Posts tagged with data viz

roots of `x²⁶•y + x•z⁶ + y¹³•z + x⁹•y¹³ + z²⁶     =   0`

$\dpi{200} \bg_white \large x^{27} \cdot y+x \cdot z^6+y^{13} \cdot z+x^9 \cdot y^{13}+z^{26}$

(Source: imaginary.org)

Just some sweet, sweet pictures of science:

San Francisco contour lines. got inspired by someone else’s version, thought it would be fun to try it out. contour data from datasf. (d3, three.js, python)

(Source: polytopes)

Two interesting ideas here:

• price impact of a trade proportional to `exp( √size )`

Code follows:

(Source: finmath.stanford.edu)

Michael Conover: Information Visualization for Large-Scale Data Workflows

• data geometry
• memes
• visual analysis of program structure
• visual analysis of propaganda
• compare last week’s analysis and share with colleagues
• `geom_bin2d` rather than `geom_point(alpha=...)` in `ggplot2`
• `ggpairs`
• automated grading: in addition to unit testing, 1) parse syntax trees of submissions, 2) define edit distance between them, 3) induces a network structure, 4) identify clusters, 5) give feedback to a representative member of the cluster and cc: everyone else

playing along with Elias Wegert in R:

```X <- matrix(1:100,100,100)                  #grid
X <- X * complex(imaginary=.05) + t(X)/20    #twist & shout
X <- X - complex(real=2.5,imaginary=2.5)     #recentre
plot(X, col=hcl(h=55*Arg(sin(X)), c=Mod(sin(X))*40 ) ,        pch=46, cex=6)
```

Found it was useful to define these few functions:

```arg <- function(z) (Arg(z)+pi)/2/pi*360     #for HCL colour input
ring <- function(C) C[.8 < Mod(C) &   Mod(C) < 1.2]        #focus on the unit circle
lev <- function(x) ceiling(log(x)) - log(x)
m <- function(z) lev(Mod(z))
plat <- function(domain, FUN) plot( domain, col= hcl( h=arg(FUN(domain)), l=70+m(domain)), pch=46, cex=1.5, main=substitute(FUN) )           #say it directly
```

NB, `hcl`'s hue`[0,360]` so `phase` or `arg` needs to be matched to that.

A billion chronically hungry people in the world via The Economist

• As you can see from the right-hand scale, during the 1990’s and 2000’s the “bottom billion” poorest people have been starving or close to it.
• Even though the right-hand scale is more important, the lines get graphical emphasis.
• Therefore the two pictures, though nearly equivalent in absolute terms, tell very different stories:
1. about a spiking crisis and increasing failure to deal with poverty during rich-world recession
2. about marginal improvements that continue despite a rich-world financial debacle.
• Both stories were told by the `Food and Agriculture Organisation`of the United Nations.
• Of course statistical bodies revise estimates all the time.
• But still this juxtaposition warns us to question the facticity of numbers appearing in charts.
• All data come from somewhere. Just because the numbers appear on a chart doesn’t make them correct.

hi-res

3D map of the large-scale distribution of dark matter, reconstructed from measurements of weak gravitational lensing with the Hubble Space Telescope.

hi-res

hi-res