Tag: scatterplot

7 Reasons You Should Use Dot Graphs, by Maarten Lambrechts

7 Reasons You Should Use Dot Graphs, by Maarten Lambrechts

In my data visualization courses, I often refer to the hierarchy of visual encoding proposed by Cleveland and McGill. In their 1984 paper, Cleveland and McGill proposed the table below, demonstrating to what extent different visual encodings of data allow readers of data visualizations to accurately assess differences between data values.

DOI: 10.2307/2288400

Since then, this table has been used and copied by many data visualization experts, and adapted to more visually appealing layouts. Like this one by Alberto Cairo, referred to in a blog by Maarten Lambrechts:

Via http://www.thefunctionalart.com/

Now, this brings me to the point of this current blog, in which I want to share an older post by Maarten Lambrechts. I came across Maarten’s post only yesterday, but it touches on many topics and content that I’ve covered earlier on my own website or during my courses. It’s mainly about the relative effectiveness and efficiency of using dots/points in data visualizations.

Basically, dots are often the most accurate and to the point (pun intended). With the latter, I mean in terms of inkt used, dots/points are more efficient than bars, or as Maarten says:

Points go beyond where lines and bars stop. Sounds weird, especially for those who remember from their math classes that a line is an infinite collection of points. But in visualization, points can do so much more then lines. Here are seven reasons why you should use more dot graphs, with some examples.


Maarten touches on the research of Cleveland and McGill, on a PLOS article advocating avoiding bars for continuous data, and on how to redesign charts to make use of more efficiënt dot/point encodings. I really loved this one redesign example Maarten shares. Unfortunately, it is in Dutch, but both graphs show pretty much the same data, though the simpler one better communicates the main message.

Do have a look at the rest of Maarten’s original blog post. I love how he ends it with some practical advice: A nice lookup table for those looking how to efficiently use points/dots to represent their n-dimensional data:

  • For comparisons of a single dimension across many categories: 1-dimensional scatterplot.
  • For detecting of skewed or bimodal distributions in 2 variables: connect 1-dimensional scatterplots (slopegraphs)
  • For showing relationships between 2 variables: 2-dimensional scatterplots.
  • For representing 4-dimensional data (3 numeric, 1 categorical or 4 numerical): bubble charts. Can also be used for 3 numerical dimensions or 2 numeric and 1 categorical value.
  • For representing 4-dimensional data +  time: animated bubble chart (aka Rosling-graph)
Play Your Charts Right: Tips for Effective Data Visualization – by Geckoboard

Play Your Charts Right: Tips for Effective Data Visualization – by Geckoboard

In a world where data really matters, we all want to create effective charts. But data visualization is rarely taught in schools, or covered in on-the-job training. Most of us learn as we go along, and therefore we often make choices or mistakes that confuse and disorient our audience.
From overcomplicating or overdressing our charts, to conveying an entirely inaccurate message, there are common design pitfalls that can easily be avoided. We’ve put together these pointers to help you create simpler charts that effectively get across the meaning of your data.


Based on work by experts such as Stephen Few, Dona Wong, Albert Cairo, Cole Nussbaumer Knaflic, and Andy Kirk, the authors at Geckoboard wrote down a list of recommendations which I summarize below:

Present the facts

  • Start your axis at zero whenever possible, to prevent misinterpretation. Particularly bar charts.
  • The width and height of line and scatter plots influence its messages.
  • Area and size are hard to interpret. Hence, there’s often a better alternative to the pie chart. Read also this.

Less is more

  • Use colors for communication, not decoration.
  • Diminish non-data ink, to draw attention to that which matters.
  • Do not use the third dimension, unless you are plotting it.
  • Avoid overselling numerical accuracy with precise decimal values.

Keep it simple

  • Annotate your plots; include titles, labels or scales.
  • Avoid squeezing too much information in a small space. For example, avoid a second x- or y-axis whenever possible.
  • Align your numbers right, literally.
  • Don’t go for fancy; go for clear. If you have few values, just display the values.

Infographic summary

Avoid bar plots for continuous data! Do this instead:

Avoid bar plots for continuous data! Do this instead:

Tracey Weissgerber, Natasa Milic, Stacey Winham, and Vesna Garovic wrote this interesting 2015 paper on bar graphs. By a systematic review of physiology research, they demonstrate we need to reconsider how we present continuous data in small samples.

Bar and line plots are commonly used to display continuous data. This is problematic, as many different data distributions can lead to the same bar or line graph. Nevertheless, the rarely used scatterplots, box plots, and histograms much better allow users to critically evaluate continuous data.

They provide many interesting visuals that underline their argument.

For instance, the four datasets below (B, C, D, and E) will all result in the same barplot (A), whereas they demonstrate quite different characteristics.

Alternatively, bar plots are often used for to display group means when observations within groups may not be independent. For instance, it could be that the bars below represent two measurement occassians, and that each of our sampled observations occurs in both. In that case, the scatterplots with connected dots may be more suitable. While the bars in plot A would represent datasets B, C, and D, these are clearly different when viewed in scatterplots. 

Also, a lot of meaningful information is typically lost in bar plots. For instance, the number of observations in a group. But also the distribution of values. While the former can be added (see B below), the latter can much better be shown in a scatter plot like C (below).

Actually, in a later blog post, lead researcher Tracey Weissgerber  shares the below visual. It highlights the distractive irrelevance of bar plot and the information that is lost (becomes invisible) when opting for a bar chart.

Tracey refactored this into a similar visual of her own:

So what can you do instead, you may ask yourself. To this question too, Tracey has an answer, sharing the below overview of alternatives options:

She made another overview which may help you pick the best visual for your data. This one takes your intention behind the visual as a starting point, though is unfortunately a bit low quality:



I’ve mentioned before that I dislike wordclouds (for instance here, or here) and apparently others share that sentiment. In his recent Medium blog, Daniel McNichol goes as far as to refer to the wordcloud as the pie chart of text data! Among others, Daniel calls wordclouds disorienting, one-dimensional, arbitrary and opaque and he mentions their lack of order, information, and scale. 

Wordcloud of the negative characteristics of wordclouds, via Medium

Instead of using wordclouds, Daniel suggests we revert to alternative approaches. For instance, in their Tidy Text Mining with R book, Julia Silge and David Robinson suggest using bar charts or network graphs, providing the necessary R code. Another alternative is provided in Daniel’s blogthe chatterplot!

While Daniel didn’t invent this unorthodox wordcloud-like plot, he might have been the first to name it a chatterplot. Daniel’s chatterplot uses a full x/y cartesian plane, turning the usually only arbitrary though exploratory wordcloud into a more quantitatively sound, information-rich visualization.

R package ggplot’s geom_text() function — or alternatively ggrepel‘s geom_text_repel() for better legibility — is perfectly suited for making a chatterplot. And interesting features/variables for the axis — apart from the regular word frequencies — can be easily computed using the R tidytext package. 

Here’s an example generated by Daniel, plotting words simulatenously by their frequency of occurance in comments to Hacker News articles (y-axis) as well as by the respective popularity of the comments the word was used in (log of the ranking, on the x-axis).

[CHATTERPLOTs arelike a wordcloud, except there’s actual quantitative logic to the order, placement & aesthetic aspects of the elements, along with an explicit scale reference for each. This allows us to represent more, multidimensional information in the plot, & provides the viewer with a coherent visual logic& direction by which to explore the data.

Daniel McNichol via Medium

I highly recommend the use of these chatterplots over their less-informative wordcloud counterpart, and strongly suggest you read Daniel’s original blog, in which you can also find the R code for the above visualizations.

Tidy Missing Data Handling

Tidy Missing Data Handling

A recent open access paper by Nicholas Tierney and Dianne Cook — professors at Monash University — deals with simpler handling, exploring, and imputation of missing values in data.They present new methodology building upon tidy data principles, with a goal to integrating missing value handling as an integral part of data analysis workflows. New data structures are defined (like the nabular) along with new functions to perform common operations (like gg_miss_case).

These new methods have bundled among others in the R packages naniar and visdat, which I highly recommend you check out. To put in the author’s own words:

The naniar and visdat packages build on existing tidy tools and strike a compromise between automation and control that makes analysis efficient, readable, but not overly complex. Each tool has clear intent and effects – plotting or generating data or augmenting data in some way. This reduces repetition and typing for the user, making exploration of missing values easier as they follow consistent rules with a declarative interface.

The below showcases some of the highly informational visuals you can easily generate with naniar‘s nabulars and the associated functionalities.

For instance, these heatmap visualizations of missing data for the airquality dataset. (A) represents the default output and (B) is ordered by clustering on rows and columns. You can see there are only missings in ozone and solar radiation, and there appears to be some structure to their missingness.


Another example is this upset plot of the patterns of missingness in the airquality dataset. Only Ozone and Solar.R have missing values, and Ozone has the most missing values. There are 2 cases where both Solar.R and Ozone have missing values.b.JPG

You can also generate a histogram using nabular data in order to show the values and missings in Ozone. Values are imputed below the range to show the number of missings in Ozone and colored according to missingness of ozone (‘Ozone_NA‘). This displays directly that there are approximately 35-40 missings in Ozone.

c.JPGAlternatively, scatterplots can be easily generated. Displaying missings at 10 percent below the minimum of the airquality dataset. Scatterplots of ozone and solar radiation (A), and ozone and temperature (B). These plots demonstrate that there are missings in ozone and solar radiation, but not in temperature.d.JPG

Finally, this parallel coordinate plot displays the missing values imputed 10% below range for the oceanbuoys dataset. Values are colored by missingness of humidity. Humidity is missing for low air and sea temperatures, and is missing for one year and one location.


Please do check out the original open access paper and the CRAN vignettes associated with the packages!


The Dataviz Project: Find just the right visualization

The Dataviz Project: Find just the right visualization

Do you have a bunch of data but you can’t seem to figure out how to display it? Or looking for that one specific visualization of which you can’t remember the name?

www.datavizproject.com provides a most comprehensive overview of all the different ways to visualize your data. You can sort all options by Family, Input, Function, and Shape to find that one dataviz that best conveys your message.

datavizproject overview

Update: look at some of these other repositories here or here.