This is a follow-up to my Visualizing Risky Words post. You’ll need to read that for context if you’re just jumping in now. Full R code for the generated images (which are pretty large) is at the end. Aesthetics are the primary reason for using a word cloud, though one can pretty quickly recognize what
dat<- data.frame(t=seq(0, 2*pi, by=0.1) ) xhrt <- function(t) 16*sin(t)^3 yhrt <- function(t) 13*cos(t)-5*cos(2*t)-2*cos(3*t)-cos(4*t) dat$y=yhrt(dat$t) dat$x=xhrt(dat$t) with(dat, polygon(x,y, col="hotpink")) i heaRt you! (R code inspired by/lifted from: DWin on StackOverflow)
![Extended (Simple) ASN Graph Visualization Example [R to D3]](https://rud.is/b/wp-content/uploads/2013/02/vc_60-300x231.png)
The small igraph visualization in the previous post shows the basics of what you can do with the BulkOrigin & BulkPeer functions, and I thought a larger example with some basic D3 tossed in might be even more useful. Assuming you have the previous functions in your environment, the following builds a larger graph structure
This is part of a larger project I’m working on, but it’s useful enough to share (github version coming soon). The fine folks at @TeamCymru have a great service to map IP addresses to ASN/BGP information en masse. There are libraries for Python, Perl and other languages but none for R (that I could find).
Folks may debate the merits of the SHODAN tool, but in my opinion it’s a valuable resource, especially if used for “good”. What is SHODAN? I think ThreatPost summed it up nicely: “Shodan is a Web based search engine that discovers Internet facing computers, including desktops, servers and routers. The engine, created by programmer John
![How Low Can It [The Mississsippi River] Go?](https://rud.is/b/wp-content/uploads/2013/01/mississippi-300x187.png)
I’ve heard quite a bit about the current problems with the Mississippi River and wanted to see for myself (with data) just how bad it is. St Louis seems to be quite indicative of the severity of the situation, so I pulled the USGS “stream” records for it and also the historic low water level
I updated the code to use ggsave and tweaked some of the font & line size values for more consistent (and pretty) output. This also means that I really need to get this up on github. If you even remotely follow this blog, you’ll see that I’m kinda obsessed with slopegraphs. While I’m pretty happy
Naomi Robbins is running a graph makeover challenge over at her Forbes blog and this is my entry for the B2B/B2C Traffic Sources one (click for larger version): And, here’s the R source for how to generate it: library(ggplot2) df = read.csv("b2bb2c.csv") ggplot(data=df,aes(x=Site,y=Percentage,fill=Site)) + geom_bar(stat="identity") + facet_grid(Venue ~ .) + coord_flip() + opts(legend.position
UPDATE: As indicated in the code comments, Google took down the cone KML files. I’ll be changing the code to use the NHC archived cone files later tonight I will (most likely) not be littering the blog with any more updates to the ‘Sandy’ code unless they are really significant. You can follow along at
As indicated in the code comments, Google took down the cone KML files. I’ll be changing the code to use the NHC archived cone files later tonight NOTE: There is significantly updated code on github for the Sandy ‘R’ dataviz. This is a follow-up post to the quickly crafted Watch Sandy in “R” post last
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
