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 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.
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.
Introducing the Data Visualization Checklist This post has been a long time coming. Ann Emery and I knew some time ago that evaluators and social scientists had a thirst for better graphs, a clear understanding of why better graphs were necessary, but they lacked efficient guidance on how, exactly, to make a graph better. Introducing the Data Visualization Checklist. Download this checklist and refer to it when you are constructing your next data visualization so that what you produce rocks worlds.
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.
Give Mockup presentations on your iPad/iPhone with Link Viewer – Mockups Product Blog – Balsamiq Hello Balsamiq friends! Today we asked Eileen and Max of Link Viewer to write up a blog post introducing their app for testing iOS Mockups. Enjoy! Understanding Data Visualisations - Seeing Data Home » Understanding Data Visualisations This resource aims to help people make sense of data visualisations. It’s for the general public – people who are interested in visualisations, but are not experts in this subject. Each section tells you something different, and it attempts to build your confidence and skills in making sense of data visualisations. You can work through the sections in any order you like.
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 Mobile BI Design Framework: The Use of Colors - The Decision Factor Blog In mobile business intelligence (BI) design, the use of colors plays an important role because colors are some of the easier components to incorporate into our mobile assets. However, this ease of use often leads to misuse and, subsequently, ineffective design of our mobile solutions. I often find that the oversight happens not because we lack the knowledge or technical capability, but because we make the wrong assumptions.
GitHub Visualization About GitHub is one of the most popular sites for online code repositories. With a large user base GitHub generates lot of activity. This activity, in the form of commits, contains the relations between repositories and contributors. How people engage with data visualisations and why it matters In this context, visualisation literacy is a necessary skill for living in our information and (big) data times. Data visualisation expert Andy Kirk of Visualising Data estimates that there are around 75 common chart types, and that’s just the ones that have names. The proliferation of visualisations and chart types can make understanding them difficult, yet it is also essential for those of us who wish to make sense of the data within them and so to participate in informed ways in data-driven conversations and society. Previous research
The Three Elements of Successful Data Visualizations Now that we’ve discussed when data visualization works — and when it doesn’t, let’s delve into what makes a successful data visualization. Although there are a number of criteria, including ease of comprehension and aesthetics, I’d like to explore the three that designers most often overlook. 1. It understands the audience. Three Ways to Show Down is Good Humans in the western culture tend to see things that trend upward as positive and lines that trend downward as bad. But what if bad is good? And not like my college boyfriend. But in the sense that a decrease is a good thing. Let’s use the example of weight loss.