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New DataViz Weekly is here, highlighting some of the new cool graphics visualizations we found around the web just recently:

  • Finding a link between undocumented immigration and crime in the United States
  • Competition in the U.S. presidential elections since 1980
  • MLB pitchers’ pitch distribution
  • Sea-surface salinity

New Cool Graphics Visualizations in Data Visualization Weekly: May 10, 2019 — May 17, 2019 Looking for Connection Between Undocumented Immigration and Crime in U.S.


There are many studies out there that reveal no connection between crime and immigration in the United States of America. But what about undocumented immigrants? Join The Marshall Project’s Anna Flagg on The Upshot as she visualizes and analyzes new data from the Pew Research Center to find out if there are any strong links between unauthorized immigration and crime rates in the U.S.

Visualizing Competition in U.S. Presidential Elections Since 1980


Data journalist Lauren Leatherby and graphics developer Paul Murray, Bloomberg Graphics, looked at a staggering number of Democratic candidates jumping into the race for U.S. president and analyzed what competition looked like during previous presidential campaigns starting from 1980. In addition to the (static) chart that visualizes the number of candidates by election cycle along with their most recent political experience, click around the cool interactive visualization to see when each candidate entered and withdrew from the race.

Charting Statcast Pitch Distribution


For all baseball analysts and fans, MLB added a new exciting data visualization tool to Baseball Savant, their “clearinghouse for Statcast data.” The new project charts the frequency of pitch arsenal by pitch speed for all MLB pitchers, showing how the velocity of curves, sliders, changeups, cutters, and fastballs have changed for individual players over years, with age. Select your favorite players and look at the animated visualizations.

Mapping Sea-Surface Salinity


Working as part of the European Space Agency’s Climate Change Initiative, a team of researchers lead by LOCEAN’s Jacqueline Boutin and Ifremer’s Nicolas Reul created what the ESA website calls “the longest and most precise satellite sea-surface salinity global dataset to date.” It is based on observations from the satellite missions measuring salinity from space. Check out a map visualizing the global sea-surface salinity with new, significantly enhanced precision.

***

Thanks for staying tuned!

In addition, don’t miss out on this week’s new major release of our JavaScript charting library, AnyChart 8.6.0 bringing Timeline Chart and Network Graph along with multiple improvements.

If you are a Qlik Sense user, you’ll be amazed to know we’ve just become a Qlik Technology Partner and launched a dedicated extension for Qlik Sense with 36 chart types.

Have a great time over the weekend and beyond!

The post Graphics Visualizations About Immigration, Elections, Pitchers, and Salinity — DataViz Weekly appeared first on AnyChart News.

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AnyChart 8.6.0 is out! Check out what new awesome interactive data visualization features we’ve added to our award-winning JS charts library!

First and foremost, two new chart types are now available out of the box — greet the main protagonists of the 8.6.0 release:

Timeline Chart in AnyChart JS Charts Library

Timeline Chart is used to display a set of events in chronological order. It is typically a graphic design showing a long bar labeled with dates paralleling it, and usually contemporaneous events. Look at interactive Timeline Chart examples in our gallery. Read AnyChart Docs to learn how to create a JS Timeline Chart using our library.

Network Graph in AnyChart JS Charts Library

Network Graph, also Graph Chart, is a mathematical structure (graph) designed to show relationships between data points. This chart type visualizes how entities are interconnected with each other. Entities are displayed as nodes (points). Relationship between them (edges) are depicted as lines. Check out interactive Network Graph examples in our gallery. Read AnyChart docs to learn how to create a JS Network Graph using our library.

Then, the new major release AnyChart JS Charts 8.6.0 is bringing multiple other improvements, not to mention bug fixes.

Take a look into what’s new, by product:

Let us know if you have requests for new JS chart types or data visualization features. Contact our Support Team with all your tips and suggestions, and we’ll be happy to consider quickly delivering what you need. As always, you know.

Enjoy AnyChart 8.6.0!

The post JS Charts Library AnyChart Adds Timeline Chart and Network Graph in Version 8.6.0 appeared first on AnyChart News.

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Take a look at another set of compelling data visualization examples we have come across these days, in DataViz Weekly on the AnyChart blog:

  • Rise of developing countries in global trade
  • Birth rates
  • Growing similarity of diets
  • Child mortality

Compelling Data Visualization Examples: May 3, 2019 — May 10, 2019 Analyzing Rise of Developing Countries in Global Trade


Developing countries are likely to be disrupting the global trade more than anyone realized. 20 years ago, 62% of bilateral trade was only between rich countries such as the United States of America, Canada, and European countries. By now this share has decreased to 47%. At the same time, the value of trade between emerging economies has increased 10-fold during the same period. Join Bloomberg’s Andre Tartar and Cedric Sam as they look into the rising power of developing countries in the global trade, sharing cool charts and maps to visually represent the figures and trends.

Visualizing Birth Rate in Every Country


Harry Stevens from Axios charted birth rates around the world based on the United Nations World Populations Prospects data. Check out the visualization to see how the average number of kids per woman in every country has been changing since 1950 and how it is expected to be changing in the future, through the year 2100. Also, don’t miss out on the comments with important insights so you know where to start looking. Actually, Axios first published this piece last year, but we’ve noticed it only thanks to this week’s repost on their feed, and obviously, it is nonetheless interesting.

Charting Growing Similarity of Diets


People around the world consume more and more similar food. That is according to the results of a new study conducted by CGIAR, an international agricultural research group that tracked almost 50 years of the corresponding data comparing what people eat, by country. On National Geographic, you can see the change in similarity of diets over 1961-2009 and read more about it.

Exploring Child Mortality Data

Worldwide annual deaths by 5-year-age-group

In the period 1950 to 1954 more than 20 million children died every year.
Since then the number of annual child deaths declined 3-fold.
And the number of older people’s deaths doubled. pic.twitter.com/jTqwKmv6EV

— Max Roser (@MaxCRoser) May 2, 2019


The chart from data scientist Max Roser shown above displays the number of annual child deaths decreased 3-fold over the last seven decades. However, we are still far away from achieving the global goal to reduce the child mortality rate to at least 2.5% in the entire world by 2030, which is one of the official UN’s Sustainable Development Goals. Learn more and see more charts on his blog Our World in Data.

***

Thank you for staying tuned for compelling data visualization examples in the Data Visualization Weekly series on our blog!

If you are a Qlik Sense user, you’ll love our corporate news of the week, about the advent of AnyChart on the Qlik platform. To put it shortly, we became an official Qlik Technology Partner and have already made 36 chart types available in Qlik Sense in our extensions for Qlik Sense. Sankey, Gantt, Word Cloud, and more. Make sure to read more in the news.

The post Compelling Data Visualization Examples on Trade, Diets, Mortality, and Birth Rates — DataViz Weekly appeared first on AnyChart News.

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“We’re excited to be partnering with AnyChart, and their extensions will provide customers with additional choices for building even more compelling visualizations.”
Mike Foster, Vice President, Strategic Partners at Qlik.
(Source: joint press release by Qlik and AnyChart as of May 7, 2019.)

AnyChart crosses a new frontier! The vast data visualization capabilities of our award-winning JavaScript charting library, designed primarily for web developers and software engineers, are now available directly to data analysts and BI experts through powerful, flexible, intuitive extensions for Qlik Sense — AnyChart, AnyGantt, and AnyStock!

Taking advantage of over 15 years of AnyChart’s leadership in the data visualization software market, we are happy to grant Qlik users an entirely new set of chart types, each of which can be built stylish in literally three clicks — with the same ease and simplicity one has come to expect from Qlik.

For unique customizations, our chart editor allows changing every aspect of the visualizations to meet the individual needs of every client and every data visualization task, basic or advanced. Chart design, tooltips, axes, labels, interactivity, and other parts can be easily modified on the fly — natively inside the Qlik environment.

Overall, we’ve already delivered 36 chart types to Qlik Sense:

On top of that, we have a consistently progressive roadmap of adding even more charts out of the box! In fact, our JavaScript data visualization library supports over 70 chart types and we plan to make all of them available in Qlik Sense. Not to mention our roadmap is also customizable! Any customer can ask us to introduce a certain new chart type or feature and we’ll do our best to add it asap, as we’ve always done. So Qlik users, feel free to get in touch and tell us what you guys actually need for your business and data analysis work — your input greatly matters!

AnyChart’s advanced charting extensions for Qlik Sense are available for download on a 30-day free trial, requiring an appropriate paid subscription to be purchased upon expiration. Licensing is straightforward and completely scalable to the size of the customer business. Additionally, all subscriptions include unlimited upgrades and support at no additional cost.

We are excited to see our extensions have been enjoying enormous interest, considerably expanding our already huge customer base from the very first days live. Actually, it’s explicable as indeed Qlik users never got access to so many built-in charts and features at once, before the advent of AnyChart on the Qlik platform.

From our huge experience in interactive data visualization, we know that better charts lead to better decisions. So we invite all Qlik Sense users, consultants, and business integrators to sign up and cooperate. The AnyChart, AnyGantt, and AnyStock extensions will definitely benefit Qlik’s customers and partners as well as turn out to be greatly advantageous to further development of Qlik itself as a global leader in visual data analytics and business intelligence.

>>> AnyChart’s Extensions for Qlik Sense Data Analytics Tool <<<

The post AnyChart Joins Qlik Technology Program and Adds 36 Chart Types to Qlik Sense in Just-Launched Extensions appeared first on AnyChart News.

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Check out new interesting examples of how plotting data on charts and maps can be both insightful and beautiful. Here is what we are happy to feature in today’s article in the DataViz Weekly series:

  • Visualizing how India-Pakistan tensions disrupt air travel
  • Plotting diversity of Brussels
  • Mapping where natural disasters tend to strike in the United States
  • Charting NBA shots

New Data Plotting Examples in Data Visualization Weekly: April 26, 2019 — May 3, 2019 Visualizing How India-Pakistan Tensions Disrupt Air Travel


After an air strike conducted by the Indian Armed Forces near the Pakistani town of Balakot on February 26 this year, Pakistan restricted its airspace. Nowadays, most of the limitations remain in place. As a result, international airlines have to take time and money-consuming detours to the south and north. This means longer flight times and larger fuel costs.

Check out the charts and maps to figure out what’s going on and why it matters. Simon Scarr and Marco Hernandez from the Reuters Graphics team provide a stunning graphics-based look at exactly how India-Pakistan tensions have disrupted air travel.

Plotting Diversity of Brussels


One of the world cities with the highest share of foreign-origin residents and home to over 180 nationalities, Brussels is indeed a melting pot. Data visualization developer Karim Douïeb offers a glance over the ethnic and racial diversity of the European Union’s capital city.

Overall, it’s a very interesting data visualization essay. Don’t miss out on the charts and maps showing what foreigners live in Brussels and where.

Mapping Where Natural Disasters Strike in America


Natural disasters cost the United States of America 247 human lives and more than $90 billion in damage last year. That’s according to data from the National Oceanic and Atmospheric Administration (NOAA). What’s more, “it turns out there is nowhere in the United States that is particularly insulated from everything,” The Washington Post’s Tim Meko concludes after conducting a visual analysis of more than a decade of data from NOAA, as well as the National Weather Service (NWS), and other sources.

Take a look at the impressive maps Tim Meko published several days ago, which show where in the United States natural disasters tend to strike: floods, wildfires, tornadoes and hurricanes, earthquakes and volcanoes, extreme cold and heat, and lightning.

Wondering how he made such stunning maps? Don’t miss out on a dedicated thread on Twitter where the author explains the whole process.

Charting NBA Shots


FiveThirtyEight published an excerpt from their contributor, ESPN analyst Kirk Goldsberry’s new book “SprawlBall: A Visual Tour of the New Era of the NBA.” In the book, he looks into the history of the three-point shots in the NBA and the way teams and players use them now. Check out the excerpt to learn how map-based visualization of shot data changed the game, aligning economic efficiency and shot selection, and leading to the fact that two-point jump shooting is currently dying.

***

It’s awesome to see you’ve looked through all of it! If you like good visualizations, stay tuned for DataViz Weekly and learn about new interesting projects every Friday.

The post Plotting NBA Shots, Diversity, Disasters, and Air Traffic — DataViz Weekly appeared first on AnyChart News.

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Wondering how to make a beautiful interactive Word Cloud using JS? Then you’re in the right place! In this data visualization tutorial, I’ll guide you through the entire development process, demonstrating it’s easier to create a JavaScript word cloud chart for an HTML5 app or web page than you might think!

Also known as tag clouds, word clouds represent a popular visual technique designed to reveal how often tags (or basically, any words) are mentioned in a given text body. Essentially, the word cloud chart type leverages diverse colors and sizes to display at a glance different levels of relative prominence.

Now that we’ve got an idea of what a word cloud is, let’s get down to learning how to quickly code one using JavaScript!

Building Basic JavaScript Word Cloud

Generally speaking, there are four basic steps to create a chart of any type using JavaScript. Just follow them, and it won’t take you long to make your text data visualization look like this:


1. Create an HTML page

First of all, create an HTML page where your JavaScript word cloud chart will appear. It has to be as follows:

<!DOCTYPE html>
<html>
  <head>
    <title>JavaScript Tag Cloud Chart</title>  
    <style>
      html, body, #container {
      width: 100%;
      height: 100%;
      margin: 0;
      padding: 0;
      }
    </style>    
  </head>
  <body>
    <div id="container"></div>
  </body>
</html>

The container that you see in the <body> section is intended for your future chart. The width and height parameters are responsible for the chart size, and you can specify them in percentages or in pixels to make the chart fill as much page space as you want.

2. Reference all necessary files

Then, add the necessary AnyChart JavaScript charting library links into the <head> section in the <script> tags:

<!DOCTYPE html>
<html>
 <head>
  <title>JavaScript Tag Cloud Chart</title>
  <script src="https://cdn.anychart.com/releases/v8/js/anychart-base.min.js"></script>
  <script src="https://cdn.anychart.com/releases/v8/js/anychart-tag-cloud.min.js"></script>
  <style>
    html, body, #container {
    width: 100%;
    height: 100%;
    margin: 0;
    padding: 0;
    }
  </style> 
 </head>
  <body>
   <div id="container"></div>
    <script>
        <!-- chart code will be here -->
    </script>
  </body>
</html>
3. Put together the data

The most valuable part of any chart is data. So you should always carefully choose the chart type depending on exactly what you want to visualize and for what purpose. Visit Chartopedia, a handy tool that will tell you more about each chart type and how to use one.

In the present case, a tag (word) cloud chart will be used to demonstrate the 15 most spoken languages. Go to Wikipedia: List of languages by total number of speakers and obtain the data from there:

Language Number of speakers Family
Mandarin Chinese 1.09 billion Sino-Tibetan
English 983 million Indo-European
Hindustani 544 million Indo-European
Spanish 527 million Indo-European
Arabic 422 million Afro-Asiatic
Malay 281 million Austronesian
Russian 267 million Indo-European
Bengali 261 million Indo-European
Portuguese 229 million Indo-European
French 229 million Indo-European
Hausa 150 million Afro-Asiatic
Punjabi 148 million Indo-European
Japanese 129 million Japonic
German 129 million Indo-European
Persian 121 million Indo-European

The magic is that all the tags in a word cloud chart will get their size and color automatically after you write the values and put the languages in categories in the data object:

// set the data
var data = [
    {"x": "Mandarin chinese", "value": 1090000000, category: "Sino-Tibetan"},
    {"x": "English", "value": 983000000, category: "Indo-European"},
    {"x": "Hindustani", "value": 544000000, category: "Indo-European"},
    {"x": "Spanish", "value": 527000000, category: "Indo-European"},
    {"x": "Arabic", "value": 422000000, category: "Afro-Asiatic"},
    {"x": "Malay", "value": 281000000, category: "Austronesian"},
    {"x": "Russian", "value": 267000000, category: "Indo-European"},
    {"x": "Bengali", "value": 261000000, category: "Indo-European"},
    {"x": "Portuguese", "value": 229000000, category: "Indo-European"},
    {"x": "French", "value": 229000000, category: "Indo-European"},
    {"x": "Hausa", "value": 150000000, category: "Afro-Asiatic"},
    {"x": "Punjabi", "value": 148000000, category: "Indo-European"},
    {"x": "Japanese", "value": 129000000, category: "Japonic"},
    {"x": "German", "value": 129000000, category: "Indo-European"},
    {"x": "Persian", "value": 121000000, category: "Indo-European"}
  ];
4. Write the JS word cloud chart code

Now, add the anychart.onDocumentReady() function to be executed when the page is ready:

<script>
anychart.onDocumentReady(function() {
    // code to create a word cloud chart will be here
});
</script>

After that, add the data into the function, create the tag cloud chart, set its title and legend, and just command to display it:

anychart.onDocumentReady(function() {
  var data = [
    {"x": "Mandarin chinese", "value": 1090000000, category: "Sino-Tibetan"},
    {"x": "English", "value": 983000000, category: "Indo-European"},
    {"x": "Hindustani", "value": 544000000, category: "Indo-European"},
    {"x": "Spanish", "value": 527000000, category: "Indo-European"},
    {"x": "Arabic", "value": 422000000, category: "Afro-Asiatic"},
    {"x": "Malay", "value": 281000000, category: "Austronesian"},
    {"x": "Russian", "value": 267000000, category: "Indo-European"},
    {"x": "Bengali", "value": 261000000, category: "Indo-European"},
    {"x": "Portuguese", "value": 229000000, category: "Indo-European"},
    {"x": "French", "value": 229000000, category: "Indo-European"},
    {"x": "Hausa", "value": 150000000, category: "Afro-Asiatic"},
    {"x": "Punjabi", "value": 148000000, category: "Indo-European"},
    {"x": "Japanese", "value": 129000000, category: "Japonic"},
    {"x": "German", "value": 129000000, category: "Indo-European"},
    {"x": "Persian", "value": 121000000, category: "Indo-European"}
  ];

 // create a tag (word) cloud chart
  var chart = anychart.tagCloud(data);

   // set a chart title
  chart.title('15 most spoken languages')
  // set an array of angles at which the words will be laid out
  chart.angles([0])
  // enable a color range
  chart.colorRange(true);
  // set the color range length
  chart.colorRange().length('80%');

  // display the word cloud chart
  chart.container("container");
  chart.draw();
});

The JavaScript word (tag) cloud chart code should be put in the <script> tag from the second step. And that’s it!

Yay! You did it! Check out the beautiful JS word cloud sample you’ve made:


Customize Word Cloud Chart Appearance

If you want to change how your JavaScript word cloud chart look, you can easily modify it. Visit the Word Cloud Chart settings and Word Cloud Chart gallery pages to see the instruction on and examples of how it can be done.

In the meantime, let’s makes some visual changes to the tag cloud built along the tutorial.

Tooltips in a word cloud

You must have noticed that in the data object, the values are in millions and billions. But it may be hard to clearly perceive such long numbers in a visual way. Therefore, it would be good to properly configure the chart tooltips with the help of the formatting parameters list:

// format the tooltips
var formatter = "{%value}{scale:(1)(1000)(1000)(1000)|( dozen)( thousand)( million)( billion)}";
var tooltip = chart.tooltip();
tooltip.format(formatter);

Now the JS word cloud sample tooltips read better, don’t they?


Angles of the tags

If you want your JavaScript word cloud to look sort of more сhaotic, you can simply change the angles at which the tags are arranged. For example:

chart.angles([0, -45, 90])

And the JS word cloud chart sample becomes a little bit wild:


Word cloud interactivity

You may also want to link tags to some web pages. If so, use the listen() method to add an event listener to your word cloud chart. In the given case, clicking on a word will lead to its Wikipedia page opening, which means you will be able to find more information about every language:

// add an event listener
chart.listen("pointClick", function(e){
  var url = "https://en.wikipedia.org/wiki/" + e.point.get("x");
  window.open(url, "_blank");
});

Here is your final interactive JS word cloud chart:


Conclusion

You see there is nothing difficult in creating beautiful interactive JavaScript word cloud charts for web pages and applications, in particular using the AnyChart JS (HTML5) charting library for data visualization.

For more information, check the official AnyChart JS Charts website. The Chart Documentation and Chart API Reference sections will help you make and tune your JavaScript charts in a way you prefer, and you can freely play with the code of the charts on the Chart Playground.

You are also welcome to check out the other basic JavaScript chart tutorials on our blog.

Now it’s time for your questions! Feel free to ask them in the comments below or by contacting our Support Team.

The post How to Create JavaScript Word Cloud Chart — Tutorial for Web Developers appeared first on AnyChart News.

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Get ready for another dose of cool visual data graphics — DataViz Weekly is here! Today we are glad to acquaint you with the following new interesting projects:

  • The Pudding explains why EU regions redraw their borders.
  • Reporters Without Borders shares its annual World Press Freedom Index for 2019.
  • Bloomberg analyzes errors in the International Monetary Fund’s spring forecasts of same-year GDP growth.
  • BuzzFeed visualizes how climate change has already transformed the planet.

New Interesting Visual Data Graphics in Data Visualization Weekly: April 19, 2019 — April 26, 2019 Explaining Why EU Regions Get New Borders


The European Union spends about one-third of its budget on funding infrastructure projects in less economically developed regions, supporting transport, education, health care, and so on. But as regions improve their GDP figures, which is the explicit purpose, they approach the brink of getting less EU investments. This is exactly why governors of some regions decide to change their borders by splitting the territory into two parts, one more developed, and one with a lower GDP that again can qualify for EU catch-up funds in full, keeping the money flowing. Interested? The Pudding uses cool data visuals to explain how the border redrawing scheme works and how Hungary, Poland, and Lithuania have already pulled it off.

Analyzing Freedom of Press Worldwide in 2019


Reporters Without Borders (RSF) published its annual World Press Freedom Index for 2019. Based on evaluations of the journalists’ independence and safety, as well as pluralism and legislation quality, it ranks 180 countries and regions according to the degree of freedom available to media on their territory. Check out RSF’s interactive map for a visual overview of the new World Press Freedom Index data, and see the ranking table for more information.

Charting Errors in IMF’s Spring Forecasts of Same-Year GDP Growth


Inspired by the release of the new World Economic Outlook report from the International Monetary Fund, Bloomberg decided to find out how good the IMF’s predictions have actually been so far and studied 3,284 same-year country forecasts published each spring since 1999. As discovered by the analysts, there used to be a really wide variation in the magnitude and direction of forecast errors. To understand how much trust should be put in IMF’s spring forecasts of same-year GDP growth around the world according to the experts’ findings, take a closer look using Bloomberg’s special interactive charts as well as explore the accompanying map to reveal the level of misses by region and another chart providing focus on the changing outlook for Greece, Ireland, Portugal, and Cyprus as bailout countries.

Visualizing How Climate Change Already Transformed Earth


This week, BuzzFeed has published a series of articles on the topic of climate change. While all of the stories are really worth-reading, we are glad to feature one with interesting visual data graphics. Peter Aldhous mapped how the average annual temperature has changed all over the world. When you click on any place on the map, you’ll also see a line chart that shows the temperature change data over time for that specific location. So you can quickly explore how climate change has already changed your city. Also, don’t miss out on the two data visualizations further down the article. The first one shows how the sea ice area has shrunk. The other displays how the sea level has changed.

***

Thanks for staying with us! Have a great weekend time, everyone! See you again soon on our blog!

The post Visual Data Graphics on EU Regions, Freedom of Press, IMF Forecasts, and Climate Change — DataViz Weekly appeared first on AnyChart News.

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At AnyChart, we believe that companies and organizations should be able to leverage their data without having to waste time on trying to make sense of raw digits and vast spreadsheets. This is why we worked hard to create a no-fuss interactive data visualization software that does not compromise flexibility while giving everyone a unique chance to visualize any data in any way they want and make correct data-driven decisions they need in an instant. It is for this reason that FinancesOnline, a reputable business directory, has included our charting solution in their list of the top data visualization tools and granted us the Premium Usability Award and the Rising Star Award for 2019!

AnyChart, according to the FinancesOnline experts, allows developers to simplify the creation and integration of any charts into any application thanks to our robust JavaScript charting library along with “awesome API functionality, documentation tools, and enterprise-grade support.” Moreover, AnyChart’s capability to export these charts into any file format and share them on different platforms is a great way to boost collaboration in the workplace and improve data transparency for stakeholders.

FinancesOnline also commended how AnyChart allows users to churn out interactive BI dashboards based on what types of data they want to monitor regularly.

Aside from snagging a spot on their list of the best data visualization tools and being honored with the awards, FinancesOnline’s experts noticed our JS/HTML5 data visualization library had built a solid reputation among industry professionals. Indeed, in recent years, we have increasingly expanded our customer database by providing not only top-notch SaaS products but also high-quality service for our clientele.

If you want to learn more about AnyChart JS Charts and find out why it is one of the trending alternatives to some of the other most popular data visualization tools, read the full AnyChart review on the FinancesOnline website. They also offer in-depth evaluations of other types of platforms in case you are looking for other kinds of software solutions for your business.

The post FinancesOnline Honors AnyChart Data Visualization Software with Premium Usability and Rising Star Awards appeared first on AnyChart News.

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Various data charting examples get published on the web every day. They are part of visual stories, analytical reports, scientific studies, and so on. Every week, we choose four of the most interesting projects with charts and maps we’ve come across and feature them in a special post on the AnyChart blog within the framework of our regular DataViz Weekly series. So here’s a new one!

Today on Data Visualization Weekly:

  • estimates of health care expenditures under the “Medicare for All” plan by U.S. Senator Bernie Sanders;
  • electoral interference instances by the United States and USSR (Russia) in 1946-2000;
  • U.S. households in each state by income level;
  • top countries by GDP per capita in 1801-2016.

New Data Charting Examples in Data Visualization Weekly: April 12, 2019 — April 19, 2019 Estimating Health Care Expenditures Under Medicare for All Plan


The “Medicare for All” plan reintroduced by U.S. Senator Bernie Sanders this week has been met with mixed reactions. As a matter of fact, it appears to be hard to conclude whether it would increase health spending in the United States or make costs lower. The New York Times tried to figure this out and asked several economists and think tanks to provide their estimates: Gerald Friedman from the University of Massachusetts, Charles Blahous from Mercatus Center at George Mason University, RAND Corporation’s analysts, Kenneth E. Thorpe from Emory University, and analysts from the Urban Institute. Take a look at the findings on whether “Medicare for All” will save billions or cost billions, in a special piece on The Upshot.

Electoral Interference by United States and Russia (USSR) in 1946-2000


The (alleged) interference of Russia in the 2016 United States presidential election has been widely discussed in media. Whatever the case, such things are not really rare in world history, and data scientist Will Geary made a nice illustration of this fact. His impressive map animation displays all known instances of electoral interference lead by the USA on the one hand, and the USSR and Russia on the other, covering the period from 1946 through 2000. Take a look and do not forget to turn on the audio to listen to speeches by American presidents Harry Truman, Ronald Reagan, and George H. W. Bush.

U.S. Households by Income Level


Famous statistician and data visualization expert Nathan Yau continues to look into the American Community Survey data. In his new project published as recently as this week on his blog FlowingData — it is #1 on the list of the 50 best data visualization blogs according to Feedspot, where our AnyChart blog with DataViz Weekly is now #2 best — he allows us to see what percentage of all households in each U.S. state belongs to the lower, middle, and upper-income level, adjusted for household size. Take a look. Also, don’t miss out on Nathan Yau’s other interesting work on the income subject, based on the same data and posted last week, which in a similar manner visualizes what qualifies as middle-income in each state of the United States of America.

Top Countries by GDP Per Capita in 1801-2016


Noble Datum, a company developing interactive data visualization content, created a cool video showing the top countries by GDP per capita over 216 years from 1800 through 2016. The authors leverage the form of a bar chart race applied to data from the University of Groningen’s Maddison Project Databases. Take a look at the world’s economic history through the lens of the criterion of GDP per capita.

***

We’ll show you more data charting examples next week. Meanwhile, have a wonderful time everyone, and stay tuned!

The post Data Charting on Health Care, Elections, Income, and Countries — DataViz Weekly appeared first on AnyChart News.

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We continue to update the Challenge AnyChart! section of our blog with new data visualization tutorials. They nicely demonstrate how powerful our JavaScript charting library is, which our Support Team is always eager to prove to everyone. In one of the first challenges, we already told you how to create a JS chart with nested axes. Since our customers keep showing interest in such forms of data presentation, now we’ll show you how to build another interesting chart with a different appearance but quite similar code — an interactive JS column chart with a multi-level X-axis.

Data Visualization Task

Here’s the issue a customer asked us to solve:

I want to display the data with subcategories in a column chart, is it possible?

To show how the nested axis should be placed, they attached the following picture:

Typically, here is what’s needed to make a chart like the one in the picture:

  • work with data using view and iterator objects,
  • use extra axis,
  • use custom scales, and
  • use weights of scale ticks.
Solution Overview

First of all, let’s modify the source data and add empty values in it to visually separate data by category.

Then, once the chart has been drawn and calculations for the scales and bounds completed, add an extra axis and set the ticks and labels in a preferred way.

Preprocessing

Before feeding the data to the chart, let’s add empty values into the data, which will make visible where one category ends and another begins.

To achieve this, add one empty value to the beginning of the array, one more to the end, and two empty items every time the category name changes.

function preprocessData(data){
  if (data.length > 0) {
    data.unshift([data[0][0]]);
    data.push([data[data.length - 1][0]]);
    for (var i = 2; i < data.length - 1; i++) {
      var previous = data[i-1][0];
      var current = data[i][0];
      if (current!=previous) {
        data.splice(i, 0, [previous], [current]);
        i = i+2;
      }
      else {
        data.splice(i, 0, [previous]);
        i += 1;
      }
    }
  }
  return anychart.data.set(data);
}

When it’s done, add subcategory names to the meta using the mapAs() method to get a different view and use them as names of the X-axis ticks this way:

chart.column(data.mapAs({'year': 0, 'value': 2, 'sub-category': 1}));
chart.xScale().names('sub-category');

Now the JS column chart itself can be drawn.

Extra Axis and Additional Scale on JS Column Chart

The time has come for adding an extra axis to the chart. To achieve this, create a function with the iterator object. It will be used for exploring the view and finding categories and subcategories.

After that, draw ticks between the categories and set up the weights. This will give the chart a better look:

var iter = data.mapAs({'category': 0, 'sub-category': 1}).getIterator();
while(iter.advance()) {
  var name = iter.get('category');
  var value = iter.get('sub-category');
  names.push(name);
  if (name && names[names.length - 1] != names[names.length - 2])
    ticks.push(iter.getIndex());
  }
  weights.push(value?0.5:0.2);
}

Axes work with scales. To be more precise, the former visualize the latter. So in order to implement the idea, a custom scale containing data of categories and subcategories is needed. Then let’s pass the values, names, and ticks to this scale:

var customScale = anychart.scales.ordinal();
customScale.values(chart.xScale().values());
customScale.names(names);
customScale.ticks(ticks);

And build the new axis on it:

chart.xAxis(1)
  .scale(customScale)
  .orientation('top')
  .ticks(true);

Finally, we synchronize the weights with the chart scale:

chart.xScale().weights(weights);

And disable the ticks on the main axis:

chart.xAxis(0).ticks(false);

As was said, everything is possible with AnyChart! Now the entire JS column chart with a multi-level X-axis, which has been created along the tutorial, is ready to be presented. Check it out right here below, and if you want, you are welcome to view and modify this sample on AnyChart Playground.


The full code is placed below. Take a look through the lines to better understand the implementation:

anychart.onDocumentReady(function () {
  var data = preprocessData([
    ['2016', 'Rel 04', 18],
    ['2016', 'Rel 06', 13],
    ['2016', 'Rel 10', 17],
    ['2017', 'Rel 02', 4],
    ['2017', 'Rel 04', 13],
    ['2017', 'Rel 06', 12],
    ['2017', 'Rel 08', 6],
    ['2017', 'Rel 10', 17],
    ['2018', 'Rel 02', 12],
    ['2018', 'Rel 06', 9],
    ['2018', 'Rel 10', 15]
  ]);

  var chart = anychart.column();

  // configure global settings for series labels
  chart.labels({position:'top'});

  // add subcategory names to the meta of one of the series
  chart.column(data.mapAs({'year': 0, 'value': 2, 'sub-category': 1}));

  // use subcategory names as names of X-axis ticks
  chart.xScale().names('sub-category');

  chart.xAxis().labels().rotation(90);
  chart.xAxis().labels().anchor("center");
  chart.xAxis().overlapMode('allow-overlap');

  // set a container and draw the chart
  chart.container('container').draw();

  // calculate an extra axis
  createTwoLevelAxis(chart, data);
});

function preprocessData(data){
  // to make beautiful spacing between categories, add
  // several empty lines with the same category names to the data
  if (data.length > 0) {
    // add one to the beginning of the array
    data.unshift([data[0][0]]);
    // add one more to the end of the data
    data.push([data[data.length - 1][0]]);
    // add two empty items every time the category name changes,
    // to each category
    for (var i = 2; i < data.length - 1; i++) {
      var previous = data[i-1][0];
      var current = data[i][0];
      if (current!=previous) {
        data.splice(i, 0, [previous], [current]);
        i = i+2;
      } 
      else {
        data.splice(i, 0, [previous]);
        i += 1;
      }
    }
  }
  return anychart.data.set(data);
}

function createTwoLevelAxis(chart, data, padding){
  // subcategory names
  var names = [];
  // ticks for axes based on main categories
  var ticks = [];
  // weights of ticks (to make spacing between categories by using
  // the empty lines created in preprocessData)
  var weights = [];
  // the iterator feature allows you to go over data, so
  // create an iterator for a new breakdown
  var iter = data.mapAs({'category': 0, 'sub-category': 1}).getIterator();
  while(iter.advance()) {
    var name = iter.get('category');
    var value = iter.get('sub-category');
    // store category names
    names.push(name);
    // when the border between the categories is identified, create a tick
    if (name && names[names.length - 1] != names[names.length - 2]) 					{
      ticks.push(iter.getIndex());
    }
    // assign weight to the tick
    weights.push(value?0.5:0.2);
  }

  // create a custom scale
  var customScale = anychart.scales.ordinal();
  // supply values from the chart to the scale
  customScale.values(chart.xScale().values());
  // names and ticks of the main categories only
  customScale.names(names);
  customScale.ticks(ticks);

  // synchronize weights with the chart scale
  chart.xScale().weights(weights);

  // disable ticks along the main axis
  chart.xAxis(0).ticks(false);

  // create an extra chart axis
  chart.xAxis(1)
    .scale(customScale)
    .orientation('top')
    .ticks(true);

  chart.xAxis(1).ticks().length(60).position('center');
  chart.xAxis(1).labels().offsetY(30);

  chart.xGrid(0).scale(customScale);

  chart.title('Year / Release');

  // format the tooltip title
  chart.tooltip().titleFormat("{%year}");

  // format the tooltip body
  chart.tooltip().format("{%sub-category}: {%value}");
}
Conclusion

Hoping you liked this article, we invite you to check out more of the similar ones in the Challenge AnyChart! section of our blog. If you have any interesting data visualization questions that might be a good fit for a tutorial like this, please send an email to our Support Team at support@anychart.com with “Challenge” in the subject line.

We will be glad to keep showing you how to create cool, sophisticated data visualizations using AnyChart JS Charts and further demonstrate the vast capabilities of our JS charting library in action.

Any feedback is always welcome. If you have something to say, please write a comment below.

The post How to Code JS Column Chart with Multi-Level X-Axis — Challenge AnyChart! appeared first on AnyChart News.

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