Mijn eerste R-doel is om de uitvoer van mijn software voor syntactische metingen als R data frames te kunnen benaderen. Hiervoor gebruik ik mede de iL04 interface van Peter Kleiweg.

Dit is nog werk in uitvoering, maar mijn toolchain genereert al een enigzins werkend R-script voor ieder metingsresultaat waarmee ik in R kan verder experimenteren, zoals je hier kunt zien...

# Ask the user for input before a new figure is drawn
oldInteractiveValue <- par(ask = TRUE)

# Include RuG/L04 import functions
library(iL04)

# Read distance table
sand1.b.dif <- read.dif("D:/DOCUME~1/Marco/pro/dat/../../out/sand1.b/sand1.b.dif")
# Read 3D-vector after Classical MDS
sand1.b.vec <- read.vec("D:/DOCUME~1/Marco/pro/dat/../../out/sand1.b/vec/sand1.b.cl.vec")
# Read longitude/latitude coordinates of datapoints
sand1.b.coo <- read.table("D:/DOCUME~1/Marco/pro/dat/../../out/sand1.b/sand1.b.coo", 
	header = FALSE, sep ="	", quote = "", dec = ".", row.names = NULL, 
	c("Longitude", "Latitude", "dx", "dy", "Location") )
# Read longitude/latitude border lines of the Dutch language area
sand1.b.map <- read.table("D:/DOCUME~1/Marco/pro/dat/nl.map.R", header = FALSE, sep = " ", 
	quote = "", dec = ".", row.names = NULL, c("Longitude", "Latitude") )

# Calculate the mean aspect ratio for the range of latitudes
aspectRatio <- mean.asp(sand1.b.map[, 2])

# Create a distance-frequency histogram
hist(sand1.b.dif)
# Plot the empirical cumulative distribution
plot(ecdf(sand1.b.dif), do.points = FALSE, verticals = TRUE)

# Plot a clustering-based partitioning on a topographic map
clustermap(sand1.b.coo[, 1:2], sand1.b.dif, labels = sand1.b.coo$Location, 
	method="ward", k = 4, cex = 0.3, bwlim=0.55, seq = 25:1, asp = aspectRatio, 
	map = sand1.b.map, xlab="Longitude", ylab="Latitude")

# Perform classical (Metric) Multidimensional Scaling on the distance matrix
sand1.b.mds <- cmdscale(sand1.b.dif, 2)
# Generic plotting of the MDS solution in 3 dimensions
plot(sand1.b.mds, type = "n", xlab = "", ylab = "", main = "Classical MDS solution in 3 dimensions")
text(sand1.b.mds, rownames(sand1.b.mds), cex = 0.3)
# Plot the results of 3D multidimensional scaling on a topographic map
mdsmap(sand1.b.coo[, 1:2], sand1.b.mds, labels = sand1.b.coo$Location, 
       main = "3D MDS on a topographic map", asp = aspectRatio, map = sand1.b.map)
# Plot a stochastic clustering on a topographic map
cccmap(sand1.b.coo[, 1:2], sand1.b.dif, labels = sand1.b.coo$Location, 
       main = "Repeated clustering + 3D MDS", asp = aspectRatio, map = sand1.b.map)
# Plot a set of links on a topographic map
linkmap(sand1.b.coo[, 1:2], sand1.b.dif, labels = sand1.b.coo$Location, 
        main = "Differences represented as lines", nmax = 600, limit = .2, 
		separate = 20, asp = aspectRatio, map = sand1.b.map)

# Restore the user interactivity input value
par(oldInteractiveValue)