Dungeons and Dragons is a fantasy tabletop role-playing game in which players undertake quests overseen by a Dungeon Master (a player who creates and narrates the story of the game). Players create their own characters. These characters can have different races, backgrounds, classes, and skills. The attributes that players choose for their characters affect their capabilities throughout the course of the game.

So, what does the data tell us about these brave adventurers? (By the way, you can see all the magic behind the visualizations in this post, as well as lots of other analyses, by clicking the button below.)

Explore the original dashboard

Who becomes an adventurer?

In D&D, a character's race is basically the species they belong to. Many of these are inspired by high fantasy fiction like The Lord of the Rings, while others are more obscure.

You can create your own donut chart for free here!

Unsurprisingly, most adventurers are human. What's more, more than 40% of characters fall into the "standard" fantasy races of Humans, Elves, and Dwarves. It looks like people love their Tolkien.

A character's background describes the character's life before they became an adventurer.

Create your own Donut Chart

When we look at the background of characters, things are much more diverse. No one background really stands out as the most popular.

Now, what do these adventurers do? Let's answer this by looking at the classes they're part of and skills they're proficient in.

Classes and Skills

A class describes what a character in D&D does. For instance, a character who is a wizard uses arcane knowledge to cast spells, whereas a ranger allows a player to live out their Aragorn fantasies.

Create your own Bar Chart

Characters can be part of multiple classes, so we count any character with at least 1 level in a class as being part of that class.

It's also interesting to look at the skills that the different classes tend to use. While some skills are decided by a character's class, players still have plenty of choice in the skills they pick.

Create your own Pyramid Chart

Most classes make good use of perception and insight, although Paladins and Sorcerers are notably worse at perception. Wizards are the least athletic class (unsurprisingly), but are also the least intimidating class. I guess vast arcane powers just don't scare people like you'd expect. Rangers and Druids seem to be the only classes who are any good with animals or nature.

Alignment

One other thing we might be interested in is the moral alignment of these characters. While most of the users in our sample (71%) decided not to include this in their character sheet, we still have a decent sample to look at.

Create your own Heatmap

Thankfully, most adventurers are good people. However, they tend pretty heavily towards Chaotic (as anyone who's run a game of D&D can certainly attest to). Let's take a closer look at the differences between adventurers who have different alignments.

Create your own Radar Chart

Right away, it's clear that the more morally unscrupulous adventurers are also by far the most charming. Evil characters also tend to be slightly smarter and more dexterous than their good-aligned counterparts, whereas good characters seem to tend towards "strong but stupid". There's less to be said about the differences between Lawful and Chaotic adventurers.

So, how do differently-aligned characters differ in terms of the classes they choose?

Instead of showing the average alignment for each class (the average adventurer for each class still sits around chaotic good), we're looking at the amount each class differs from the average alignment. Mostly, there's nothing surprising here - paladins are more lawful, and barbarians and sorcerers are more chaotic. Warlocks and wizards are drawn more towards evil, whereas druids prefer to be good.

Doing the same type of analysis with skills is also interesting.

Those who lie or steal are more likely to be chaotic or evil, whereas those skilled in medicine tend towards lawfulness and goodness. Adventurers who are proficient in religion tend to be good, which is interesting - it seems there aren't many devout followers of evil gods around. What's more, it seems that there's a relationship between the "goodness" of skills and their "lawfulness".

Full character comparison

We can take all this information and use it to compare each of our characters with every other character! Each point in the chart below represents a character, colored according to class. Points are clustered based on ability scores, skills, background and race. Based on this chart, we can see which types of characters are most similar to which other types of characters. Hover over points for more info!

Try t-SNE yourself

So, are you thinking of changing up your character the next time you hit the D&D tabletop? Make sure you tell us!

Keen to learn more? Click here to see tons of other visualizations that we didn't have space to include in this post! 

Explore the original dashboard

(Featured Image © 2006  Sawan Kumar Pandey. License: CC BY-SA 4.0)

Where have meteorites fallen?

First, let's see where on the globe all these meteorites have fallen. A lighter line indicates that the fall happened longer ago; a darker line indicates a more recent fall. The larger the meteorite, the longer the line it is represented by. You can interact with the globe with your mouse, or view and edit the original dashboard and R code by clicking the button below.

Explore the original dashboard

This seems to show that meteorite hits are concentrated around certain areas. This raises the question: are meteorites more likely to fall in certain places? There is some research to suggest that meteorites might be more likely to fall closer to the equator. However, this hypothesis doesn't appear to be borne out by our data. There is no obvious clustering around the equator visible on our globe.

Meteorites and land area

So, our meteorites don't seem to be more likely to fall at the equator. Here's another hypothesis: they're falling completely at random. If this was true, we would expect a reasonably even distribution around the globe. By this logic, the bigger a country's land area, the more meteorites should fall there. To examine this, let's chart each country by the number of meteorites in our dataset that fell there.

Avoid the United States! Our dataset contains 145 meteorites that have fallen there. India also looks dangerous, with 124 meteorites having fallen there.

This map makes it pretty clear that there's not an even distribution according to land area. If our hypothesis was correct, Russia and Canada should both have a higher number of meteorite hits than the United States.

We can chart the land area of countries against the number of meteorites that fell there. From the scatterplot below, we can see that there is a weak relationship. If the number of meteorites fallen was correlated with land area, we would expect to see a linear relationship. Clearly, this is not the case.

If we chart the number of meteorites we would expect to see if meteorites fell in a linear relationship with land area, this is what it looks like.

Compared to the map made from the real meteorite data, this looks very different. Obviously, land area does not determine how many meteorites fall in certain countries. But if that's the case, then what does?

Meteorites and reporting bias

Rather than being related to land area, it actually seems as though meteorite strikes are related to population size. India and the United States, the two countries with the most meteorite hits, are both densely populated. This is likely indicative of a reporting bias, as meteorites are more likely to be seen and reported if they fall in densely populated areas.

Just as there's a large reporting bias in favour of meteorites that fall in densely populated areas, there's also a bias in favour of larger meteorites. This is logical, as a one kilogram meteorite is going to be much more noticeable than a ten gram meteorite. The chart below shows all the meteorites that have fallen in the last 10 years. As you can see, there are far more meteorites over 1 kilogram (shown in red) than under 1 kilogram (50 g - 1 kg is shown in yellow; under 50 g is shown in green).

Explore the original dashboard

However, it's not all doom and gloom! The odds of being hit by a meteorite are extremely low. You're far more likely to die in a car crash or a fire than you are to die from a meteorite strike. It's also more likely that you'll be killed by lightning or a tornado - both of which are extremely rare. However, there's bad news too - you have a higher chance of being hit by a meteorite than you do of winning the lottery.

All the visualizations in this post were made by writing R code in Displayr. You can create online, shareable dashboards with R code - and best of all, it's free!

Start creating your dashboard

Learn more about creating online dashboards here!

An obvious way to measure music's popularity is through the charts. Billboard has been charting the "Hot 100" songs weekly since 1958. Nowadays, chart rankings are based on a combination of sales (physical and digital), radio play, and online streaming in the United States. Billboard states that the approximate weighting is 35-45% for sales, 30-40% for airplay, and 20-30% for streaming. An annual "year end" chart is also released, ranking that year's songs by calculating a cumulative total of sales, airplay, and streaming. The table below shows the 2017 Year-End chart.

But that's not the only way we can see which songs are popular. 140 million people worldwide are active users of Spotify, with around half of those using the paid Premium service. The Swedish streaming service has a vast catalogue of 20 million songs. Each year, Spotify releases a playlist of the 100 most streamed songs for that year. These are the most streamed songs of 2017.

We can see that there are similarities, but that the lists are far from identical. We can visualize the differences in the rankings using a slope graph.

Ed Sheeran's Shape of You wins out on both lists! In fact, this pop hit has been streamed over a billion times since its release in January 2017.

After that, though, the lists diverge almost immediately. While there are almost no songs in the top 10 on either list that don't appear anywhere on the other list, the orders are far from identical. Luis Fonsi's Despacito is another winner of these rankings, appearing at number 2 on Billboard and numbers 2 and 3 on Spotify (courtesy of the Daddy Yankee remix). Kendrick Lamar's HUMBLE is also a clear favorite at 3rd on Billboard and 5th on Spotify. After that, it's more contentious. Bad and Boujee by Migos, 4th on the Billboard list, is only at number 36 on Spotify! Even more dramatically, Sam Hunt's Body Like a Back Road is 6th on Billboard but 71st on Spotify! Clearly, the most popular songs depend on who you ask.

The most popular artists

If you're a fan of some of the songs we've been discussing, you might have noticed that there are several artists whose songs appear multiple times on one or both lists. Is there one artist who stands out as the most popular artist?

Answer: kind of, but not by much. This chart displays all the artists who have at least 2 songs on at least 1 list. Ed Sheeran and The Chainsmokers both have 4 songs on the Spotify list, but The Chainsmokers inch ahead by scoring 3 songs on Billboard as opposed to Ed's 2. Perhaps as we would expect, most artists who score at least 2 top 100 hits appear on both lists. The only notable exceptions are Martin Garrix, with 3 songs on Spotify but none on Billboard, and Gucci Mane and Rae Sremmund, both with 2 hits on Billboard but none in the Spotify top 100.

The most popular genres

What about genres? Does one style of music dominate the charts? Fortunately, we can use Spotify's Web API Console to find out. This nifty tool allows you to search for artists and see which genre Spotify classifies their music into. These genres range from the obvious ones like pop, hip hop, and EDM, through to genres you didn't even know existed. (One artist in this dataset - Starley - is classified into the somewhat nebulous genre of "aussietronica". If anybody can enlighten me as to what, exactly, this means, please let me know!)

Genres - Spotify

Genres - Billboard

We can see that the Spotify list is more disposed towards pop music, with almost half the list classified as pop. Conversely, the Billboard list contains more hip hop (35% as opposed to 19%), as well as more modern rock, R&B, and country.

We can see these differences more clearly if we plot the genres next to each other (you can see a larger version here).

If we had to pick a winner, it'd be pop, but both lists have a range of genres represented.

So, can we in fact quantify the most popular music? The answer would have to be - sort of. Ed Sheeran, if you're reading this, the data says that you're the winner! (Also hi, I'm a fan). But really, there's a whole world of music out there. Go forth and explore it!

All these visualizations were made in Displayr; you can make your own for free! You can also check out more of our cool Data Stories.

This is a bit of a trick question because there's no way to objectively quantify who is the greatest supervillain. You'd run into problems straight away trying to determine what constitutes 'greatest'. Is it the scariest? The one with the biggest crime empire? The one with the biggest secret lair filled with a wall of cameras watching your every move? Or the one most likely dangle you over a pool of sharks or feed you to a pet tiger? Oh wait, those last ones are all Bond villains. But you get the point, the options are endless.

What about a list of top supervillains?

Since it's too difficult to pick just a single greatest supervillain, maybe a list of the top eighteen supervillains in cinema history will be easier. We thought we'd take a look at who some experts and movie buffs picked. The first place we had to look is the iconic American Film Institute's (AFI) list of the top fifty heroes and villains of all time. AFI chose to define a villain as "a character(s) whose wickedness of mind, the selfishness of character and will to power are sometimes masked by beauty and nobility, while others may rage unmasked. They can be horribly evil or grandiosely funny, but are ultimately tragic." When making their picks, AFI also asked the panel of jurors to consider the cultural impact and lasting legacy.

We also looked at the Complex and Time Out's lists, which were compiled by the team of film writers and reviewers on staff. To make sure we also got the opinions of our regular cinema goers, we also included the crowdsourced Empire Online and Ranker lists. Now that we've covered our bases, let's take a look at this data visualized with a ranking plot.

Here are your top 18 supervillains according to the data!

The ranking plot automatically placed each publication in alphabetical order but straight away we can see that loads of the villains mentioned by Empire Online also appear on the Ranker list (albeit in a different order), and these two lists share the most commonalities. This makes sense as they were the only two that were crowdsourced - the other lists feature more eclectic picks like Mr. Potter (It's a Wonderful Life) and Alex Forrest (Fatal Attraction). Complex has its top pick as John Doe (Se7en), a name no other list nominated.

Create your own Ranking Plot

But looking at this ranking plot, can we pick out a clear winner? Unfortunately, not really. If we had to pick, it'll be Darth Vader (who appears in all five lists), just beating The Joker (who appears in only three but occupies top spots). Rounding out the top three is Hannibal Lecter, who makes it onto four lists and is generally ranked highly.

Do our supervillains have any commonalities that make them more likely to top the lists?

This led us to wonder, is there a magical formula to create a supervillain? Are there any traits they all share?  Let's investigate gender first...

Surprise surprise, the overwhelming majority of supervillains are male. Women continue to be vastly underrepresented on the silver screen, supervillains or not, with reports that in 2017, just 24% of leads in the 100 highest-grossing films were women.

I think we're long overdue for a woman supervillain, don't you?

What about race? Well, this probably also doesn't surprise you, but the majority of villains on the list are white. Of the 70% of white supervillains, only 5% of them are women. The next category belongs to those of an undetermined race. These include such luminaries as The Wicked Witch of the West, Freddy Krueger and Sauron (please, Lord of the Rings fans, do not write to tell me that Sauron was actually an Elf a long time ago or something.)

CORRECTION: I've been informed by an esteemed colleague that Sauron was actually a Maiar which is some kind of spirit (again, Lord of the Rings fans, please do not write to me to correct this correction). 

Let's move on to age. Is there an age in which regular folks or even villains turn into supervillains? The answer according to data is no.

In fact, most of our supervillains are of undetermined age. This includes delights like the Alien from Alien and Scar from The Lion King. It also includes characters like Agent Smith from The Matrix and someone who is technically a god, like Loki.

Beyond that, most of our supervillains seem to be heading towards middle age. Maybe it's the mid-life crisis effect?

Create Your Pie Chart

What is the top motivation for supervillains?

Let's say those factors above like gender and race and age are all things that our supervillains are born with and therefore out of their control. What about the motivations for their dastardly crimes? Now, the following correspondence analysis may not have been the best way to visualize this, we admit. Mostly because the overlap in similarities between Revenge, Obsession, Power/Control, means that we have quite a few supervillains clustered closely together. But it does reveal some common motivations and who falls where.

Obviously, we can see that motivations of power and control, obsession and revenge are shared by most. We can see a couple of main clusters.

1. The group including Darth Vader and Lord Voldemort that are evenly split between World Domination and Power/Control.
2. The group clustered towards Profit/Greed including most of our crime lords and Phyllis Dietrichson (Double Indemnity).
3. A tight cluster around Obsession, including Alex Forrest (Fatal Attraction) and Hannibal Lecter.

Then there are those of undetermined motivation which includes HAL 9000 (2001: A Space Odyssey) and the poor old Shark from Jaws, who is probably just trying to catch a meal and survive.

How does it all end for supervillains?

You might be expecting that it always ends badly for supervillains. The good guys always prevail in the end, right? But does this hold true? And is there a relationship between motivation and how things end up for our supervillains? Let's take a look at the data with the help of a Sankey diagram.

It's true, most of our villains did end up rather deceased, but it's perhaps not as big a disparity as you might think. However, don't confuse alive with a happy ending, some of those poor buggers clinging to life may as well be dead. But were there any particular motivations that led to a particular result? Well, if you're the jealous or obsessive type, you may want to look away now. It seems like jealousy and obsession only lead to particularly nasty outcomes like death or exile. The same goes for revenge, so if the movies teach us anything, it might be to turn the other cheek when we feel wronged. However, if you are motivated by a desire to cause chaos and advance anarchy, your prospects look okay.

Create your own Sankey Diagram

So it seems like the Greatest Supervillain of all Time (the GOAT Supervillain, if you like) is pretty undecided. If you picked Darth Vader, you can feel pretty confident in your choice. If you picked anyone else, I look forward to you arguing your case with me! See you around the Dark Side.

Want more fun data stories? We got you. 

How do you represent yourself to potentially millions of men and women? How do you stand out in a sea of other profiles? What kinds of things should you include? After all, your bio is essentially your dating CV! We don't blame you for stressing about it. Luckily, we're here to help you craft your profile, with tips backed up by data.

With apps like Tinder, photos are clearly a major aspect of your profile, but an often neglected aspect is the Tinder bio. A recent university study found Tinder profiles with bios had a 4-fold increase in the number of matches received as compared to profiles with no text. I decided to take a look at what both men and women were putting in their profiles and what we can learn from them.

Let's Get Swiping

To get started I created 2 generic male and female Tinder profiles which served as my search engines into the Tinderverse. Using tinderjs and matching all genders and orientations, I was able to scrape over 5000 profiles within Sydney, Australia. To get a good representation of both genders, I chose an equal distribution of male and female profiles to analyse. Here's a snapshot of the types of profiles collected:

Nothing too surprising here, the majority of the people I found via Tinder are in the Sydney metropolitan area and within the 20's - 30's age bracket, though it is interesting seeing a spike of profiles in the 50's-60's age range.

Searching for the Quintessentially "Tinder" Profile

The aim of this analysis was to divine out any patterns from all the Tinder profiles out there and work out how we can apply this information when writing our own.

A quick text analysis of the scraped profiles shows some interesting observations. Clearly there are a lot of common values that the Tinderverse shares. You're not going to make many enemies if you have an Instagram account, love travelling and hanging out with your friends (maybe for a coffee or a chat).

There are still some stark gender differences here though:

Clearly, women are far more into their pets and going on adventures than guys are - or at least they like to say it more. Men, on the other hand, like to talk about going on dates, having a good chat, and otherwise being social. We can hazard a guess that women are describing their ideal partner - someone who likes going on adventures and can tell a funny joke - and possibly that tattoos are kind of a deal-breaker! On the other hand, men tend to describe what they want to do on a date - chat, go for coffee and taking it easy (or that they're easy, this ones a bit confusing). So here's some advice: to craft a better Tinder profile, consider avoiding these over-used terms. Perhaps the two genders could learn from one another - mix it up! Men out there should try talking about their ideal partner, and women should introduce some fun date ideas.

How about length?

The sheer number of choice people have on Tinder means attention spans are dwindling. 23% of Tinder profiles had no words in their bio and over 60% of profiles contained 30 words or less. Whatever you have to say, you probably should be saying it succinctly. But don't forget the earlier stat - people with text in their bios had four times the number of matches. You definitely want to be saying something!

Emojis are 

One way of conveying information about yourself concisely is through emojis and a lot of people seem to agree. Over 44% of profiles contained emojis, of which, an average of 4 emojis were used per profile. Here are some of the most popular ones:

Emoji usage is wide and varied, but a common theme appears to be substituting words with their emoji counterpart rather than conveying reactions or emotions. Travel, dogs, drinks and coffee are common crutches for most people, as we saw earlier. So think outside the box! Your profile might end up being a confusing mess of indecipherable emojis (upside down face anyone?) but hey, at least you'll stand out.

Writing Your Bio

When it comes to online dating, everyone has a different approach. Whether you want to stand out or just seem like a normal person, there is no silver bullet that will work for everyone. However, your Tinder profile doesn't exist in a vacuum. How it appears to the people you're trying to attract is informed by the all the other profiles they see. Being aware of what everyone else is saying should inform how you want to present yourself, as well as avoid the dating profile equivalent of awkwardly talking about the weather. The data definitely tells us there are some established cliches that you'll want to avoid. In an app that encourages users to be picky, you'll likely attract more attention by avoiding the stereotypes.

The top takeaways we can give you are:

• Be concise - users are used to reading short profiles
• Avoid talking about interests that everyone likes (I'm looking at you, travel!)
• Avoid generic date talk - everyone likes going for a coffee and having a chat! What do you like?
• Use emojis! But maybe some of the less popular ones.

Check out more Data Stories, or see the original Displayr document here!

Kickstarter describes itself as a platform to help "artists, musicians, filmmakers, designers, and other creators find the resources and support they need to make their ideas a reality." Since the launch of the crowdfunding platform in 2009, almost 150,000 projects have been successfully funded. 15 million people have backed projects on Kickstarter, pledging a total of USD $3.8 billion. Some Kickstarter projects have raised thousands or even millions of dollars. But what are the odds of your project being successfully funded?

For this analysis, we looked at a dataset of over 300,000 Kickstarter projects from 2009 to 2018. It contains data including the project's name, category, state (outcome), goal, deadline, and how much was pledged. From this data we can start to look at some of the factors that influence the outcome of a Kickstarter project.

The projects

Kickstarter is designed specifically to fund creative projects. The platform provides 15 categories which projects must fit into. The first thing we can do is look at which categories most projects fall into.

We can see that projects on Kickstarter are diverse, with no one category dominating the field. The biggest categories are Film & Video and Music, but they're closely followed by Publishing, Games, Technology, Design, and Art.

Project Success

Sadly but perhaps not surprisingly, here we see that more Kickstarters fail than succeed. Over half the projects in this dataset failed, and there's only a 35% success rate.

However, all hope is not lost! On the Kickstarter website, they state that 78% of projects that raised at least 20% of their goal were successful. The chart below shows that this statistic is supported by our dataset, with 77% of projects which raised 20% or more of their goal succeeding.

We can also break this down by category to see which categories are most likely to succeed. Here we've excluded projects that were cancelled, suspended, are undefined, or were live when this data was collected. This leaves only projects which either failed or succeeded, which comprise 88% of the total dataset.

Dance, which comprises only 1% of all projects on the site, leads the pack with a 65% success rate! This is closely followed by Theater and then Comics and Music. These are the only four categories in which a project is more likely to succeed than fail. At the other end of the scale, Technology and Journalism look dire, with 76% of these projects failing.

Create your Column Chart in Displayr

The impact of your goal

What could be the reason for this? Kickstarter allows users to set the goal for their projects at whatever they want. In this dataset, goals range from $0.01 to over $160 million. (It's likely that most of the projects with goals this high or low are joke projects, but judging from the project names, at least some of them seem to be serious.) We can compare the median goal of each category to its success rate to see whether they are correlated.

Technology, tied for the lowest success rate, stands out with a median goal of $20,000 - twice as high as the next highest category. We can't say for sure that this is the reason for its low success rate, but it does seem logical that it would play a part. The four categories with a success rate of more than 50% - Dance, Theater, Comics, and Music - all have relatively low median goals of under $5000. However, Crafts, with the lowest median goal at just $2345, has the third worst success rate. This is interesting, but clearly not the whole story.

If we exclude projects which are undefined, live, suspended or cancelled, Kickstarter projects have a 60% failure rate.

The visualizations below show how the rate of success changes depending on the goal of the projects.

The impact of your backers

Projects on Kickstarter are funded by backers - users who pledge money towards a project's goal. While creators can certainly take steps to improve their chances of success, ultimately every Kickstarter project lives or dies by its backers (or lack thereof). However, the visualizations below show just how important it is to have a large number of backers.

Will your Kickstarter succeed?

Success or failure on Kickstarter depends on a wide range of factors. It's extraordinarily difficult to predict what will go viral, and it's impossible to predict with certainty which Kickstarter campaigns will succeed. However, we can make a guess based on the variables we've discussed in this article. Below is a decision tree visualized as a Sankey diagram. This visualization shows whether a campaign is likely to succeed or fail based on its category, goal, and number of backers. You can zoom in by scrolling, and hover over the forks to see the statistics.

Create your own CART decision tree

Ultimately, what can we say about how best to succeed in your Kickstarter campaign? Make sure you're trying to fund a Dance project and that you don't need more than $1000. Oh, and make sure you have at least 1000 friends who are willing to back you!

Make your own column chart here, or a Sankey decision tree here! Check out the rest of our Data Stories for more.

Basal metabolic rate

An organism's metabolic rate is the amount of energy needed per second to keep that organism alive. For humans, this is about 2,000 calories per day, or about 90 watts. This means that a human's metabolism could power one 90 watt lightbulb.

An animal's basal (resting) metabolic rate differs depending on the size of the animal. A rat's basal metabolic rate is about 1.5 watts. From this, we can figure out how many rats are equivalent to one 90 watt lightbulb.

If you're good at math, you would have already worked this out. It takes the combined metabolisms of 67 rats to power one 90 watt light bulb! No doubt once this knowledge becomes widespread, RatLights^TM will take over the lighting industry. Finally, a convenient source of renewable energy. Take that, solar power!

But if it takes 67 rats to power a lightbulb and only one human to power the same lightbulb, what other creatures can we use? An average rat weighs between 0.3 and 0.5 kg. An average adult human, on the other hand, weighs anywhere from 55 to 80 kg. How many light bulbs could we power with an animal that weighs, say, ten times more?

Cowpower

Here we encounter an interesting feature of metabolic rates:  they don't scale in a linear relationship with body size. An average adult cow weighs 750 kg - roughly ten times as much as an average adult human - but a cow can't power ten times as many light bulbs as a human. A cow's basal metabolic rate is about 400 watts. Rather than the 10 light bulbs we'd expect if metabolic rate scaled perfectly with weight, we can only power 4.4 light bulbs with poor old Bessie. Given how impractical it would be to keep a cow in your living room to power your lights, I'm not sure the extra 3.4 light bulbs is worth it. We'll have to think bigger.

Whalepower

Now we're talking! The blue whale, the largest animal in the world at over 150 metric tonnes, has a metabolic rate of 12,000 watts! This equates to a staggering 133 light bulbs! Forget about RatPower - the true energy of the future is clearly WhalePower^TM. The power of only one blue whale can provide light for you and both your neighbors! It's OK, you can thank me later.

Whalepower (artist's impression).

This piece is inspired by Scale, by Geoffrey West. 

All the pictographs in this post were created in Displayr. You can learn how to make your own here, or check out more of our Data Stories!

Marketing is crucial for a tech company. It's essential to convincingly and memorably illustrate why a consumer should buy your phone, as opposed to any of the other hundreds of models they could opt for. The language used by brands to describe and market their latest products can provide many interesting insights about how these products are marketed, and why companies choose to market these phones in this way.

The data behind smartphone marketing

Luckily, we can use data science to help us illustrate the differences between different brands! The table below contains every sentence on the "overview" or "highlights" page for Apple's iPhone X, Samsung's Galaxy S9/S9+, and Google's Pixel. This includes reviews which have been quoted on the page, but does not include headings, footnotes, or annotations.

Straight away we can see that the iPhone X page contains more sentences than either the Galaxy or the Pixel (scroll to the last page and you'll see that it only contains sentences from the Apple website). A table like this can be useful to compare how these phones are described. But it's messy. A clearer way visualize this data is with a series of word clouds that will highlight the words most frequently used.

Apple iPhone X word cloud

Samsung Galaxy S9 word cloud

Google Pixel word cloud

This gives us an immediate picture of some of the key differences in the language used to market these different phones. As you'd expect, some types of words are common to all three smartphones. This includes words related to the physical characteristics of any smartphone - like the screen, the camera, or the battery. It also includes words like "impressive", "revolutionary", and "beautiful", which all have an emotive value and serve to convince the reader of the value of that particular model. But just looking at these visualizations, we see differences. The Galaxy word cloud looks like it contains a lot of words relating to cameras and photography. By contrast, both the iPhone and Pixel word clouds look more varied. Let's see if some other visualizations will highlight these differences for us.

Comparing and contrasting

By looking at how frequently different words are used, we begin to see what the marketing teams at these companies think is the key selling feature of their smartphone. One of the most interesting features of these word clouds is the varied prominence of the product's name. In the Apple and Google clouds, "iPhone" and "Pixel" are among the largest words displayed. But in the Samsung cloud, "Galaxy" is small and hidden. If we graph the number of times the product name appears on the site, we get this:

The word "Galaxy" only appears once on the "Highlights" page designed to convince people to buy a Galaxy! There are 492 total words in the dataset, meaning the density of the product name is only o.2%. By contrast, the density of the word "iPhone" in the Apple dataset is 1.7%. Looking at the word cloud for the Galaxy, the word that jumps out at you is "camera". If we plot the frequency of the word "camera" or "cameras" across all smartphones, we get this:

 The word "camera" appears 13 times on the Galaxy page! This equals a density of 2.6% - significantly higher than the density of the word "iPhone" on the iPhone X page. From this alone, we get an idea of the marketing angle of Samsung compared to either Apple or Google.

Another interesting metric is the number of times the brand - Apple, Samsung or Google - is referred to on the smartphone's page.

This one's closer, but still interesting. Samsung is referred to only half as many times as Apple, and only a third as many times as Google. It's particularly notable that the Pixel page, the shortest at only 239 words, mentions Google 6 times - a density of 2.5%!

What does this mean?

What does all this tell us about how these smartphones are marketed? The Pixel's page mentions Google extremely frequently - by density, five times more often than the Galaxy's page mentions Samsung. I suggest that this is because Google is a far more universal brand than Samsung, and thus lends more credibility. Especially as the Pixel is a relatively newer entry into the market, it makes sense to leverage the brand name of Google.

By contrast, the word most frequently used on the Galaxy page is "camera". Clearly, Samsung relies heavily on the phone's camera as a selling point - more so than is true for either the iPhone X or the Pixel 2. I think this is because Samsung can't rely on its own brand name to promote the product as much as either Apple or Google can, so it has to refer to a physical feature of the phone that makes it stand out.

However, I asked around the office, and there were some other interesting theories. One is that it's because Samsung is associated with the catastrophe of the exploding Galaxy Note 7s from 2016, so they don't want to remind people of that by using their brand name too much. Another is that Samsung, as an Asian company, perceives that Western people will trust it less than they would trust American tech companies. Whatever the reason, it clearly has a big impact on Samsung's marketing strategy.

And as for Apple? "iPhone" is the word most frequently used on the iPhone X's page. I think this is for the same reason that Google is so frequently mentioned on the Pixel's page. Except in the case of Apple, the iPhone name itself has become so ubiquitous that it's unnecessary to refer back to Apple to build credibility. The iPhone X already has brand credibility purely because it's an iPhone.

So there you have it! Do you think I'm right about smartphone marketing? Let us know!

See how we did this analysis in Displayr, or check out more of our Data Stories!

The Environmental Impact of Plastic

Plastic takes over 400 years to break down, meaning that every piece of plastic which has ever been made still exists. In 2016 alone, an estimated 335 million metric tons of plastic were manufactured! Only about 9% of plastic waste is recycled. Much of the rest eventually ends up in the ocean where it is consumed by marine life. Birds mistake plastic waste for food, which can lead to starvation as their stomachs become full of plastic. Alarmingly, 95% of Great Shearwaters, 93% of Blue Petrels, and 80% of Northern Fulmars have plastic in their stomachs. Clearly, this is a huge problem!

Alternatives to Plastic Bags

Various alternatives to LDPE (single-use) bags are in use. One solution is for retailers to charge a small fee for a high-density polyethylene bag - a more durable plastic bag intended for reuse. Most supermarkets also offer 'green' bags and encourage customers to bring their own bags. These measures are intended to reduce the impact we have on the environment. But studies have shown that it actually takes more resources to manufacture alternatives to single-use bags! Potentially this could have a greater impact on the environment. Let's take a look at the data...

Based on a life cycle analysis of their manufacturing process, energy, and resource consumption, single-use plastic bags appear more environmentally friendly! For the amount of resources it takes to produce a 'green' bag, you would have to re-use at least ten times for it to have its intended effect. A heavy LDPE bag requires 4 uses, while a paper bag needs 3. But over 80% of Australians said they reuse supermarket bags as bin liners. How does this change our comparison?

If 40% of all single-use plastic bags were reused, you would need to use your green bag 14 times to have the same environmental impact. If all plastic bags were reused, you'd need to use it 26 times! The plastic bag ban is looking shaky as a way of saving the world from plastic pollution!

Reducing Our Impact

Of course, this analysis is only based on the resources needed to make different kinds of bags. We haven't taken into account the typical usage patterns of these bags or their impact on specific species or habitats. Single-use plastic bags are known to cause a myriad of problems, particularly once they enter the ocean. But banning plastic bags alone isn't enough to save the planet. If we just replace one type of bag with another without changing our usage patterns, the world could end up worse off than when we started. The key to minimizing the environmental impact of our shopping bags is to reuse them as many times as possible. Banning plastic bags isn't useful on its own - instead, we need to focus on changing our habits.

Did the data surprise you? Learn how we made these vizualisations, or check out more fascinating data stories!

If you've ever wondered which country drinks the most wine, some familiar suspects might have flashed across your mind. Maybe you thought of the famously snobbish wine drinkers, the French? Maybe those Italians and Greeks, whose idea of a healthy diet includes a glass a day? Or perhaps those notorious beer drinkers, the Germans, might also like their wine? Could it be the United States, always so proficient in the consumption stakes? Let's have a look at our contenders.

Decanting the Data

Countries by wine consumption in mhl
If you picked the United States, raise a toast! Americans consume the most wine out of every nation on the planet, clocking in at a whopping 32.6 million hectoliters. That's right, hectoliters, as in a hundred liters. The next four countries are our usual suspects, with France, Italy and Germany representing Europe as wine central.

China rounds out the top five. But if the rest of our nations want to hold onto their spots, they better hit the bars. China is climbing fast both in terms of national wine production and consumption. Mirroring the meteoric rise of the United States from sixth in the world in the early 1990s to claiming the top spot for consumption in 2016, China has seen a similar rise. China also now only trails Spain in total area of wine cultivation and is expected to overtake Spain within the next few years. With wine becoming increasingly popular in China and a population of over 1.4 billion, the potential boom for the Chinese market is sky-high.

But if you think you know who the booziest country in the world is now, think again. Just looking at the data for total consumption doesn't tell us the whole story. Looking at our visualization for wine consumed per capita for the same countries, we're thrown for quite the plot twist.

                 Countries by wine consumed per capita in liters

The United States doesn't even make the top three! Instead, that honor goes to the Portuguese, followed by the French. In fact, the U.S. even falls short of their great rivals to the North, the Canadians. Ooh, la la indeed! Perhaps surprisingly, the Swiss round out our top three. Our data visualization has a custom color palette from dark reds to light pinks, representing the variation in amounts.

Interestingly, China actually falls bottom of this metaphorical wine glass with only 1.1 liters consumed per capita - highlighting just how massive the gap in their market is and how monumental their potential for growth is. Does anyone want to partner up and open a bar in Shanghai?

But wait...there's more!

Top destinations for wine consumed per capita 

Andorra and the Vatican City come out of absolutely nowhere to trounce the rest of our top countries for wine consumed per capita. This visualization shows some of the most concentrated areas of wine drinkers. Tiny Vatican City, with an area of 44 hectares and just 1000 citizens, manages to pack away a stunning 56.9 liters per capita. It must be all those clergymen running about!

So who really is the booziest country in the world?! Well, technically, it's Andorra! 

Interactive Geographical Map 

Of course, a pictograph bar chart like we've done above isn't the only way to show this data. A cool visualization you might want to use to show your own data is the interactive geographical (choropleth) map. Hover over each country and the map will tell you how many liters of wine each country consumes per capita. Like the pictographs, this map is also color-coded to represent which countries consume more or less wine. You can also zoom in and out of this visualization as you please.

Were you surprised that the answer is Andorra? We sure were! If you want to recreate these visualizations, you can access and play with the original Displayr document here. Even better, you can read the step-by-step tutorial for how we created them here, and do the same thing with your own data!

Learn how we made these vizualisations, or check out more fascinating data stories!