johncolby

require(plyr)
set.seed(12345)

n = 10
n.sum = 2

# Simulate data
a = sample.int(10, n, replace=T)
df = data.frame(n=1:n, a)

johncolby

original = expand.grid(ID1=c('a', 'b', 'c'), ID2 = c('red', 'green', 'blue'))
original$value = 0

replacement = data.frame(ID1='b', ID2='red', value=1)

rownames(original) = paste(original$ID1, original$ID2)
rownames(replacement) = paste(replacement$ID1, replacement$ID2)

original[rownames(replacement), ] = replacement

johncolby

Pf1 Pf2 Pf3 Pf4
0.4 0.3 0.1 0.6
0.1 0.7 0.7 1
0.4 0.9 0.5 0.7
0.3 0.4 0.4 0.7
1 0.8 0.4 0.2
 0.4 0 0.3
 0.4 0.8 0.5
 0.1 0.2 
 0.2 0.2 

johncolby

# $Id: textplot.R 1213 2007-11-01 20:20:10Z warnes $

textplot <- function(object, halign="center", valign="center", cex, ... )
  UseMethod('textplot')


textplot.default <- function(object,
                             halign=c("center","left","right"),
                             valign=c("center","top","bottom"),
                             cex, ... )

johncolby

macOS hiDPI retina scaling options compared

These are some photos comparing different macOS retina scaling options. All use a non-vector test pattern displayed at 100% (AAPM TG18). All photos are taken at roughly the same distance, using the same crop. Forgive the slight hand-held motion blur, and focus on these variables:

• Pixel size
• UI size
• Pixel perfection (has any interpolation taken place?)
• Real estate (how much of the image is displayed in this representative patch?)

Dell U2713H