Pie Charts - Angelys Valdez

Pie Charts

Pie charts are charts that display data out of a whole (100%). They are important for us to understand how parts relate to a whole. The slices display how much each category contributes to the total. Even without values of the percentages, the audience can compare the sizes of slices and instantly spot differences. People understand slices of a pie as a universal visual language. 

Figure 5.1

College can often be described as the best time of your life, but it can also be the most stressful. A 2025 survey evaluated stressors for college students. Academic world, financial concerns, relationships/social life. family responsibilities, and health/well-being were rated out of 100%. Academic workload was the highest stressor ranked at 40%. This could be because of deadlines, time management, etc.

Design

A pie chart was the best design for this type of dataset. This is because it is supposed to rank percentages. This chart is also in descending order, so it is a lot easier to understand than perhaps a bar chart. The pie chart allows for easy comparison of the causes of stress in college.

 

Pie Chart

Pie Charts are to show reflective answers.

On Today's topic, we have cosmetics there is so much for sale in market from purchase of makeup and skin care to anything to cure your skin needs. Looks like every other month there is a new influencer with a new skin line, this industry is billions of dollars' worth. With social media around you can easily grasp attention and lure yourself into buying a whole collection of products you don't really need. You are most likely buying the brand and the person who is selling it to you. 

This pie chart shows the percentages of items that all per take in the industry from celebrities and influencers we follow.

We don't realize what all goes into the encouraging and influencing but when we see what is in our counters our credit card statements we fall more into reality and see the financial damage because everything costs a pretty penny. 

 Cosmetics industry - statistics & facts | Statista

PIE CHART- Dawson Joseph Week 5

 PIE CHART

This week, our chapters went over a lot of different types of graphs that could be derivatives from Pie chart. For me, learning just that little bit much more about the pie chart almost reinforced what I already thought about pie charts. They are extremely valuable pieces of information that are best to use when we want to split percentages especially when we don't have that many values.

DATA SET+PIE CHART

Now for my data set I used college undergraduates split up by their ethnicity. For me, I think the pie chart was the best way for me to determine what it is we want to see. Since I didn't have more than 5 data sets and my data equaled to 100, I don't really see the use for any other type of graph to be used. I actually had to use excel and watch some videos on how to properly use it as well as reference back to our chapter about doughnuts because of the fact it showed us the data legends. Below, you could find my pie chart.

Link used for my data set can be found below as well:

https://custommapposter.com/article/college-enrollment-statistics-in-the-u-s-bestcolleges

Pie Chart

 When a Pie Chart Actually Makes Sense: U.S. Electricity 

 A strong use case

For this project, I visualized the U.S. utility-scale electricity generation mix in 2023 using a pie chart. The dataset sums to a meaningful whole (100%) and contains five clear categories: natural gas, nuclear, coal, renewables (total), and petroleum & other. That setup fits the textbook criteria for a pie chart: showing parts of a whole, a small number of categories, and differences that are visually distinct enough to compare.

https://www.eia.gov/energyexplained/electricity/electricity-in-the-us-generation-capacity-and-sales.php?utm

The audience’s task is to grasp the concept at a glance, not to make ultra-precise comparisons between similar values. Visualization research generally finds bars are more accurate for exact comparisons, but pies are acceptable when the primary question is “what share does each piece contribute to the whole?” and the slice count is limited.

Data (EIA, 2023): Natural gas 43.1%, Nuclear 18.6%, Coal 16.2%, Renewables (total) 21.4%, Petroleum & other 0.8%. These categories are mutually exclusive and sum to 100%, which is essential for a valid pie.

https://www.eia.gov/energyexplained/electricity/electricity-in-the-us-generation-capacity-and-sales.php?utm

I built a flat (non-3D) pie chart with direct data labels showing both the category and percent, a descriptive title, and an in-chart source note. The focus is on clarity: no exploding slices, no gradients, no shadows. Avoiding 3D prevents distortion of perceived angles and areas.

If the goal were precise comparison between similar slices, a bar chart would support more accurate judgments for a viewer because length is read more reliably than angle or area. If I needed to emphasize the total while also leaving room for annotations in the middle, a donut chart would be acceptable as a stylistic variant of a pie.

Blog Post 5 - Pie Charts Joelc Lopez

 I honestly think with the data see that I have it might've benefited the graph if it was in a bar format. I think in all the bar graph has a better comparative element than the pie graph. While the catagories are not alot it does hzve a sense of flair and ogrnaization to it that makes it very easy to read. What helped was the chart design tab. This made it easy to see what visuals work better than others.

Usages and Designs for Pie Charts

Usage and Design of Pie Charts

    Pie charts are a type of data visualization that organizes data as a whole, represented as a circle or pie that is divided into slices, each slice is a category that is proportional to the value of the overall data. In terms of percentage values, each slice added together should equal 100%. The purpose of a pie chart, as Tableau states, "helps the user compare the relationship between different dimensions (Ex. categories, products, individuals, countries, etc.) within a specific context." (Tableau). However, due to the human mind's difficulty with interpreting area, volume, and curvature, it is best for visual clarity that pie charts are made up of only 3-4 slices or categories, with a maximum of 5 slices if necessary for the subject at hand. Typically, a pie chart is chosen to emphasize a specific category that stands out among the rest, crafting a narrative around the significance of that particular slice in the context of the larger data subject.

Figure 1: Pie Chart

    Using the 2024 Browser Market Share Worldwide dataset from StatCounter GlobalStats, I created a pie chart that visualizes the market share (usage) of common browsers from across the world. Arranging the chart to showcase the top 4 browser categories, such as Chrome, Safari, Microsoft Edge, and Firefox, with the other browsers, like Opera or Samsung Internet, combined together and represented as the Other category. As you can see, the difference between the areas of each slice helps to highlight the significant portion of the chart held by Chrome, indicating that in 2024, Google Chrome dominated the global market share over other browsers, such as Safari and Edge. With the second version of my pie chart, reinforce that story by shadowing the other distinctive colors of gray while keeping the Chrome slice as its bright red, as demonstrated under the Great Examples of Pie Charts section in Tableau's Pie Chart article, to quickly inform viewers which slices are significant and most important to the narrative of the visual and reiterate the area difference between the smaller and larger slices through careful color choices. 

Figure 2: Highlighting Slices

    During the design, I implemented recommendations from articles such as Pie Charts Best Practices by Anastasiya Kuznetsova, who suggested data labels with percentages be positioned outside their respective slices,  removing the legend, and matching the font with the slice's color for conformity, which I was unable to do in my version of Excel without changing all of the font colors to the same one. As well as following along with visualization and Excel guides, Effective Data Visualization 2nd Edition by Stephanie Evergreen, who advised removing the white space/outline between slices that Excel automatically applies, and since Chapter 1 - Why We Visualize, never use 3D pie charts as 3D exacerbates the visual clarity issue already present with pie charts due to slice area being hard to decipher for the human mind. 

Alternative Formats for Dataset

    However, even with a validly designed pie chart, other visualizations can be used to visualize the worldwide browser market share in 2024. One prominent example would be a bar chart that utilizes the lengths of the data bars, either arranged horizontally or vertically, to compare the categories. Bar charts should be used instead of pie charts when handling at least 5 or more categories, as the bar chart will provide much more visual clarity for an audience to be able to interpret the differences between the lengths of the bars, rather than the constricting areas of a pie's slices. Employing a similar design method as demonstrated in Figure 2, my bar chart utilizes the browser category as its y-axis and share (usage) percentages as its x-axis, while lightly shading the smaller data bars and highlighting Google Chrome's bar of 72% in red to stand out among the rest. As well as direct the audience's attention to the central focus of the visualization that in 2024, Chrome held the lead in the most used browser across the world.

Figure 3 - Alternative Chart

References:

Kuznetsova, A. (2024, July 13). Pie charts best practices. Medium. https://nastengraph.medium.com/pie-charts-best-practices-2f8ac3b73c80

 (n.d.). What is a pie chart?. Tableau. https://www.tableau.com/chart/what-is-pie-chart

Evergreen, S. (2019). Effective Data Visualization (2nd ed.). SAGE Publications

Browser market share worldwide. StatCounter Global Stats. (n.d.). https://gs.statcounter.com/browser-market-share/all/worldwide/2024

Pie Charts Blog Post

 Pie Charts Visualizations 

A pie chart is a graph, in a circular shape, that is divided into slices. Each slice in the pie graph represents parts of a whole. Typically, pie chart slices are split into categories of a certain topic.  The sizes of a slice in a pie graph represent the proportion or percentage of the whole, contributing to the total. 

Above is an example of a pie chart representing a dataset on a survey on favorite ice cream flavors. Each slice represents a proportion of people's vote on their favorite flavor, between Chocolate, Strawberry & Vanilla. The total number of people participating in the survey is 100. If you look at the chart, you can indicate that out of 100 people, 45% people voted for Vanilla, 30% voted for Chocolate & 25% voted for Strawberry. 

Just by viewing the sizes of the slices, you can indicate that the greatest would be the Vanilla voters, representing nearly half of the total, and the Strawberry voters being the smallest, representing a quarter of the total. The total being 100, all three percentages add up to the total. 

Pie charts are most effective when using a small number of categories. The suggested number would be 5 categories or fewer. Using the comparison of three slices makes it clear and easy to understand. Pie charts are simple and clear to give viewers and readers a quick visual summary of data, turning numerical data into a visual form. 

Pie Charts

 The data I chose to create my pie chart was the percentage of households that own a pet in the country. I found that 66% own a pet while 34% don't. I think this data is perfect for a pie chart. While you can use other graphs like a bar chart or stacked bar chart, I think a pie chart visually displays the information the best. A pie chart is a type of graph that displays a part to a whole in this case being how many households in the country own a pet. 

Making and Evaluating a Pie Chart

I chose a dataset showing the distribution of internet users worldwide as of February 2024, by age group. This dataset is a strong fit for a pie chart because it represents a part-to-whole relationship, the percentages of total internet users across age groups sum to 100%. Additionally, the dataset has a limited number of categories, which keeps the chart simple and ensures that the differences among slices are visually distinguishable.

(Figure 1). Pie chart depicting internet users worldwide by age group.

Each slice is labeled with the percentage, and the title clearly describes the data being visualized. There are no 3D effects or exploded slices because these can distort perception and make it harder for viewers to accurately compare segments. Instead, distinct colors are used for each age group and arranged the slices from youngest to oldest clockwise, starting at the 12 o’clock position, which makes it easier for the audience to interpret the proportions intuitively. It does go over the limit of 5 slices, but it doesn't add much visual clutter and is beneficial to the purpose of the visualization.

While a pie chart works well in this case, I do believe a horizontal bar chart can excel at allowing viewers to compare exact values across categories because of the consistent baseline, but in this scenario, the part-to-whole relationship is the main story, not precise comparisons. Each age group is a part of the total people using the internet regardless of age. The pie chart communicates the relative contributions of each age group to total internet users immediately and visually, making it more effective for my intended purpose.

I reinforced several key design tips for pie charts: keep the number of slices low, label clearly, use consistent colors, and avoid unnecessary embellishments. I also realized that thoughtful ordering of slices can help guide the viewer’s attention to the largest categories first, enhancing comprehension. By following these guidelines, the pie chart becomes a clear, engaging, and accurate visual representation of the data.

References:

My Data Set: https://www.statista.com/statistics/272365/age-distribution-of-internet-users-worldwide/

Pie Data Visualization

So for my assignment this week I decided to do a bit of a twist on the traditional pie chart. With Thanksgiving on the horizon I thought it would be a fun idea to look at some data for the most popular pies eaten at Thanksgiving.

I found a survey that was conducted last year by Business Insider. In the article it showed a traditional bar graph to display the results of the survey. While a bar chart could work, I though it could be simplified and presented better in the form of a Pie Chart.

I made a chart on numbers (which is the Mac equivalent of Excel) and made the following chart. While its not the best chart it still does a good job of breaking each section down into a legible form. But I decided to take it a bit further, I went into Canva, and turned the chart there into something a bit more attractive.

Overall, I would say it looks a lot more presentable and has a better time of getting the point across. Overall, I think the data better is served as a Pie chart (no pun intended) as it does breakdown things into a more understandable and legible form at a glance that I believe a bar chart might not have the same impact as.

Source:
Most Popular Thanksgiving Pies Survey - Business Insider

Pie Charts

 Making and Evaluating a Pie Chart

Step 1: Identify a Strong Use Case

For my dataset, I chose to visualize global video game sales by platform using the “Video Game Sales” dataset available for free on Kaggle. This dataset includes over 16,000 games with their sales figures by platform and region, making it a great choice for exploring the distribution of total sales.

To keep things simple, I focused on my childhood favorite consoles platforms by total global sales:

• PlayStation 2 (PS2): ~1,250 million units
  
  
• Xbox 360: ~970 million units
  
  
• Wii: ~920 million units
  
  

These totals represent the cumulative global sales of all games released on each console, providing a clear “parts of a whole” relationship.

This dataset is ideal for a pie chart because:

• It shows proportions of total video game sales across major platforms.
  
  
• The data represents a complete whole (100%) when summed together.
  
  
• There are three categories, which fits within the recommended range for a clean and readable pie chart.
  
  
• Differences among platforms are noticeable enough to be easily compared visually.
  
  

Step 2: Create Your Pie Chart

Below I have added my clean pair chart that includes all three categories with their respected colors to match. Along with a key to indicate the unit sales.

Step 3: Evaluate Your Design Choice

The pie chart effectively shows how the total video game market is distributed among leading platforms. It’s visually intuitive  readers immediately see that PlayStation 2 dominates, while the other consoles take smaller portions.

However, if my goal was to compare exact sales values, a bar chart might be better, since it allows for more precise visual comparison. A stacked bar chart could also show total platform sales with additional context (like regions). Still, for showing each platform’s overall share of total sales, the pie chart is the most straightforward and visually appealing choice.

Step 4: Reflect on Design Tips

While researching and creating the visualization, I learned several important guidelines for effective pie charts:

• Limit the number of slices: Keep it between 3–6 to maintain readability.
  
  
• Avoid 3D effects: They can distort proportions and mislead the viewer.
  
  
• Order logically: Arrange slices from largest to smallest to guide the viewer’s eye naturally.
  
  
• Keep colors consistent: Use a simple palette that’s accessible for all viewers.
  
  

Step 5: Summary

In summary, the pie chart of global video game sales by platform successfully communicates how different consoles contributed to total sales. The visualization clearly shows PlayStation 2’s dominance, followed by Xbox 360, and  Wii. I learned that the simplicity of a pie chart can make proportions easier to understand  as long as it’s well-labeled, uncluttered, and used for the right kind of data.

This exercise showed that even a basic visualization can tell a meaningful story when paired with thoughtful design and a strong dataset like Kaggle’s Video Game Sales.

Pie Charts

by Nicole Cardillo

Pie charts are one of the most common visualizations that we see when analyzing data. The circle or the "pie" is made up of slices which each represent a certain percentages. The total amount of slices should have a sum of 100%. It is key to not have too many slices in a pie chart to avoid a busy visualization. Pie charts with too much information may distract the viewers and take away from the main point of the graphic. Instead, pie charts should be made of about five categories that are relevant to the topic and story that is being told through the graph. 

I created the pie chart below to reflect popular music genres among college students. The categories for genres consist of Pop, Hip-Hop, Rock, and Country, along with a slice for other miscellaneous genres. 

This pie chart is a valid visualization for this type of data because it clearly displays a small amount of categories, along with the value for each slice of the pie chart. Additionally, it is easy to tell the differences in slices as each percent is significantly smaller/larger than the surrounding slices. Often, a common misconception when analyzing a pie chart is accurately deciphering between different sized slices.  Storytelling With Data explains, "This example demonstrates one limitation of data encoded as pie slices: humans’ eyes aren’t well-equipped to compare areas" (Storytelling With Data). For instance, if we are given two slices that are 19% and 20%, respectively, it will be extremely difficult for the human eye to see which slice is which. In this case, labels or a completely different visualization would be more beneficial to the data set.

A bar graph would also be suitable for my data. These values are comparable to one another; turning the data into a bar graph would give me a clear visualization of which values are largest/smallest. Despite this, I still believe a pie chart is the best visualization for this data because it is so simple. The total of each percentage for music genre sums to 100%, making this data appropriate for a pie chart. Furthermore, humans are more drawn to circles. While a bar graph has the ability to reflect this data just as effectively as a pie chart, humans will desire to analyzing the pie chart. Author Manuel Lima explains, "Today, when someone looks at a pie chart they are recognizing a visual pattern they see everywhere, everyday, in both the natural world and our man-made culture. They are recognizing a graphical arrangement that our ancestors have been exposed to for countless generations" (Medium). Overall, circles are historically the most appealing geometric shape to the human eye. It is all around us in nature and thus maintains our attention as we are more prone to analyze data visualizations like pie charts.

I think one of the best practices when creating pie charts like the one I made is to keep it short and simple. My pie chart reflects five percentages that add to 100%. This short amount of categories provide me with the space to label inside the slices. I think labeling inside the slices is more beneficial than a legend because it keeps our focus on the pie itself, rather than having to look back and forth to match a color to a slice. 

Ultimately, pie charts are one of the most common data visualizations in our world. While they are not ideal for a large amount of raw data, they are great for simple datasets! 

References:

Ricks, E. (2020, May 14). What is a pie chart and when to use it. Storytelling with Data. https://www.storytellingwithdata.com/blog/2020/5/14/what-is-a-pie-chart

Lima, M. (2018, July 26). Why humans love pie charts. Medium. https://medium.com/@mslima/why-humans-love-pie-charts-9cd346000bdc

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Pie chart - Armani Johns

    My dataset shows the results of a hypothetical survey on people’s preferred music genres. It includes categories such as Hip-Hop, Pop, R&B, Rock, and Country, with each genre representing a portion of the total responses. A pie chart fits this data well because it shows proportional data that adds up to 100% and it has only a few categories that are easy to compare visually.

    A pie chart is a good choice for this data set because it clearly shows how each music genre makes up a portion of the total preferences. It’s easy to see which genres are more popular at a glance. Technically a bar chart could work too, but it wouldn’t show the sense of proportion as clearly as a pie chart does and i think this is a much more compact way to convey the preferences of the listeners.

    For design choices, I kept the colors simple but distinct so each slice is easy to tell apart. I also chose to have the percentage number on each slice because it makes it even easier to read. I now know that pie charts work best when they are describing a small number of data points and that don't need much nuance to understand.

            Overall, I learned that simplicity is what makes pie charts most effective.

Lollipop Charts - Armani Johns

 

    For this week’s post, I decided to make a lollipop chart using a dataset that displaying how many hours per week people spend on different digital activities like social media, streaming, and gaming. I made it in Excel using the method shown in Chapter 5, where you turn a line chart into a clean visual with circular markers and drop lines to represent each value.

    What I like about the lollipop chart is that it gives you the same information as a bar chart but looks a lot cleaner. Instead of thick bars, you just get thin lines with dots connecting them to their position on the y axis. The dots naturally draw your attention to the data points value, which makes the data easier to compare at a glance than even a bar chart, at least in my opinion.

    I think lollipop charts work best when you have a few categories and you want to emphasize the values quickly rather than chart design. It greatly reduces visual clutter making it very well suited for small datasets. Overall, it’s a simple but effective chart type that makes information look modern and easy to comprehend.

Benchmark Comparison – S&P 500 – Armani Johns

Benchmarking is when we compare something to a standard or an average to gauge it performance. It’s a simple way to tell if something is doing better or worse than it we expect it to. In charts, benchmarks help give context so we’re not just looking at random numbers or movement, they show us what’s normal.

The chart I found shows the S&P 500 with a moving average line over it. The moving average works as the benchmark here because it smooths out all the short-term movements and gives a sense of the overall trend of the market. This makes it easy to see when the market is performing above or below the usual. This is very useful for investors who want to track long-term direction and use that information to make decisions.

I think this benchmark is really clear and effective because it makes long term movement simple to read. Even if you don’t know much about stocks, you can tell what’s going on just by looking at it. The moving average line stands out just enough to help you make out what's going on without cluttering up the chart, making judgements simple.

Source: https://www.marketwatch.com/investing/index/spx/charts?mod=mw_quote_advanced

Wealth Inequality in the United States

The wealth distribution of the United States is a good choice for a pie chart for multiple reasons. 

1. As a DISTRIBUTION, there is an inherent part-to-whole relationship waiting to be visualized.
2. There are only a handful of slices which avoids visual cluttering. 
3. I knew beforehand that the majority of people would own very little (but not to this degree), so it would be good to emphasize and focus on that little slice. 
1. The book "Effective Data Visualization" states "Keeping that rule of thumb in mind, pie charts work best when you have roughly four or fewer wedges and the wedges are either really different from one another in angle or are really similar to each other" 
2. The article "What is a pie chart?" by the website Storytelling With Data states "There are two primary use cases for a pie chart: 1. If you want your audience to have a general sense of the part-to-whole relationship in your data and comparing the precise sizes of the slices is less important. 2. To convey that one segment of the total is relatively small or large."
3. The wealth distribution pie chart follows these guidelines; there are few slices and the main message is simple: the wealth slices of the elite few are generally huge in comparison to the wealth slice of the majority. 

Beyond these reasons justifying my choice of dataset, the pie chart is even better for some formatting decisions. 

1. I followed the book and made sure to get rid of the white borders that Excel has between the slices by default; "But research shows that readers can “read into” these white borders and get a sense that the group making the data is disjointed or unstable (Ziemkiewicz & Kosara, 2010)."
2. Furthermore, I decided to do the other slices in a grayscale (which may be a bit odd). The alternative was having all of the other slices in one shade of gray, which would require borders to differentiate the slices, and borders just don't look good in pie charts, in my opinion. 

I only have a few formatting concerns.

1. I have the double-packed data labels which include both the name of the wealth slice and the value of the wealth. I originally wanted the names on the outside and the values on the inside, but now I think it may be more legible in this consolidated positioning, avoiding looking back and forth as the graph is fairly big. 
2. Both the book ("We tend to read pie charts in a clockwise order (in Western cultures), so start the largest slice at noon and arrange the wedges such that they run greatest to least clockwise around the circle.") and the article ("The right pie is sorted in descending order starting with the largest category (pineapple). This takes into account our natural construct of reading around a circle.") advocate for sorting the slices by size, but I felt that it was more sensible to just keep the order of the groups to avoid confusion. 
3. I originally wanted the groups split by quintiles instead of having wildly different sized groups (but I couldn't find the data) but ultimately, the way it turned out with the entire 50% in that tiny slice is still very impactful (although with a quintile version, the top quintile would have been very huge; and I wonder if that would have been more impactful). 

Lastly, what could represent this better? A bar chart would allow for precise length comparisons to easily show which shares are bigger and a stacked bar chart would emphasize the part-to-whole relationship, but they're not dramatically better than the pie chart which includes the percentages for easy comparison, and it inherently shows a part-to-whole relationship. However, I learnt from my AP Micro and Macro Economics classes about the Gini coefficient and the Lorenz curve, which I think is the best because it better illustrates the populations of these groups, but it's not very beginner-friendly. Anyway, here is a Lorenz Curve of Federal Reserve data from 2019. The Lorenz Curve shows that the wealthiest few own the most of the nation's wealth, and the line of equality demonstrates what the curve would look like if everyone owned an equal share of the nation's wealth. 

Making and Evaluating a Pie Chart

 The Creation of a Pie Chart

    This dataset from the American Kennel Club 2024 displays the percentages of popular dog breeds, for example, Labradors, French Bulldogs, and Poodles. A good choice to represent this kind of data is a pie chart, since we’re adding up percentages in five categories, totaling up to 100%. Making it easy to understand which breeds are more popular than others. Showing the comparison first in size, and then viewing the percentages either on the side of each slice or in the middle. An article titled “Story Telling with Data” elaborates on what data is considered good for this kind of chart, speaking about how each slice represents one component and all slices added together equal the whole, which, in my chart, can be applied.

    Looking at the pie chart above, the data could be used in another kind of chart. A bar chart allows the data to be understood more easily as well, but since it’s a percentage, it might not work the best. There’s also a donut chart, which would display the same sort of concept as the pie chart, but it won't display the main goal, which would be the data being compared to the total, to not compare exact numbers. What I learned so far from comparing pie charts to other types of charts is that bar charts show details better, while pie charts do a better job of showing proportions. Design choices like color and labels have an impact on how to understand a chart. Some design guidelines I learned from “Story Telling with Data” are that a pie chart shouldn’t have an overload of slices; a good amount would be a range of 5-7. Avoiding 3D pie charts is for the better since it’s hard to see properly and distorts the data. Also that legends aren't needed; just applying the percentages with what the percentages represent is the easiest and best option. 

Resources:

Story Telling with Data

Lollipop Graph

This graph was a difficult to do so many adjustments to make it look neat, the graph was based off women and how they pass there summer doing activities and the percentage of them that do them. 

In this graph I choose to align them horizontal it makes my graph look cleaner with only four categories being compares my graph looks much bigger. 

I think having this option to represent data is used by many when comparing numbers of revenue. 

 

lollipop chart Christopher Eng

 A lollipop chart is essentially a bar chart with the bar replaced by a thin line and a circular marker (the “lollipop”) at the end. This design emphasizes the exact data value while keeping the graphic light and uncluttered. It’s perfect for showing comparisons between categories when precise numbers matter as much as visual impact.

Below is my example chart, which shows the popularity of different fruits based on randomly generated survey data.

When should we use a lollipop chart?

• Comparing Categories 
• Emphasize data points
• Keeping visuals neat and simple
• Replace Bar charts

Lollipop Charts

 Lollipop charts are a style variation of a bar chart that uses a line with a dot or circle at the end instead of a solid bar. They are a visually appealing alternative to bar charts that can help when comparing many categories. They are often used to highlight individual data points. They can provide a cleaner look and be more easier to understand especially when there are many categories. In this simple Lollipop Chart I used data from how many scoops of ice cream people normally order. 24% of people said 1 scoop. 60% said 2 scoops, 12% said 3 scoops, 4% said 4 scoops. 

Lollipop Charts - Patrick Johnson

Lollipop Charts

by Patrick Johnson

What are Lollipop Charts?

Lollipop charts are a type of data visualization that possess elements from both a bar chart and dot plots to create an minimalized, cleaner version of a bar chart by replacing the data bars with thins lines anchored to the x-axis (vertical lollipop chart) or y-axis (horizontal lollipop chart) that represent the value of the data that are marked at the end by a circle/dot to indicate the total value of the category. Similar to the bars, the lollipops are designed to compare or rank the quantitative value between a larger group of data that would otherwise clutter or be visually aggressive in a traditional bar chart

My Lollipop Charts

For my lollipop charts, I used the Data Viz Project website to create the charts, as the site offers a wide range of charts/graphs that used a data tab, which can used exported Excel files in csv. format, and the design tab that allows you to select the type of chart used and customize attributes of the charts from data labels to color/size of the lollipops. Although it only offers one credit that allows you to download the chart once before asking for I designed a vertical (Figure 1) and horizontal (Figure 2) lollipop chart comparing 10 of the most common cereal brands and their calories per servings, discovering that Honey Nut Cheerios has taken the lead by having 367 calories per serving. 

Figure 1 (Vertical Lollipop Chart)

Figure 2 (Horizontal Lollipop Chart)

However as you main noticed in both figures, that not only are the data values outside and on top of the lollipops, there are some that are also missing. For the first issue, unfortunately, Data Viz Project does not allow users to change the position of the data value label to be displayed inside the lollipop. However, while trying to correct the missing data, I believe I discovered that it was caused by the site not displaying the data due to screen size, so by zooming out I can see the values on top the lollipops, but only if I do not remove the data labels. As you can see in Figure 3, this disrupts the visual clarity as the data labels can obscure the value. So for the future, I would recommend searching for other data visualization alternatives that enable you to use a lollipop chart template or use Excel and go through the tedious manual process of creating a lollipop chart to avoid these issues.

Figure 3 (All Data Values w/Labels)

Sources

Lollipop chart. Data Viz Project. (2023, August 16). https://datavizproject.com/data-type/lollipop-chart/

Healy, Y. H. and C. (n.d.). Lollipop chart. Lollipop chart – from Data to Viz. https://www.data-to-viz.com/graph/lollipop.html

Visualizing Progress in Performances vs. Benchmark Assessments - Angelys Valdez

When comparing performance data, simplicity is key. A lollipop chart gives insight into how to compare academic performance vs. benchmark assessments. This chart is more minimalistic than other chart designs. There is no complex visualizations with these graphs. The axis labels state exactly the percentage of what the data means.

What the Chart Shows

The chart displays three categories of performances. Performance reached 50% which falls short of the 65% benchmark. In category 2, performance improved to 58%, which still does not reach the 65%. The last category's performance closed the gap at 67% surpassing the 65% benchmark. 

Why Use a Lollipop Chart?

    Unlike bar charts, a lollipop chart uses comparative distance rather than the volume of data. The lines         and markers represent the key points of the data. This is useful in presentations and visuals, so the                 audience can understand this easily. It is an effective way to communicate progress and measure                 growth.

Lollipop Charts

A Lollipop charts is essentially a bar chart that is a lot slimmer. They replace thick bars with thin lines topped with dots, making your data pop without all the visual clutter. They're perfect when you want to compare values across categories but don't want chunky bars dominating your entire chart. I would use them when you've got multiple data points to compare (like rankings or percentages) and want your audience to focus on the actual values rather than getting distracted by bulky shapes. They're especially clutch for presentations where clean, modern visuals matter.

I grabbed a survey from Kaggle.com of 125 college students about their facorite types of snacks. French fries was the most showing at around 23%, followed by pizza at 19% together they make up nearly half of the favorite snacks. Classics like chips (at around 12%) and chocolate (around 11%) hold strong in the middle tier. Pasta and Ice Cream tied at around 8% each. Then Lastly was Mac abd Cheese at around 6%, with Popcorn being the most unfavored snack at a round 3%. The data shows college students normally gravitate heavily toward comfort foods that are quick and easy enough to prepare or are already pre made and ready to eat!