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Data Vizualization

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React-vis. Data visualization library based on React and d3.

react-vis

See the live demo at Overview A collection of react components to render common data visualization charts, such as line/area/bar charts, heat maps, scatteplots, pie and donut charts, tables with fixed headers and tree maps. Some notable features: Shiny - Tutorial. The How to Start Shiny video series will take you from R programmer to Shiny developer.

Shiny - Tutorial

Watch the complete tutorial here, or jump to a specific chapter by clicking a link below. Clarity or Aesthetics? Part 3 – Tips for Achieving Both. Previous Post (Part 2 of 3): A Tale of Four Quadrants We started this series by introducing the notion of a two dimensional plane on which to assess all data graphics, and then followed it up with an example of visualizations in four different quadrants on the plane to illustrate the differences between the two axes, clarity and aesthetics, that define the plane.

Clarity or Aesthetics? Part 3 – Tips for Achieving Both

Now, let’s review some of the basic principles & tips that you will find in the data visualization resources out there. All I have done here is I have applied these well-known best practices to the four quadrant system. Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization. iWantHue. Data visualisation tools. 30 Simple Tools For Data Visualization. There have never been more technologies available to collect, examine, and render data.

30 Simple Tools For Data Visualization

Here are 30 different notable pieces of data visualization software good for any designer's repertoire. They're not just powerful; they're easy to use. In fact, most of these tools feature simple, point-and-click interfaces, and don’t require that you possess any particular coding knowledge or invest in any significant training.

Data Insights. Made Social. Tour. Modest Maps. Raw. Chartkick. Simplify your admin dashboard - create new charts in seconds!

Chartkick

Works with Rails, Sinatra and most browsers (including IE 6) A perfect companion to groupdate, hightop, and active_median Get handcrafted updates for new features Usage. Ember Charts. A charting library built with the Ember.js and d3.js frameworks.

Ember Charts

It includes time series, bar, pie, and scatter charts which are easy to extend and modify. The out-of-the-box behavior these chart components represents our thoughts on best practices in chart interactivity and presentation. Features. Springy - A force directed graph layout algorithm in JavaScript. Demos - Draggable shapes. Using Google Charts - Google Charts. Google Charts provides a perfect way to visualize data on your website.

Using Google Charts - Google Charts

From simple line charts to complex hierarchical tree maps, the chart gallery provides a large number of ready-to-use chart types. The most common way to use Google Charts is with simple JavaScript that you embed in your web page. You load some Google Chart libraries, list the data to be charted, select options to customize your chart, and finally create a chart object with an id that you choose. Then, later in the web page, you create a <div> with that id to display the Google Chart. That's all you need to get started.

Charts are exposed as JavaScript classes, and Google Charts provides many chart types for you to use. All chart types are populated with data using the DataTable class, making it easy to switch between chart types as you experiment to find the ideal appearance. Ready to create your first chart? SVG Graphics Library for JavaScript HTML5 :jsDraw2DX. Cube. Time Series Data Collection & Analysis Cube is a system for collecting timestamped events and deriving metrics.

Cube

By collecting events rather than metrics, Cube lets you compute aggregate statistics post hoc. It also enables richer analysis, such as quantiles and histograms of arbitrary event sets. Envision - demos. Gephi, an open source graph visualization and manipulation software. 30 Simple Tools For Data Visualization. There have never been more technologies available to collect, examine, and render data.

30 Simple Tools For Data Visualization

Here are 30 different notable pieces of data visualization software good for any designer's repertoire. They're not just powerful; they're easy to use. In fact, most of these tools feature simple, point-and-click interfaces, and don’t require that you possess any particular coding knowledge or invest in any significant training. Reverse Snowflake Joins-online demo. Visualizing huge SQL SELECT film.film_id AS FID, film.title AS title, film.description AS description, category.name AS category, film.rental_rate AS price, film.length AS length, film.rating AS rating, GROUP_CONCAT(CONCAT(actor.first_name, _utf8' ', actor.last_name) SEPARATOR ', ') AS actors FROM category LEFT JOIN film_category ON category.category_id = film_category.category_id LEFT JOIN film ON film_category.film_id = film.film_id JOIN film_actor ON film.film_id = film_actor.film_id JOIN actor ON film_actor.actor_id = actor.actor_id GROUP BY film.film_id; Algorithm (start with neato) Distance between nodes Please try the new version of SnowflakeJoins Powered by: Python , Graphviz , Pyparsing and.

Reverse Snowflake Joins-online demo

Reverse Snowflake Joins. 2 Related Work. Next: 3 H3: 3D Hyperbolic Up: Interactive Visualization of Large Previous: 1 Motivation There are several relevant threads of related work. We have already discussed some of the core information visualization data- and task-based taxonomies in Chapter 1. We begin with the previous work in the deliberate use of distortion to show as much context as possible around a focus point. The bulk of this chapter is a discussion of the many previous systems for drawing graphs and hierarchies, both topologically and geographically. Our main focus when reviewing previous systems for automatic graph drawing is their limited scalability. One of the important challenges in a visualization system is how to present as much important information as possible given a finite display area. The disadvantage of simply providing navigation controls is that users often lose track of the position of their current viewport with respect to the global structure.

Firefox Web Browser — Getting Started with Mozilla Firefox — mozilla.org. Welcome to Firefox! We'll show you all the basics to get you up and running. When you're ready to go beyond the basics, check out the other links for features you can explore later. Choose the page that opens when you start Firefox or click the Home button. Colorbrewer: Color Advice for Maps. Colorbrewer: Color Advice for Maps. jQuery. jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML.[2] It was released in January 2006 at BarCamp NYC by John Resig. It is currently developed by a team of developers led by Dave Methvin. Used by over 80% of the 10,000 most visited websites,[3] jQuery is the most popular JavaScript library in use today.[4][5] The set of jQuery core features—DOM element selections, traversal and manipulation—enabled by its selector engine (named "Sizzle" from v1.3), created a new "programming style", fusing algorithms and DOM-data-structures; and influenced the architecture of other JavaScript frameworks like YUI v3 and Dojo.

Microsoft and Nokia bundle jQuery on their platforms.[7] Microsoft includes it with Visual Studio[8] for use within Microsoft's ASP.NET AJAX framework and ASP.NET MVC Framework while Nokia has integrated it into the Web Run-Time widget development platform.[9] jQuery has also been used in MediaWiki since version 1.16.[10] Crossfilter.

Fast Multidimensional Filtering for Coordinated Views Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records; we built it to power analytics for Square Register, allowing merchants to slice and dice their payment history fluidly. Since most interactions only involve a single dimension, and then only small adjustments are made to the filter values, incremental filtering and reducing is significantly faster than starting from scratch. Raphaël—JavaScript Library. Creating Sparklines. A sparkline chart is characterized by its small size and data density. Typically displayed without axes or coordinates, sparklines present trends and variations associated with some measurement, in a simple and condensed way. Whereas the typical chart is designed to show as much data as possible, sparklines are intended to be succinct.

The example uses the Superstore sample to create a sparkline that shows profit for various product categories over a 12-month period. Step 1. Building a Bullet Graph. A bullet graph is a variation of a bar graph. It is generally used to compare a primary measure to one or more other measures in the context of qualitative ranges of performance. Occasionally, bullet graphs are used to compare the same measure across multiple categories, such as using the data from one region as the threshold for other regions. Video. D3.js Tips and Tricks. DVDC HTML5 Interactive Examples. 25 (Free) 3D Modeling Applications You Should Not Miss. 3D-modeling tools help turn individual ideas into beautiful models and prototypes for a variety of fields.

These tools allow building and customizing models from the ground up, no matter if you are a keen beginner or a professional engineer. Popular in various industries such as film, animation, gaming, architecture, and interior design, 3D models are key aspects of various projects. Choosing the best modeling software is often difficult because of various aspects and the wide range of features available in these tools. A Pen by Andrew Trice. DVDC HTML5 Interactive Examples.