choose to display the absolute value of percentages. Note that the data labels are small on bubble charts, so they might not be the best choice for comparing categories with long, descriptive names.
Best for: showing multivariate data, comparing more than two variables, visualizing trends and patterns
A histogram shows you the frequency of numerical data by using rectangles. The vertical axis represents the frequency of a variable. The horizontal axis shows the variable value, e.g. months, weeks, days.
A histogram is the ideal chart type for comparing the distribution of numerical data in different time intervals or ranges. Histograms should not be mistaken for bar charts, even though they look similar.
Best for: data distribution analysis, identifying outliers, data preprocessing.
This chart type is ideal for showing the stages of a process in parts stacked on top of each other and visually enhancing them with colors. The more items you have in a certain stage, the wider that part of the chart is.
This makes funnel charts ideal for taking a look at processes and identifying bottlenecks. In Luzmo, there are three different layout types for this chart: dynamic width, dynamic height, and equal and linar.
Best for: lead generation and conversion charts, website conversion analysis, sales and marketing funnel visualization.
A heatmap or a heat map is a type of chart that shows values for a main variable across two axis variables as a grid with squares in different colors. The variables are split into ranges similar to a histogram or a bar chart.
The typical heatmap chart has darker colors corresponding to the larger values in variables. This makes it easy to see patterns and make conclusions with a quick glance at a heatmap.
PS. another name for a heatmap is a density map, which is coincidentally, something entirely different from a chart.
Best for: data exploration and pattern recognition, correlation analysis, risk assessment and portfolio analysis.
A Gantt chart is a subtype of a bar chart that shows different categories over a time period. You can use it to visualize the start and finish of a project in time period blocks. Besides data visualization, Gantt charts are also commonly used for project management to ensure that projects stay on track and within deadlines.
Best for: project planning, dependency management, task scheduling.
When you want to compare two or more categories based on different variables, a radar chart is the ideal choice for multivariable comparison. If you have a lot of data and a high number of variables and want to show them in one chart without it looking like a mess, you want to use a radar chart.
To make the radar chart more suitable for your needs, you can change the opacity of the color to make it more or less intense. A radar chart is often used for mapping out skillsets, e.g. for HR or training purposes.
Best for: multi-dimensional data comparison, performance evaluation, feature comparison
You can use a waterfall chart to show a running total where the values are added or subtracted. This is a good type of chart to show how an initial value gets impacted over time with positive and negative values.
The values can be based on the time or category. Other names for this chart are a flying bricks chart or a Mario chart.
Best for: financial statement analysis, project budget analysis, sales and revenue analysis.
If you want to show a hierarchical structure in a fun way, this type of graph is a great choice. Alternatively, if you want to show proportions between different values within a single category, you can use a treemap.
Even though it’s called a treemap, this visualization type uses squares for visualizing categories. Every category has a colored rectangle area and subcategories are within these rectangles. The bigger the rectangles, the bigger bigger the part-to-whole ratio.
Best for: hierarchical data visualization, nested categories, quantitative data display.
Although it looks very similar to a treemap, marimekko charts use stacked column charts of different widths, to help you detect relationships between categories and subcategories.
Marimekko charts are great for displaying survey results, because you can easily compare how different groups respond to certain questions. It is, however, a more complex visualization, which is why we prefer the treemap as a more intuitive visualization. But by no means is it a bad data visualization.
When you need to visualize data in terms of performance, bullet charts are one of the most logical choices. They allow you to show the progress of multiple categories in one place.
These chart types are most commonly used to compare a performance forecast with the actual numbers. If you want to track if your business or a certain department is meeting certain goals, this is a great chart type to use.
Best for: goal and target tracking, performance assessment, tracking efficiency and productivity.
Also known as the Japanese candlestick chart or a K-line, this is the ideal choice for visualizing and tracking financial data. It consists of two parts:
When creating this type of chart, the body is filled by default when the open price is higher than the close price. This means you’re in a bear market. If the body is empty, this is a bull market.
Because it's specific to the financial sector, you may not find this chart in every data visualization tool's assortment.
Best for: financial use cases - market sentiment, price reversals, support and resistance levels.
Dating back to the 19th century, the Sankey diagram is one of the oldest ways to show data values in visual form. This type of diagram is ideal for showing flows and amounts of traffic. The higher the quantity of the flow, the wider the streams in the diagram.
The flow can break out into multiple branches or subcategories, indicating the size connection between them.
Best for: energy flow analysis, material flow analysis, environmental data.
This is a statistical thematic map that uses colors that match a number range. It’s most commonly used to summarize a geographic characteristic in a geographic area. You’ll see these maps when viewing information such as income per capita or population density.
Best for: geographic insights, data distribution, special patterns
A symbol map overlays quantitative values over geographical locations through the use of symbols. These maps are easy to read and they have been around for centuries. When preparing presentations of data studies or something happening to a geographical area, go with this chart type.
Best for: geospatial data, location analysis, spatial trends
This chart type is ideal when comparing the distribution of two categories against each other on a few levels such as age groups, years and others. It’s commonly used for showing demographic data represented by two categories (male and female), grouped into different categories such as age groups.
Best for: population demographics, market segmentation, economic data
If you want to visualize hierarchies without using traditional chart types, this is an interesting choice. Instead of using bars, circles can be used to show hierarchy. The larger the circle, the larger the category, packing up smaller categories within it.
Best for: hierarchical data, proportional data, data exploration
Closely akin to the circle pack diagram, the sunburst chart shows hierarchical data, and is great for advanced data exploration. Instead of using embedded circles, it uses concentric circles. For each category you add, you’ll add another ring or circle to the visualization. The innermost circle is at the root, while the outer circles represent deeper levels of the hierarchy.
The Venn diagram is a simple visualization that uses overlapping circles to show relationships between your data. Each circle represents one category; the overlap between the circles indicates which characteristics they have in common.
Because data visualizations don’t always have to be serious, enjoy this example Venn diagram showing all the firing, hiring, and re-hiring happening in The Office.
As you can see, there are a lot of chart types to choose from. The right chart for your application will be the one that shows the most relevant data in a way that is clear and easy to understand. However, finding that chart can be very challenging.
To help you out, you can use dashboard templates in Luzmo to find the most commonly used charts, along with their use cases. If you’re looking for inspiration, these templates will show you how to go from your data points to a useful visualization.
On top of that, this flow chart is another handy tool to help you figure out which chart type to use.
And if you’re ready to build an embedded analytics dashboard of your own, we can help you do that in a matter of hours - not weeks. Sign up for Luzmo today and go from raw data to interactive insights, rapidly fast!
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
