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Choosing the right type of chart or graph visualization 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 analyzing and the questions you want to answer.
But simply casting your data onto a static graph can at best 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 and answers to questions at the speed of thought.
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:
Figure 1: Tell stories with bar charts. Are film sequels profitable? In this example of a bar chart, you quickly get a sense of how profitable sequels are for box office franchises. Select the chart and use the drop-down filter to see the profit for your favorite movie franchise.
Figure 2: Combine bar charts and maps.Don’t settle for a bar chart that leaves you scrolling to find the answers you seek. By combining a bar chart with a map, this dashboard showing public pension funding ratios in the U.S. provides rich information at a glance. When California is selected, for example, the bar chart filters to show state-specific information. Check out another state to see their funding ratio.
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.
Dana Zuber, Vice President - Strategic Planning Manager
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:
Figure 3: Basic lines reveal powerful insight.These two line charts illuminate the increasing popularity of “Black Friday” as an epic event in the United States. It’s quick to see that Thanksgiving lost ground to the popular shopping period in 2008.
Figure 4: Transform line charts into area charts.Often when you have two or more sets of data in a line chart it can be helpful to shade the area under the line. In this chart, it’s easy to tell that companies in the technology sector raised more capital than real estate in 2011.
Figure 5: Combine line charts with bar and trend lines.Line charts are the most effective way to show change over time. In this case, GE’s stock performance over a one-year period is joined with trading volume during the same time frame. At a glance you can tell there were two significant events, one resulting in a sell-off and the other a gain for shareholders. Click the graph and use the filter to select a different date range.
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:
Figure 6: Use pies only to show proportions.Pie charts give viewers a fast way to understand proportional data. Using pie charts on this map shows the distribution of oil rigs on land vs. offshore in Europe.
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:
Figure 7: Provide street-level data on a map.Maps are a powerful way to visualize data. In this visualization you can zero in on every LEED certified building in the United States based on their street address. Select any state or city to find the greenest buildings in that area.
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:
Figure 8: Who is most expensive to insure?Use an informative icon or “mark type” such as the female and male icons for additional detail in your scatter plot. Select the graph and filter to see how demographics change insurance premium forecasting for an employer.
Figure 9: Can you spot the fraud?Using scatter plots is a quick, effective way to spot outliers that might warrant further investigation. By creating this interactive scatter plot, an insurance investigator can quickly evaluate where they might have fraudulent activity.
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:
Figure 10: Manage project effectively.A Gantt chart is the centerpiece of this dashboard, providing a complete overview of tasks, owners, due dates, and status. By providing a menu of tasks at the top, a project manager can drill down as needed to make informed decisions.
Figure 11: Who served the longest?With a quick glance, this Gantt chart lets you know which U.S. senator held office the longest and which side of the aisle they represented. Select the visualization and use the drop down menu to see criteria such as party.
Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. 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:
Figure 12: Add data depth with bubbles.In this scatter plot accentuated with bubbles, the varied size and color of circles make it quick to see how the game’s players compare. Click this dashboard then scroll over the bubbles to get instant access to more detailed information about each character.
Figure 13: Oil imports and exports at a glance.It’s easy to tell who buys and sells the most oil with green bubbles for net exporters and red for net importers overlaid on this map. Select a country on the map and the dashboard reveals details about consumption history.
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:
Figure 14: Which houses are selling? This histogram shows which houses are seeing the most sales in a month. Explore for yourself how the histogram changes when you select a different month, county, or distress level.
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:
Figure 15: Have you hit your quota?Tracking a sales team’s progression to hitting its quota is a critical element to managing success. In this quota dashboard, a sales manager can quickly select to view her team’s performance by quota percentage or sales amount as well as zero in on regional achievement.
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:
Figure 16: Who buys the most books?In this market segmentation analysis, the heat map reveals a new campaign idea. High- income households of people in their sixties buy children’s books. Perhaps it’s time for a new grandparent-oriented campaign?
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:
Figure 17: Highlight table shows spending difference.This highlight table compares two 2012 budget proposals for the U.S. Click the table to learn more.
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:
Figure 18: Support cases at a glance.This treemap shows all of a company’s support cases, broken by case type, and also priority level. You can see that Document, Feedback, Support and Maintenance make up the lion share of support cases. However, in Feedback and Support, P1 cases make up the most number of cases, whereas most other categories are dominated by relatively mild P4 cases.
Figure 19: Visualizing World GDPIn this treemap-bar chart combination chart, we can see how overall GDP has grown over time (with the exception of 2009, when GDP fell), but also which regions and countries comprised most of the world’s GDP. Since 2001, the region ‘The Americas’ made up most of the world’s GDP, until 2007 for three years. You can also see that GDP for ‘The Americas’ is made up of largely one rectangle (one country), whereas ‘Europe’ is made up of rectangles that are more similar in size. Click a rectangle to see which country it represents and how much GDP was produced (and how much per capita).
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:
Figure 20: Comparing the sales prices of homes.For this time period, the median prices of homes sold were highest in San Francisco, but the distribution was wider for Los Angeles. In fact, the most expensive home in Los Angeles was sold at several times greater than the median. Hover over a point to see its geographic location and how much it sold for.
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