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Librairies Javascript pour gérer les graphiques

Librairies Javascript pour gérer les graphiques
Related:  visualization

Looking 4 data visualization Gallery: U.S. Federal Budget Back to Gallery Home Let’s begin with some tilted 3D pie charts and work our way toward a more revealing visualization. Here are the above 1993 and 2012 pie chart pairs, with Receipts and Outlays converted to flows in two separate Sankey diagrams: Diagram Notes: Receipts are shades of green, Outlays are shades of blue. The above diagrams lay out the relative proportions without any distortion from 3D tilting. Now that we have the two diagrams, though, what if we want to compare the two years to each other? Then the above diagrams are not that useful—the 2012 budget could be ten times the size of 1993’s (or vice versa) and these diagrams would still quietly imply that the budgets’ sizes are similar. We need some actual amounts to compare, not just percentages. Searching for actual federal budget numbers leads to the U.S. I found the numbers underlying the pie charts’ percentages in the following GPO documents: I rendered the negative outlay (and kept the diagram balanced) by: Summary:

Data, BigData, DataViz, Data Scientist, OpenData… reprenons du début Un des principaux objectifs de ce blog est d'aborder les différents aspects de la diffusion de l'information géographique. Cela commence forcément par évoquer la source de toute information: la donnée. Autour du concept de la Data, gravitent de nouveaux phénomènes comme le Big Data, la Data Viz ou l'Open Data … Des journalistes aux décideurs en passant par les spécialistes techniques, tout le monde s'en fait désormais l'échos. La Data La Data constitue le cœur de tous nos systèmes d'information et jouent donc un rôle essentiel dans la prise de décisions économiques, stratégiques ou politiques. GeoData Sans qu'il soit évident de le démontrer, il est communément admis que 80% de nos bases de données ont une composante géographique, on parlera donc parfois de GeoData. Big Data, le nouveau défi de la Data On estime qu'en 2013, l’humanité produit autant de données en 2 jours qu’elle ne l’a fait en deux millions d’années. Un nouveau métier: Data Scientist Et l'Open Data dans tout ça ?

Ann's Blog | The Dataviz Design Process: 7 Steps for Beginners Does data visualization leave you feeling like this? If so, this beginner-level post is for you! Data visualization requires two skillsets: technical skills to create visualizations in a software program and critical thinking skills to match your visualization to your audience’s information needs, numeracy level, and comfort with data visualization. If you’re interested in learning more about technical skills, check out my Excel for Evaluation chart tutorials and my Dataviz Challenges. If you’re interested in learning more about critical thinking skills, read on! Step 1: Select a single message to highlight in your chart This is, admittedly, the least linear of all the thinking steps in the design process. Who is your audience? Your audience should be your primary consideration. What’s your reader’s numeracy level? And P.S., if you can’t think of how your chart will add value for the readers, don’t make one. Which chart is right for my data? How much precision is necessary? Phew! Woohoo!

Dataviz : les outils gratuits indispensables mar132012 La visualisation des données (dataviz) est devenu la nouvelle marotte des rédactions connectées. m0le'o'blog vous propose une petite sélection des meilleurs outils gratuits. Révolu, le temps où les connaisseurs d'Excel étaient les seuls à pouvoir transformer des tableurs complexes en graphiques lisibles par tous ? Beaucoup de développeurs en font le pari en lançant leurs propres applications. m0le'o'blog en sélectionne quelques-unes pour vous. Cliquez sur le titre pour arriver sur la page d'accueil des applications. Image : capture d'écran sur La société Tableau Software fait le pari d'un logiciel tourné sur la visualisation des données. Avantages le large éventail de visualisations disponibles, notamment gréographiquesla possibilité de travailler offline une fois le logiciel installél'exportation sur Internet optimisée Inconvénients l'ensemble fait vraiment usine à gaz et il faudra forcément vous plonger dans les tutoriels pour vous y retrouver

Graphics in R Please direct questions and comments about these pages, and the R-project in general, to Dr. Tom Philippi. Introduction One of the strengths of R is graphical presentation of data. One consequence is that most pdf introductions to R and introductory books on R include chapters on the basics of R graphics, or include graphical examination of the data integrated with the statistical analyses. Base Graphics & Extensions plot(X) is the core function for producing a graph of an R object X. The syntax for plot is plot(x,...), so all additional parameters specifying axes and colors and symbols and titles are passed on to the underlying graphics system, with different parameters meaninglful for different forms of graphs. sciplot Advanced graphics: lattice and ggplot Graphics in other packages ⇑ To Top of Page lattice Lattice graphics provide the ability to generate quite complex graphics to present informative views of complex datasets. ggplot2 Field and Analysis-specific Graphics in Other Packages RGraphExampleLibrary: R Example Graph Library Introduction I wondered why there wasn't a website with output of the many examples of R packages. Well, here it is. This site contains hundreds of plots and graphics from the example sections of over 90 packages. Using the library Just click on the 'packages' tab for an overview of R packages.After selecting a R package a list of small plots is displayed. Arrg, It Does Not Work ... The example code and log output is currently not available. The Making of ... Essential ingredients: How to Master Data Visualization Data Visualization Data visualization is the study of the visual representation of data, meaning “information which has been abstracted in some schematic form, including attributes or variables for the units of information”. According to Friedman (2008) the “main goal of data visualization is to communicate information clearly and effectively through graphical means. Data Visualization Strategy Edward Tufte @EdwardTufte delivered a great presentation about data visualization strategy for the star studded Tech@State event audience. If your display isn’t worth 1000 words, to hell with it – @EdwardTufte Data Visualization Tools & Services Google Data Visualization Services Google Data Visualization Service provides a variety of charts that are optimized to address your data visualization needs. Sencha ExtJS Data Visualization Tools It has always been hard to draw things in web applications. Highcharts Data Visualization Tools Current Data Visualization Twitter Game Board (Edward Tufte) (StateDept)

Tableau 8.1 and R There are many reasons for this: The cost: While commercial distributions exist, open-source R is free. The rich features: R has an estimated user community of 2 million, which includes thousands of contributors from different domains expanding the language’s capabilities through new libraries. The quality: R libraries are enhanced by domain experts and field-tested by the large user base including other experts with real datasets in real analysis scenarios. The learning resources: Thanks to the active user community, plenty of tutorials and sample code are readily available. When we were working on building a bridge between Tableau and R, we wanted to enable three core scenarios and types of users. Give Tableau users access to a rich, ever-expanding collection of statistical analysis and data mining libraries to help them gain deeper insights from their data. Analyze your data source of choice at the speed of thought The rest is the same Tableau experience you know and love!

The Volume, Velocity, Variety, and Visualization of Big Data Today’s Big Data Scientists are being challenged with discovering actionable insights from the Volume, Velocity and Variety of data resources in cost-effective innovate ways. This is the foundation of Big Data Trends and has tremendous value when understanding is Visualized by today’s Big Data Artists. Big Data Artists and Data Storytelling Data comes to life in the hands of Data Artists. Big Data Virtualization Processing unstructured, semi-structured, and structured data can be accomplished with open-source tools such as Hadoop, MongoDB, Node.JS, with multiple programming languages including Java and Python. Big Data Visualization Getting the right information to the right person at the right time in the right way is a big deal. Data Visualization Tools Great artists experiment with a variety of material and tools to create their masterpieces. RapidMiner Data Mining System: RapidMiner is an open-source system for data mining. Sencha Ext JS is an “Enterprise grade JavaScript Framework”.