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POR: split a data-frame, apply a function to all row-pairs in each subset
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copied!<p>I am new to R and am trying to accomplish the following task <code>efficiently</code>.</p> <p>I have a <code>data.frame</code>, <code>x</code>, with columns: <code>start</code>, <code>end</code>, <code>val1</code>, <code>val2</code>, <code>val3</code>, <code>val4</code>. The columns are sorted/ordered by <code>start</code>. </p> <p>For each <code>start</code>, first I have to find all the entries in <code>x</code> that share the same <code>start</code>. Because the list is ordered, they will be consecutive. If a particular <code>start</code> occurs only once, then I <i>ignore</i> it. Then, for these entries that have the same <code>start</code>, lets say for one particular <code>start</code>, there are 3 entries, as shown below: </p> <p>entries for <code>start=10</code></p> <pre>start end val1 val2 val3 val4 10 25 8 9 0 0 10 55 15 200 4 9 10 30 4 8 0 1</pre> <p>Then, I have to take 2 rows at a time and perform a <code>fisher.test</code> on the <code>2x4</code> matrices of <code>val1:4</code>. That is,</p> <pre>row1:row2 => fisher.test(matrix(c(8,15,9,200,0,4,0,9), nrow=2)) row1:row3 => fisher.test(matrix(c(8,4,9,8,0,0,0,1), nrow=2)) row2:row3 => fisher.test(matrix(c(15,4,200,8,4,0,9,1), nrow=2))</pre> <p>The code I wrote is accomplished using <code>for-loops</code>, traditionally. I was wondering if this could be <b>vectorized</b> or improved in anyway. </p> <pre>f_start = as.factor(x$start) #convert start to factor to get count tab_f_start = as.table(f_start) # convert to table to access count o_start1 = NULL o_end1 = NULL o_start2 = NULL o_end2 = NULL p_val = NULL for (i in 1:length(tab_f_start)) { <b># check if there are more than 1 entries with same start</b> if ( tab_f_start[i] > 1) { <b># get all rows for current start</b> cur_entry = x[x$start == as.integer(names(tab_f_start[i])),] <b># loop over all combinations to obtain p-values</b> ctr = tab_f_start[i] for (j in 1:(ctr-1)) { for (k in (j+1):ctr) { <b># store start and end values separately</b> o_start1 = c(o_start1, x$start[j]) o_end1 = c(o_end1, x$end[j]) o_start2 = c(o_start2, x$start[k]) o_end2 = c(o_end2, x$end[k]) <b># construct matrix</b> m1 = c(x$val1[j], x$val1[k]) m2 = c(x$val2[j], x$val2[k]) m3 = c(x$val3[j], x$val3[k]) m4 = c(x$val4[j], x$val4[k]) m = matrix(c(m1,m2,m3,m4), nrow=2) p_val = c(p_val, fisher.test(m)) } } } } result=data.frame(o_start1, o_end1, o_start2, o_end2, p_val)</pre> <p>Thank you! </p>

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