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Get Your Data into Gephi: A Quick and Basic Tutorial. If you’re interested in learning more about information visualization by trying out some yourself, here’s a quick tutorial on how to get a very basic dataset showing character relationships from a piece of literature into Gephi!

Get Your Data into Gephi: A Quick and Basic Tutorial

You might also check out my posts on the Bloomsday Ulysses visualization project (this year’s more in-depth analysis, last year’s smaller project), Gephi tutorials (how I used Gephi for my “View DHQ” DH knowledge networks project, Gephi terminology and ideas for exploration), and ACH Microgrant visualization work. Basic Gephi Dataset Creation In the Bloomsday project, we recorded data about what character interacted with which other characters, and used a scale of 1-7 to indicate the perceived intimacy of those interactions (e.g. from one person thinking of another person, to an involved conversation between two people).

Here’s an example of these two columns in the Bloomsday visualization dataset: Source Target Weight John Jim 2 John Jane 1 Jane Jim 2. Gephi makes graphs handly. d3.js: Examples of Basic Charts. By Ben Lorica (last updated Apr/2012) The set of tools I use to create charts include Excel & R (for generating static images), Processing, Protovis, and the Google Visualization API (for interactive graphics).

d3.js: Examples of Basic Charts

I tend to customize the charts I create so any tool I choose to learn & use needs to be flexible in that regard. I use Processing and R for prototyping and designing visualizations that I plan to deliver on the web -- the final product is either a static image or something done through Javascript. Both Protovis and the Google Visualization API use JSON and Javascript, and are great for delivering charts on a web browser. Recently the creators of Protovis announced that they would cease development, and instead focus their efforts on a new visualization library called d3.js: D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document.

Paired Bar Charts. Examples: Horizontal Bar charts. Visualization: Geochart - Google Chart Tools. Combining Plots. R makes it easy to combine multiple plots into one overall graph, using either the par( ) or layout( ) function.

Combining Plots

With the par( ) function, you can include the option mfrow=c(nrows, ncols) to create a matrix of nrows x ncols plots that are filled in by row. mfcol=c(nrows, ncols) fills in the matrix by columns. # 4 figures arranged in 2 rows and 2 columns attach(mtcars) par(mfrow=c(2,2)) plot(wt,mpg, main="Scatterplot of wt vs. mpg") plot(wt,disp, main="Scatterplot of wt vs disp") hist(wt, main="Histogram of wt") boxplot(wt, main="Boxplot of wt") click to view. Gnuplot demo script: histograms.dem. Stacked/Grouped Multi-Bar Chart. Diagrams Online. D3 tooltip using jQuery tipsy.

Ordinal Scales · mbostock/d3 Wiki. Wiki ▸ API Reference ▸ Scales ▸ Ordinal Scales Scales are functions that map from an input domain to an output range.

Ordinal Scales · mbostock/d3 Wiki

Ordinal scales have a discrete domain, such as a set of names or categories. There are also quantitative scales, which have a continuous domain, such as the set of real numbers. Scales are an optional feature in D3; you don't have to use them, if you prefer to do the math yourself. However, using scales can greatly simplify the code needed to map a dimension of data to a visual representation. A scale object, such as that returned by d3.scale.ordinal, is both an object and a function.

Swimlane using d3.js. Line Graphs Using d3.js « Ben J. Christensen. Simple examples of line graphs implemented using d3.js: Simple Line Graph Line Graph with Dual-scaled Axes Line graph over time with multiple data points UPDATE: I added an interactive version with scrubbing and dynamic updating.

Line Graphs Using d3.js « Ben J. Christensen

Like this: Like Loading... Filed under: Code, User Interface. A Bar Chart, Part 1. Say you have some data—a simple array of numbers: var data = [ 4 , 8 , 15 , 16 , 23 , 42 ];

A Bar Chart, Part 1

An Introduction to R. Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics.

An Introduction to R

R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). This manual provides information on data types, programming elements, statistical modelling and graphics.

This manual is for R, version 3.1.0 (2014-04-10). Copyright © 1990 W. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Preface This introduction to R is derived from an original set of notes describing the S and S-PLUS environments written in 1990–2 by Bill Venables and David M.

Producing Simple Graphs with R. 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. Crossfilter uses sorted indexes (and a few bit-twiddling hacks) to make this possible, dramatically increasing the perfor­mance of live histograms and top-K lists.

For more details on how Crossfilter works, see the API reference. Example: Airline on-time performance. Parallel Coordinates. Parallel coordinates are one of the most famous visualization techniques, and among the most common subjects of academic papers in visualization. While initially confusing, they are a very powerful tool for understanding multi-dimensional numerical datasets. The Technique The usual way of describing parallel coordinates would be to talk about high-dimensional spaces and how the technique lays out coordinate axes in parallel rather than orthogonal to each other. But there’s a much simpler way of looking at it: as the representation of a data table. This one describes car models released from 1970 to 1982, and contains their mileage (MPG), number of cylinders, horsepower, weight, and year they were introduced (among others).

Now imagine each of these columns being mapped onto a vertical axis in the image above. To make some sense out of this, the easiest way is to forget the part about records that span the entire width, and look at the space between each pair of axes. Brushing Limitations. jQuery UI.