February 1, 2022

The Data Visualization Tools Wars

in TOOLS

Not-so-long time ago, in our own galaxy, programmers, coders, and designers wrestled for dominion over the world of data visualization. They argued over base code and usability, drop-down menus vs coding, and whether they should be based on the desktop or in the browser. They debated what color and font tools should be included and how much data wrangling could be done.

It seems to me that the dust has settled on the Data Visualization Tools Wars. At least for now. There will surely be more fights to come as technology changes (e.g., augmented and virtual reality), digital consumption evolves (e.g., mobile technology), and access to data expands.

I thought I would share what I see as the major data visualization tools currently in use (arranged alphabetically). Every tool has its own camp of people who argue their tool is the best, but I’m a believer in just using the best tool for the specific task. As you likely know, I do a lot of my data visualization work in Excel, but I don’t run regressions or create online interactive dashboards in Excel because it’s not the best tool for those jobs. I’m also not going to ask every person I work with to learn JavaScript to create simple line charts.

Representation of data visualization tools along a spectrum from lower barrier to entry on the left to higher barrier to entry on the rightSource: One representation of data visualization tools from my book, Better Data Visualizations.

These are the tools I often suggest to people new to the field. I’m sure you have your favorite tool as well, so feel free to put them in the comment section below. You can also go over to my data visualization tools collection to see more or visit the fantastic collection from Andy Kirk at VisualisingData.com.

Coding Languages

Some argue that everyone should learn to code. I think that’s a worthy idea—and I think there are lots of things to learn just from understanding how to think through code—but I also think it’s practically true that not everyone has the time or inclination to code. That being said, there are a lot of advantages to conducting data analysis and data visualization with code, namely that code makes work more reproducible and often more accurate than point-and-click-type tools. There are three primary coding languages used for data visualizations:

- D3. D3 is a JavaScript library for manipulating objects based on data and was developed by Mike Bostock along with Jeff Heer and Vadim Ogievetsky at Stanford University in the early 2010s. Most of the interactive data visualizations we currently see on the web are run on D3. Bostock has moved to his new company, Observable, which fosters collaboration through code (and, apparently like others, I don’t totally understand what it is). Unlike D3, Observable has a pricing model for teams and organizations. There are a lot of other tools that are built on a D3 platform that don’t require the same kind of coding prowess. I like HighCharts as one example (free for non-profits)–and which may have the best user guide of any tool on this list–but others include Plotly and Vega.

- R. In my experience in the data visualization field, the R programming language is one of the fastest growing coding languages. For data visualization, it is especially useful because it can be combined with the statistical analysis that R has always been useful for. The data visualization engine in R, ggplot, was developed by Hadley Wickham and is based on the Grammar of Graphics originally conceptualized by Leland Wilkinson, who passed away late last year (and was on the podcast last September). (In my opinion, Hadley’s book with Garrett Grolemund, R for Data Science, is the best book available.) To my mind, the biggest advantage of the GG approach is that you can easily layer graph encodings together.

- Python. I’ve never coded in Python, so can’t say much about its ease of use or effectiveness, but a lot of people like Python, especially for its ability to facilitate web scraping. The biggest thing I hear from Python users is being able to easily build notebooks, specifically Jupyter Notebooks, which is a web-based application that can show both code and text. I’ve been learning RMarkdown over the past few months, which, as I understand it, works similarly to Jupyter Notebooks and has a variety of pros and cons. I think this post does a good job comparing the two. Given my lack of knowledge here, I’ll leave it to others to tell me where I’m wrong in the comments section below.

Dashboarding Tools

There are lots of ways to define what we mean by a “dashboard,” but for purposes of this list, I take it to mean a connected set of interactive data visualizations that enable the user to explore the data. With that being said, there are two primary tools that have risen above the rest—PowerBI and Tableau. I’m not a huge dashboard user and I’m aware that other tools might fit better into some circumstances, fields, and organizations, but these are the two that I see most commonly in the data visualization field.

- PowerBI. There are likely two reasons behind why I’ve seen so much growth in PowerBI usage over the past two years or so: First, it’s directly connected to the Microsoft Office suite, which means it is easy for many people to drop right into their existing workflow. Second, it’s cheap compared to many other tools, especially because most people and organizations already have the Microsoft Office suite. The basic PowerBI Pro package is $9.99 per user per month, but it comes free with Microsoft Office 365. That being said, I don’t think the PowerBI visualizations look as glossy as those that can be created in Tableau but if your goal is to sit around a table with your colleagues and work with your data, maybe that doesn’t matter. PowerBI is also not currently available on Macs.

- Tableau. Probably the most popular dashboarding tool in the data visualization field is Tableau and the Tableau community is certainly the most active on social media. There are a lot of good things to say about Tableau, including the ability to quickly change between visualizations and its growing data cleaning and reshaping tools. As a definite introductory user (though, I’m working to build up these skills this year), I find the claim that you just “drop in your data and make a bunch of visualizations” not so true—your data need to be in the right format for Tableau to use. I think pricing is probably why there is not more Tableau usage—licenses for the desktop version are $70 per user per month. So, if you’re a small organization of, say, 5 people, Tableau desktop is going to cost you $4,200 for the year.

Browser-based Tools for Interactive Visualizations

This is where we saw some of the most interesting movement in the area of data visualization tools over the past few years. Creating relatively easy-to-use, drag-and-drop-type tools in the cloud both enables greater sharing/collaboration capabilities and the ability to create interactive visualizations that can be embedded on any website. One of the downsides is that these tools are difficult to expand or hack to extend what’s in the various menus. It’s also true that your data is going to necessarily be loaded into the cloud and not sit on your desktop, which introduces all sorts of considerations around data privacy and security.

- Datawrapper. Now a team of 19 people based mostly in Germany, Datawrapper was founded by Mirko Lorenz, Gregor Aisch, and David Kokkelink. My understanding is that the initial discussions for this kind of tool that could help data journalists formally started at a roundtable discussion on data journalism in 2010 at Stanford University. The beta version was launched in 2012 and quickly hit 10 million chart views about a year later. Since then, Datawrapper has become immensely popular among many people creating interactive data visualizations, especially in smaller newsrooms. They have a somewhat more limited set of visualizations in the library relative to Flourish (see next) and focus on the more standard visualization types. Their table templates are probably the best you’ll find among this set of tools. The pricing model is, well, basically free to any individual—it then jumps up to $599/month for teams and if you want to include custom branding and specific exporting options to your visualizations.

- Flourish. Launched in 2016, Flourish is an online data visualization tool that enables users to create highly-stylized, interactive visualizations without needing to know any code. What I like about Flourish is that they seem to keep up with the data visualization field. Remember bar chart races? As soon as those took off, Flourish added it to their gallery of visualization types (of course, in retrospect, maybe it wasn’t worth the time?

😉). They have a wide range of visualization types available, including some not-so-standard graphs and charts. Pricing starts for free for testing and educational use and then moves to $69/month for individuals who need their data and assets to be kept private.

- RAWGraphs. Okay, RAWGraphs is interesting. It started in 2013 as an open-source project from a team based in Milan, Italy. The first iteration of RAWGraphs focused on some standard graphs plus a few that were harder to make in standard tools, like streamgraphs and Sankey diagrams. The tool basically sat there without much enhancement, but over the past one or two years, they raised a bunch of money and have added a larger set of graphs to the library. It will be interesting to see where they go from here. Pricing? It’s completely free. You can also run an instance of RAWGraphs directly on your own machine by downloading the code from the site, which makes it somewhat more flexible (i.e., hackable) than Datawrapper and Flourish, but, of course, you would need coding knowledge on how to do it.

Infographics

Like dashboards, there are a lot of ways to define what we mean by an “infographic.” For me, I just think of infographics as a combination of static charts, graphs, diagrams, text, photographs, icons, and other visual elements pulled together into a single, cohesive view. Over the past few years, we’ve seen a big increase in the number of browser-based tools that has helped democratized the skills needed to create effective infographics (which can be both good and bad). I know this must bug a lot of graphic designers, but I’m all for tools that make it easier for me to create and lay out different graphic objects. That being said, if I’m creating a really polished-looking visualization—especially if it’s for a print project—I’m likely to reach out to a professional graphic designer.

- Adobe Creative Suite. The best infographics are likely going to be made in a custom graphic design tool like Adobe Illustrator. But a tool like Illustrator is not easy to use and requires a lot of practice. The data visualization tool in Illustrator is well known to be pretty terrible—as a novice user, I could never understand why you would need to delink the graph and the data to properly style and format a graph. Pricing is also a consideration, though it’s better now in the subscription model than a few years ago. The entire Adobe Creative Suite goes for $52.99/month and Illustrator on its own goes for $20.99/month.

- Canva. Of all the browser-based tools in this category, I like Canva the best. It has a really nice user interface, a good library of photographs (the icon library isn’t great), and ability to create and save styles and formats. The other thing I like about it is that you can easily create social media posts for different platforms with the click of a ‘resize’ button. The free version of Canva is good but limited in the various libraries—the Pro version is $12.99/month and includes 5 different people, so a pretty good deal. If you are an educator (with a .edu email address), you can get a deep discount.

- Infogram. A decent set of infographic and social media templates here but I’m not a huge user or lover of this tool. They also have a set of dashboarding templates, which I’ve never used but occasionally stumble upon out in the wild. Personally, I wouldn’t use it to do hard-core data analysis or enable deep data exploration–but for a basic interactive graph or two it might work well (though you should always ask yourself whether interactivity is necessary). I will say that they have a decent number of data visualization-driven infographics here rather than the more diagram-y/design-y ones you might find on some other sites. Infogram pricing starts at the free version and then moves up to the Team package at $149/month.

- Venngage. Similar to Infogram in lots of ways with a longer list of potential templates and printed products. Be careful that the graph options do not always follow what we might consider best practices—that is, there’s a lot of 3D here. Like other tools, Venngage pricing starts at a free version and increases from there, to a $39 per user per month package.

Drag-and-Drop Tools for (mostly) Static Visualizations

There are obvious pros and cons with drag-and-drop tools like Microsoft Excel and Google Sheets: On the one hand, everyone has them and they are relatively easy to use. On the other hand, they have obvious limitations of what’s in the drop-down menus (even with some clever tricks and hacks) and limited ability to create interactive data visualizations. I say “(mostly)” in the title of this section, because it is possible to add some interactivity to visualizations with these tools, but they are relatively limited relative to other tools in this list.

- Excel. If you know anything about PolicyViz, you know that I’m a big fan of Microsoft Excel. It is also (still) the most popular data visualization tool reported in the annual Data Visualization Society State of the Industry report. Everyone has it and if you know a few hacks and tricks, you can do more with it than just what’s in the drop-down menus. That being said, it’s a drag-and-drop tool and has some basic limitations. You can’t run a bunch of code to create a new graph (which is why I spend so much time encoding graph elements with data—to make Excel look more like code). You can get Excel on its own for $160 or buy the entire Microsoft Office 365 suite for $6.99/month.

- Google Sheets. It’s kind of like Excel’s little cousin—it’s very similar but lacks some of the mechanisms of its more mature relative. The obvious advantage of Google Sheets is that it’s easier to share and collaborate than with Excel, where the web-based version still lacks a lot of the features of the desktop version. Google Sheets (and other Google tools) are free with a Google account.

Wrap Up

I’m sure people regularly use tools that are not on this list, but this is what I see most commonly and what I usually recommend to students and workshop participants. I can’t tell you what tool is best because it will depend on your preferences, your needs, your audience’s needs, and the structure of your organization. Personally, I use nearly all of these tools in my work (except for Python–sorry, I have no idea how that works!) depending on the needs and context–and I’ll often use multiple tools within the same project. Remember, these are just tools–none are right and none are wrong–just use them well to create effective visualizations that help you and your reader, user, and audience member.

Update. After this post was published, a number of people remarked that there are numerous other data visualization tools I didn’t mention here. Again, these are the tools that I have seen used the most often in the field and the ones that I use and recommend to others. My little network of people and organizations I work with certainly affect those tools. That being said, here is a list of other tools folks have sent my way, many of which I have never heard of:

- Altair

- Amplitude

- Datylon

- eCharts

- Google Data Studio (This was a big omission on my part. I use GDS for my dataviz catalog and have seen others use it as well)

- Inkscape

- Julia

- Looker

- Metabase

- Mode

- Omnisci

- QGIS

- Semiotic

- Sisense

- Zing Data