Choosing the right type of chart or graph visualization can be key to conveying the most important insights in your data on sight. This article will help you determine which chart is best for the type of data you’re analyzing and the questions you want to answer.
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.
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.
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.
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.
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.
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.
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. 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.
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.
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).
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.
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.
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.
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.
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