connecting... the dots

Today's post is a guest post written by Daniel Zvinca. When Dan first reached out to me via email about a blog post I'd written, I thought to myself, That name sounds familiar... Why do I know that name? Some time later, it struck me—it was because I'd recently read one of Stephen Few's quarterly updates that introduced the Zvinca plot named after—you guessed it—Daniel Zvinca.

Dan has a mechanical engineering background, spent much of his career developing business related data applications in the ERP area and beyond, and today enjoys, among other things, considering and practicing data sense-making and data visualization. He lives in Romania (Romanian is his first language), but most of his projects were implemented in Belgium, where he runs a small IT company together with his business partner, Wilfried Van den Bosch.

Dan and I have had some good conversations in blog comments as well as behind the scenes (I'll use this as a opportunity to mention how impressed I am at the technical conversations we have with him speaking in a non-native language). At one point, we were discussing lines and all the ways they can be used. I've run into the scenario several times recently in workshops where it's become clear that many people are under the false impression that lines can only be used for continuous data. That's actually not true. The guideline is that with graphs that use lines, you have to make sure that those lines make sense. In some cases, that can be true for non-continuous data as well. At any rate, Dan and I were discussing this at one point and I invited him to pen a guest post outlining the many uses of lines in data visualization. The following is his post. I hope you'll find it eye opening how many different ways we can use lines in data visualization.

A good communication requires reasonable knowledge of a common language in order to succeed

I had the experience of learning English by starting with a minimum vocabulary and only a few grammar rules. At one point my job required me to travel in another country where English was commonly accepted for communication, even though none of my coworkers were born in a natively English-speaking country. After a while I thought I had a reasonable command of English, but all my confidence collapsed during a 2-hour meeting in London. I could not understand half of what the participants were saying and I probably misunderstood the other half. I am still not sure if they used the cockney dialect or another language but, I am sure it felt awkward to me. Obviously, we can’t properly communicate if we don’t speak the same language, in some cases even the same dialect. The same rule applies so well to data visualization as an extension of our communication language.

The line, a powerful encoding graphical element

One of the basic entities we see in different forms of display is the line. Actually, we use the line term to describe what in geometry is called a line segment. Sometimes we use it to describe curved graphs as well. Just to make sure we are on the same page, in data visualization a line has a finite length, being delimited by two points in a two-dimensional space (paper, screen). Besides its ends coordinates, a line also features a geometric property called slope. This is defined by the rise over the run, the change of “Y” over the change in “X.” The most common data visualization form using lines to encode data is, obviously, the line chart, but there are many other graphs that use lines to encode information. It might be useful to see a few of them and the roles that lines have to encode information. Basically, in data visualization we use lines to:

  1. Encode end points position.
  2. Show connection between two points.
  3. Show orientation, direction or sense.
  4. Encode variation. A slope shows the change in vertical direction over the change in horizontal direction.
  5. Show pattern of change. A group of connected lines show pattern of change, possibly indicating trends.
  6. Define separation. A line can indicate the separation between two regions.

Although important, I deliberately skipped the use of lines for reference constructions like axis, ticks or gridlines. Before I dive into the subject, I would like to remind a couple of things. First, in data visualization we encode two types of variables: quantitative and categorical. Examples of quantitative variables: Cost, Price, Volume of Sales. Examples of categories: Country, Customer, Months of the year, Days of the week. I should also mention that some of the quantitative variables can be considered categories in certain contexts where they can be used for grouping or aggregation purposes. Another thing I want to remind is the nature of information assigned to variables useful to describe the axis of a graph. Psychologist Stanley Smith Stevens developed Scale of Measurement, a classification that is widely accepted in the Data Visualization world. According with this, there are four levels or scales of measurement:

  1. Nominal (items have no particular order and no quantitative meaning),
  2. Ordinal (items have an intrinsic order, but not necessarily a quantitative meaning),
  3. Interval (items have an intrinsic order and the same difference between consecutive values), and
  4. Ratio (items have an intrinsic order, same difference between consecutive values and have a zero as reference).

With these two pieces of information we may consider that most of the graphs are forms of display that encode one or more variables (quantitative and/or categorical) with the help of one or two scales of measurement (nominal, ordinal, interval, ratio). Let’s have a look at the different roles the lines can play across several forms of display.

Line graphs


The most common form of display that uses lines to encode values, is the line chart. While in most of the graphical tools a line chart is considered just an alternative to a bar chart, they are important differences between these two graphs beyond the variable types they share. A bar chart encodes the values of a quantitative variable across a categorical variable for comparison purposes. A line graph displays the variation of a quantitative variable across the items of a categorical variable. Its main purpose is to show the pattern of change across all the items of a categorical variable. Each end of a line encodes the value of the quantitative variable (Y) associated with an item of the categorical variable (X). 

In line graphs, the slopes encode the variation of the quantitative variable between two successive categorical items. For this to work the change in X direction has to make sense. In Data Visualization it is widely accepted that a line chart works fine with interval (and implicit ratio) scales, for which the difference between consecutive items of categorical scale has a quantitative sense.

For instance, a time series (fits into the definition of interval scale) works well with line charts. A chart showing the sales over the months of a year, the sequence March ($50M), April ($60M), May ($40M) can be interpreted as: Sales increased (in one month) from March to April by $10M, but then significantly decreased in May. Trying a similar exercise and use a line chart to encode the sales across the product categories ordered alphabetically, we might have Computers ($40M), Mobile Phones($70M), TV’s ($20M). The interpretation of the slope would be something like: Sales increased from Computers to Mobile Phones, but then significantly decreased for TV’s. It doesn’t make sense. We can repeat the same exercise and order the product categories in descending order of sales adding as prefix their rank: 1. Mobile Phones($70M), 2. Computers ($40M), 3. Televisions ($20M). This time we may read the graph as: If we look at figures from sales rank perspective, the variation from 1st category (Mobiles) to 2nd category (Computers) is as large as $30M, almost as much as the value of 2nd category... This time it works because each category is displayed in its rank position, so actually our categorical variable is the rank (1, 2, 3, …), an integer variable for which we can obviously have clear metrics defined.

In case we don’t remember the classification of S.S. Stevens mentioned above, we can consider that line graphs can be used only with those categorical variables that:

  1. Have an intrinsic order,
  2. The change (difference) between consecutive items makes sense, and
  3. All the changes between consecutive items have a similar meaning.

Please notice that I avoided the strict definition of interval scale that requires the same difference between consecutive items, in the favor of more general, similar meaning to make possible the inclusion of logarithmic and fractional scales. 

I made a short list of examples of categories that can or cannot be used with a line chart.

  • January, February, March (it works, intrinsic order, difference between any two consecutive items is 1 month)
  • January, February, September (it doesn’t work, intrinsic order is there, yet the difference between February and January is 1 month, but between September and February the difference is 7 months)
  • Monday, Tuesday, Wednesday (it works, intrinsic order, difference between any two consecutive items is 1 day)
  • 1, 2, 3 (it works)
  • 2, 1, 3 (it doesn’t work, not ordered)
  • 1, 2, 4 (it does not work, order exists, but the difference between consecutive elements is different)
  • 3, 2, 1 (it still works, descending order, the difference between consecutive elements is -1)
  • Apple, Oranges, Pineapples (doesn’t work, no intrinsic order)
  • 1st Oranges, 2nd Apples, 3rd Pineapples (it works, the rank is actually the categorical variable)
  • 1, 10, 100, 1000 (it works, logarithmic scale, but does not fit into S.S. Stevens' classification)
  • 1, 1/2, 1/3, 1/4 (it also works, fractional scale, but does not fit into S.S. Stevens' classification)



A slopegraph is a form of display that shows the variation of one quantitative variable over two categorical variables. The quantitative variable value is encoded by Y, first categorical variable is identified by the lines (S-Category, S from Slope), and second categorical variable is encoded by X (X-Category). Each end of a line encodes the quantitative variable value (Y) and X-Category while the line itself identifies the S-Category.

On his site, Edward Tufte writes “Slopegraphs compare changes usually over time for a list of nouns located on an ordinal or interval scale.” I agree, but I think that slopegraphs usage can be extended just fine to show the comparison between two groups of any categorical type, therefore they can belong to a nominal scale as well. Cole wrote this post about a slopegraph showing the comparison between groups.

When there are more than two elements of the X-Category, slopes comparison has to make sense across the entire graph, therefore the above mentioned line graph rules apply (intrinsic order, differences between any two consecutive items have sense and similar meaning). Or, if you prefer, follow Edward Tufte guidance, but you might consider also the cases that do not fit S.S. Stevens’ classification (for example, logarithmic, fractional, and cyclic scale).

Frequency Polygon


A frequency polygon is similar to a histogram. It displays the distribution of a quantitative variable over bins defined for the same quantitative variable. Instead of using bars to encode values it uses lines to connect the encoded values. A frequency polygon is the preferred form of display when we need to look for the distribution shape. I haven't figured out why it is called a polygon (this is the name used in geometry for closed two-dimensional figures), but I assume that polyline was not good enough. Each end of a line uses Y to encode the frequency value (the counter of the values that belongs to certain bin) and X to encode the bin position. The slope can be interpreted as the frequency variation between two consecutive bins.

Parallel Coordinates


Parallel Coordinates is a form of display that shows relations between multiple variables. Used in multivariate analyses, Parallel Coordinates usually works better with quantitative variables. Categorical variables also work, but in their case the lexicographic order is used to define the scale. Each line end uses Y to encode the value of one quantitative variable and X to identify the variable. Parallel Coordinates are used to discover relationships between variables. For this form of display the lines have just two roles: to encode the values and to connect correspondent values of two adjacent variables. Unlike Line graphs and Slopegraphs, the line angle has no meaning for this form of display. To remind the definition of a slope as the change of Y over the change in X, we cannot give any quantitative sense to X, other than a conventional position for variables axis.

Pareto Chart


Pareto charts are one of the few cases where it’s acceptable to use a secondary axis. This chart shows the values of a quantitative variable (encoded by bars) and their cumulative values (encoded by lines) calculated in the descending order of values. A correct design should have the two scales synchronized to make sense of dual data encoding/decoding in variable unit of measure and in percentages. The line uses Y to encode the quantitative value and X to encode the category. The slope of one line can be interpreted as the change of the cumulative value between two consecutive ranking positions.   

Connected Scatterplots


Connected scatterplots are scatter plots that have connection lines between the encoded X and Y positions given by a third ordered variable (very often time). The only role of the line is to show the order of the pairs. Sometimes the lines can be decorated with arrows to indicate the parsing order. There were many discussions over the years about the utility of connected plots. I participated in one of them on Stephen Few’s forum. I need to admit that since then I found a very useful particular type of connected scatterplot, that is often assimilated with a line graph, but is not. This is a design I made a few years ago in a forum as a respond to one participant question. Is the below line graph correct or not (the elections are not equally spaced in time)? The graph was designed to show the decline of the interest in politics by measuring % of participants from total possible electors for all elections organized between 1949 and 2009.  


My makeover is a correct and useful connected scatterplot, but it is not a line chart. The slopes of the lines connecting consecutive events indicate the participation change from one event to another and the distance in time between events can vary. This particular connected scatterplot has the third variable (connection order) the same with X variable (time).

Contour Plots


Contour plots are forms of display that encode 3 quantitative variables with a continuous variation, two of them encoded accurately by X and Y position and the third one (commonly Z, elevation) encoded by the variation of a color intensity. A sequential palette usually works best. The lines which shape the contours have the role of delimiting the bins of the third variable (Z-levels).

Tukey Bagplot


Tukey Bagplot is the two-dimensional generalization of a boxplot. Investigating the distributions of both variables with independent boxplots does not reveal anything about the simultaneous behavior of paired values. The dark gray area is called “the bag” (containing 50% of the points), the light gray area is called “the loop” (the other 50% of the points minus the outliers) and the outer polygon of the light gray area is called “the fence.” Without going into details, a Tukey Bagplot reveals similar metrics as a boxplot: location (the depth median, white cross), spread (bag size), correlation (bag orientation), skewness (the shape of the bag and the loop), and tails (the points near the boundary of the loop and the outliers in red). Even if the drawn polygons go through different points, they are just conventionally computed convex hulls, used to enclose sets of values. In this case, the lines have just a separator role.

This was by no means an exhaustive list, but it gives a good indication of the many roles lines can play in different forms of display. There are many other graphical forms that feature lines: regression lines, dendrograms, hierarchical trees, and the list goes on. Do you know of any other roles for lines in visual displays? What are your thoughts on this subject? Leave a comment!

Note from Cole: Dan, HUGE thanks for writing this post and teaching us all about the many different roles of lines in data visualization!


SEARCH STORYTELLING WITH DATA: © 2010-2018 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.

#SWDchallenge: education, color, and words

Throughout my life, I’ve known February to be Black History month. Interestingly, though, that’s simply on account of my age, as this year marks only the 43rd year we celebrate and recognize African American achievements in the US and Canada—achievements that took place against a backdrop of inequalities and often injustices politically, economically, and socially. For me, I believe that one of the most important pillars to ensuring access and opportunity for all (as well as ending ignorance and racism) is education.

To raise awareness and celebrate Black History Month, storytelling with data is collaborating with, Tableau Public, #MakeoverMonday, Viz for Social Good, and Data for Democracy to ignite the imaginations and talents of our respective community members around the datasets and data stories connected to Black History. Each week’s focus is on a different sub-topic. I’ve decided to make this month’s #SWDchallenge to be centered on education, specifically the access, benefits, opportunities, and ignorance-curbing power. Create a visual with this in mind and let’s use data to recognize the importance—today perhaps more than ever before—of education in our society.

Your work doesn’t stop there. Last month, the challenge was to create an annotated line graph (nearly 90 people shared their creations!). I felt that singling out a graph type here would be too limiting, however (we’ll come back to that in future challenges). Rather than dictate a type of visual, this month we will put into practice a specific tip I find myself giving often when it comes to creating effective visual stories: be thoughtful in your use of color and words.

This may sound like simple advice. It is, I suppose, but there are nuances and the impact of these two straightforward elements executed well can be huge—and can even help overcome other design issues. Let’s talk a bit more about each of these.

Color, used sparingly, is one of your most strategic tools when it comes to the visual design of you data stories. Consider not using color to make a graph colorful, but rather as a visual cue to help direct your audience’s attention, signaling what is most important and indicating where to look. Note that for this to be effective, the use of color must be sparing. If we use too many colors, we lose the ability to create sufficient contrast to direct attention.

Words used well will both ensure your visual is accessible as well as indicate to your audience what you want them to understand in the data. There are some words that must be there: every graph needs a title and every axis needs a title (exceptions will be rare!). Don’t make your audience work or make assumptions to try to decipher what they are looking at. Beyond that, think about how you can use words to make the “so what?” of your visual clear. I advocate use of a “takeaway title”—meaning, if there is something important that you want your audience to know (there should be), put it in the title so they don’t miss it. Also, when your audience reads the takeaway in the title, they are primed to know what to look for in the data. When I’m putting a graph on a slide, I’ll use the slide title for the takeaway (and put a descriptive title on the graph). When the graph is on its own, I’ll often title with both—typically “descriptive title: takeaway.”

As illustration, below is an example. Here, I’ve shown the progression (no need to do this for your challenge, you can simply share the final product) from base graph, then added color, and finally words. Notice how we immediately know what to look for and where to look in the final graph.

Education color words.png

To recap the #SWDchallenge: find some data of interest related to education (you have free range within this: academia, higher education, black scholars, access, how education has helped ensure progress and opportunity, etc.). has curated a short list of datasets, or you can find even more in this list of publicly available data. Analyze the data to determine the specific story you’d like to tell. Harness the power of color and words to create your visual story. DEADLINE: Wednesday, 2/14 by noon PST. Specific submission details follow.


  • Make it. Identify your data and create your visual with the tool of your choice. If you need help finding data, check out this list of publicly available data sources.
  • Share it. Email your entry to by the deadline. Attach your image as a .PNG. Put any commentary you’d like included in my follow up post in the body of the email (e.g. what tool you used, any notes on your methods or thought process you’d like to share); if there’s a social media profile or blog/site you’d like mentioned, please embed the links directly in your commentary (e.g. Blog | Twitter). If you’re going to write more than a paragraph or so, I encourage you to post it externally and provide a link or summary for inclusion here. Feel free to also share on social media using #SWDchallenge and #VisualizeDiversity and/or upload to the page.
  • The fine print. I reserve the right to post and potentially reuse examples shared.

I look forward to seeing what you come up with. Thank you for helping to celebrate Black History Month and the importance of education in our society. Stay tuned for the recap post!


SEARCH STORYTELLING WITH DATA: © 2010-2018 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.

it depends...

“It depends.” These two simple words can answer a number of questions raised when it comes to visualizing data and communicating with it effectively. In this session, Cole discusses 10 common data visualization questions where the answer is “it depends” and discusses what it depends on and the critical thought process required for success. Cole also answers reader questions on considerations between lower and upper case in data visualization related text and Excel resources.

    Listening time: 45:00. Links mentioned during the podcast:

    Subscribe in your favorite podcast platform to be updated when new episodes are available. If you like what you hear, please rate or review the SWD podcast. You can find past episodes on the podcast page, including sessions focusing on how I've built storytelling with data, feedback in data visualization and discussion of what is story?


    SEARCH STORYTELLING WITH DATA: © 2010-2018 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.

    area graph to highlight a line

    I don't use a lot of area graphs. But I found myself pausing on one that was submitted as part of the recent annotated line graph #SWDchallenge. It was created by Mike M. and the interesting thing to me was that the focus of this particular area graph wasn't on the area so much, but rather on the line that separated the areas.

    This apparently stuck with me, because I found myself recommending a similar approach in a recent client makeover. 

    The original graph looked something like the following (data has been modified to protect confidentiality):

    Area to highlight line_1.png

    This is collections data from a bank. In case you aren't familiar with how collections work, typically an automated dialer makes calls to overdue accounts. The grey bars above represent total dials made. When someone answers the phone on the other end, the dialer connects them to a collections agent, who talks to the person who hasn't paid their bill and tries to get them to make a payment. The accounts where a person is reached (a collections agent talks to someone) are considered to be "worked," which is what the teal bars above represent. The penetration ratio, depicted by the black line, is...hmm. What is a penetration ratio exactly? This one threw me. I'm familiar with penetration rate, which would be the proportion of accounts that were worked out of the total dialed. So in other words, if penetration rate is 33%, we worked a third of the accounts. The ratio seems less straightforward. I think to describe it, it would be something like "if the penetration ratio is 3, it means we dialed 3x more accounts than we talked to." This seems unnecessarily complicated. Let's see if we can make some changes to how we show this data to make it more straightforward. Oh, and let's use that cool idea that I picked up from Mike M, too.

    First, I'm going to remove the secondary y-axis on the right side of the graph and the data (Penetration Ratio) that goes with it. That gets us a simple two-series bar chart:

    Area to highlight line_2.png

    In the above, we see accounts worked (teal) and total dials made (grey). Dials made is the sum of accounts that were worked and those that weren't reached. So I'm going to change this data slightly—from dials made in grey to those not reached—and stack the bars on top of each other.

    Area to highlight line_3.png

    We can get the same information out of the view above as the previous one: we can see total dials made (overall height of bars) and within that, the portion that were worked and the portion that were not reached. Notice that because worked series is on the bottom of the stack, we can easily see how it has varied over time. Total dials made have decreased over time, so has the number of accounts we've worked. But are we working a lower proportion of total dials now than we have historically? It's hard to tell here. Let's shift to 100% view to answer that question:

    Area to highlight line_4.png

    With the 100% stacked bar, we lose the context that overall call volume (total dials made) has decreased over time. But that's ok, because we know it now, so we can state it in words: "Call volume decreased 47% over the course of the year." With the 100% view, we can see that the proportion of accounts that we are working has decreased recently. So in spite of reduced call volume, we are reaching a lower proportion of accounts. Interesting. Perhaps we can make that a little easier to see?

    Let's remove the space between the bars and turn this into an area graph:

    Area to highlight line_5.png

    Bingo! With this view, we can see the proportion of accounts that were worked out of the total dialed. The white line separating the teal from the grey now represents the penetration rate. We can make this clear by adding some text and calling out the most recent data point:

    Area to highlight line_6.png

    I might add a headline that says something like, "Despite decreasing call volume, penetration rate hit a 12-month low in December." And like that, we've used an area graph to highlight a line.

    What do you think? Do you like this approach? What might you do differently? Where else could an approach like this work? Leave a comment with your thoughts!

    You can download the Excel file with the above visuals.