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Cédric Scherer

@cedricscherer.com

🧙‍♂️✨📊 Independent Data Visualization Designer, Consultant & Instructor ♢ PhD in Computational Ecology ♢ Interested in all things data & design ♢ #DataViz with #rstats, #ggplot2, #Figma and more ♢ he/him

1.3k followers473 following189 posts

Overview Posts Replies

CScedricscherer.comSep 10, 2024 2:30pm

Thanks Matthias!

CScedricscherer.comSep 10, 2024 2:20pm

I'm in Washington DC for a few days to host a workshop 🧑‍🏫 If you're around, feel free to reach out—I'd love to connect and hang out!

CScedricscherer.comJul 10, 2024 9:27am

📊 Have you ever needed to create a bar chart when data is aggregated in groups of different ranges? While researching the pros and cons, I couldn't find a consensus on what the "best" approach is. As often, "It depends" is the best recommendation I could find. #dataviz #datavis #datavisualization

A traditional column chart. The height of each rectangle encodes the share per group. However, as the percentages on the x-axis are aggregated in groups with irregular ranges, should we visualize the data in a different way?

In the made-up example shown, the groups on the x-axis are aggregated as <5%, 5-10%, >10-25%, >25-50%, and >50%. The groups >10-25% and >50% both have bars with a height indicating the same frequency of 27%.

A numeric x-axis. The height of each rectangle still encodes the share per group. But now, the width of each rectangle encodes the range used to aggregate the data.

As the area scales quadratically, the area of the last group is much larger than that of the third. But both have a 27 percent share.

Percentages encoded by area. To prevent rectangles with larger x-axis ranges from giving a false impression of greater importance than those with similar shares, we can map the share to the area instead of the height.

This might be correct, but how can we ensure that viewers understand and interpret it correctly?

A comparison of pros and cons when visualizing frequency by height (simplicity, conventional, comparability versus overemphasis of wider ranges) or area (accuracy, proportionality versus complexity, unconventional)

CScedricscherer.comJul 9, 2024 5:59am

Impressive!

CScedricscherer.comJul 5, 2024 7:45am

Cool stuff! I hoped the page was built in R as well but seem not.

CScedricscherer.comJun 30, 2024 3:10pm

Thanks Ilya! Hope all is good on your end 👐

CScedricscherer.comJun 29, 2024 11:08am

A different perspective on the same data: points versus goals for all teams during the group phase of the #UEFA #EURO2024.

Scatter plot of points (y) versus goals (x) showing all teams from the UEFA EURO 2024 group phase. Each dot represents a team, with blue indicating teams that qualified for the round of 16 and orange indicating teams that were eliminated. The size of the team labels corresponds to their goal/shot ratio (efficiency), with larger labels indicating higher efficiency.

CScedricscherer.comJun 29, 2024 10:55am

All teams in one panel and only one color encoding: qualified for the round of 16 or eliminated. The larger the team name, the better the goal/shot ratio. #EURO2024 #DataViz #ggplot2

Scatter plot of goals (y) versus shots (x) showing all teams from the UEFA EURO 2024 group phase. Each dot represents a team, with blue indicating teams that qualified for the round of 16 and orange indicating teams that were eliminated. The size of the team labels corresponds to their goal/shot ratio (efficiency), with larger labels indicating higher efficiency.

CScedricscherer.comJun 29, 2024 10:50am

🏆⚽ As we head into the round of 16 of the UEFA EURO 2024, which teams do you think were the most efficient? Germany scored the most goals and shots in the group phase! Serbia, France, and Belgium were the least efficient. #EURO2024 #DataViz #rstats #ggplot2

Scatter plot of goals (y) versus shots (x) with six panels, one for each group in the UEFA EURO 2024 group phase. Each dot represents a team, with the color indicating their rank: blue-green shades for teams that qualified for the round of 16 and orange shades for those that were eliminated. The size of the dots corresponds to the points each team has scored. Germany stands out with the largest dots, indicating the most goals and shots.

Reposted by Cédric Scherer

MFdatavisfriendly.bsky.socialJun 27, 2024 2:26am

The #rstats docs.r4photobiology.info/ggpp/index.h...

Grammar Extensions to ggplot2

Extensions to ggplot2 respecting the grammar of graphics paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to plots using native data coordinates, while geom_table_npc(), geom...

Cédric Scherer

@cedricscherer.com

1.3k followers473 following189 posts