Vega: A Visualization Grammar
Vega is a visualization grammar, a declarative format for creating, saving, and sharing interactive visualization designs. With Vega you can describe data visualizations in a JSON format, and generate interactive views using either HTML5 Canvas or SVG.
For a basic setup allowing you to build Vega and run examples:
yarnto install dependencies for all packages. If you don't have yarn installed, see https://yarnpkg.com/en/docs/install. We use Yarn workspaces to manage multiple packages within this monorepo.
yarn testto run test cases, or run
yarn buildto build output files for all packages.
yarn build, run
yarn serveto launch a local web server — your default browser will open and you can browse to the
"test"folder to view test specifications.
This repository includes the Vega website and documentation in the
docs folder. To launch the website locally, first run
bundle install in the
docs folder to install the necessary Jekyll libraries. Afterwards, use
yarn docs to build the documentation and launch a local webserver. After launching, you can open
http://127.0.0.1:4000/vega/ to see the website.
For backwards compatibility, Vega includes a babel-ified ES5-compatible version of the code in
packages/vega/build-es5 directory. Older browser would also require several polyfill libraries:
<script src="https://cdnjs.cloudflare.com/ajax/libs/babel-polyfill/7.4.4/polyfill.min.js"></script> <script src="https://email@example.com/runtime.min.js"></script> <script src="https://firstname.lastname@example.org/dist/fetch.umd.min.js"></script>
Visual Analytics is the scientific visualization to emerge an idea to present data in such a way so that it could be easily determined by anyone.
It gives an idea to the human mind to directly interact with interactive visuals which could help in making decisions easy and fast.
Visual Analytics basically breaks the complex data in a simple way.
The human brain is fast and is built to process things faster. So Data visualization provides its way to make things easy for students, researchers, mathematicians, scientists e
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Visual analytics is the process of collecting, examining complex and large data sets (structured or unstructured) to get useful information to draw conclusions about the datasets and visualize the data or information in the form of interactive visual interfaces and graphical manner.
Data analytics is usually accomplished by extracting or collecting data from different data sources in the form of numbers, statistics and overall activity of any organization, with different deep learning and analytics tools, which is then processed using data visualization software and presented in the form of graphical charts, figures, and bars.
In today technology world, data are reproduced in incredible rate and amount. Visual Analytics helps the world to make the vast and complex amount of data useful and readable. Visual Analytics is the process to collect and store the data at a faster rate than analyze the data and make it helpful.
As human brain process visual content better than it processes plain text. So using advanced visual interfaces, humans may directly interact with the data analysis capabilities of today’s computers and allow them to make well-informed decisions in complex situations.
It allows you to create beautiful, interactive dashboards or reports that are immediately available on the web or a mobile device. The tool has a Data Explorer that makes it easy for the novice analyst to create forecasts, decision trees, or other fancy statistical methods.
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Why do we visualize data?
It helps us to comprehend _huge _amounts of data by compressing it into a simple, easy to understand visualization. It helps us to find hidden patterns or see underlying problems in the data itself which might not have been obvious without a good chart.
Our brain is specialized to perceive the physical world around us as efficiently as possible. Evidence also suggests that we all develop the same visual systems, regardless of our environment or culture. This suggests that the development of the visual system isn’t solely based on our environment but is the result of millions of years of evolution. Which would contradict the tabula rasa theory (Ware 2021 ). Sorry John Locke. Our visual system splits tasks and thus has specialized regions that are responsible for segmentation (early rapid-processing), edge orientation detection, or color and light perception. We are able to extract features and find patterns with ease.
It is interesting that on a higher level of visual perception (visual cognition), our brains are able to highlight colors and shapes to focus on certain aspects. If we search for red-colored highways on a road map, we can use our visual cognition to highlight the red roads and put the other colors in the background. (Ware 2021)
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Visualization is an interactive representation of (abstract, complex) data that can help human to perform the task more effectively. It helps us see patterns in broader contexts that specific statistical questions do not reveal. Also, it helps us drive insights and questions that even predefined analytical queries do not elicit.
In this blog post, I will critique one good and one bad visualization.
In the below visualization, the three maps accurately show the life expectancy in the years 1800, 1950 and 2015. We can easily interpret how life expectancy has changed over the last three centuries. In the year 1800, people could expect a life span of only 25–40 years, irrespective of the location of their birth. As the new century (1950) began, newborns had the chance of longer life (over 60 years) but it is highly dependent on the location of their birth. People in continents like North America have a higher life expectancy as compared to people born in Asia. In recent decades every country has made very substantial progress in health and many other aspects.
Life Expectancy in 1800, 1950 and 2015 [source]
Globally the life expectancy increased from less than 30 years to over 72 years; after two centuries of the progress, we can expect to live even twice as long as our ancestors.
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