Introduction
Pie charts are an ideal way to display parts of a whole, especially when showing proportional data. In this post, I’ll illustrate why the pie chart is a perfect fit for visualizing the global market share of mobile vendors, where each segment represents a clear percentage of the total market. With clear, distinct categories, a pie chart effectively communicates the dominance of each vendor relative to its competitors.

Visual: Mobile Vendor Market Share Worldwide – September 2024

For this data, I’ve created a pie chart that clearly shows Apple, Samsung, and Xiaomi as the top three competitors, with other vendors representing smaller segments. This format enables viewers to quickly assess the relative size of each brand’s market share.

Argument for Using a Pie Chart
The pie chart works well here because:

• Distinct Categories: Each vendor represents a distinct, easily identifiable part of the mobile market.
• Relative Comparison: With Apple leading at 27.6%, the chart’s circular form highlights the gap between top competitors like Samsung (22.91%) and others.
• At-a-Glance Readability: For readers looking to grasp key takeaways on market share distribution quickly, the pie chart’s simplicity and proportional representation make it an excellent choice.

Alternative Visualisations
Though the pie chart serves this data well, a bar chart could be used to show exact values more precisely. However, in this context, the pie chart’s layout emphasises the proportions without requiring precise numeric comparison, which is sufficient for understanding general market dominance.

Design Tips for Effective Pie Charts
Here are some design tips that helped me make an effective pie chart:

1. Limit the Number of Slices: Too many slices make pie charts hard to read. Keeping it to significant players (Apple, Samsung, Xiaomi, etc.) helps maintain clarity.
2. Use Contrasting Colors: Each segment should stand out visually without overwhelming the chart. For this chart, I used distinct, brand-associated colors for the top brands.
3. Label Percentages Directly on the Chart: Avoiding a legend makes it easier for readers to associate values with segments directly.

Pie charts are a popular, but often misused, tool for displaying data. They are best used when illustrating proportions or parts of a whole. Typically, they are divided into slices to represent numerical values as percentages with each slice corresponding to a category. These characteristics make pie charts ideal for comparing the relative size of different components.

Displaying proportions, categorical data, and non-complex comparisons are all instances in which pie charts are a proper choice for graphical visualization. Pie charts also offer visual simplicity for the audience and can easily be displayed in presentations or reports because of their simple and straightforward appearance.

The pie chart below displays the proportion of streamed Spotify songs for a survey group, organized by decade.

The data in this pie chart, showing the distribution of most played Spotify songs by decade, is a good choice for pie chart representation because it effectively illustrates proportions and highlights significant contrasts.

One of the key strengths of this data set is that a significant majority (91.14%) of the most streamed songs for this survey group were released between 2013 and 2023. The pie chart visually emphasizes this dominance with a large, unmistakable slice. The overwhelming size of the 2013 – 2023 segment makes the dominant trend immediately clear, which is exactly what pie charts are designed to do — highlight proportions.

Although there are several other decades included in the survey, their contributions are relatively small. The remailing decades, such as 2003 – 2012 (4.00%) and 1993 – 2002 (2.27%), have much smaller slices. A pie chart allows for quick, visual comparisons between these smaller values without having to actually read through numbers. For instance, it’s easy to see that the 2003 – 2012 period represents about double the share of 1993 – 2002, but both are much smaller compared to the dominant period shown.

I believe that visualizing data such as this is important because it allows artists and producers to quickly assess whether they should be making new music or if older music should be re-worked and released. If the data showed that Spotify users were more consistently streaming songs from 1983 – 1992, then artists and producers may want to use songs from that time period in their own work in an effort to boost streaming numbers.

Pie Charts are a great way to show different pieces of a whole. In other words, as long as your data can be represented as 100% it can be represented as a Pie Graph. Pie Graphs are the easiest to understand at a glance and require little to no in depth explanation. When your data can be represented as 100% as a whole, the only reason where you wouldn’t use a pie graph is when most of the separate values are so similar that they can not be differentiated at a glance. In those cases the best graph to use would be a bar graph.

Pie charts are something that we are used to seeing, and have been practically since elementary school. They are commonly used for comparisons because they help people visualize a difference and also allow them to understand different categories and severities of things. For this reason, it is always important to make sure a pie chart is used properly, otherwise information can become too overbearing and confusing.

When tasked with creating a pie chart, I felt that it was important to pick something that was equally important to me and had data that was easily comparable, as this is what would work best for a pie chart. The image below shows the pie chart I created with sample data taken from Statista about the percentage of college students who are diagnosed with some sort of mental disorder.

One thing that not many people are aware of (unless they were or are in college) is the amount of stress and hard work that comes with the role as well. More often than not, these stresses cause college students to be diagnosed with some sort of mental disorder, and I felt that as a college student who is also suffering from the stress and pressure put on me, this was a good thing to talk about. The pie chart features five different types of mental disorders that college students are most commonly diagnosed with, with anxiety and depression being the highest amongst them. 

I think this data was best shown in this pie chart because it puts the numbers and diagnoses into perspective about how significant and problematic the pressures of college are on a student. Due to the two large percentages taking up more than half of the circle, you get an idea of the severity of it.

As previously mentioned, this sample data was taken from a larger data set on Statista. However, Statista showed this information on a horizontal bar graph (as shown below).

While the bar graph is still a great way to display this information, I think that taking the higher percentages/more common disorders and displaying them on the pie chart created a more urgent and dramatic effect that may not have been seen or fully acknowledged in the bar graph. On the other hand, if I were to include all of this information in the pie chart created, I do not think the pie chart would have as much of an effect or impact as it currently does.

In all, a pie chart is a great way to display certain types of information, including my sample data set on mental disorders in college students. It shows comparison of different categories and allows for easy interpretation and understanding of data. One thing that I discovered while creating this pie chart is that Excel has several hidden tools and features to help you create the pie chart to your liking, including pre-made color palettes that make it easy to keep a theme, as well as different examples of pie charts using your data. For instance, I found that it looked better to have the percentage and category labels on the outside of the pie chart rather than the inside due to clutter and I was able to do so with a simple click of a button. I think it is important to note these features because certain types of information and pie charts may need to be created depending on the data used, and Excel provides easy navigation to do just that!

By Nick Ligameri

When you think about charts, the pie chart is probably the first chart that comes to your mind.  It is one of the most well-known types of chart.  Because of this notoriety, pie charts tend to be misused.  This misuse leads to pie charts getting a negative reputation.  According to storytellingwithdata.com, there are two main reasons to use a pie chart.  These reasons are to how a part (slice) relates to the whole (pie) and to show that one slice is larger or smaller compared to the pie. 

The graph shown above just so happens to convey both of these uses very well.  Today, 24 October 2022, is a day after the Philadelphia Phillies won the National League Pennant and secured their spot in the World Series.  I thought it would be appropriate to show how dominant the Phillies have been in the post-season.  They have played eleven games so far and have won nine of them.   

Storytellingwithdata.com explains that we perceive the area that a slice takes up.  This pie graph shows that the Phillies’ nine wins cover most of the pie.  It also shows that the Phillies are winning a high percentage of their playoff games.  This is why this specific pie chart does a great job showing how the Phillies’ nine wins convey a sense of the part-to-whole relationship and is relatively large compared to their two losses.  

The design of this graph was kept very simple.  We are only working with two numbers so there is no need to get crazy on the design.  The colors that I used were the Phillies’ colors, red and blue, for the two slices. The message I am trying to convey to the audience is that the Phillies are a dominant force right now, and this design shows this in a straightforward way.  The data I used was sourced from www.baseball-reference.com.  I had to click on a few links and do some basic counting and math to aggregate the data I wanted.   

Go Phillies!