Choosing the right type of chart or graph visualisation can be key to conveying the most important insights in your data – on sight. This paper will help you determine which chart is best for the type of data you're analysing and the questions that you want to answer.
Learn more than just how to create a graph
Displaying your data as a static graph can answer simple questions. We'll go beyond and teach you about combining graphs on a dashboard, adding filters, and what charts pair well together. From a horizontal bar graph to a box-and-whisker plot, you'll be slinging graphs at the speed of curiosity. The result is business insight, clear communication of data and answers to questions at the speed of thought.
1. Bar chart
Bar charts are one of the most common ways to visualize data. Why? It’s quick to compare information, revealing highs and lows at a glance. Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data.
When to use bar charts:
- Comparing data across categories. Examples: Volume of shirts in different sizes, website traffic by origination site, percent of spending by department.
- Include multiple bar charts on a dashboard. Helps the viewer quickly compare related information instead of flipping through a bunch of spreadsheets or slides to answer a question.
- Add color to bars for more impact. Showing revenue performance with bars is informative, but overlaying color to reveal profitability provides immediate insight.
- Use stacked bars or side-by-side bars. Displaying related data on top of or next to each other gives depth to your analysis and addresses multiple questions at once.
- Combine bar charts with maps. Set the map to act as a “filter” so when you click on different regions the corresponding bar chart is displayed.
- Put bars on both sides of an axis. Plotting both positive and negative data points along a continuous axis is an effective way to spot trends.
Tableau is one of the best tools out there for creating really powerful and insightful visuals. We’re using it for analytics that require great data visuals to help us tell the stories we’re trying to tell to our executive management team.
2. Line chart
Line charts are right up there with bars and pies as one of the most frequently used chart types. Line charts connect individual numeric data points. The result is a simple, straightforward way to visualize a sequence of values. Their primary use is to display trends over a period of time.
When to use line charts:
- Viewing trends in data over time. Examples: stock price change over a five- year period, website page views during a month, revenue growth by quarter.
- Combine a line graph with bar charts. A bar chart indicating the volume sold per day of a given stock combined with the line graph of the corresponding stock price can provide visual queues for further investigation.
- Shade the area under lines. When you have two or more line charts, fill the space under the respective lines to create an area chart. This informs a viewer about the relative contribution that line contributes to the whole.
3. Pie chart
Pie charts should be used to show relative proportions – or percentages – of information. That’s it. Despite this narrow recommendation for when to use pies, they are made with abandon. As a result, they are the most commonly mis-used chart type. If you are trying to compare data, leave it to bars or stacked bars. Don’t ask your viewer to translate pie wedges into relevant data or compare one pie to another. Key points from your data will be missed and the viewer has to work too hard.
When to use pie charts:
- Showing proportions. Examples: percentage of budget spent on different departments, response categories from a survey, breakdown of how Americans spend their leisure time.
- Limit pie wedges to six. If you have more than six proportions to communicate, consider a bar chart. It becomes too hard to meaningfully interpret the pie pieces when the number of wedges gets too high.
- Overlay pies on maps. Pies can be an interesting way to highlight geographical trends in your data. If you choose to use this technique, use pies with only a couple of wedges to keep it easy to understand.
When you have any kind of location data – whether it’s postal codes, state abbreviations, country names, or your own custom geocoding – you’ve got to see your data on a map. You wouldn’t leave home to find a new restaurant without a map (or a GPS anyway), would you? So demand the same informative view from your data.
When to use maps:
- Showing geocoded data. Examples: Insurance claims by state, product export destinations by country, car accidents by zip code, custom sales territories.
- Use maps as a filter for other types of charts, graphs, and tables. Combine a map with other relevant data then use it as a filter to drill into your data for robust investigation and discussion of data.
- Layer bubble charts on top of maps. Bubble charts represent the concentration of data and their varied size is a quick way to understand relative data. By layering bubbles on top of a map it is easy to interpret the geographical impact of different data points quickly.
5. Scatter plot
Looking to dig a little deeper into some data, but not quite sure how – or if – different pieces of information relate? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further.
When to use scatter plots:
- Investigating the relationship between different variables. Examples: Male versus female likelihood of having lung cancer at different ages, technology early adopters’ and laggards’ purchase patterns of smart phones, shipping costs of different product categories to different regions.
- Add a trend line/line of best fit. By adding a trend line the correlation among your data becomes more clearly defined.
- Incorporate filters. By adding filters to your scatter plots, you can drill down into different views and details quickly to identify patterns in your data.
- Use informative mark types. The story behind some data can be enhanced with a relevant shape.
6. Gantt chart
Gantt charts excel at illustrating the start and finish dates elements of a project. Hitting deadlines is paramount to a project’s s uccess. Seeing what needs to be accomplished – and by when – is essential to make this happen. This is where a Gantt chart comes in.
While most associate Gantt charts with project management, they can be used to understand how other things such as people or machines vary over time. You could use a Gantt, for example, to do resource planning to see how long it took people to hit specific milestones, such as a certification level, and how that was distributed over time.
When to use Gantt charts:
- Displaying a project schedule. Examples: illustrating key deliverables, owners, and deadlines.
- Showing other things in use over time. Examples: duration of a machine’s use, availability of players on a team.
- Adding color. Changing the color of the bars within the Gantt chart quickly informs viewers about key aspects of the variable.
- Combine maps and other chart types with Gantt charts. Including Gantt charts in a dashboard with other chart types allows filtering and drill down to expand the insight provided.
7. Bubble chart
Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. People are drawn to using bubbles because the varied size of circles provides meaning about the data.
When to use bubbles:
- Showing the concentration of data along two axes. Examples: sales concentration by product and geography, class attendance by department and time of day.
- Accentuate data on scatter plots. By varying the size and color of data points, a scatterplot can be transformed into a rich visualization that answers many questions at once.
- Overlay on maps. Bubbles quickly inform a viewer about relative concentration of data. Using these as an overlay on map puts geographically-related data in context quickly and effectively for a viewer.
8. Histogram chart
Use histograms when you want to see how your data are distributed across groups. Say, for example, that you’ve got 100 pumpkins and you want to know how many weigh 2 pounds or less, 3-5 pounds, 6-10 pounds, etc. By grouping your data into these categories then plotting them with vertical bars along an axis, you will see the distribution of your pumpkins according to weight. And, in the process, you’ve created a histogram.
At times you won’t necessarily know which categorization approach makes sense for your data. You can use histograms to try different approaches to make sure you create groups that are balanced in size and relevant for your analysis.
When to use histograms:
- Understanding the distribution of your data. Examples: Number of customers by company size, student performance on an exam, frequency of a product defect.
- Test different groupings of data. When you are exploring your data and looking for groupings or “bins” that make sense, creating a variety of histograms can help you determine the most useful sets of data.
- Add a filter. By offering a way for the viewer to drill down into different categories of data, the histogram becomes a useful tool to explore a lot of data views quickly.
9. Bullet Chart
When you’ve got a goal and want to track progress against it, bullet charts are for you. At its heart, a bullet graph is a variation of a bar chart. It was designed to replace dashboard gauges, meters and thermometers. Why? Because those images typically don’t display sufficient information and require valuable dashboard real estate.
When to use bullet graphs:
- Evaluating performance of a metric against a goal. Examples: sales quota assessment, actual spending vs. budget, performance spectrum (great/good/poor).
- Use color to illustrate achievement thresholds. Including color, such as red, yellow, green as a backdrop to the primary measure lets the viewer quickly understand how performance measures against goals.
- Add bullets to dashboards for summary insights. Combining bullets with other chart types into a dashboard supports productive discussions about where attention is needed to accomplish objectives.
10. Heat maps
Heat maps are a great way to compare data across two categories using color. The effect is to quickly see where the intersection of the categories is strongest and weakest.
When to use heat maps:
- Showing the relationship between two factors. Examples: segmentation analysis of target market, product adoption across regions, sales leads by individual rep.
- Vary the size of squares. By adding a size variation for your squares, heat maps let you know the concentration of two intersecting factors, but add a third element. For example, a heat map could reveal a survey respondent’s sports activity preference and the frequency with which they attend the event based on color, and the size of the square could reflect the number of respondents in that category.
- Using something other than squares. There are times when other types of marks help convey your data in a more impactful way.
11. Highlight table
Highlight tables take heat maps one step further. In addition to showing how data intersects by using color, highlight tables add a number on top to provide additional detail.
When to use highlight tables:
- Providing detailed information on heat maps. Examples: the percent of a market for different segments, sales numbers by a reps in a particular region, population of cities in different years.
- Combine highlight tables with other chart types. Combining a line chart with a highlight table, for example, lets a viewer understand overall trends as well as quickly drill down into a specific cross section of data.
Looking to see your data at a glance and discover how the different pieces relate to the whole? Then treemaps are for you. These charts use a series of rectangles, nested within other rectangles, to show hierarchical data as a proportion to the whole.
As the name of the chart suggests, think of your data as related like a tree: each branch is given a rectangle which represents how much data it comprises. Each rectangle is then sub-divided into smaller rectangles, or sub-branches, again based on its proportion to the whole. Through each rectangle’s size and color, you can often see patterns across parts of your data, such as whether a particular item is relevant, even across categories. They also make efficient use of space, allowing you to see your entire data set at once.
When to use treemaps:
- Showing hierarchical data as a proportion of a whole. Examples: storage usage across computer machines, managing the number and priority of technical support cases, comparing fiscal budgets between years.
- Coloring the rectangles by a category different from how they are hierarchically structured.
- Combining treemaps with bar charts. In Tableau, place another dimension on Rows so that each bar in a bar chart is also a treemap. This lets you quickly compare items through the bar’s length, while allowing you to see the proportional relationships within each bar.
13. Box-and-whisker plot
Box-and-whisker plots, or boxplots, are an important way to show distributions of data. The name refers to the two parts of the plot: the box, which contains the median of the data along with the 1st and 3rd quartiles (25% greater and less than the median), and the whiskers, which typically represents data within 1.5 times the Inter-quartile Range (the difference between the 1st and 3rd quartiles). The whiskers can also be used to also show the maximum and minimum points within the data.
When to use box-and-whisker plots:
- Showing the distribution of a set of a data. Examples: understanding your data at a glance, seeing how data is skewed towards one end, identifying outliers in your data.
- Hiding the points within the box. This helps a viewer focus on the outliers.
- Comparing boxplots across categorical dimensions. Boxplots are great at allowing you to quickly compare distributions between data sets.