R, How to extract blocks of values from correlation similarity matrix? -


i have correlation similarity data frame, example 1 shown below:

      1         2         3         4 1 1.0000000 0.9991846 0.7648333 0.3517951 2 0.9991846 1.0000000 0.7563048 0.3569311 3 0.7648333 0.7563048 1.0000000 0.6568740 4 0.3517951 0.3569311 0.6568740 1.0000000

i'd extract clusters of values above threshold (0.95,in case). i'd have following returned each 1 separate data frames:

      1         2         1 1.0000000 0.9991846  2 0.9991846 1.0000000         3 3 1.0000000         4 4 1.0000000

i have thought subsetting or using logical operators, such as: 

subset(blah, blah[1,] >.95) blah > .95

which think right direction, i'm kind of stumped how approach problem? appreciated?

here solution no looping:

mytabletxt <- "      1         2         3         4 1 1.0000000 0.9991846 0.7648333 0.3517951 2 0.9991846 1.0000000 0.7563048 0.3569311 3 0.7648333 0.7563048 1.0000000 0.6568740 4 0.3517951 0.3569311 0.6568740 1.0000000" mytable <- read.table(textconnection(mytabletxt), header = true, row.names = 1) mytable <- mytable[mytable > .95] newbloc <- mytable==1 & c(1, mytable[-length(mytable)])==1 blocid <- rep(1:sum(newbloc), c(which(newbloc), length(newbloc) + 1)[-1] - which(newbloc)) blocsplit <- split(mytable, factor(blocid)) lapply(blocsplit, function(x)     tmp <- as.data.frame(matrix(x, ncol = max(c(1, length(x)/2))))) # $`1` #          v1        v2 # 1 1.0000000 0.9991846 # 2 0.9991846 1.0000000 #  # $`2` #   v1 # 1  1 #  # $`3` #   v1 # 1  1

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