{"id":62871,"date":"2012-05-31T00:00:00","date_gmt":"2012-05-31T00:00:00","guid":{"rendered":"https:\/\/rockcontent.com\/blog\/the-pros-and-cons-of-scatterplots\/"},"modified":"2025-09-15T15:35:09","modified_gmt":"2025-09-15T18:35:09","slug":"the-pros-and-cons-of-scatterplots","status":"publish","type":"post","link":"https:\/\/pingback.com\/en\/resources\/the-pros-and-cons-of-scatterplots\/","title":{"rendered":"The Pros and Cons of Scatterplots"},"content":{"rendered":"

Scatterplots may not be used too often in infographics, but they definitely have their place. <\/p>\n

They can show large quantities of data and make it easy to see correlations between variables and clustering effects. <\/p>\n

As a quick overview and analytical tool, scatterplots are invaluable and work with almost any continuous scale data. <\/p>\n

Unfortunately, scatterplots aren’t always great for presentation. Several problems occur frequently, and it’s best to be aware of each when using scatterplots for analysis or presentation. <\/p>\n

A scatterplot works by placing one dimension on the vertical axis and a different dimension on the horizontal axis. <\/p>\n

Each piece of data is represented by a point on the chart. Variations on scatterplots introduce differently shaped or colored points for categories and differently sized points for quantitative data. <\/p>\n

Occasionally, people use pie charts as the points in scatterplots to show even more data with a part-whole<\/a> relationship.<\/p>\n

\"\"<\/a><\/figure>\n

The major cause of problems with scatterplots is discretization of values. <\/p>\n

This happens when decimal places are rounded off, measurements are not accurate enough, or a data field is categorical. <\/p>\n

The scatterplot below uses a standardized dataset about cars<\/a>. <\/p>\n

The problems with this scatterplot all derive from the x-axis; number of cylinders. There are so few values that cylinders is really a categorical scale being represented using numbers. <\/p>\n

This causes overplotting problems so there are hundreds of values all stacked on top of each other. <\/p>\n

This makes it difficult to see the full quantity of values in the dataset, and correlation and clustering is harder to find with so few possible values on the x-axis.<\/p>\n

\"\"<\/a><\/figure>\n

If you are dead-set on a scatterplot, there is not much you can do to remedy such a severe case of discretization, but in slightly better cases, there are some possible fixes. <\/p>\n

Translucency is a powerful tool for dealing with overplotting. <\/p>\n

Another possible mitigation technique is removing the fill of the mark. Both methods have advantages and disadvantages, and the combination of the two can also be useful. <\/p>\n

In practice, it\u2019s often a bit of trial and error. You fiddle around with the transparency slider and sometimes it just… doesn\u2019t help as much as you hoped, especially when points are stuck right on top of each other. And if you\u2019re working with some clunky data visualization software (not naming names), \u201cremoving the fill\u201d might turn your dataset into a blob of ghostly rings, which can be more confusing than enlightening. Still\u2014every so often, using both translucency and hollow points will at least give you a fighting chance to see overlapping data, even if it never feels perfect.<\/p>\n

There\u2019s also the reality that audiences don\u2019t always \u201cget\u201d scatterplots straight away. Folks used to bar charts or line graphs might find themselves squinting at a mass of tiny dots, wondering if they missed the point (pun intended). If I had a dollar for every time someone asked where the trend line is, I could probably buy better graphing software. But sometimes that\u2019s the cost of nuance\u2014scatterplots reveal complicated stuff, only if you know where to look and what questions to ask.<\/p>\n

Unfortunately, these methods are not a cure-all solution. It is still possible to have so many points or perfectly aligned points that pile up beyond the opacity range.<\/p>\n

\"\"<\/a><\/figure>\n

Ideally, avoiding data dimensions with low precision or few unique values is the best way to prevent these problems. <\/p>\n

Sometimes data just doesn’t belong in a scatterplot and you should visualize another dimension instead.<\/p>\n

In the case below, two continuous scales are shown and the overall shape of the group indicates negative correlation between the two dimensions.<\/p>\n

\"\"<\/a>   <\/p>\n

If you really need to show categorical data, consider visually encoding it as color. <\/p>\n

The following chart does have dimensions with lower unique value counts (data from Fisher’s Iris Data<\/a>), however it does a good job of showing how color can help call out clusters. <\/p>\n

\"\"<\/a>  <\/p>\n

Scatterplots definitely have limitations, most of which come from characteristics of the data. <\/p>\n

When used correctly, however, they are great for overviews, finding outliers, and for showing patterns between some dimensions. For a data visualizer, a responsibly used scatterplot can be a very valuable tool.   <\/p>\n

Drew Skau<\/a> is a scatterbrained PhD Computer Science Visualization student at UNCC<\/a>, with an undergraduate degree in Architecture.<\/em><\/p>\n

\n
\"Guide<\/a><\/figure>\n<\/div>\n

}}<\/p>\n","protected":false},"excerpt":{"rendered":"

Scatterplots may not be used too often in infographics, but they definitely have their place. They can show large quantities of data and make it easy to see correlations between variables and clustering effects. As a quick overview and analytical tool, scatterplots are invaluable and work with almost any continuous scale data. Unfortunately, scatterplots aren’t […]<\/p>\n","protected":false},"author":1,"featured_media":53027,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[],"class_list":["post-62871","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\nThe Pros and Cons of Scatterplots - Pingback<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/pingback.com\/en\/resources\/the-pros-and-cons-of-scatterplots\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Pros and Cons of Scatterplots - Pingback\" \/>\n<meta property=\"og:description\" content=\"Scatterplots may not be used too often in infographics, but they definitely have their place. 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