When it comes to data analysis, visualizations are key. With them, you can summarize large amounts of information and make things easy to understand through graphical representation.
An ineffective illustration puts your work at risk. In other words, it weakens your message and could damage your reputation. Fortunately, you don’t have to be an expert in statistics to create data visualizations.
Choosing the way you represent your data is one of the toughest challenges for a data analyst. If you don’t get this right, you can ruin the whole process.
Hence, if you want an effective solution to your analysis, you need to consider how the results look. Take a look at this guide and see what data visualization to use in different cases.
Choosing the Right Data Visualization
Making a poor choice in this area can harm your work. After all, you don’t want your readers to make mistakes or misread your information.
To make this easier, think about different chart types. The right visualization is the one that best illustrates your point. In other words, the message must be clear.
If you need extra help, ask yourself the following questions:
- What is the relation between my data sets?
- Is it necessary to understand data distribution and identify outliers?
- Do I want to compare values? In that case, will I compare many values or a single one over a certain period?
- Is it interesting for me to analyze trends within my data?
- Is this data visualization an important part of my data processing?
Once you’ve answered these questions, you can choose the type of chart you need. Next, you’ll find a description of the most popular ones.
10 Popular Data Visualization Types
Bar Charts or Column Charts
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If you’re still wondering what data visualization to use, take a look at these two options. Their main function is to organize information into rectangular bars, helping you compare values. Thus, you can analyze parts of a whole or set up your data into different categories.
All in all, bar charts and column charts are similar. Yet, the former has more space for labeling and comparison.
In general, bars can be organized vertically or horizontally, and they are proportional to the values they represent. Thus, one axis represents discrete values while the other represents the different categories to compare.
Bar charts are the right visualization option when you have limited space.
Some tips to use a bar chart wisely:
- Ideally, they should have two dimensions and very few distracting elements.
- Bars must have the same width and the same space between each other. Make sure that the space between bars doesn’t have the same length as the bar width.
- The only exception to the rule above is when a bar chart displays groups of nominal values.
- Choose the color according to the message. Color must convey a specific message that cannot be communicated through the axis labels.
Using Line Charts
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In this case, the graphic indicates values by the vertical positions of the points that connect the lines. Line charts work well when we don’t have a meaningful baseline. We also use them instead of a bar chart when the number of bars would be overwhelming.
A line chart shows the results of a continuous variable, which is usually time or money. They are also great for analyzing trends, acceleration, deceleration, data fluctuations, and patterns. If you want to compare different data sets, this data visualization tool is the best one. Moreover, it helps you make projections.
When it comes to illustrating performance trends throughout a certain period, line graphs are a great option.
Tips to use line charts effectively:
- Your line chart will be unreadable if it has over four lines. Provide additional data density using selection boxes.
- The points and lines should be easy to distinguish. They also must illustrate specific values while showing the overall trend.
- Hovering is a great tool. Use it to display the values on each point.
How to Use Pie Charts or Donut Charts
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The essence of a pie chart is to illustrate the relationship of one value to the whole. It shows relativity quite well. Yet, keep in mind that more than five sections can make your data visualization hard to read. In other words, it will be harder to compare results.
Sometimes, pie charts show values that are very similar to each other. If the difference is too narrow, use alternative visual styles. For instance, the exploded pie wedge chart can help you emphasize certain information. If you’re working with a donut chart, you can insert an element in the center to better illustrate the point.
Tips to get the best data visualizations through pie charts:
- The slices of a pie chart must add up to 100%. To make sure the math is accurate, add the numerical data and the percentages to the chart.
- Organize slices according to their size.
- Try not to compare over five categories, so that your slices are identifiable.
Similar to line charts, area charts have color patterns filling the area between the x- and y-axis. With this type of chart, you show relations between a part and its whole; for instance, the performance of a salesperson in comparison to the yearly sales. Thus, you can analyze individual and overall trends.
Use an area chart if you want to show cumulative totals in a specific period through percentages or numeric values. Generally, area charts compare two or more categories being the ideal option to illustrate performance. Show how different items contribute to the whole.
Best uses of area charts:
- Using transparent colors is a good idea, so that the information is readable.
- Displaying more than four categories can result in clutter.
- Analysts should place highly variable data on the top of the chart. Thus, your data visualization will be easy to read.
Effective Uses of a Radar Chart
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Also called a “spider chart,” a radar chart displays multiple values over different variables. These can help you understand the relative differences within your data sets.
A good comparison is a bicycle wheel, where each point is assigned on an individual axis, or “spoke.” It starts in the center of the graph. Thus, each axis is connected through circular grid lines. Information is plotted on each spoke, which is also connected to several straight lines.
As a result, you’ll see a polygon where you can easily read outliers and commonalities.
- Plotting too much data into one radar chart is not the best idea. If you do this, your chart’s information will overwhelm the reader.
What Data Visualization to Use: Waterfall Charts
With a waterfall chart, you can represent how a certain value is positively or negatively affected by other values. These can be based on time or categorical data. In the final column, you’ll be able to read the cumulative value.
This is a great option for your data visualization, especially if you want to show how a value changes. Readers will be able to see the changes and the final result as well.
For instance, you can show how different departments of a company contribute to the overall income. Thus, assessing results and performance becomes easier.
Best practices for a waterfall chart:
- Don’t use it if you want to include more than one number or metric in your data visualization.
- Contrasting colors will help you highlight separate data sets.
- Use a cool color scheme to indicate when a value decreases. On the other hand, warm colors should illustrate increases.
Learn How to Use Heat Maps
A heat map helps you show the relationship between different data sets when variables are not numerical values. On the other hand, you can use this to display numeric data only, such as in 2D histograms or 2D density curves.
You can use a heat map for different things. Many tech companies use this tool as an indicator of user experience on website design, apps, and other online resources.
Location assessment is another great use of heat maps. Entrepreneurs, for example, can use it to decide the location of their next store. With a heat map, you can have a clear picture of the characteristics of an area.
Best uses of heat maps:
- Using a simple outline will help you avoid distractions.
- The most effective way to illustrate changes in data visualization is through color. Use a single color in different shades and you’ll notice the difference.
- Don’t use multiple patterns when creating heat maps.
There is a debate concerning tables. While some people say that they are not data visualizations, the fact is that they have a place in the data visualization world. Tables are the options you use when you need to compare data analysis on categorical objects. You can display both graphics and data points, icons, sparklines, and bullet charts.
A table is the ideal graphic to display two-dimensional data visualizations and organize them into categories. There’s also the possibility to display large data sets.
Here are some useful tips for tables:
- If the numbers represent specific units, make sure to add them to each cell and not only to the header. This applies to units like percentages, dollars, and years.
- Use commas in large numbers to indicate thousands. If all numbers in a sequence are of the same minimum size, truncating the value is a good idea.
- Display only the amount of precision that is necessary for the table. In other words, always keep its purpose in mind.
- Avoid cluttering by using fewer than 10 different rows within the same table.
Using Scatter Plots
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Scatter plots provide a great solution for displaying values from two variables. When you have a large amount of data, you can illustrate relationships using this chart. The reader will see the data in a set of points. The value from one variable will determine the position of the horizontal axis, while the other one will be represented on the vertical axis.
Thus, these variables will correlate negatively or positively, or they can be not correlated at all. Also, these correlations can be weaker or stronger depending on the proximity to the lines.
Good uses of a scatter plot chart:
- If you want to add more data, incorporate more variables.
- The y-axis must start at zero for the graph to show data sets accurately.
- Don’t use more than two trend lines, so that your scatter chart is easy to understand.
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With bubble charts, you can use data visualizations in three dimensions. Compare categories according to their relative size, position, and value. These graphics are similar to scatter plots, with the only difference being that bubbles substitute points.
A good example of how to use a bubble chart is a real estate comparison. For instance, you can compare the European and American markets by setting up your values as price (x-axis), income (y-axis), and category (townhome, land, detached, condo, etc.).
Using a bubble chart effectively:
- Identify all axes clearly (even the third one).
- If the points are too close together, you might want to use an unfilled circle instead of a dot. Thus, readers will be able to see overlapping points.
- Use the x-axis to represent time for better clarity.
FAQ on the Data Visualization To Use
Which chart should I use for my sales data?
Oh, sales data, that’s a classic. You’ve got a bunch of numbers, right? And you want to see trends, compare performances, maybe? Start with a line graph if you’re tracking changes over time.
Bar charts are your friends for comparing different categories. And if you’re all about proportions, like how each product contributes to total sales, pie charts can be pretty sweet.
How do I choose the right visualization for complex data?
Complex data, huh? Sounds like a bit of a puzzle. You want to avoid turning your audience into that confused math lady meme. For multidimensional data, heat maps can show you patterns you didn’t know were there.
Scatter plots are great for showing relationships between variables. And don’t shy away from bubble charts; they can add another layer of info with bubble size. Just keep it clean, not cluttered.
What’s the best way to visualize time-series data?
Time-series data is all about the flow, like a river of numbers through time. Line graphs are the go-to here; they’re like timelines that show how data points change.
Want to highlight variations, like seasonal spikes in sales? Seasonal plots can make those stand out. And if you’re feeling fancy, a time series decomposition plot can show trends, seasonal effects, and residuals. It’s like breaking down the melody of your data into harmonies.
Can I use a pie chart for my survey results?
Survey results, right? You’ve got all these people’s opinions, and you want to show what slice of the pie each opinion holds.
Pie charts work if you’ve got a few clear-cut categories. But if you’ve got a ton of responses or the differences are subtle, consider a bar chart.
It’s easier to spot the small differences without squinting. Just make sure your pie isn’t a messy splat of too many slices.
What visualization tool should I use for real-time data?
Real-time data’s like a heartbeat, constantly changing, right? You need something that can keep up. Dashboards are the way to go.
Tools like wpDataTables, Tableau, Power BI, or even some custom D3.js magic can give you live updates. Think of it as your data’s pulse monitor, showing you the life of your data as it happens. Just make sure your visuals are easy to read at a glance.
How do I make sure my visualization is accessible to everyone?
Accessibility, that’s key. You want everyone in on the story your data’s telling. Use color contrasts that stand out even for those with color vision deficiencies.
Add text descriptions, alt text, for screen readers. And keep your designs simple. No need for a visual fireworks show. Think about it like you’re making sure everyone can enjoy the movie, not just those in the fancy seats.
What’s the difference between an infographic and a standard chart?
Infographics are like the comic books of data visualization. They mix charts with illustrations, text, and stats to tell a more compelling story.
Standard charts are more like just the facts, ma’am. They’re straightforward, focused on the numbers without the frills. Infographics can be super engaging, especially for social media or presentations. But remember, with great power comes great responsibility—don’t let the design overshadow the data.
How do I visualize qualitative data?
Qualitative data’s a bit like jazz; it’s all about the themes and patterns, not so much the numbers. Word clouds can show you which terms pop up most often.
Thematic analysis maps can help you visualize the connections between different themes. And when you’re sharing stories or feedback, sometimes a good old-fashioned quote can hit harder than any chart ever could.
When should I use an interactive chart over a static one?
Interactive charts are like a playground for your audience. They can click, hover, zoom, and really get into the data.
Use them when you want to offer a deeper dive, like exploring a complex dataset or letting users discover their own insights. Static charts are your workhorses, though—perfect for reports or any time you want to control the narrative. Think of it as the difference between a novel and a choose-your-own-adventure book.
What are some common mistakes to avoid in data visualization?
Common mistakes? Oh, there’s a few. Overcomplicating is a biggie. You don’t want your chart to look like a Jackson Pollock painting—unless you’re into that sort of thing.
Keep it simple. Misleading scales or cherry-picking data points can make your visualization tell a fib. And watch out for too much jargon or technical lingo. It’s like explaining a joke; if you have to explain your chart, you’ve lost the punchline.
Final Thoughts on What Data Visualization to Use
Good data visualization is the best ally for your business. When it comes to storytelling, this is a powerful tool. Organizations can change the way they understand data and use it to grow.
If you’re wondering what data visualization to use, take a look at this guide. Think about what message you want to send and choose the right variables. Then, all you have to do is set up the plots and label each chart. With these clear data visualization options, your data management capabilities will improve considerably.
If you liked this article about what data visualization to use, you should check out this article about dynamic data visualization.