Imagine you have a portfolio of seven products and you want to measure sales for each, the sum of each product’s sales value equals the amount. You can create a part-to-whole relationship with each product’s contribution towards sales with the help of a pie chart.
If you want to add more sense to your data then start using pie charts. Pie charts are commonly used under data visualizations. It is also widely condemned and misused. Good thing is that it has a proper use case and also ease to use.
In this post we’ll be looking upon
- Way it works
- How to create a pie chart.
- Two cases where a pie becomes necessary.
- How to spot whether my data is better served as something other than pie.
- Tips to create an effective pie.
What does a pie chart mean ?
A pie chart conveys the part-to-whole relationship of your data. Visualize a pie chart (variety is left to your choice) here each slice represents an element and all slices put together look a whole. Perhaps pies are commonly used as we all like things to be whole and figure out how the various pieces relate to the whole.
How to create a pie chart?
Let’s go with an example. Imagine you have a portfolio of six products and you want to measure sales for each, the sum of each product’s sales value equals the amount.
Step 1: Select the data for which you want to apply a pie chart
Step 2: Select a pie chart
Manual method: Go to insert >> Select pie chart
Shortcut Key: Click on Alt key and press N & Q
Step 3: Click on 2d pie, a pie will be created.
Step 4: Besides to pie chart you have multiple options to filter, add element, add different styles.
In which case pie charts become necessary ?
If you ask me, I will say pie charts are way better than any other visualizations for expressing a part to the whole relationship. When you see or hear a percentage off or part off then its time to use a pie chart.
Two case where a pie chart becomes necessary:
- If you wish to treat your audience with a general sense of the part-to-whole relationship that relates your data and comparing that precise size of the slice is less important.
- To make clear that one portion of the pie chart is comparably small or large.
Sales example which we have given under How to create pie charts suits for the first case.
With the help of below two charts below (by the size of the slices), I can come to a conclusion which product gave me the highest sales and which product gave me the least, this example suits the second criteria.
How to spot whether my data is better served as something other than pie
In reality, pies shouldn’t be used for calculating the relative sizes of categories, comparing data across pies, and visualizing percentages that do not add to 100%. Let’s have look at an example of each.
Comparing by size: Pie charts can be created in seconds as it breaks downs pretty quickly, but if we need our audience to have a more understanding of our data than simply saying whether it is big, small, or about the same. To make clear about the present, consider the following example. If I ask you to compare the size of portions C and D, which would you conclude as larger? or, how can you estimate how bigger it is?
This example demonstrates one limitation of data encoded as pie partitions: humans’ eyes aren’t that perfect to compare areas. The challenge becomes even more noticeable without a consistent baseline—like a y-axis in a horizontal bar chart—against which to make the visual comparison. As illustrated in the following, comparing the size of C vs D is much easier in the graph on the right. We’re also comparing one dimension (length) across two categories, instead of irregularly-shaped areas.
Comparing data across pies: This becomes a more difficult task as the things you wish to compare are separated visually and are located in different areas on each pie. To be clear, check out the pair of pies below
Let’s say the primary comparison you want to highlight is how Tier B varies across the two breakdowns. Tier B is in different places across the pies—as a direct result of the variation between the two datasets. Because of this spatial separation, different positioning within the pie, and the irregular shapes of the segments, it’s hard to accurately judge the difference in magnitude.
Showing data that doesn’t sum to 100%: If the slices don’t sum 100%, it gives no meaning as the pie must depict a meaningful whole.
How do I create an effective pie
Here are some quick design tips to make pies more effective.
Avoid 3D and exploding effects: These components introduce unnecessary clutter destroys data. To be clear see the below picture
Don’t add too many slices: There is no rule or an approach to be followed consider only your dataset and be clear with the need of your audience. You can improve readability by either de-emphasizing small categories with similarity of color (as shown in the following middle pie with grey) or by aggregating into an “All other” category, as illustrated on the right.
Sort your data accurately: Check out the two pies below. On the left, the data is sorted alphabetically (beginning with apples at zero degrees). The right pie is sorted in descending order starting with the largest category (strawberry). This takes into account our natural construct of reading around a circle.
Eliminate legends: Stop using default thing and label the things manually as it reduces the task of going back and forth between the legend and the data.
Try to use colors: Using colors is very important as it keeps our audience focused and get their attention. view below image which image looks better First one or the second one?.
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