Category Archives: ideas

You can still cheat in Tableau! Examples of dark practices

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Tableau is famous for following the best of the best practices in the DataViz world – it has no 3D effect, the pie chart is marginalized, there are no curved lines, no dashed lines, and the colour palettes are near perfect. One will question whether dashed lines are really a bad practice, another one will easily recreate curved lines with the “data-densification” technique, and here I will argue that there are still plenty of ways to make misleading charts in Tableau.

May this article draw your attention to what the dark practices might be, and whether to use them or to recognize and fight them is the decision to make for each reader.

Dramatic growth

This is the simplest and probably most used dark practice in real life – simply cutting the axis above zero. Whether it is a good or dark practice depends a lot on the context. Line chart allows to do this “legally”, but here I’m adding some more distortion by changing colours to a more dramatic hue and additionally encoding the size of a bubble to a variable.

Not so easy comparison

Tableau allows for bubble charts that look cool, but are rarely useful to communicate changes in values until your aim is to hide that subtle change. If there is a KPI on your dashboard you don’t really want anyone to see changing – use bubbles. Leaving out the printed numbers would render this visualization practically useless.

However, the same bubbles could actually emphasize the difference if placed one above the other – this would be more like an unnecessary overdesigning rather than dark practice.

Not so easy comparison again

Bubbles are very effective to obscure comparisons between categories. The same could be achieved also with colour, and Tableau allows it to happen so easily! Of course, the top and bottom values are easily seen, but the middle is quite muddy, it would be very difficult to sort them without printed numbers.

Both dark practices shown here would be difficult to reproduce on Excel.

Not so easy comparison by stacking

I’ve seen such stacking more as a result of trying to make a cooler chart rather than a dark practice (it was a donut chart by the way). But it could be used to conceal the true difference in numbers. Additionally – make the part you want to look bigger much more saturated, and put the border of the same colour around both bars. No smart reader would be fooled by such arrangements, but if there are more charts to digest on the page, the message might slip unnoticed.

Hiding a relation

Scatterplots are not that easy to read and get for the untrained audience and that is why they are not used often, however they are perfect to show a relation between two variables. Line charts might be used to show the relation, but they could be used to hide that relation – removing lines and making markers a bit oversized effectively obscures the actual direction of data.

Disguised negative number

This was the actual problem I faced in my job – how to explicitly show profit numbers and somehow make the loss at one period be not that very visible. First of all, we could make the profit and loss to be the same colour, then move the number of loss above – as those of profit, then remove the reference line and finally make the bars thinner. Again – acute reader would quickly recognize dark practices used against him, but only if he is not overwhelmed with charts.

3D still possible

And finally the trick as old as Excel – 3D pie charts are notorious for distorting reality and more often than not they distort it unintenionally, however clever masters of dark practices utilize this feature to their benefit.

With smart preparation of data, dynamic 3D charts are still possible in Tableau. The one seen here is drawn “by hand”, feeding Tableau exact coordinate of each point. A more saturated colour and annotation further serve exaggeration of our company’s market share.

Click the tabs in the visualization above to see all the interactive versions of charts in this article.

Dark practices

Dark practice is not making bad decisions about data vizualizations. It’s making smart intentional decisions to distort viewers’ perception of the data.

Dark practice is not faking the data, it is making the data to appear showing what it might not be showing.

Dark practice is not lying, it is not telling the truth.

Is it easy to replicate the default settings of one charting software in another charting software?

chart planet Posted on by

The question is it easy to replicate the default settings of one charting software in another charting software bothered me for some time. Are the default settings more universal or less universal? Do different vendors have different attitudes towards what should be the default setting?

I chose to work with a line chart because different software interprets differently how to arrange multiple series in a bar chart – some tools stack them, some not. By adjusting this arrangement I would lose the defaultness, while without any corrections the charts would be less comparable. I made all charts squared, so they fit better in the grid.

This comparison does not include online tools like Datawrapper because a significant part of their settings are the interactions – tooltips, highlights, etc and here only static images are compared. I would like to include JMP, but my trial period has already expired. Python and Javascript libraries are not included, because I don’t know how to use them.

Insights about the defaults:

  • All have horizontal gridlines, ggplot2 and Tableau have even vertical ones.
  • Only Google Data Studio and Tableau have highlighted the zero line, although Tableau highlight is barely noticeable.
  • Blue and red or orange are within the first three choices in every palette.
  • ggplot2 looks exceptional with its grey panel.
  • The default settings of Tableau make the least sense because they are configured for more charts with more legends. One chart with one legend looks a bit weird.
  • Grey squares at the top right of Google Data Studio charts are how the control buttons are rendered as an image.

Insights about the comparisons:

  • Of course, ggplot2 manages to replicate even the most complex cases. The biggest challenge was using Google font from Google Data Studio because the library”showtext” which seemingly allows achieving this does not work well with ggplot2.
  • Settings of ggplot2 itself were the most difficult to replicate.
  • Tableau was the only software that could not replicate the exact colours of lines, because a colour must be chosen from a predefined palette there.
  • It was quite annoying that Power BI and Google Data Studio could only export to PDF, however they are not meant to make pretty pictures after all.
  • Somehow square charts from Excel lost the squareness after saved as images.
  • Google Data Studio insisted on a black line indicating zero and refused to show vertical gridlines. Maybe I just don’t know this tool well enough or maybe these are the limits.
  • Adjusting the limits of the x-axis was always a challenge, the y-axis is often allowed for way more freedom.
  • Adjusting legends was always the most difficult part. Legend is what distinguishes one tool from another.

I believe this exercise is of little use, but it was fun to do it!

Tools of dedicated dataviz professionals

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There are variety of tools to do dataviz and for someone who is just starting it is quite difficult to select which one to learn. One possible criteria might be whether this tool is used by professions focused on dataviz. R is focused on statistical analysis, Excel on spreadsheets, and python might be used for everything. So what is the tool which dedicated dataviz professionals use? The conclusion is that there is no such tool, because dataviz is more like a skill useful in many cases (financial reporting, UX design, journalism, etc.) rather than a profession by itself.

dvs 2020

This is my submission to the Data Visualization Census Survey 2020 Challenge by Data Visualization Society a bit updated from the original based on comments by the jury. Visit their site to see the winners!

>> My original entry <<

Let’s Bring Some Data Density to Financial Presentations

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If you’re working in the financial department of a company or just dealing with financial data, you may be familiar with the Previous-Budget-Actual bar chart. It’s frequently used, occupying critical presentation and dashboard space, but sadly it lacks data density, contributing only three observations.

previous budget actual

Managers rarely have time to thoroughly explore financial presentations, that is why you need to make as much information available as quickly as possible — an easily readable data-dense visualization serves this purpose well. In my pursuit to make denser bar charts, I found five techniques that I will share here:

full article at Nightingale – the Journal of Data Vizualization Society.

Visualizing profit loss statement

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Probably of the most often visualized datasets in the world are profit-loss statements of corporations month after month presented to executives in powerpoints. Millions of them are made and quickly thrown away faster than new Covid-19 case charts. It’s quite strange how few resources are there discussing visualizations of financial data.

Majority of those who do care about finance seem to favour waterfall charts (like this analyst) and for a good reason, because they provide insight a simple table does not have. Some utilize sankey or flow diagrams because they show flows of money in quite an intuitive way (cool example – a squared sankey in Tableau). Also, there are dashboards completely ignoring the specific structure of financial data – good (because it’s bad) example is this dashboard made by “somebody with good Tableau but limited finance knowledge” as said in one comment.

I consider myself as somebody with good financial knowledge so I tried finding a better way to visualize profit loss statement solving some limitations of waterfall and sankey charts:

  • waterfalls look much better when visualizing increases and decreases, but the distribution of revenues or expenses look weird done in this way.
  • sankeys simply cannot visualize negative results in an intuitive way
  • financial activity result is often presented as one line in profit-loss statements and might be positive or negative, so in a usual sankey diagram it would not have a fixed position independent of its value

And here is the result. Sankey is used for basic distribution of revenue or expenses and could be used for showing the further breakdown. The waterfall comes in after Operating profit is calculated and net results of financial and other activities are simply added and subtracted (in this specific case they’re tiny). Profit tax is again visualized as sankey, because usually, it has the same sign as profit. Also, mind the colours, black is used to show positive flows (revenue), blue (which is also provided in the brandbook of Amazon) is used for negative flows (expenses).

This approach allows for visualizing negative results as well. Where else could we look for companies with negative profits except the aviation industry! Here is the profit-loss statement of Lufthansa, a German air carrier. The visualization is a bit awkward at first, but once we get into the waterfall everything becomes simple and intuitive. Also, notice that profit tax is negative – it improves the result.

Of course this way to visualize profit-loss is far from perfect:

One issue is related to labels – when a rectangle is too small, the number does not fit inside. It is easy to solve, however – if labels are necessary they can be shown beside the names of categories.

Another issue is a comparison between time periods – this type of chart can show only one period. However some cross-company insights still could be made – just compare profit margins of Amazon with those of Aramco – the Saudi Arabian Oil Company:

I hope these charts will inspire further discussion about financial data visualization and more great ideas.

Chart idea: Comparative Stacked Bars

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The Stacked Bar chart is one of my favourites, I even made the same stacked bar chart with 9 online tools – but it has one major weakness, it’s difficult to compare changes of its segments over time. I tried finding a way to improve it and here let me introduce the Comparative Stacked Bars:

Comparative Stacked Bars

Triangles show the absolute increase or decrease of each segment. They are colour-coded to make it even easier to read.

It’s limitations:

  • Too many is too many – if there are too many categories the triangles will make the chart look messy and difficult to read. But even more difficult it would be without triangles.
  • Too small is too small – if the change is too small the triangle might become invisible. But without them, the changes would get invisible much sooner – just observe the top segment in the above chart.

The example above was made in R, and the example below was made in Tableau. Unfortunately I have no not-overly-complicated solution for Excel. If anyone knows how to implement it properly, please let me know!

Chart idea: Cake Chart

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The pie chart faces a tremendous amount of criticism for attempts to show part to the whole relations. Of course – it is easily the single most misused chart! However more and more data visualizations practitioners are writing articles to defend it with Robert Kosara being the most thorough and methodical in my opinion.

Here I will offer an alternative which is something like a mix of square pie chart, marimekko and packed bars. Let me introduce the Cake Chart:

It’s features:

  • Always a regular square, total area = 100%.
  • Each segment is a bar for easy comparison, but its area represents the percentage.
  • The height of the chart is distributed evenly among bars.
  • Only the selected number of largest categories are shown separately.
  • All other categories are aggregated into the irregular steppy gray area.
  • If there are 5 categories shown separately, then the largest cannot be larger than 100% / 5 = 20%. For X categories to be shown – the maximum value cannot be larger than 100% / X.

Weaknesses:

  • Aggregated other categories are difficult to compare to bars.
  • There are limitations how long the longest bar can be without destroying the squariness of the square.
  • Square is not as intuitive to be 100% as circle.

Strengths:

  • Bars are super easy to compare.
  • It’s still a regular shape.

It’s quite obvious how to make it – make the panel square, make its background grey. However, making the Others category selectable in Tableau is a challenge and here is the result:

Same chart – 9 online charting tools

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Imagine the situation – we want a beautiful and interactive chart on our website but have no knowledge of D3 or other fancy stuff, so, to pick a solution I am quickly trying them all and making a recommendation based on this experience.

CONTINUE READING >>>

Templates of charts with lines for Tableau

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Here are my templates of various charts with lines (often curved) to reproduce when needed.

Templates of Radial charts for Tableau

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Here are my templates of various radial charts to reproduce when needed.

Templates of various non-standard charts for Tableau

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Here are my templates of various charts to reproduce when needed. Not all of them are useful, some are just fun to make.