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I’ve been working on a series of short, focused and practical data visualization courses that will be published on wisevis.com, my consulting firm’s site. The plan is to add two new courses each month (minimum), update and move my current dashboard courses and convert my e-books to the online course format. There will be free and paid content.

Course for fun: How to make a pie chart

I couldn’t resist the temptation of creating a course on How to make a pie chart as a “Hello World!” message. It’s free, so you don’t even need to enroll.

Although it is free and about pie charts, this course shares with other similar courses two key characteristics. First, a generic (meaning tool-agnostic) discussion on the merits and weaknesses of pie charts. Second, a section where you’ll find step-by-step instructions on how to make the chart using several tools. I’m starting with two examples for Excel (regular and pivot chart) and I’ll add more soon.

Making complex charts in Excel

I like the course structure above, but in some cases the chart is so complex to make using a specific tool that it can’t share a course with other tools. That’s the case with horizon charts.

Since Excel doesn’t support small multiples, the common solution is to make a chart and then clone it. There are several problems with this technique, though. In this course I explore a different approach that allows us to display small multiples using a single chart. Beware: I think it’s worth it, but it’s not for the faint of heart!

Practical data visualization courses

A third group of courses is about practical data visualization: color, design, chart selection. The first course in this group discusses efficiency and specifically how our data and design choices impact the overall chart size. Why is this relevant? Because efficient screen real estate management allows you to design better and more informative dashboards or fit a chart into smaller (think smartphone) screens.

Coming soon

There are other key areas I’ll be addressing, like dashboard design, practical approaches to a few areas, like color, or choosing the right chart. The way many Excel users structure their workbooks and their data is painful to see. I want to address that as well, including risk-minimizing strategies (every formula is a potential risk vector.)

Tools

In the data visualization course I used two charts made with Flourish and with Datawrapper. I can’t avoid Excel, but I’ll try to minimize its presence when discussing tool-agnostic concepts. And you’ll find charts made in PowerBI, Tableau, JMP and Charticulator.

Short and specific: low investment in time and money

A word about free and paid content. I’d really like to make all courses freely available. Unfortunately, I can’t afford that (I’m open to suggestions, though).

At first, I wanted to create a large (and necessarily expensive) data visualization course. But I liked the idea of making multiple and very specific courses that require low investment in time and money. They are also more flexible, and it’s easier to change them or add more content depending on user feedback (each course offers a one-year access, including updates).

When more content is available I’ll offer bundles designed around learning paths. A special and limited membership that grants you full access to current and future courses is also available. Contact me if you’re interested.

So, take a look at the courses, and if you have any suggestions or comments, please let me know.

The original post is titled Announcing: Wisevis data visualization courses , and it came from The Excel Charts Blog .

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A single horizon chart is easy to make in Excel using overlapping columns or areas (the trick is to structure the data the right way). But the horizon chart is a variation of small multiples, so what makes sense is to stack them to compare multiple entities. That’s problematic in Excel.

But many charts can be done in Excel using a scatter plot, and making a passable version of a horizon chart is no exception. Can you figure it out? I have been trying to make a reasonably flexible chart without VBA and today I’m finally adding it as a bonus file to my Wordless Instructions packages (no wordless instructions for this chart yet).

The advantage of creating a horizon chart in a single chart instead of multiple charts is that you have much more control over sorting, synchronizing and making the design consistent. I do prefer making horizon charts with area charts instead of bar charts, but I’m not sure if that’s possible.

So, here are a few examples. Monthly employment rate by US state compared to the US rate:

Horizon chart in Excel

You can switch between names and codes to make more room for the chart.


Horizon chart in Excel

It’s easy to switch between sorting keys:


Horizon chart in Excel

Playing with bin width:


Horizon chart in Excel

I like the idea of mixing the horizon chart with the cycle plot. It’s possible to do it but needs more work and better data (maybe there is a hidden image in this stereogram):


Horizon chart in Excel

No significant editing needed when pasting new data (it adapts to a different number of entities):

Horizon chart in Excel

The current options and legend:

It gets slow with more data (with 500 months x 50 states x 12 classes it becomes really slow, but the computer I’m using is slow for everything Excel). Other than that, I’m very pleased with the results. Area charts are more pleasing to the eye, but I was able to show that some interactivity is possible, something you can’t do with multiple individual charts. And not having to use VBA is great.

This works with Excel 365 and probably Excel 2013 for Windows. I don’t think it works on a Mac.

I promised to add a few bonus files to the Wordless Instructions packages, and this is the first one. There are no wordless instructions for this chart yet, because I genuinely don’t know how detailed these instructions need to be. So, any feedback is welcome!

P.S. Last week, on my flight to the JMP Summit in Copenhagen I used the same table and could make the horizon chart in a few minutes. Haven’t tested it with Tableau yet, but I assume it should be as easy. Conclusions: how you structure your data matters; Excel is a pain when it comes to small multiples.

I should try it in PowerBI.

Kidding.

The original post is titled Horizon charts in Excel [bonus file] , and it came from The Excel Charts Blog .

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I need to learn PowerBI, as soon as possible (per client request). So, I spent much of last week using it. I wrote about the depressing experience on Twitter. I also commented on this post, and its author, Vitali Burla, invited me to show an example of a chart that can be done in Tableau but not in PowerBI.

And I was like, oh God, this is so easy it hurts. But I kept it to myself.

I am now more or less familiar with the interface of both Tableau and PowerBI, but I still need many hours to understand what makes them powerful (the languages behind calculations and metrics). I don’t know enough about the tools, but I tend to know exactly what type of visualization I want, and why. This helps.

The official truth

According to the Microsoft marketing machine, PowerBI is much better than Tableau on  the data side, and it is as good as Tableau on the visual side. The former is probably true, or will be in the near future. The latter is not, and it’s hard to imagine an alternative reality where that happens.

Let me be clear here. I don’t dismiss the data side. We need products that make connections, data preparation or statistical analysis easier and faster. Data and visuals complement each other and in most projects they share a common purpose: finding and communicating insights. Also, I’m sure in many cases PowerBI will fit the requirements perfectly.

The scenario

Here is the scenario: you need to display the distribution of a few hundred or thousand data points. You do have an aggregate view, but you’ll need to check for outliers, select data points that display an interesting behavior, etc. Perhaps it makes sense to split the distribution by the categories of a meaningful variable.

A business analyst could use this display to analyse sales territories or store performance. I don’t have business data that fits the bill, so I’m going to use population data instead. I’ll use population density in European countries at a regional level (NUTS3). A second chart splits the distribution by country. If you want to play with the data you can download the file here (xls).

The visual

This is a fairly straightforward chart. You use a scatterplot with a constant y and plot the data along the x-axis. Can’t be simpler.

There can be an issue, though. When you have many data points you risk overlap. To minimize the risk, you can use different markers, smaller markers, transparency, or add a bit of noise to the values on the y-axis, so that data points don’t overlap on the vertical scale (this is called jittering). There are other techniques to reduce overlap, like shorter scale range, increase chart width or use log scales.

Assume this is a proof-of-concept chart that would need a bit more design time.

Benchmark: the Excel version

Let’s start with an Excel version. I added a first series with a single y value, a second series where the value of y is the rank of population of each country, and a dummy series to display country codes. 

The most efficient marker to display data along the x-axis is a vertical line. Why most applications don’t offer this option of marker I beyond my understanding. Anyway, in Excel, you can use an image for a marker, so import a picture of 1×10 pixels and there you go, a vertical line. 

I prefer a a more flexible approach. Instead of a picture, I use error bars. Here is the result:

Strip plot, Excel version The Tableau version

Since Tableau doesn’t include a vertical marker, I imported a custom shape. No big deal. Next, I set opacity at 20% and used a log scale. Here is the result when all data points are plotted along a single line: 

Strip plot in Tableau

I’m pretty happy with the chart. It’s easy to see the most common range of population density values, but there are variations that only the combination of transparency and the marker’s small graphical footprint allow me to see.

What if you wanted to split the distribution by country? Simple: You just need to drag the right field and it’s done. For this second chart I increase opacity a bit, since overlap is less problematic.

Strip plot in Tableau The PowerBI version

Let’s see if we can get the same results with PowerBI. 

Strip plot in PowerBI

I guess not. I can’t import a custom marker, the available markers are gigantic for this task, and there is no transparency. Removing fill color improves a bit, but clearly not enough. The result is a continuous black line that hides relevant detail.

Chart height is also a problem. This is the minimum before the axis title gets hidden. As you can see, the Tableau design is much more compact. This can potentially impact display on small screens. It’s also interesting to note that Tableau does a better job at defining and describing the scale. 

Now, what about the chart detailing the distribution by country? Here is the best I could come up with:

Strip plot in PowerBI

This is not one chart but two: I used a hidden bar chart to display country codes.

The process is slightly unintuitive, and before you can come up with something you get this message or similar a few hundred times:

I will not try to make sense of what I feel is a conceptual mess around “details”, “categorical” vs “continuous” and “Don’t summarize”. I do need to emphasize the most basic conceptual error: assuming that the x-axis and the y-axis have a different nature. This is a wrong assumption and now you’re basically doomed.

The Key Influencers visual

Some people noted that the new Key Influencers visual demonstrates how great the PowerBI visuals are. No. Actually it demonstrates the opposite. I’ll accept that the analysis behind the visual is basically magic. This makes the visual even sillier and unworthy of all that magic. It also shows that Microsoft is much better at the data side than at the visual side.

The custom visuals marketplace

Call me old-fashioned, but I believe a “self-service BI” tool should not have what feels like a half-baked chart engine. Not that PowerBI should have all the options for every imaginable chart, but the basic ideas should be right, and some consistency wouldn’t hurt, either (example: color option means transparency option, always.)

Microsoft recently decided to allow developers to monetize their custom visuals in the marketplace. That’s great, and I hope this improves the overall quality of the custom visuals. I know there are a few excellent premium visuals. If you expect to need more than a simple bar or pie chart, I advise you to factor in these extra costs.

Takaways

If you believe the built-in visuals in PowerBI are comparable to the ones found in Tableau, choose the option that best applies to you: 1) you’re too gullible and should search for resources beyond the Microsoft marketing machine; 2) you need to increase your data visualization literacy; 3) your needs are simple and you don’t need more than this (and that’s OK!).

The whole point of this exercise is to show that the built-in visuals and the visualization process in Tableau and PowerBI are fundamentally different. Tableau demonstrates a more consistent conceptual framework and that can be seen in practice, while PowerBI visuals are messy and outdated.

That said, it’s possible that the data side in PowerBI is in fact stronger that Tableau’s. If that is true, and you need that, having someone evaluating and, if needed, developing custom visuals could be your best bet.

It’s possible that I’m terribly wrong. Help me see the truth, but please don’t start with “use the custom visuals or R”, because that’s not the point.

The original post is titled Comparing Tableau and PowerBI visuals , and it came from The Excel Charts Blog .

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When I saw Paris for the first time I was like, meh.

Not Paris’ fault. This was the second leg of a trip that started in Prague, and I was still in a process of digesting the city’s overwhelming beauty. After a couple of days, I was able to enjoy Paris, not in full, but in what made it different from Prague. For full disclosure, I prefer a different kind of beauty I find in other places, like, you know, London.

I hope you’re starting to suspect that this has nothing to do with cities and tourist trips. It has everything to do with data visualization and the multiple approaches to it.

I was reading Why visual literacy is essential to good data visualization, an interesting article by Benjamin Cooley (Twitter handle @bendoesdata) where he discusses the difference between visual literacy and data literacy and why it matters. I was calmly nodding along, but then I reached this paragraph, which marks an unexpected turn of events:

Data literacy is an essential starting point. But let’s be real: most people don’t want to look at a page full of bar charts. With more data available than ever before, the world is suffering from insight-fatigue, staring at dashboards day after day of red and green indicators showing positive and negative performance.

It is followed by a stock photo captioned “Just look at this generic business guy. He’s been staring at 3d pie charts and bar graphs all day”. As a generic business user, I recognize myself in this picture as much as a graphic designer would, if it pictured a generic graphic user tired of staring at automated infographics all day.

What’s the cure for the poor generic business user? Color theory, “feelings of certain shapes”, reading patterns of web users. In other words, we need more beauty.

The Invisible – Visible continuum

If using information to support decision-making is how I get my job done, all I want is insights, not pretty pictures (Ben Shneiderman). I don’t understand this notion of “insight-fatigue”, unless it means a more general burn out consequence of badly designed dashboards.

I agree that probably most people don’t want to look at a page full of bar charts. But the answer is not finding something less boring than bar charts. The answer is to create visuals that get out of the way. In other words, make them invisible (that’s what good design is all about, so they say?).

Benjamin mentions a Truth – Beauty continuum. I don’t see it as a continuum, because beauty (aesthetics) is always there, and truth should always be there.

That’s why I would prefer, instead of a Truth – Beauty continuum, an Invisible – Visible continuum. A great dashboard should be invisible. This means removing barriers to getting the insights I need, and that includes the dashboard not calling attention for itself, for good or bad reasons. I don’t care if it is a screen filled with bar charts or some well-chosen xenographics.

Now, I have nothing against a dashboard or an infographic that was designed specifically to be aesthetically pleasing. Designing for the visible side of the spectrum increases attention and engagement, and make the object more memorable. And makes you the cool kid. There are some trade.offs, though. If you search for “insights fatigue” I’m not sure if you’ll find a lot of relevant results, but search for “beauty fatigue” and you’ll get a lot to choose from. My Prague – Paris trip was an obvious case. I also mentioned London to express the idea that beauty can be overwhelming, but the sense of beauty is not universal: a stronger attraction is coupled with stronger rejection.

There are, of course, more earthly matters: beautiful objects tend to be unique, and that means they cost more. Since this is business, I need a cost-benefit analysis: how much this beautiful dashboard will cost me to implement, maintain and update? If it uses non-conventional visuals, it means that users will need extra training. And will they be able to get better insights and faster?

So, I don’t think beauty is the answer. It certainly is one of many possible situation-dependent answers along the the full Invisible-Visible spectrum. And we should humbly recognize that our talents and skill set will not always fit the requirements. That’s OK. Believe me, I know. I’m a generic Excel user.

The original post is titled Data visualization: beautiful Paris? , and it came from The Excel Charts Blog .

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How do Excel and Tableau compare when actually making a chart? I couldn’t find such post, so I wrote one. I’ll create a simple chart, a population pyramid, and comment on the process. To make it a bit more interesting, we’ll compare a certain population in 1986 with the estimates for 2050.

The Data

Let’s start with the data. Most tools draw a clear distinction between data sources and data display. In Excel, this distinction is often blurred because users don’t follow basic best practices. This is the first of several (more than seven, I’m sure) deadly sins Excel users often commit: badly structured data.

Nice table, but should you use it as the data source?

There is nothing terribly wrong about the table above. But it is designed to display data, so it should never be used as a data source for making charts, because it will severely limit your freedom to explore the data, and increases update and maintenance costs. This is the first case of badly structured data.

Also, Excel allows you to enter the data wherever you want in a spreadsheet, or even in multiple spreadsheets. If you know what you are doing, that can be immensely useful. If you don’t, you’re going to find yourself in some form of spreadsheet hell.

Data scattered in a spreadsheet

In Tableau, the distinction between a) data sources, and b) the objects (display tables, charts) that use them is clear. Also, Tableau is a lot less forgiving when it comes to data structures.

Let’s get back to the first table. To add more data you’ll probably add more columns, and you’ll have to recreate the charts, adding the individual data series. In Tableau, the table structure (number of columns) will not change, which makes data exploration much easier. (This is not Tableau specific: you can, and should, use this table structure in Excel.)

Contrary to a popular believe among Excel users, this is not a detail. The flexibility you love in Excel sooner or later will turn against you, while hard-to-read tables will prove very flexible when exploring the data. Try to add multiple years and regions to the first table and watch how quickly it gets out of hand.

Encoding in Tableau

Encoding means that you associate a data point with a visual object (a bar, for example) and one or more of its properties (height). Because you do that for all data points in a field / series, you can compare bar heights, which is much easier than comparing the actual values in the table.

In Tableau, these visual objects are called marks. You let Tableau select a mark for you (depending how you structure your chart) or, preferably, you select the mark yourself.

Think of marks as words: even if they are similar they don’t mean exactly the same. Choosing different words will change your message and make it more or less effective. For the same data, bars will focus attention on pairwise comparisons, while lines will emphasize the overall pattern. This is an editorial choice, not something you should let the computer decide.

Now, suppose you decide (wisely) that bars are not the right mark/chart type, because change between 1986 and 2050 is not clear. Perhaps lines are better, or areas, or…  In Tableau, since you already have a structure, you simply switch marks until you find one that feels right.

Now that you selected a mark, the next step is to assign data to some of its properties. You do that by dragging fields into “shelves”, at the top and on the left (I’m oversimplifying).

Tableau interface

The most important property is Position (along the x and the y axes). That’s what the shelves at the top are used for. Other properties, like color or size, are available on the left. Here is how the total population profile looks like in 1986:

Population pyramids using different marks.

So, for each data point you have two coordinates: a quantitative one (total population) varying along the x-axis (“Columns”), and a categorical coordinate (age group) varying along the y-axis (“Rows”)

Encoding in Excel

In Excel, things are more complex. First, it uses this outdated concept of “chart types”. They are OK for a casual conversation but not for such a massive tool like Excel.

Choosing a chart from the Excel chart library

The Excel charts library is a mess of visual objects and properties. What’s the point of having columns and bars, each with the same sub-types, when “Direction” could be defined as a property? This window is the Excel equivalent of Tableau shelves (never consciously realized Excel uses the concept of “Legend Entries” for series):

Because of this mess, when you click Add, you get one of three different windows, depending on the chart type: the X-Y window, for scatter plots, the X-Y-Size window, for bubble charts, and a third version for all the rest (I think):

Because Excel uses these “special cases” (and not a generic X-Y structure), it’s easy to find examples that don’t fit them. For example, there is no concept of vertical area chart. If you want to make a vertical line chart, sorry, but that’s also not possible, unless you use a connected scatter plot. But, because the y-axis needs to be numeric, in our case we must transform the age groups into a quantitative scale. We can do that by taking the lower limit of each age group (0, 5, 10…). Note that, while I was able to add copies of the same data to the top shelf in Tableau, in Excel the charts are independent of each other:

Making population pyramids in Excel.
Design Data

Design data are data you add to your chart to help you achieve certain visual effects, improving the display of the real data. A good example of display data is jittering, whereby you add a small amount of random data to minimize point overlap.

I don’t like the traditional design of a population pyramid. Both sexes should be displayed to the right side of the y-axis, not male to the left and female to the right. It would be easier to compare them without breaking the axis logic (there is a positive and a negative side). But let’s assume it’s OK to display the series Male to the left side of the axis. How do you do that in Excel? I wanted to use two axis and reverse one of them and align them at the origin. So far, no luck. The alternative is to use a stacked bar with a fake first series that must remain hidden. The second option is to multiply the data for -1. Obviously you have to take care of the scale, because it is wrong in both of these charts. I did that in the third example below.

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After creating wordless instructions for making charts in Excel, here is the Tableau version. This post discusses similarities and differences between both tools. Check out the e-books at the bottom!

How to make a chart

To make a chart, you must select the data, encode the data into visual objects, format those objects, and add text (title, labels, annotations). When using point-and-click applications these tasks are essentially visual: select from menu items, click, drag and drop icons. So, when writing instructions for making charts using these tools, there is no reason not to use a visual language. If it works for traffic rules or IKEA’s assembly instructions, surely it can handle making charts! Also, using wordless instructions means that we can break much of the language barrier (potentially reaching a much wider audience), and unify instructions (to some extent) across tools.

My first wordless instructions experiment: Excel

In my previous post, I show that it’s possible to create wordless visual instructions to help making charts in Excel. I defined a structure shared across instructions pages: one chart per page, and up to eight cards per chart displaying intermediate steps. The data set (or a sample) is also displayed, as well as the formulas for calculated fields. The example below shows how to make a doughnut chart.

I’m not assuming these instructions can be read as easily as a plain English sentence. Many icons are self-explanatory, the meaning of others can be derived from context and some others must be learned. This handy multi-language dictionary (in English, Portuguese, French, and Spanish) can help you with that.

These instructions are not limited to making basic charts. Some of the 50 charts below are fairly complex for Excel, and they can all be made following wordless instructions.

The Excel charts created by following the visual instructions. Going further: wordless instructions for Tableau

I’m very familiar with Excel, so understanding these instructions isn’t hard for me, but it was encouraging to see how easily my test subjects (my 13yo kids) were able to follow them as well. Having somewhat proven that creating visual instructions can be done for a tool like Excel, the next question was: can it be done for other tools? So, I tested the same idea with Tableau.

Tableau is a different environment (understatement of the year?). A fundamental difference is that Tableau is a structured environment where you assign data to visual marks, instead of choosing a chart type from a chart library to display data scattered all over the sheet. If you’re coming from Excel, you’ll need some time to adjust.

I followed a similar set of constraints: one chart per page, cards to display the intermediate steps, the data set added to one of them. “Shelves” are an interesting concept in Tableau that you can’t find in Excel: they allow you to see the underlying structure and let you move things around by dragging and dropping, or using other visual marks. It made sense to include them, so for Tableau there are fewer and wider cards per page, to accommodate both the chart and the shelves.

I had no intention of forcing compatibility between the instructions for both tools. When possible, I used the same icons with similar meanings, but above else I wanted to respect the differences between the tools (more on this later.) Here is how to make a dot plot in Tableau.

Wordless instructions to make a dot plot in Tableau Results

Since I already had instructions for making many charts in Excel, it made sense recreating them in Tableau by selecting a chart, and adapt the instructions for Tableau. I wanted to use this as a learning exercise, since my previous experience with the tool was limited. I got stuck plenty of times (often because I was thinking as an Excel user). But the Tableau community is really amazing, and I was able to find the right answers most of the time.

The Tableau charts created by following the visual instructions.

Now, if you are familiar with Tableau, and took a moment to think about the original list, you know how silly this idea of replicating the charts was. Some charts are simple variations in Tableau (change an option), but require a lot of work in Excel. The step chart is a good example: in Tableau, you change an option to switch between a regular line chart and a step chart, while in Excel you have to start from scratch and play with error bars to get the same result.

While some charts are simple to make in Tableau, circular charts tend to be harder: as far as I know, you can’t rotate a pie chart to start at a desired angle, for example. And then there are the error bars. How I missed them! I don’t often use them to show, well, errors, but I use them in Excel for everything else, from fake bar charts to fake grid lines, to fake sticks in a lollipop chart. You can imagine how flabbergasted I was when I discovered that Tableau users have been living all these years without true error bars!

Tableau vs. Excel

I don’t want to turn this into another Tableau vs. Excel post, but this exercise made a few things clearer to me:

  • If your needs are simple and you can/want to stick to the Excel chart library I don’t see much benefits from switching to Tableau.
  • Except for circular charts, many of the more advanced Excel charts felt pretty basic when made in Tableau (I hated that feeling!).
  • For similar charts, you need to take more micro steps in Excel than in Tableau.
  • In Excel, my kingdom for a horse small multiples.
  • Encoding data into variables (color, size…) is way easier in Tableau.
  • Combining multiple marks is easier and more flexible in Excel than in Tableau.
  • That comes at a price: in Excel, all those steps need to be recreated to change perspective, while exploring and moving things around is at the heart of Tableau.
What’s next?

Compared to Excel, I believe the instructions for Tableau are more consistent and use a few new ideas. This needs to be cared first. My ultimate goal is to have a generic visual language that can be applied to any point-and-click tool. I’m still unsure if this is feasible, but it’s my reference point. Using both Excel and Tableau gave me the opportunity to compare in more detail the process of making charts. However, using then raised a number of challenging questions:

  • Is it really possible to unify them into a single set of icons and grammar rules?
  • How much of it is tool-specific?
  • What’s the right balance between more abstract, tool-agnostic icons, and concrete, easier to recognize icons that tend to be associated with a single tool?
  • How much of this can be automated?
  • How easy is to adapt it to other tools?

So, next steps: improve compatibility between instructions for both tools and make more complex charts, especially in Tableau.

The e-books

Do you want to learn how to make the charts above? Do you think these visual instructions can help you? You can get the Excel or the Tableau e-book for USD $27 each, or a bundle for only USD $37. With your purchase you get access to any updates until the end of 2019.

The original post is titled Wordless instructions for making charts: Tableau Edition , and it came from The Excel Charts Blog .

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Context: why written instructions suck.

Data visualization is a young discipline, and we use a lot of words to discuss and try to figure out what works, why, and when. I do think experts could/should use more visuals in these exchanges, among them or when communicating with a general audience (to be honest, my own data visualization book would probably benefit from such advice.)

When I started this project, I wanted to focus on the how-to part, and the end product should be a dry step-by-step guide on how to make charts in Excel. But, because I was using words, leaving the why and the what-for parts out was harder than expected. Words have a will of their own, and they multiply like rabbits if you’re not paying attention. Having to weed them out when you’re not even using your mother tongue can be exhausting. And then one day I realized I could get rid of them, all of them. “Wordless” became my favorite word.

We need words to explain or justify, but written instructions can range from confusing to useless (can you imagine written instructions in aircraft safety cards or traffic signs?) Even if they require some learning, visuals tend to be more universal and immediate.

There is another good reason to go wordless. Most people can communicate in English, but it doesn’t mean they are comfortable with it to fully understand an argument or even to follow instructions. Living in a small, poor, and non-English speaking country (Portugal), I’m keenly aware of this. Small countries also mean small or non-existent markets, especially for niche products. There are no translated data visualization books here, and only one book was published by a local author (in 2006!).

Going wordless, IKEA style

A few years ago, I realized that I tend to gravitate towards a low-cost, ephemeral, and functional type of data visualization or, in other words, IKEA-style data visualization (I suspect most people wouldn’t take this as a compliment, but I would). This project made me aware that there is more to learn from IKEA.

For many of us, assembling a bed or a table is a complex task. That’s why simple and clear instructions are essential for the success of a knock-down furniture business. IKEA’s wordless instructions manuals send a unified message than can be understood by most people across cultures and languages (easier said than done, though).

If you can assemble a BILLY bookcase, I’m sure you can use wordless instructions to make a chart in Excel.

The sign system

We can agree that aircraft safety cards and IKEA instructions display a real-world situation that is easily recognizable, while many traffic signs are abstract and you have to learn what they mean. We’ll use this one as our model.

The image below provides instructions on how to make a doughnut chart. If you follow the steps you should get a similar chart. Create a small table like the example displayed and try it out!

In general, to make a chart you need to select an object, perform some kind of action and apply formatting options. Colors identify each category.

Try to read these instructions. For example, step 2 means “select the chart area and do not apply Fill or Line”. Step 7 means “select the labels and display percentage and category name”.

I tried to use self-explanatory icons whenever possible, but their meaning is also available in multiple languages. The ebook comes with English, Portuguese, French and Spanish translations, but more can be added to a table in a shared Google sheet (the file is open access and unprotected, so anyone can add a new language).

There are few simple charts that most people can make without instructions. Think of them as training wheels the will help you familiarize with the icons and their meaning. This is a first set of 50 charts (hopefully there will be more soon!), so no chart is particularly complex.

Where to go from here?

By design, this ebook focus on the how-to, and excludes explicit data visualization guidelines. But the examples stay clear from Excel chart defaults and formatting options. There are a few minor concessions, but no capital sin. I like to expose readers to these implicit guidelines while using the instructions.

This is my first attempt to create a sign system, so a few inconsistencies remain. I’d like to iron them out, optimize the design and automate a few steps. I’m curious about using these visual instructions to create more complex charts or evaluate their flexibility when applied to other point-and-click tools.

Buy the ebook!

You can buy the ebook here.

The original post is titled [New ebook] Wordless instructions for making charts in Excel , and it came from The Excel Charts Blog .

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So our usually calm data visualization corner on Twitter was shaken by this tweet:

Forget pepperoni – mushroom is Britain's most liked pizza topping (65%), followed by onion (62%) and then ham (61%) https://t.co/5kYikXOEtF pic.twitter.com/AJezMfJHbk

— YouGov (@YouGov) March 6, 2017

quickly followed by this one:

We're very sorry for the confusion, but this is NOT a pie chart – it is just a top-down photo of a pizza with some topping stats pointed out https://t.co/gHQM4D7NHu

— YouGov (@YouGov) March 6, 2017

What was all the fuss about? Well, Elijah Meeks summarizes it nicely:

Instead of teaching people data visualization literacy, we just made them pedants on the look out for pie chart fights. https://t.co/zuv3nT3xn0

— Elijah Meeks (@Elijah_Meeks) March 6, 2017

So, was this much ado about nothing? I don’t think so.

Putting a few percentages near all things circular is a dangerous thing to do on social media. It invites scrutiny by the pie-charts-are-bad mob, so do it at your own peril. The fine line between humor and trollish behavior is often blurred. If it is round like a pie chart, percentages are displayed as in a pie chart, has slices as a pie chart, then it can only be a pie chart. So let’s have some fun!

People tend to follow the path of least effort. That means using  defaults and following the rules. There is nothing wrong with defaults and rules, but some are stupid, and some need context to be understood. I’m often worried that “no pies” is the only part of the message heard beyond the choir.

Now, I don’t have the time to check all the replays to the original tweet, but I couldn’t find a mention to the elephant in the room. If you look closely, there is a note below that says, among other things, “Other items not depicted include: onions (62%), chicken (56%)….”. So, because @yougov couldn’t find a stock photo depicting onions and chicken these very large items where relegated to a footnote.

This is the real issue: molding the data to fit your clever (?) design, and it happens too often. This pizza-not-pie incident is just silly and light. But think about it a bit: replace “molding the data” with “alternative facts” and “design” with “ideology” and things suddenly become very serious. There are some red lines, don’t let l’air du temps blur them too.

The original post is titled On alternative pizzas, pie charts and datavis pedantry , and it came from The Excel Charts Blog .

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