Nissan, Tesla, BMW, and many other car companies use Dash or are customers of Dash Deployment Server (DDS). To see what Dash is all about, check out Plotly’s Dash Gallery, or check out our recent post on Dash apps. Interested? Get in touch.
This post is the first in a two-part series. We’ll share some fresh visualizations on the world of electric cars: who’s leading the way, the costs of going electric, which manufacturers are most represented out on the road, and more.
🌟 Before we introduce this seriously cool visualization, we’d like to announce that plotly.py has surpassed 5 million downloads! Thank you for helping us reach this milestone! 🙌
Once Upon a Time 🕰
It was the year 2008. If you wanted to create 3-D plots for interactive scientific data visualization, the Mayavi Python library was your go-to.
The charts may have looked slick for their time, but it doesn’t take a rocket scientist to tell you that we’ve come a long way.
See for yourself:
Cone plots (also known as 3-D quiver plots) represent vector fields defined in some region of the 3-D space.
A vector field associates to each point of coordinates (x, y, z) a vector of components (u, v, w).
In this post, we’ll explore how Plotly’s cone plots can be used to visualize atmospheric wind 💨, magnetic fields, a trajectory of the Rössler System, and tangent vector fields to a surface.
The scatterplot matrix, known acronymically as SPLOM, is a relatively uncommon graphical tool that uses multiple scatterplots to determine the correlation (if any) between a series of variables.
These scatterplots are then organized into a matrix, making it easy to look at all the potential correlations in one place.
SPLOMs, invented by John Hartigan in 1975, allow data aficionados to quickly realize any interesting correlations between parameters in the data set.
In this post, we’ll go over how to make SPLOMs in Plotly with Python. For extra insights, check out our SPLOM tutorial in Python and R.
FIPS codes are five-digit codes that are assigned to each U.S. county.The first two digits identify the state and the last three identify the county.
Think of it like a fancy version of a ZIP Code or postal code that distinguishes a county.
FIPS codes are easier to utilize in data and information systems than state and county names. This makes datasets that come packaged with FIPS codes a portal into national, state, and county-level graphical and/or statistical analyses for a variety of topics.
It was the year 1860. Robert FitzRoy, of England and New Zealand, was using the new telegraph system to gather daily weather observations and produce the first synoptic weather map. He coined the term “weather forecast” and his were the first ever to be published daily.
Fitzroy probably would have never imagined in his wildest dreams what the weather map scene would be like 160 years into the future…
Data visualizations such graphs, charts, presentations, and reports represent the intersection point of your company’s data, statistics, and numbers (i.e. science) and its brand, style, and personality (i.e. art). However, this “marriage” of science and art will only be a successful one if the platform it is presented on is simple, effective, and beautiful.
“Design is not just what it looks like and feels like. Design is how it works.”
-Steve Jobs
This is where Plotly’s dashboards, reports, and presentations come in — to bridge the gap between what your creation looks and feels like, and how it works. Plotly gives you the tools and examples to make beautiful dashboards and reports, even if you’re not trained in design.
In this post, we’ll teach you how to be situationally aware when it comes to choosing graph types. Additionally, we’ll show you how easy it is to apply your brand’s style guide, from the logo to the color palette, to a Plotly product. Inspiration for this post stems from IBM’s Design Language data visualization guidelines and therefore we created some IBM-branded graphs, reports, and presentations in Plotly.
We recently provided some dashboard and slide deck templates that you can use — now we’ll expand on that work, personalizing your figures and applying industry-leading techniques to ensure the best visualizations possible.
How it works
Let’s go through several different chart types. We’ll address how to best utilize each type of graph according to IBM’s data visualization language. We’ll later use these to construct our Dashboard and Report.
Line chart, costs vs revenues: “Line graphs are used to track changes over short and long periods of time. When smaller changes exist, it’s better to use line graphs than bar graphs. Line graphs can also be used to compare changes over the same period of time for more than one group.”
Line chart, units sold over time: “Avoid [line charts] if not comparing values over time, as it might create confusion. Select a bar graph in this case.”
Bar chart, revenue by year: “Rectangular bars with lengths proportional to the values they represent. Bar graphs should be used to compare different values that are hierarchically equivalent. Using a colored element among gray elements makes the focus of the chart clear to the reader.”
Stacked bar chart, evaluation of manager by department: “A variant of the bar graph, where each rectangle is divided in multiple parts. Stacked bar charts are good for comparing elements across categories. In this example, a diverging palette is especially effective — the two opposing values have a positive/negative connotation.”
Pie chart, comparison of revenue subdivision: “Use only when the different values add up to a total and there is a need to highlight percentages.”
Bubble chart, students by faculty: “Used to show values among categories or groups with circles, avoiding any kind of axis. Use it as alternative to bar chart.”
Map, temperature variations across the USA: “Cartography is used to display geographical data. Use it when the focus of the analysis is geography, and when it’s important to zoom in on different places.”
What it looks like
Notice we’ve applied IBM’s color palette and logo to our dashboard and slide deck below [click here to reference the color palette]. If you’re on a Mac, you can use Digital Color Meter to ‘color pick’ off your screen. Windows users might try paint.
Pro tip: simple touches like this not only make your figures look more sleek but also reputable, professional—it readies it for the big stage (i.e. a presentation for the execs) in a jiffy.
When using Slide Deck, make your graphs as large as possible and use at least 50 size-font for slide titles; match your charts background color to the color of the slide deck (we recommend white or light gray).
Your presentation should tell a story and not be a lecture — your goal should be to not only convey information to your audience, but inject some of your passion and expertise about the topic(s) into them. Consider it a journey that you are taking together and reach the finish line at the same time!
A limited text-plentiful graphic combination will set you on the right path…
“Above all else, show the data.“
-Edward Tufte, 1983
Once referred to by the New York Times as the “Leonardo da Vinci of data,” the work of data visualization expert Edward Tufte has long been an inspiration to Plotly.
One of Tufte’s key principals is that good graphics present their message as simply as possible. To do this, he defined the “data-ink ratio” to turn this ‘so called’ simplicity into more practical ideas.
Definition 1:
Data-ink: the non-erasable core of a graphic.
Definition 2:
Data-ink ratio =
1. Data-ink divided by the total ink used to print the graphic. 2. The proportion of a graphic’s ink devoted to the non-redundant display of data information. 3. One minus the proportion of a graphic that can be erased without loss of data information.
While the data is “freely available” on the charts, most of it isn’t downloadable or “unlocked” as such. By re-plotting it in Plotly, we “free the data,” so to speak, opening it up to future investigation as other users can easily download it, play with it, and explore it.
In this post, we present five charts that speak volumes about the state of health around the globe, examining in particular key areas such as healthy life expectancy, the prevalence of overweight adults, smoking prevalence, high-risk drinking prevalence, and deaths in the United States.
Plotly graphs are now embeddable in Medium, as demonstrated below. To learn how to embed can’t-miss Plotly graphs in Medium, check out our how-to post.
Make sure your Plotly graphs look as sharp on your desktop computer as they do on your mobile device – our post on mobile charts has the details.
1.8 million robots were in operation globally during 2015 according the the International Federation of Robotics, a number that is forecast to balloon to 2.6 million in the year 2019.
While these “automatically controlled, reprogrammable machines” may not make workers in certain industries immediately redundant, the data show that robotic penetration is trending upward. The consequences of this leap in technology loom large for the worker.
Baxter is the flagship product built by Rethink Robotics and is billed as America’s first adaptive manufacturing robot. Source: The Washington Post.
Later in 2017, the Brookings Metro program is expected to release a new state and metro mapping of where the effects of automation may be most disruptive, using some high-quality estimates of the susceptibility of occupations to substitution.
For an opinion on the impact of robots, artificial intelligence, and machine learning, give Brookings’ Darrel M West’s piece a read.
In the meantime, we explore data from the International Federation of Robots (some made available by Brookings) showing where robot have so far made their significant impression.
As you’ll learn below, their incidence is well-aligned with the United States’ highly automated advanced manufacturing sector.
According to comScore’s 2017 U.S. Cross-Platform Future in Focus, as of December 2016, 69% of digital media is consumed via a smartphone or tablet, with the remaining 31% going to desktop computers.
This is in stark contrast to data from late 2013, when the smartphone/tablet vs desktop media consumption battle was very nearly 50%/50%.
Amidst the height of the digital age, sharp mobile graphics are more critical than ever.
So the next time you’re building a Plotly masterpiece, consider using our new “mobile” graph feature. It’s super easy and will ensure that your graphs look as fly on mobile as they will on the 100 inch big screen.
