stuff.R

# set up matrix
x = matrix(rnorm(165*165),ncol=165)
# calculate distance between all pairs and
# convert to matrix
df1 = as.matrix(dist(x))
# show dimensions
dim(df1)
# set the names of the rows and columns (convenience only)
colnames(df1) <- paste("sec",1:165,sep="_")
rownames(df1) <- paste("sec",1:165,sep="_")

# This little function takes a dataframe or matrix
# and the number of columns to retain in the output
# It finds the ordering of the values in the rows
# and then matches that ordering with the names from
# the columns of the data frame.
rows2names <-  function(df,n_columns) {
  cnames = colnames(df)
  newdf = data.frame(apply(df,1,function(y) {
    tmp = order(y)
    return(cnames[tmp][1:n_columns])}))
  colnames(newdf) <- cnames
  return(t(newdf))
}

# apply the function
df2 = rows2names(df1,5)
# note that the first column is always the
# same as the row number, as we would expect
head(df2)

###############################################
# an alternative that takes one from the distance matrix
# to a graph based on the rankings


# The first step is to make a rank matrix.
rankm = apply(df1,1,rank)
# Now, make an adjacency matrix where two nodes
# are considered "adjacent" if their rank is high
# enough. The max rank is specified.
maxrank = 5
adjmat = rankm < 5
library(igraph)
g1 = graph.adjacency(adjmat,mode='undirected',diag=FALSE)
g1
plot(g1,layout=layout.spring)