Tutorials · mbostock/d3 Wiki Wiki ▸ Tutorials Please feel free to add links to your work!! Tutorials may not be up-to-date with the latest version 4.0 of D3; consider reading them alongside the latest release notes, the 4.0 summary, and the 4.0 changes. Introductions & Core Concepts Specific Techniques D3 v4 Blogs Books Courses D3.js in Motion (Video Course)Curran Kelleher, Manning Publications, September 2017D3 4.x: Mastering Data Visualization Nick Zhu & Matt Dionis, Packt. Talks and Videos Meetups Research Papers D3: Data-Driven DocumentsMichael Bostock, Vadim Ogievetsky, Jeffrey HeerIEEE Trans.
40+ Visualization Tools for dashboards and social business There is so much data that it is an incredible task to actually comprehend it. Like looking at a TV screen full of static… trying to rationalize if one pixel means something. In order to make the data actionable we need to really focus on tools to clarify what we are looking at. We need to think about the process of tuning into a specific channelof segmenting dataof communicating the significance of that dataof doing SOMETHING with the data To that end I’ve collected another list of tools that specifically help with visualizing different types of information. Keep in mind that some of these tools just need ‘some play time’ where you trial and test different types of data until they begin to make sense to you, your audience, and your business. You should always keep in mind If you have a visualization tool we’d love to check it out. “Easy to Use” Visualization Tools Social Media Oriented Tools Google+ RipplesThis is a hidden gem, often overlooked by many. Charting Oriented Tools Word Based Tools
10 Fun Tools To Easily Make Your Own Infographics People love to learn by examining visual representations of data. That’s been proven time and time again by the popularity of both infographics and Pinterest. So what if you could make your own infographics ? What would you make it of? It’s actually easier than you think… even if you have zero design skills whatsoever. Below are my two favorite infographic-making web 2.0 tools that I highly recommend. Click the name of each tool to learn more! Visual.ly One of the more popular ways to discover infographics, Visual.ly actually just launched a design overhaul of their website. Dipity Want to get a beautifully simply visualization of data over time? Easel.ly I absolutely love Easel.ly. Venngage Venngage (likely named for Venn diagrams) is a double threat. Infogr.am One of the most simple tools, Infogr.am lets you actually import data right into the site and then translate it all into useful visualizations. Tableau Public Photo Stats This one’s an iPhone app that’s worth trying out. What About Me?
Visage | Branded Reports ONLINE CHARTS | create and design your own charts and diagrams online Folium: Python Data. Leaflet.js Maps. — Folium 0.1.2 documentation Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in on a Leaflet map via Folium. Concept Folium makes it easy to visualize data that’s been manipulated in Python on an interactive Leaflet map. The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. Base Maps To create a base map, simply pass starting coordinates to Folium, then create the map: import foliummap_osm = folium.Map(location=[45.5236, -122.6750])map_osm.create_map(path='osm.html') Live example Folium defaults to 960 x 500 pixels (to make it easy to generate maps for bl.ocks ). map = folium.Map(location=[45.5236, -122.6750], width=500, height = 300) Folium also supports two zoom parameters: zoom_start: The starting zoom level.max_zoom: The maximum possible zoom. Tilesets Example: Markers Simple Markers Live example
Gallery · mbostock/d3 Wiki Wiki ▸ Gallery Welcome to the D3 gallery! More examples are available for forking on Observable; see D3’s profile and the visualization collection. Please share your work on Observable, or tweet us a link! Visual Index Basic Charts Techniques, Interaction & Animation Maps Statistics Examples Collections The New York Times visualizations Jerome Cukier Jason Davies Jim Vallandingham Institute for Health Metrics and Evaluation Peter Cook Charts and Chart Components Bar Chart Histogram Pareto Chart Line and Area Chart Pie Chart Scatterplot and Bubble chart Parallel Coordinates, Parallel sets and Sankey Sunburst and Partition layout Force Layout Tree Misc Trees and Graphs Chord Layout (Circular Network) Maps Misc Charts Miscellaneous visualizations Charts using the reusable API Useful snippets Tools Interoperability Online Editors Products Store Apps
Gephi, an open source graph visualization and manipulation software Method: Data visualization with D3.js and python - part 1 - Next Genetics View the demo hereHTML source is at the bottom of the post Computers and the internet have changed academia in dramatic ways from greater sharing of data to a larger sense of community. Science journals are now all digitized and available online either through your web browser or downloadble as a .pdf. Even with all the technology available for presenting data, most published papers still only contain static figures. I am not undervaluing the importance of having nicely formatted figures and graphs. But I do want to show how data can be presented with all the tools available now. Science papers are generally viewed on a computer through a web browser like Chrome, Firefox, or Safari which use javascript/html/css for displaying information. Here are a bunch of examples of interactive figures made using browser technologies, specifically D3.js. How a web page displays information A brief primer on javascript, html, css, and how they are interpreted by the browser: Javascript and D3.js Data input
OVERNEWSED BUT UNINFORMED || STEFAN BRÄUTIGAM Overnewsed but uninformed – zum durchblättern Overnewsed but uninformed – anklicken, vergrößern und genießen Overnewsed but uninformed – Bonusposter ^ Beam me up Chartist - Simple responsive charts You may think that this is just yet an other charting library. But Chartist.js is the product of a community that was disappointed about the abilities provided by other charting libraries. Of course there are hundreds of other great charting libraries but after using them there were always tweaks you would have wished for that were not included. Highly customizable responsive charts Facts about Chartist The following facts should give you an overview why to choose Chartists as your front-end chart generator: Simple handling while using convention over configurationGreat flexibility while using clear separation of concerns (Style with CSS & control with JS)Usage of SVG (Yes! These projects and wrapper libraries are known to me right now that either use Chartist.js or wrap them into a library for usage in a framework. Cross-browser support Note that CSS3 animations on SVG CSS attributes are not supported on all browsers and the appearance may vary.
Les 20 meilleurs outils de datavisualisation au banc d’essai Que vous soyez absolument novice ou codeur amateur, il existe aujourd’hui sur le web une impressionnante palette d’outils (presque) gratuits pour réaliser des datavisualisations. Banc d’essai. Note : j’ai volontairement éliminé les outils (a) entièrement payants (b) trop moches pour être utilisés dans des rédactions (c) en Flash. Ce billet n’est consacré qu’aux outils de “visualisation”, et non de scraping ou de traitement des données (un autre billet suivra bientôt). ↑1 » Pour les novices/pressés : le clé-en-mains Ces outils gratuits ou freemium permettent de générer des graphiques ultra-rapidement en copiant-collant des données d’un tableur. Le meilleur – Datawrapper : Simple d’utilisation, sobre, rapide, Datawrapper est tout à fait satisfaisant pour la plupart des visualisations courantes. Les + : la possibilité de personnaliser les couleurs, de mettre en évidence une série, la navigation par onglets entre les différentes séries. Les challengers : Ils ne nous ont pas convaincu : Sources :
Chiqui Esteban. Infrographics, digital narratives, data visualization