Grading 5 Visuals From r/dataisbeautiful [March ‘22]

We love sifting through the super popular subreddit, Data is Beautiful, for data visualization inspiration. We graded five visualizations from March 2022.
Matt Talbot
Matt Talbot
CEO of Superchart
April 13, 2023
Grading 5 Popular Data Visualizations and Storytelling with Data from r/dataisbeautiful [March 2022]

If you are a data geek like us (and 17.2m other subscribers), you probably already love the super popular subreddit “Data is Beautiful.” 

To save you some time and energy, we have sourced five interesting data visualizations posted in the subreddit in Q1 2022 for your inspiration.

In this article, we will break down each visualization to talk about its strengths and weaknesses, including some recommendations on how to make it even better. 

An Eye-Opening Decline in the % of Women Getting Computer Science Degrees

25.6k Upvotes (as of Mar 10, 2022)

Link to Original Reddit Post

data visualization breakdown grading pros cons design best practice
Distribution of Women Bachelor's by field
Interestingness: A
Clarity of Information: C 
Use of Color & Styling: C

This chart does a great job at calling attention to the central startling statistic - there has been a meaningful decline in computer science college education for women. 

The bright pink color draws your eye in immediately and strengthens the understanding that this data is about women. 

However, there is one thing that is immediately confusing - what do the percentages mean? It’s not that 80%+ of women have degrees in health professions? 

The chart is showing the percentage, out of 100% for each area of study, that are earned by women, overall. So, per the chart, 8 out of 10 degrees earned in health professions are earned by women.

This is unclear though because the Y-axis is labeled “% of women getting degrees” which is a confusing and incorrect label. 

Labeling the Y-axis appropriately with a title like “% of degrees earned by women” would be more clear and immediately make the chart more effective. 

Finally, the light gray color is pushing the boundaries of visibility and accessibility in a big way. We like using gray and muted colors for supporting information, but in this case, the gray is just too light. 

Overall, this data is interesting and makes us curious as to why women are earning fewer computer science degrees, but the graph could use some improvement.

A Simple Way to Visualize the Age Distribution of the United States 117th Congress 

3.8k Upvotes (as of Mar 10, 2022)

Link to Original Reddit Post

data visualization breakdown grading pros cons design best practice
117th Congress
Interestingness: A-
Clarity of Information: A-
Use of Color and Styling: A

First off, this is a great idea for a data visualization. It’s a simple data set, but it’s somewhat creative and unexpected. Kudos to AskMrScience!

This chart is very clear and well executed and it immediately drew my attention. The data matches what you would expect intuitively (that congress is dominated by members in their 50s and 60s), so it’s satisfying to see the details.

Because of the great details and use of colors, this visualization is one where you engage with it longer than you would imagine. 

This version we have above was a modification of the original by the same creator who added the bands for the generations based on feedback from the reddit community. How cool is that! We love the addition of the generations and it made the chart even more engaging. It's always great to hear of analysts incorporating feedback to make their insights more prevalent.

However, this chart also had some issues. 

The gray band should be labeled in a legend, so this was a point that was missed in the clarity score. 

Further, it would be nice to have the mean and median on the visualization if possible. This is merely a suggestion and we think it would be possible to work in. 

Overall, this is a fun and well executed data visualization. Bravo! 

Most Used Ingredients in French Cooking based on “Mastering the Art of French Cooking” by Julia Child et al

13.3k Upvotes (as of Mar 10, 2022)

Link to Original Reddit Post

data visualization breakdown grading pros cons design best practice
Top 20 Ingredients in Mastering the Art of French Cooking
Interestingness: A-
Clarity of Information: B
Use of Color and Styling: D

Looking at this chart, you are immediately drawn to how much more butter is used in French recipes than any other ingredient, nearly twice as much as the next highest, egg. Pretty interesting and makes sense intuitively. French cooking is butter rich! 

I love anything with butter, but this chart is testing me a little bit. It is an interesting subject matter - how many times an ingredient is used based on recipes in a well known French cookbook - but the colors are really unnecessary and frankly, the whole chart is downright ugly.

In this case, the colors of the columns aren’t really corresponding with anything important about the information. Rather, it just adds unnecessary color and confusion. 

Outside of the color, the Y-axis should say number of mentions, not “Quantity.” Does quantity mean the weight of the ingredients used in each recipe? Or, across all recipe ingredients, is this the most used? A common mistake is to use a poor axis label, and it strikes again here. 

We will always mark down any X-axis with diagonal labels and in this chart, there is enough room and few enough ingredients where horizontal labels would have worked just fine. In this situation, a bar chart may have also been a more effective choice than a column chart.

Overall, pretty cool subject (and kudos to the person that tabulated this!) but the chart itself leaves a lot to be desired. 

Who Americans Spend Their Time With, by Age

3.2k Upvotes (as of Mar 10, 2022)

Link to Original Reddit Post

data visualization breakdown grading pros cons design best practice
Who Americans Spend Their Time With, by Age
Interestingness: B+
Clarity of Information: B+
Use of Color and Styling: A-

The main takeaway here is an insight that grips you, and not in a way that feels great: as we age, we spend a lot more time alone. If this chart makes you feel a bit sad, well then kudos to the creator for clearly communicating information in a way that is meant to cut through the noise.

This chart does a few things really well: first off, the axis labels are both clear and the explanation text under the chart title helps clarify any questions about the data set (is this time daily or weekly, for example).

Also, the use of six distinct colors make it easy to follow each line, especially on the left side of the chart where they are most clustered together.

However, there are some ways to make the clarity even better. The lines are very clumped in the early years, so maybe they could be ground into five year chunks which may make the changes more readily apparent. Also, the X-axis is missing a label. Finally, why not convert the minutes into hours? When we see 400 minutes, we immediately begin to do the mental math on how many hours that is in total.

Our only styling recommendation would be to use a heavier weight font for the line labels to make it a bit easier to read. It’s not bad as thin, but a medium weight on the font would make a big difference for readability. 

20 Most Reviewed Places on Google Maps

7.8k Upvotes (as of Mar 10, 2022)

Link to Original Reddit Post

data visualization breakdown grading pros cons design best practice
20 Most Reviewed Places on Google Maps
Interestingness: A
Clarity of Information: A-
Use of Color and Styling: C

The top 20 most reviewed places on Google Maps is a great idea for a data set and there is no way I would have guessed that Mecca in Saudi Arabia held the number one spot. Super interesting! 

Also seeing that the most reviewed places are spread all over the world shows the reach and power of Google Maps. 

The information displayed on this visualization is super clear - it’s simply ranking the most reviewed places. Not a lot to it.

However, there are some additional data points that would be nice to be included such as the reviews per place and the overall rating for the place. We understand that there isn’t a ton of real estate available on the visualization to add a lot of additional information, but that really leads to the next point…

While it is cool to see these places mapped on a geo-based visual, is it really the best way to display this information? Maybe so, but a well designed table view may actually have been better in this case. 

From our perspective, the overall visual is a little hard to read and the support text under the place title is very, very hard to read. 

Overall, I’m glad I know the most reviewed places on Google Maps now (that’s great party conversation fodder) and the creator did a great job making the information clear, but we do think better styling and potentially better use of color could have made an impact here.

In Conclusion

Data is Beautiful is an amazing subreddit community full of data visualization geeks that love to share. If you are not already subscribed, we recommend you go do that now. Look out for our next review of 5 more visualizations in Q2!

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