What Is A Good Graph To Use When You Have A Lot Of Individual Data Points

Data Visualization – How to Pick the Right Chart Type?

Making sense of facts, numbers, and measurements is a form of fine art – the art of data visualization. There is a load of data in the sea of noise. To turn your numbers into knowledge, your job is non simply to carve up noise from the information, but besides to present information technology the right way.

Many of the states come from the "PowerPoint generation" — this is where the roots of our understanding of data visualization and presentation lie. Unfortunately, information technology is far from anything related to good, and I stand before you as guilty myself.

And if y'all remember I'm too cynical about this, don't accept only my discussion for it.

PowerPoint could be the most powerful tool on your figurer. But it's not.  Countless innovations fail considering their champions use PowerPoint the way Microsoft wants them to, instead of the right way.
– Seth Godin, Marketing proficient

There is no question that PowerPoint has been at least a part of the trouble because it has affected a generation. It should accept come with a warning label and a good gear up of design instructions dorsum in the '90s. But it is besides a copout to blame PowerPoint — information technology is just software, not a method.
– Garr Reynolds, Presentation proficient

– Marker Goetz

To avoid common pitfalls in your presentations, it wouldn't hurt to review the nuts of data visualization .

In this article, I'll try to undo some of the damage by sharing some of the best practices for information visualization and representation and, hopefully, relieve some kittens in the process.

Data Visualization Best Practices

At that place are 4 basic presentation types that you tin can apply to present your data:

• Comparing
• Composition
• Distribution
• Human relationship

Unless you are a statistician or a data-annotator, you are nigh likely using but the ii, about commonly used types of data analysis: Comparing or Composition.

Selecting the Right Chart

To decide which nautical chart is all-time suited for each of those presentation types, first yous must respond a few questions:

• How many variables practise you desire to show in a unmarried nautical chart? One, two, three, many?
• How many items (information points) will yous brandish for each variable? But a few or many?
• Will you lot display values over a menstruum of time, or among items or groups?

Bar charts are adept for comparisons, while line charts work better for trends. Scatter plot charts are good for relationships and distributions, merely pie charts should be used but for elementary compositions — never for comparisons or distributions.

There is a chart selection diagram created by Dr. Andrew Abela that should assist you lot pick the right chart for your data blazon. (You can download the PDF version here: Chart Selection diagram.)

Let'due south dig in and review the most commonly used nautical chart types, some instance, and the dos and don'ts for each chart type.

Tables

Tables are essentially the source for all the charts. They are best used for comparison, composition, or relationship analysis when in that location are only few variables and information points. It would not make much sense to create a nautical chart if the information can exist easily interpreted from the table.

Use tables when:

• You need to compare or look up individual values.
• You require precise values.
• Values involve multiple units of measure out.
• The information has to communicate quantitative information, only not trends.

Use charts when the information presentation:

• Is used to convey a message that is contained in the shape of the data.
• Is used to show a human relationship between many values.

For example, if you want to show the rate of change, like sudden drop of temperature, it is all-time to use a chart that shows the slope of a line because rate of modify is not hands grasped from a tabular array.

Column Charts

The column chart is probably the most used chart blazon. This nautical chart is best used to compare dissimilar values when specific values are important, and it is expected that users will look up and compare individual values betwixt each column.

With column charts you could compare values for different categories or compare value changes over a period of time for a unmarried category.

All-time practices for column charts

• Utilize column charts for comparison if the number of categories is quite pocket-size — upwardly to five, but not more vii categories.
• If 1 of your data dimensions is time — including years, quarters, months, weeks, days, or hours — you should always set time dimension on the horizontal centrality.
• In charts, time should always run from left to right, never from top to bottom.
• For column charts, the numerical axis must offset at zero. Our eyes are very sensitive to the height of columns, and we can describe inaccurate conclusions when those confined are truncated.
• Avoid using pattern lines or fills. Use border but for highlights.
• Simply apply cavalcade charts to show trends if there are a reasonably-low number of information points (less than twenty) and if every information signal has a clearly-visible value.

Column Histograms

Histogram is a common variation of cavalcade charts used to present distribution and relationships of a single variable over a set of categories. A good example of a histogram would be a distribution of grades on a school exam or the sizes of pumpkins, divided past size grouping, in a pumpkin festival.

Stacked Cavalcade Charts

Use stacked column charts to show a limerick. Practice non employ also many composition items (non more than than three or four) and make sure the composing parts are relatively like in size. It can go messy very speedily.

Before moving to the adjacent chart type, I wanted to show y'all a good instance of how to ameliorate the effectiveness of your column chart by simplifying it.

Credit: Joey Cherdarchuk

Bar Charts

Bar charts are essentially horizontal column charts.

If you have long category names, it is best to apply bar charts because they give more space for long text. Y'all should also apply bar charts, instead of column charts, when the number of categories is greater than seven (but not more than 15) or for displaying a prepare with negative numbers.

• A typical utilise of bar charts would be company traffic from top referral websites. Referring sites are normally more than five to seven sites and website names are quite long, so those should be improve horizontally graphed.
• Another example could be sales operation by sales representatives. Once more, names can be quite long, and at that place might be more than 7 sales reps.

Bar Histogram Charts

Merely like column charts, bar charts tin can exist used to nowadays histograms.

• A practiced histogram example is a population distribution past the age (and sex). Think those Christmas-tree graphs?

Stacked Bar Charts

I'thousand not quite sure most a skillful application of stacked bar charts — except when at that place are only a few variables, composition parts, and the emphasis is on limerick, not comparing.

Stacked bars are not good for comparison or human relationship analysis. The merely mutual baseline is along the left axis of the chart, and so you lot tin can only reliably compare values in the outset series and for the sum of all serial.

Line Charts

Who doesn't know line charts? We used to draw those on blackboards in school.

Line charts are amidst the most frequently used chart types. Use lines when you accept a continuous data gear up. These are all-time suited for trend-based visualizations of data over a menstruation of time, when the number of data points is very high (more than twenty).

With line charts, the accent is on the continuation or the flow of the values (a trend), simply there is all the same some support for unmarried value comparisons, using data markers (but with less than 20 data points.)

A line nautical chart is also a adept culling to column charts when the chart is small.

Timeline Charts

The timeline chart is a variation of line charts. Obviously, any line chart that shows values over a period of time is a timeline chart. The only departure is in functionality — nearly timeline charts volition let you zoom in and out and compress or stretch the time axis to see more details or overall trends.

The most common examples of a time-line chart might be:

• stock market price changes over time,
• website visitors per day for the past thirty days,
• sales numbers by day for the previous quarter.

The Dos and Don'ts for Line Charts

• Use lines to present continuous data in an interval calibration, where intervals are equal in size.
• For line charts, the axis may non offset from zero if the intended message of the chart is the charge per unit of change or overall trend, not exact values or comparison. It'due south best to start the centrality with zero for wide audiences because some people may otherwise interpret the chart incorrectly.
• In line charts, time should ever run from left to right.
• Practice not skip values for consistent information intervals presenting trend information, for instance, certain days with zero values.
• Remove guidelines to emphasize the trend, rate of change, and to reduce distraction.
• Use a proper aspect ratio to testify important information and avoid dramatic slope furnishings. For the best perception, aim for a 45-degree gradient

Expanse Charts

An surface area chart is essentially a line chart — good for trends and some comparisons. Area charts will fill up the expanse below the line, then the best use for this type of chart is for presenting accumulative value changes over time, like item stock, number of employees, or a savings account.

Do not employ area charts to nowadays fluctuating values, like the stock market or prices changes.

Stacked Surface area

Stacked expanse charts are all-time used to show changes in composition over time. A skillful example would be the changes of marketplace share among pinnacle players or revenue shares by product line over a flow of fourth dimension.

Stacked area charts might be colorful and fun, but y'all should use them with caution, because they tin can quickly become a mess. Don't utilize them if you need an verbal comparison and don't stack together more than 3 to v categories.

Pie Charts and Donut Charts

Who doesn't love pies or donuts, right? Non in data visualization, though. These charts are among the most often used and also misused charts. The one above is a expert case of a terrible, useless pie chart - too many components, very similar values.

A pie chart typically represents numbers in percentages, used to visualize a part to whole relationship or a composition. Pie charts are non meant to compare individual sections to each other or to stand for exact values (you should use a bar chart for that).

When possible, avoid pie charts and donuts. The human listen thinks linearly but, when information technology comes to angles and areas, nearly of united states tin can't judge them well.

Stacked Donut Charts

I would not recommend using stacked donut charts at all! I mean, similar, never! Y'all might think that you could use a stacked donut to present limerick, while assuasive some comparison (with an emphasis on composition), but it would perform desperately for both. Use stacked column charts instead.

Hither's a practiced example of how to use pie chart effectively.

Credit: Joey Cherdarchuk

The Dos and Don'ts for Pie charts

For those of yous who even so feel sentimental most the sometime PowerPoint Pie charts, and want to keep using them, there are some things to keep in mind.

• Make certain that the total sum of all segments equals 100 pct.
• Use pie charts simply if yous have less than six categories, unless there's a clear winner you want to focus on.
• Ideally, there should be merely two categories, like men and women visiting your website, or only one category, like a market share of your company, compared to the whole market.
• Don't employ a pie chart if the category values are almost identical or completely different. Yous could add labels, but that's a patch, not an improvement.
• Don't use 3D or blow apart furnishings — they reduce comprehension and show incorrect proportions.

Besprinkle Charts

Scatter charts are primarily used for correlation and distribution analysis. Good for showing the human relationship betwixt two different variables where 1 correlates to another (or doesn't).

Scatter charts can also prove the data distribution or clustering trends and help you spot anomalies or outliers.

A good case of scatter charts would exist a chart showing marketing spending vs. revenue.

Bubble Charts

A bubble chart is a great option if you need to add another dimension to a scatter plot chart. Scatter plots compare ii values, but yous can add together bubble size equally the third variable and thus enable comparison. If the bubbling are very like in size, use labels.

We could in fact add the fourth variable by colour-grading those bubbling or displaying them as pie charts, but that's probably too much.

A good example of a bubble chart would be a graph showing marketing expenditures vs. revenue vs. profit. A standard scatter plot might evidence a positive correlation for marketing costs and revenue (plain), when a chimera chart could reveal that an increment in marketing costs is chewing on profits.

Utilise Scatter and Bubble charts to:

• Nowadays relationships between 2 (scatter) or three (chimera) numerical variables,
• Plot two or 3 sets of variables on one x-y coordinate plane,
• Turn the horizontal axis into a logarithmic scale, thus showing the relationships between more widely distributed elements.
• Present patterns in big sets of data, linear or not-linear trends, correlations, clusters, or outliers.
• Compare large number of data points without regard to time. The more data you lot include in a scatter chart, the better comparisons y'all can make.
• Nowadays relationships, merely non exact values for comparisons.

Map Charts

Map charts are practiced 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 similar coordinates, country names, country names or abbreviations, or addresses, you can plot related information on a map.

Maps won't be very good for comparing exact values, because map charts are usually color scaled and humans are quite bad at distinguishing shades of colors. Sometimes it's improve to use overlay bubbling or numbers if yous need to convey exact numbers or enable comparing.

A good case would be website visitors by state, country, or city, or product sales past state, region or urban center.

But, don't use maps for absolutely everything that has a geographical dimension. Today, almost whatsoever data has a geographical dimension, but it doesn't hateful that you should display it on a map.

When to use map charts?

• If you lot want to display quantitative information on a map.
• To present spatial relationships and patterns.
• When a regional context for your information is important.
• To get an overview of the distribution across geographic locations.
• Only if your data is standardized (that is, it has the same data format and calibration for the whole set up).

Gantt Charts

Gantt charts were adapted by Karol Adamiecki in 1896. But the proper noun comes from Henry Gantt who independently adjusted this bar chart type much afterwards, in the 1910s.

Gantt charts are good for planning and scheduling projects. Gantt charts are essentially projection maps, illustrating what needs to be done, in what order, and past what deadline. You lot can visualize the total time a project should take, the resource involved, as well equally the order and dependencies of tasks.

But projection planning is not the only application for a Gantt nautical chart. It can too be used in rental businesses, displaying a list of items for rent (cars, rooms, apartments) and their rental periods.

To brandish a Gantt chart, you would typically need, at to the lowest degree, a start engagement and an end engagement. For more advanced Gantt charts, you'd enter a completion percent and/or a dependency from another task.

Gauge Charts

Gauge charts are good for displaying KPIs (Key Performance Indicators). They typically display a single central value, comparing it to a color-coded performance level indicator, typically showing green for "skillful" and red for "trouble."

A Dashboard would be the about obvious place to utilize Approximate charts. There, all the KPIs will be in one place and will give a quick "health cheque" for your project or visitor.

Gauges are a great pick to:

• Evidence progress toward a goal.
• Represent a percentile measure out, similar a KPI.
• Show an exact value and meaning of a single measure.
• Display a single chip of information that can exist quickly scanned and understood.

The bad side of estimate charts is that they take upwardly a lot of space and typically only show a single point of data. If there are many gauge charts compared confronting a unmarried performance scale, a column chart with threshold indicators would be a more effective and meaty pick.

Multi Axes Charts

There are times when a simple chart just cannot tell the whole story. If y'all desire to show relationships and compare variables on vastly different scales, the best option might be to have multiple axes.

A multi-axes nautical chart volition let you plot data using ii or more y-axes and one shared 10-axis. Merely it comes at a toll. That is, the charts are much more difficult to read and sympathize.

Multi-axes charts might be good for presenting mutual trends, correlations (or the lack thereof) and the relationships betwixt several data sets. Simply multi-axes charts are non skillful for exact comparisons (because of unlike scales) and y'all should not utilize this type if you need to show exact values.

Apply multi-axes charts if you lot want to:

• Display a line chart and a column chart with the same X-axis.
• Compare multiple measures with different value ranges.
• Illustrate the relationships, correlation, or the lack thereof between two or more measures in one visualization.
• Save canvass space (if the nautical chart does not become too complicated).

Data Visualization Do's and Don'ts – A General Conclusion

• Time axis. When using time in charts, set up it on the horizontal centrality. Fourth dimension should run from left to right. Do not skip values (fourth dimension periods), even if there are no values.
• Proportional values. The numbers in a nautical chart (displayed equally bar, surface area, bubble, or other physically measured element in the chart) should be directly proportional to the numerical quantities presented.
• Data-Ink Ratio. Remove any excess data, lines, colors, and text from a nautical chart that does not add value. More than almost information-ink ratio
• Sorting. For column and bar charts, to enable easier comparison, sort your data in ascending or descending order by the value, not alphabetically. This applies likewise to pie charts.
• Legend. You don't demand a legend if you have simply one data category.
• Labels. Utilize labels directly on the line, cavalcade, bar, pie, etc., whenever possible, to avoid indirect await-up.
• Aggrandizement adjustment. When using budgetary values in a long-term series, make sure to adjust for aggrandizement. (European union inflation rates, U.s. aggrandizement rates)
• Colors. In whatsoever chart, don't utilise more than 6 colors.
• Colors. For comparing the same value at different time periods, use the same color in a different intensity (from low-cal to dark).
• Colors. For different categories, use unlike colors. The almost widely used colors are blackness, white, reddish, green, blue, and yellow.
• Colors. Keep the same color palette or style for all charts in the series, and same axes and labels for similar charts to make your charts consistent and piece of cake to compare.
• Colors. Check how your charts would look when printed out in grey-scale. If you cannot distinguish color differences, you should change hue and saturation of colors.
• Colors. Seven to 10 percent of men have colour deficiency. Keep that in heed when creating charts, ensuring they are readable for colour-blind people. Use Vischeck to test your images. Or, endeavor to apply color palettes that are friendly to color-blind people.
• Information Complication. Don't add too much information to a single nautical chart. If necessary, split up information in two charts, utilize highlighting, simplify colors, or change chart type.

Recommended Books on Information Visualization and Presentation

Other Resources & Further Reading Well-nigh Data Visualization

• How to Cull the Best Chart for Your Data
• Tufte'south principles
• Chart do'due south and don'ts
• Introduction to Data Visualization: Chart Do'due south and Don'ts
• Aspect Ratio and Banking to 45 Degrees
• Remove to amend (Pie chart edition)
• Remove to ameliorate (the information-ink ratio)
• Remove to better (the data tables edition)

What Is A Good Graph To Use When You Have A Lot Of Individual Data Points,

Source: https://eazybi.com/blog/data-visualization-and-chart-types

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