fabiochiu/medium-articles huggingface dataset:
Dashboards 101: Rolling Your Days
By: Alex Selby
Tags: Data, Redshift, Dataviz, Visualization, Sql

You need to crawl before you can run when it comes to data. So many leaders want to bite right into trendy Machine Learning & AI techniques before they even have simple aggregations. Fortunately, tracking your company’s progress doesn’t need to be complicated — it’s really about asking the right questions (e.g. Are users doing what I want them to do?) — and looking at the trend over time. Was there a dip in sign-ups? Did more users activate than normal? What was the holiday impact?

I was taught to do this by truncating dates to a day/Monday/first of the month/first of the year, counting the users, and graphing it. Unfortunately, the common methods for doing this don’t tell the whole story.

For the below visualizations, I pulled some TLC Application data (Taxi & Limo Commission) from New York Open Data. Be sure to checkout this website if you haven’t already. It’s amazing what has been open sourced!

Here is a distinct count by day of all TLC applications in NYC:

Just from eyeballing, I might wonder what happened in mid-August, but for all I know that day is an anomaly. I can see there is an overall upward trend once September hits, but that’s about it. If someone can look at this chart and derive value, their insight wouldn’t be worth much.

Here is an aggregation at the week level:

The bars are smoother, but what is happening within the week is lost here. Was September 29th just an off week, or is there always a dip going into the next month? It’s also impossible to know how your current week is going. Looking at Oct 13th, maybe we should shut the business down (or wait until next Monday to check how we’re actually doing).

Neither of these graphs paint an accurate picture of how your business is performing. With Daily Aggregation, the spikes are huge and random. With Weekly Aggregation, you have to wait 7 days to know how you’re doing.

Enter the Rolling Aggregation:

A rolling aggregation means a sum of the last X days of data. Monday to Monday, Tuesday to Tuesday, etc. This graph clearly shows us the ebb and flow of our applications, while also quickly pointing out spikes as they happen — for example the post-Labor Day application jump (Monday, 09/09/2019) which was not as visible on the other charts. By aggregating with this method, we knew on 09/10/2019 that applications had jumped the day before.

Some flavors of SQL have functions to achieve the above, called “window functions”. To achieve a rolling sum, start with data aggregated by day then sum across rows.

Don’t Forget to Fill your Dates!

A word of warning, don’t forget to fill your dates! If you have a day with zero applications, that means your database will have no entries for those values, and your aggregation will be incorrect. Make sure you fill in your dates with zeroes before you tell the wrong story!
The Beginning, the Middle, and the New Beginning
By: Duncan Geere
Tags: Guides And Tutorials, How To, Data Visualization, Storytelling, Dataviz

Good things come in threes. Flavours in a tub of Neapolitan ice cream. Underpants in multipacks. Bears. There’s something about the magic number three that’s endlessly satisfying to the human brain. That’s why good stories have a beginning, a middle, and an end. Three equal-ish parts, which give a story balance. Which make a story satisfying.

Much is written about the value of storytelling in all walks of modern life, from pitching an investor to buying a cup of coffee. In data visualization these days, it’s practically a religion. And this is no bad thing.

But there’s a problem. The classic beginning » middle » end model doesn’t map so well onto the goals of data visualization, because a data viz is very rarely the end of a story. Much of the time, you want the viewer to do something specific afterwards. You want them to make an informed decision, or buy something, or sometimes just sign up to a mailing list.

So I’ve developed a tweaked version of this framework, which has served me well as a mental model when I’m making visualizations. I call it “The Beginning, the Middle, and the New Beginning.”
Drawing an Arc with Canvas/SVG. To be more specific, an arc is a part…
Tags: JavaScript, SVG, Fron End, Data Viz, Canvas

If you wish to draw an arc in a Web page, then you should probably use Canvas or SVG.

To be more specific, an arc is a part of an ellipse. So we can imagine that, in order to define an arc, at least we need the following parameters:

The center of the ellipse, noted as x and y, long and short axis radius, noted as rx and ry, rotation, start angle, and end angle.

Canvas

Canvas has two corresponding API, arc and ellipse.

Arc is used for drawing an arc on a circle so the x radius and y radius are the same, and there is no rotation.

The last parameters of those two APIs are anticlockwise, which is a boolean to state if the arc is drawn from start to the end in anticlockwise. This is because, when we have start angle and end angle, there are actually two arcs fulfilling the condition so we need this parameter to define if to choose the anticlockwise one.

SVG

For SVG however, we need to provide the start point and the end point.

There are two parameters that we don’t know yet. One is called large-arc-flag, and the other is sweep-flag.

large-arc-flag is used to state if the arc is larger than half ellipse. This sounds easier than the Canvas design, but in fact, it’s more complicated because angles are not always between 0 to 2π. Different situations should be considered.

As for sweep-flag, it is a flag to state which arc to use. When we have the radius, rotation, start point, and end point, there are actually two ellipses that meet the condition and thus making four arcs.

According to large-arc-flag, we can exclude two of them. For example, if it is true, we can exclude the two small arcs. By definition, when sweep-flag is 1, the arc is in a positive angle, which is clockwise. So now, we can determine one certain arc.

Someone may ask why we should learn drawing an arc in such a hard way.

This is what we need to understand when creating ECharts, a JavaScript visualization tool supports both Canvas and SVG rendering.

Watch the YouTube video for more detailed explanation: https://studio.youtube.com/video/QXOPYyhifDc/edit
Our next Data Visualization Foundations class will be this November!
By: Alan Mcconchie
Tags: Design, Data Visualization, Workshop, Dataviz, Maps

Enrollment is now open for Stamen’s next Data Visualization Foundations class, scheduled for November of this year at Stamen Design. This hands-on class, which we developed with dataviz expert Curran Kelleher (author of the online platform for teaching & learning data visualization vizhub.com) will teach you the fundamentals of visual communication with complex information. It’s well-suited for beginner-to-intermediate practitioners and anyone who wants to develop more dataviz literacy.

The class will take place over the weekend of November 16th and 17th, 2019, in-person at the Stamen offices in San Francisco. Sign up now, before spots fill up!

June 2019 Data Visualization Foundations Class

Hey students! This is a reminder that our classes are half-price for full-time university students. It always makes sense to supplement to your college studies with some hands-on experience using the cutting-edge tools of the trade!

So if you or your company wants to present data effectively using data visualization, needs in-depth background knowledge about the art and science of data visualization, or needs to acquire data visualization literacy, this event is for you! These are small, intimate, hands-on sessions in a studio focused on data visualization, not formal lectures in a big theater. Come, bring your data and your ideas, and you will experience custom-tailored delivery and selected content emphasis based on your individual needs.

Wait no longer, take use of this great opportunity and follow the link below to register now!

Data Visualization Foundations, November 16 & 17!

If you’d like to be notified of future Stamen data visualization or cartography classes, sign up for our announcement list.
Paper → Visualizations
By: Rajat Gangrade
Tags: Underground, Tunneling, Dataviz, Engineering, Phd Application

Data visualization and infographics give you answers (and raises questions), you did not know you had. It is very much like storytelling. As a story-teller, you want to capture the imagination of the audience and dazzle them at the right moment.

Over the past week at the Annual Tunneling Short-Course at Underground Construction and Tunneling Center at Colorado School of Mines, I led the ‘3D Visualization of Geotechnical Information’ lab. The objective of the lab is two-fold (a) to introduce the participants to 3D visualization of the geotechnical information from an actual tunneling project and; (b) to disseminate the knowledge of how developing 3D visuals can aid in communicating uncertainty, risks, make informed decisions for planning, design and construction methodology.

I would like to share a few visuals from the 3D geotechnical lab and discuss their utility in tunneling projects.

(1) A deterministic interpretation of the geological-geotechnical conditions (extracted from a geotechnical baseline report) is superimposed along the centerline of the tunnel alignment. Visual is static but with knowledge from the boreholes drilled at an offset from the alignment, are most often, not considered into developing geological-geotechnical profiles.

But why is the knowledge of sub-surface domain important at lateral extents from the tunnel alignment? — To evaluate ground-deformation induced strains in surface structures and utilities.
Plastic bottle tops would tower over i360 and The Shard
By: Irene Soler
Tags: Public Art, Brighton, Plastic Pollution, Dataviz, Design

In 2018, The Appreciation Society organised the project ‘A Drop in the Ocean’ to get citizens involved in collecting plastic bottle tops from Brighton & Hove’s* beaches.

How ‘A Drop in the Ocean’ compares

We focused on bottle tops because most of them aren’t currently recycled.

Every plastic bottle top found on our beaches and intercepted in cafes along the seafront from March to October 2018 was logged and then most of them used to create a colourful installation.

In just 8 months, enough bottle tops were collected on and along the Brighton & Hove seafront to build a stack over 2.5 times the height of the British Airways i360, the UK’s highest tallest moving observation tower, over 3 times the height of the London Eye, and over 100m higher than The Shard, London’s tallest skyscraper. And these were only a fraction of the bottle tops that were used, discarded and washed ashore along this sliver of coast.

The aim was to show how even small bits of plastic add up. And how seemingly small changes could add up too.

This is what we discovered about bottle tops along Brighton & Hove’s beachfront.

The Final Bottle Top Count
Can money buy happiness?
By: Greta Gasparac
Tags: Data Visualization, World Happiness Report, Happiness, Dataviz, Data Science

Photo by Sharon McCutcheon on Unsplash

This is an entry for Reddit’s DataViz Battle for the month of June 2019. See all plots and code here. Shout out to r/dataisbeautiful mods for preparing these battles every month!

Data

The World Happiness Report is a landmark survey of the state of global happiness. More than 150 countries are included every year and the results can be of great help when experts need to assess country’s progress and recognise areas where improvement is needed. Details about the report and the report itself can be found here.

The given datasets contained results from 2015, 2016 and 2017. The report assumes there are 7 factors contributing to country’s overall happiness:

Economy (GDP per Capita),

(GDP per Capita), Family,

Health (Life Expectancy),

(Life Expectancy), Freedom,

Trust (Government Corruption),

(Government Corruption), Generosity.

These are all numerical values representing the extent to which a certain factor contributed to the calculation of the Happiness Score compared to Dystopia aka the world unhappiest country, a hypothetical country where these factor values equal to the world’s lowest national averages for each of the mentioned six factors. This brings us to the 7th factor of the dataset - DystopiaResidual, which is the sum of Dystopia Happiness Score and the residual value/the value of unexplained components for each country.

Country’s overall happiness score can therefore be expressed with the following equation:

HappinessScore = Economy + Family + Health + Freedom + Trust + Generosity + DystopiaResidual.

How happy is happy?

First let’s get familiar with our data. Happiness Score is a numeric value ranging from 0 to 10, but what does it mean? How happy are the happiest/unhappiest countries? Which parts of the world have the highest score?

Let’s take a look at top 10 happiest and unhappiest countries from 2017:

Apparently, even the happiest countries (only) achieve about three quarters of absolute happiness, which, in this case, is represented by the score of 10. We can see 7 out of top 10 happiest countries are in Western Europe and 7 out of 10 top unhappiest countries come from Sub-Saharan Africa.

Putting these numbers on a map gives us a better view.

Happiness scores in 2015, 2016 and 2017.

Maslow’s hierarchy of needs.

I will try to explain the results using Maslow’s hierarchy of needs, a five-tier model of human needs. According to Maslow, in order for motivation to occur at the next level, each level must be satisfied first.

Indeed, the highest happiness score was recorded in economically stable countries, where there are no war zones and people’s needs for safety and security are fulfilled, so they can feel love/belonging, gain self-esteem and achieve self-actualisation.

What makes us happy?

Let’s split the happiness score into factors and take a look at how each of the them contributes to region’s happiness.
Candlesticks — A dataviz marvel. No, this post is not about technical…
By: Harish Daryani
Tags: Data Visualization, Candlesticks, Dataviz, Technical Analysis

Image by Isaac Smith on Unsplash

No, this post is not about technical analysis of a stock. I am writing this to bring out the beauty of data visualization or dataviz as it’s fondly referred to in the data community.

Let’s look at an example.

As you know, stocks are traded on the exchange whenever buyers and sellers agree on a price. This could happen very frequently i.e. multiple trades in minutes or seconds. For the purpose of this example, let’s say there are tens or hundreds of trades of a stock happening at different prices in a minute.

You are an analyst interested in tracking the price of this stock over a period of time. What are your options ?

Line chart

The simplest one. You can simply plot the price on the y-axis over days on the x-axis as shown below.

Line chart of Apple stock (Source : www.seekingalpha.com)

While this is good start, it only shows closing price of the stock on any given day.

The above line chart will not convey any information about intraday movements of the stock. How can we represent this additional information ?That’s where candlesticks are helpful.

2. Candlesticks

The chart below is a candlestick version for the same time period.

Candlestick chart of Apple stock (Source : www.seekingalpha.com)

Looks clunky isn’t it. Let’s zoom in a bit to last two weeks only (ignore the bottom bars, those are volumes not prices).

Candlestick chart of Apple stock (Zoomed in)

Each daily candle conveys following information about the stock price.

1.) Open/Close price — Horizontal bases of the candle

2.) High — Top wick of the candle

3.) Low — Bottom wick of the candle

4. Note that, color has been used as an additional dimension to indicate whether stock closed higher than its open price (Green) or vice versa (Red). This means, for Green candles lower horizontal base of the candle is the Open price and upper horizontal base is the Close price (vice-versa for Red).

While the above example shows candlesticks by day, they can be plotted by hours or even minutes.

Candlesticks patterns are used by traders to understand demand, supply and price discovery of a stock. Traders take positions based on these patterns (which is beyond the scope of this discussion).

The takeaway is, there are elegant ways of representing high velocity data by overlaying them into existing visualization options like line charts. Candlesticks are just an example.
What #BlackLivesMatter and #AllLivesMatter say on Twitter
By: Irakli Kavtaradze
Tags: Sentiment Analysis, R, Twitter, Dataviz

Family of Nutsa Abash in Abkhazia, during The Soviet Union. Photo was taken from the following post about the history of black people in Abkhazia: https://abkhazworld.com/aw/blogs/1754-afro-abkhazians

What #BlackLivesMatter and #AllLivesMatter say on Twitter

That people who use two different hashtags — BlackLivesMatter (BLM) and AllLivesMatter (ALM)— use different languages and differ in their opinions is noticeable to anyone who has spent a single day on Twitter. But to see how they differ exactly, I examined their tweets with several R packages, but most extensively with tidytext and rtweet.

I have retrieved the tweets from June 27 to July 5th for ALM and from June 30 to July 1st for BLM; 25,132 and 86,922 in total, respectively, all in English.

For the start, let’s see what are the top 15 words used in each hashtag (Fig.1). Mostly there’s an overlap in word usage but differences are there, especially in word frequency. First, notice that ALM uses #blacklivesmatter hashtag, as it is a reaction to BLM movement. Authors of those tweets try to refer and talk to those who tweet with BLM hashtag. ALM hashtag contains more reference to RealDonaldTrump and #bluelivesmatter, while in BLM ‘support’,’movement’ and ‘justice’ are among top 15 words.
Bored of using Bar Charts??
By: Vasavi Jaya Krishna Manda
Tags: Data Analytics, Data Visualization, Tableau, Dataviz, Visualization

5 Ways to get creative with bar charts

Bar charts are one of the most used chart forms I use when presenting data insights at work. But I often feel like it gets a bit monotonous and repetitive especially when you need to present a lot of KPIs on the dashboard.

Here are five different variations you can use to show data using bar charts.

1. Using Rounded Bars

The way it works is basically you provide two-measure names to tableau and draw a line using those.

a. You start by drawing a normal bar chart by dragging dimensions to the columns shelf and measure to the rows shelf.

A Normal Bar chart

b. Next add another measure on the rows shelf as min(0). It is basically a point at 0 and will act as the second point for drawing the line.

c. Now we need to change the chart type to Line.

Line Chart

d. Add Measure Names to path shelf, Measure Values to Rows shelf and move both measures to Measure Values Shelf as shown below.

e. Now, just adjust the size of the line and make it a little thicker. Adjust the axis if needed.

You are done!
Poor color choices that discriminate the part of your audience
By: Weronika Gawarska-Tywonek
Tags: Inclusive Design, Data Visualization, Data, Color Blindness, Dataviz

Poor color choices that discriminate the part of your audience

Have you ever wondered if the visualization that you’ve created was understandable? If the chart you’ve chosen delivered the right message? Or if the data told the story you want? As you are reading this article, I assume that you did.

However, have you ever checked if the colors that you’ve used are understandable for everyone? You might think that red is red, end of the story. Well, unfortunately, it’s not that simple and color perception depends on the cone cells present in our eyes. If you lack some cone types, then you might have some trouble distinguishing certain colors (if you are interested in more technicalities I recommend this site).

So using some colors together on a scale discriminates against some of your audience. And this is not as uncommon as we might think. Up to 8% of men and 1% of women are struggling with some form of color vision deficiency, which means that worldwide, there are approximately 300 million people that might have trouble when reading our charts.

Luckily, changing to a color-blind-friendly palette is very easy. We can just use the one that is already created by ColorBrewer or we can check how our palette will be perceived using Color Blind Vision Simulator (and adjust if needed).

This week's example is a chart that shows GDP per country in Latin America and the Caribbean — to be more precise, a special version created especially for color-blind people. This “color-blind-friendly” chart adds supplementary encoding of the data by assigning not only the color but also the icon.

So what is wrong with this chart beside the fact that icons are rather inconsistent and random, and I admire anyone able to properly identify the icon of each of the Caribbean countries?

Original dataviz by Brasilemmapas

Good data visualization solutions✔️

Elements that work in this chart

Showing overview and details — By showing both colors and labels it’s possible to analyze the map on two levels — general and detailed one. We can get the overview by scanning the map or analyze it more thoroughly by reading the labels. I would just rethink the number of digital points provided to avoid obscuring the data.

Bad data visualization choices❌

Elements that don’t work in this chart
Framing effects: visualising the Snooker World Championship
By: Gavin Freeguard
Tags: Data Visualization, Dataviz, Snooker

From visualising BEIS to visualising baize; try not to baulk at the foul puns; now I’ve done that I’ll give it a rest and take a break. Cue the actual content…

Following my attempt to visualise the Six Nations, I thought I’d give the snooker world championships a go. There are lots of numbers, lots of ways of telling the story of a match or a frame, and lots of colours (which made it one of the hits of BBC2’s early colour programming, though obviously comes with severe downsides for those with colour blindness).

The below is very rough, but what do you think?

Match play

Step charts showing the rhythm of a match seem like a good place to start — the contrast between Gary Wilson’s 10–9 victory over Luca Brecel (one edging ahead of the other) and Stuart Bingham’s over Graeme Dott by the same score (Bingham raced into the lead, Dott made an epic comeback) is clear.

Bonus chart here showing Brecel scored more points than Wilson, but still lost.

I reckon a series of small bar charts, one for each frame showing each player’s score (perhaps stacked bars to show any breaks over 50), would give a handy dashboard summary of a match. But there are other options for showing the story of individual frames.

In the frame

Step charts are again a good place to start. The ones above show the score by shot, rather than by time (there’s not that much of a difference in the Selby vs Zhao frame shown above, but that might not be the case in all matches — e.g. extended safety battles with a long time between the shots).

But these dot plots and strip plots are more fun. Dots are a natural way to visualise snooker balls, but the strip plots are better for condensing a lot of information into a smaller space. The difference between a tense frame with the momentum going back and forth, a high break (Trump’s 141), and winning the frame in two visits is clear in the charts.

If this data — score by shot — were easily available (rather than having to sit through a match and mark it manually), one could imagine a chart showing all the frames in a match and its ebbs and flows. (And if anyone wanted to publish such data…)
Dataviz within Adobe Illustrator: the results.
Tags: Data Visualization, Dataviz, Illustrator, Design

With a goal to dive deeper into the growing need for smarter dataviz solutions within Illustrator, we recently posted an online questionnaire.

And the people (n=244) have spoken! Scroll on down to check out the results.

We’ve also spent some time looking into what we learned from reviewing all the responder comments — keep an eye out for the upcoming post on those specifics.

Dataviz in Illustrator: Survey Results

You may be surprised to see that most of the respondents are Dataviz Professionals and Graphic Designers. This is because it’s the specific network that we focused our efforts on promoting the survey too.

The roles listed in ‘Other’ correspond with the trend that dataviz and chart-making experience is becoming more of an expectation in a wider variety of roles.

Unsurprisingly, Graphic Designers all use Illustrator. What is interesting to see though is that most respondents supplement this Illustrator with 2–3 (sometimes more!) tools for data visualization.

The less programming “skills” an individual possesses, the more they request better graphing tools in Illustrator.

Respondent comments are almost unanimous: Illustrator generally has too many limitations for advanced graphing. So, “Definitely Yes”, most want better charting tools available. More about this in an upcoming post!

Most wanted: Sankey & Treemap; both of which will be available very soon within the Datylon Graph plug in. Also look out for more on the Sankey vs. Alluvial topic!

The survey is still available to complete if you wish, we are still using the results to enrich our dataviz offering. Your comments can certainly make a difference to the solutions we bring to you.
Outlier — Apply to Speak!. Outlier Online is an upcoming…
By: Mollie Pettit
Tags: Data Visualization, Outlierconference, Conference, Visual Design, Dataviz

Outlier Online is an upcoming conference hosted by the Data Visualization Society, to be held on February 4th & 5th, 2021. We invite you to fill out this speaker application form if you’d like to give a talk or hold a workshop at the conference. Speaking at Outlier is a great way to build out your network, show off your skills to potential clients and collaborators, and help the next generation of data visualization professionals by sharing your knowledge.

We’re aiming to recruit as diverse a range of speakers as we can. Diversity can mean diversity of race, gender, geographic location, tooling, background/focus, visibility, and seniority, to name a few. This leads to what we feel is extremely important — the diversity of perspective. The conference aims to reflect and celebrate the global visualization community by supporting speakers from around the globe, spanning across all time zones, and supporting talks given in multiple languages.

Never given a talk before? No problem! Do you feel too junior to apply? You have a unique perspective to share! Do you want to give a talk in your first language (that might not be English)? Fine with us! In fact, we strongly encourage you to apply.

Apply!

The deadline for talk and workshop submissions is December 1st, 2020. We’re looking forward to seeing all of your applications. Apply here. Good luck!

Got your application in already? Here’s how you can continue to help:

Help us Spread the Word

In an attempt to get a diverse lineup of speakers, we are collating a list of organizations to which we could reach out and advertise the call to speakers. Here is a list of organizations that we’ve flagged as groups to reach out to. Who are we missing? Add it to the list! And if you have a contact there, reach out to us and let us know.

Also, you could re-tweet!

Translate this article!

We’re serious about our attempt to make speaking at Outlier as accessible as possible. Therefore, we’d love it if you would be willing to translate this article into other languages you speak. To do this, simply translate this text and send it to this email address with “Article translation” in the subject line. We will post them and add them to this article! (Alternatively, you could opt to post the translated article on your medium account and then send us the link.)

Translated Articles:

Thanks for all your help! :D

Thanks to the events committee: Duncan Geere, Nadieh Bremer, and Maxene Graze.
Upcoming Data Visualization Conferences
By: Mollie Pettit
Tags: Data Visualisation, Data Visualization, Outlierconference, Conference, Dataviz

Let’s take a look at what events are coming up! This is meant to be a living document, to be updated as new things arise. Is anything missing? No fret; toss it into this very short survey! (Last updated: 2/17/2020)

S-H-O-W

When: April 8-9, 2021

Where: Online & Utrecht, the Netherlands

Links: Website, Twitter, LinkedIn, Facebook

Time Zones Targeted: ~Europe

“How can we help our audience to make more sense of data visualisations? How can we help to make it more understandable? One way is to work with rules. Rules about colour, axis, size, shapes, interactivity, etc. We can’t expect our audience to take the time to decode complex and beautiful visualisations, but we can design the conditions to aim for that. If we as practitioners use rules in a clear way, the reader can understand a data visualisation more intuitively. On the other hand, even with all the rules, every data visualisation is unique and different. The creativity is obviously in the rules. We can use the creativity to play around with and search for new and exiting ways of expression to connect with our audiences. At the second S-H-O-W conference in November 2020 we will examine, learn and discuss the rules. We look at how to make them and break them.”

Alright, that’s what we’ve got so far! If you hear about more, fill out this form and we’ll add to the list if it’s relevant.

Cheers! :)

Note: I realize that there are a lot of data science / analytics conferences out there that may be data-visualization tangential; however, if I started listing every data science conference in existence, this list would get VERY long very quickly. Therefore, I only included conferences with a clear data visualization focus.
Why Tableau?
Tags: Dataviz, Tableau

“Catchy things always attract people”

When we are talking about data viz, we have to keep in mind that data viz is an important aspect of any data scientist, Business analyst, and Data analyst. So when it comes to visualizing the data for any kind of Business or for academia we first look for excel or Power BI because it is available free in the market, and almost 60 % of people are using both the tools for data visualization.

In recent times Tableau is becoming popular in the data viz domain but why? Tableau is not free but still, it is replacing other tools available in the market. In recent times almost 60–70% of people are using tableau and the popularity is also exponentially increasing.

Why do I like tableau? Tableau comes with two components one is tableau prep for data preparation or preprocessing and the second is tableau desktop. what I am observing that people are not much talking about the tableau prep but for me, it's more important than tableau desktop . As a Data scientist, most of the time I used to preprocess the data and make data ready for modeling, and for this purpose I mostly used Tableau prep for processing .40% of my work is done by tableau prep which is unique compare to excel and Power BI.

I know most of the people who are using excel will not agree with me, but I can say you that using tableau prep I used 90% mouse, and only 10 % of work is done by keyboard which is unique and also if you don’t want to write SQL code than this tableau is very very helpful for you, You can do it in excel but again you have to used keyboard most of the time.

I can only say based on my experience that it's very easy and user friendly . You can do your almost 90% of your job by drag and drop.

“Tableau Desktop” you will love it, it comes with some good features like storytelling which is same as PowerPoint. If you want to present some business information you can use this storytelling features and it is very catchy and informative,

Customization with very little effort wether its dashboard or sheet .you will love it.you can integrate your tableau with R and Python Programming. You can connect many types of files like text, CSV, Excel, etc as well as it provides facility to connect with a different type of server like SQL , Oracle, etc,.

Tableau is developing each and every day and comes with great features in every update. I am using the 2020.3 version (2020 august).

If you ask me till now I do not seeing any kind of disappointing stuff, maybe there is some but yeah I will surely tell you 80 % of chance that you will never disappoint with the tableau.

If you are not started with any kind of visualization tools then I will recommend tableau. Go for it, you will thank me later.
Congressional Districts with the Most National Parks
By: Daniel Mcglone
Tags: Politics, Dataviz

In the time of social distancing, people have been reconnecting with outdoor spaces and activities. The United States National Park System contains approximately 131,000 square miles of public and private land managed by the government. That land is made up of parks, monuments, and other types of federally-designated preserved places. The largest of which is the majestic Wrangell–St. Elias National Park and Preserve in Alaska.

Photo of Wrangell–St. Elias National Park by Jim Witkowski on Unsplash

Below, we map the total land area of National Parks and Sites by U.S. Congressional district.

Unsurprisingly, Alaska’s At-Large Congressional District contains the most land area for National Parks in the entire U.S. Alaska is home to nine out of the ten largest parks in the entire system including Denali and Gates of the Arctic. Alaska has been represented by Republican Don Young since 1973. The only park in the top ten largest not in Alaska is Death Valley, located in California’s 8th Congressional District, represented by Republican Paul Cook.

Photo of Death Valley National Park by Jeremy Bishop on Unsplash

National Park Sites

In addition to parkland, the National Park system also manages hundreds of different sites with 19 different naming designations. This covers places like National Battlefields, National Historic Sites, National Seashores, and even the White House. Which Congressional Districts have the most National Park Sites? See the map and chart below.

The Congressional Districts with the most National Park Sites tend to be in the western U.S. in large districts such as Arizona’s 1st and New Mexico’s 3rd Congressional District. Arizona’s 1st contains the breathtaking Navajo National Monument. The district is represented by Democrat Tom O’Halleran.

Tsegi Phase villages built by the Pueblo People. Photo Courtesy NPS.

We hope this inspires you to get out and enjoy the great outdoors near you. It should be noted that COVID-19 precautions vary based on state, local, or tribal rules depending on the park you plan on visiting. So you should always check ahead to see the status of your favorite National Park.
DATA SCIENCE, THE NEW SOCIAL STUDIES
By: Damilola Dorcas
Tags: Data Visualization, Data, Dataviz, Data Analysis, Data Science

DATA SCIENCE, THE NEW SOCIAL STUDIES

Before the advent of technology, humans spent more time relating to one another. We learned to depend more on one another with less dependency on machines.

You trust the Post Man or the Post Office to help you deliver your letters or mail, you trust the words of your teachers more because they had more experience in the field, We spent more time talking to each other than inanimate things hence why Social studies was taught as the study of people in relation to each other and to their world.

But as the relationship between people declines, there needs to be a new way to study people in relation to each other and their world, hence data science.

Data is just a bunch of numbers to those who don’t know the true power of data and a ton of information and foreseeable insights to those who are aware, those who can pick up trend lines while working with data. Thinking of data in zeros and ones make people lose understanding of the real meaning of data.

Data is majorly decisions that individuals, firms, countries, continents have taken or are taking per time.

The world is producing more than 2.5 Quintilian data each day and 90% of all of the world’s data has been created in the past few years. That is a very huge amount of information out there.

To a certain degree, Data Science is concerned about who we were, who we are, and who we are becoming and also how a particular situation was, and the future of it. You get to know of people’s desires, prejudices, fears, opinions, and questions. For every moment we spend on the internet, mobile phones, laptops, we leave our footprints. These footprints aren’t meant to haunt us, they help us make better and informed decisions per time with the general goal of a better world.

In the current scheme of things, the fabric of the world isn’t woven by the wool of interactions among people but majorly by the relationship that exists between humans and inanimate objects (screens, internet, and machines) as people tend to tell the computer the truth about what they think about a particular issue, how they regard a particular situation to be through their online engagements ranging from searches to the videos they view online. This relationship that the human has with the computer is regarded to be a private one but as private as it may look, it is an unguarded moment as these footprints can always be seen.

But then, as your digital footprint is your true self, don’t be scared to leave them, it shows you have lived and you are living.

Thank you for reading.

Kindly share your thoughts and I will be glad to hear from you.
Outlier Tickets Now Available!
By: Mollie Pettit
Tags: Data Visualisation, Data Visualization, Outlierconference, Dataviz, Community

Outlier tickets are officially on sale! You can buy one right now — head on over to the ticketing page to make sure you don’t miss out on the very first conference from the Data Visualization Society.

Rather than asking everyone to give the same price, we are providing multiple pricing options. This is for one simple reason: we don’t want the price of a ticket to be a barrier for anyone who wants to be there.

There are three price tiers to choose from. All of them come with the same all-access offerings. No VIP attendees here!

Price Options

Support Outlier ($299)

Have you had a good year? Is your company paying for your ticket? Offering a little extra helps make it possible for people with less financial resources to attend. We appreciate it enormously! :D

Standard Ticket ($149)

We’re working hard to create a quality event and we feel this is good value for what we’ve got planned. We ask that people pay this price if they’re able.

Income Conscious ($49)

We want you to be here and we don’t want cost to be a barrier. If you can’t afford the standard ticket, please consider this cheaper option. :)

Free Tickets Available

We genuinely don’t want cost to be a barrier that keeps you from attending. If you want to come, but you can’t afford a ticket, send an email to us with the subject line “Free Outlier ticket, please” and we’ll send you a ticket, no questions asked. We trust that people who request free tickets are doing so because they’re unable to pay.

Additional Support

Want to contribute additional funds? Please feel free to add the additional amount to the free-form field on the ticket form!

If you’d like to support our conference in non-monetary ways, you could:

Call for Speakers

Also, just a quick reminder that we have an open call for speakers! Deadline: December 1st, 2020.

We hope that you decide to join us. Looking forward to seeing you there!!
PUBG Mobile Pro League SEA Season 2 Scrapping and Analysis Using R.
By: Ahsan Amri Rohman
Tags: Pubg Mobile, Dataviz, Data Analysis, R Programming, Pmpl

Loading library, scrapping and Prepare Data.

Because I only do scrapp on single page, so I don’t need to make a function. In the data preparation step, I added a new column, namely placement points based on team placement for each round and total points (kill + placement point).

The data have been prepared can be seen below.

Visualize.

In this section, I transform the data into a graph/plot to make easier to understand.

From the Line chart above, it can be seen that the team competition in rounds 4 to 11 is very tight, but the first position can still be maintained by Aerowolf Limax (AW LIMAX) from Indonesia and finished in 2nd place. Team Secret Jin (TSJ) from Thailand won 4 times WWCD managed to finish in position 3, Team Bigetron Red Aliens (BTR RA) from Indonesia became the champion of PUBG Mobile Pro League SEA Season 2 with WWCD 4 times.

The team with the most consistent position in the upper (lower better) placement was Aerowolf Limax. Out of 15 rounds, 11 of them were in the top five, although without WWCD this team was able to occupy the Runner Up.

Based on the barchart above, Bigetron Red Aliens became a champion with 197 points, which is a big difference from the Runner Up Aerowolf Limax points. The top 5 teams received an invitation to an even bigger tournament, the PUBG Mobile Global Championship (PMGC). The global tournament was attended by the best teams from each region, including China, Korea-Japan, Southeast Asia, South Asia, Europe and America.

From the top 5 teams, Aerowolf Limax excels in overall placement points, while Bigetron Red Aliens excels in total kills. Both of these teams are from Indonesia and must be watched out for by other teams in the next PMGC tournament.

From this tournament, the teams with higher placements (lower better) have relatively more kills. Overall, each round the team only got kills under 10, only a few got more than 10 kills.

Bigetron Red Aliens in a certain round reached nearly 20 kills. Even the 5 rounds played got more than 10 kills. The greatness of the Bigetron Red Aliens team in getting kills was extraordinary. Very deserving of the title of champion.
Creating empathy with Visualization
By: Gulrez Khan
Tags: Data Visualization, Dataviz, Visualization, Empathy

We spend hours and hours writing the code, cleaning the data and applying the best visual principles to communicate our stories with data but sometimes the best stories come with minimal code and yet making a deep emotional impact. Last week, I came across this visual “When Children Lack Nutrition” from Alberto Lucas Lopez published in National Geographic and gave me goosebumps. Here Alberto has done the infographic on Childhood Malnutrition and the size(circumference) of the pictures below actually depict the mid-upper arm circumference(MUAC) of kids in the respective regions. For example, you see the East Ghouta region in Syria the MUAC is just 8.7 cm and the circle circumference shown in the infographic is created that size of 8.7cm. While this in itself invokes lots of thinking and emotions what makes it more compelling is the scale on the right border.

Further, the below GIF makes it more real and does a great job in creating empathy for the topic touched here. Deservingly, It also won the Best of Show award in Malofiej27 Infographic Summit held recently.

Here’s another great visualization (created by some unknown artist) on the massacre that happened in Christchurch wherein around 50 people died when a gunmen killed while they were worshipping in a mosque. As you could see below, the graphic shows 50 men & women standing in worship and shown as a Fern Leaf which is represented as the symbol of New Zealand. It amazes me how you could create such a powerful story with a single image without any word — any annotation.

Mona Chalebi of The Guardian Newspaper is another great artist/Data Analyst who does these very creative projects — mostly with pen and paper and yet convey the issue in a very powerful way. Here, very creatively she has done the visual showing the pay gap for different races by using a dollar bill. Imagine how would have this looked like if this was a bar chart instead of these dollar bills. It would have been the same data but these dollar bills adds in creating the empathy for the subject.

I feel working day in and day out with the big data, churning millions of records and creating all those complicated visuals using R, Python, Power BI, excel and what not sometimes it’s good to take a break and take some inspiration from these acclaimed artists and their heart touching creations.

What would you create next? 😊
Learn Power BI: 10 Free Resources
By: Jenna Eagleson
Tags: Data Visualization Tools, Dataviz, Data Analysis, Learn, Power Bi

When getting started with a new tool, it can be hard to know where to start. Here is a collection of free resources to get started on your Power BI journey:

1. Dax and Power Query/M

2. Data Modeling

3. Ongoing Learning

4. Community and Practice

Let’s get started!

DAX and Power Query/M

When I first started with Power BI, one of my biggest obstacles was understanding the differences between DAX and M and calculated columns vs. measures. In short, power query is the engine you use to load and format your data into Power BI. M is the language behind power query — you can write out functions using M, or you can use power query by pointing and clicking. DAX is kind of like the Power BI version of what you put in the formula bar in Excel (that is a super oversimplification so please don’t come back and yell at me once you get more familiar). Here is a more thorough overview of the differences.

1. SQBLI to Learn DAX: Data Analysis Expressions (DAX) is something you will become intimately familiar with as you work more in Power BI. You are also not likely to get very far in your Power BI journey without running into Marco Russo and Alberto Ferrari who are the brains behind SQLBI and a wealth of Power BI resources — lots of free and some paid.

2. DAX Guide (also from SQLBI): a reference guide for DAX formulas. When using a new formula, this guide gives you the required arguments along with examples for how to use it appropriately. This is a good one to bookmark and refer back to regularly!

3. DAX Patterns (also from, you guessed it, SQLBI!): super helpful walk-throughs of some common DAX patterns like time intelligence calculations. As you start using DAX more you’ll see that it can get complicated quickly. Walking through some of these example patterns helps gain an intuitive understanding of how to work with complex problems efficiently in DAX.
Creating your own color palette for data visualization-Part 1
By: Abhinav Malasi
Tags: R, Data Visualization, Dataviz, Color Palette, Python

Creating your own color palette for data visualization-Part 1

Photo by Steve Johnson on Unsplash

Do you face difficulty choosing the right color combinations for your data visualizations? Are you still stuck using the color palettes most of the programmers are using? If you want to stand out then this post is for you.

Have you ever wondered, how designers, painters, cinematographers, and photographers choose the perfect color combinations? The perfect visualizations depicted by them are carefully curated by understanding what they want their audience to perceive. The perfect blend of colors and their vision provides an engaging visualization that not only is soothing to the eye but is aesthetically appealing. The trick used by the above-mentioned professions is that they know how to use the color wheel to make that perfect blend of colors. The color wheel was first made by nonother than Sir Issac Newton. Sir Issac Newton has contributed in various fields but let’s keep our focus on the current topic of the color wheel.

Color wheel

There are two types of color wheels, one is digital and the other one consists of paints used by painters. The major difference between these two color wheels is the difference in the components of primary colors. The digital color wheel consists of RGB (red-green-blue) and for paints, it is RYB (red-yellow-blue). Mixing two primary colors result in secondary color and a combination of primary and secondary color results in tertiary color.

Color theory

The color theory guides choosing color combinations for visual effects or creating a color mix that can be vibrant, contrasting, or harmonious in nature. This is evident in nature as well, if one sees the rainbow, the transition of colors from red to blue are in harmony and relaxing to the eyes. And on this principle, the color wheel is made.