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exporting to \\*.csv files" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -27,8 +35,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ + "## Time to First Event Data\n", + "\n", "We initialize the data frame with 3 columns and 1024 samples\n", "\n", "1. The first column contains the subject `subject`, randomly selected from 64 identifiers.\n", @@ -38,373 +51,464 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "data": { "text/html": [ - "
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" ], "text/plain": [ - "1024×3 DataFrames.DataFrame\n", - "│ Row │ subject │ time │ event │\n", - "├──────┼─────────┼──────┼───────┤\n", - "│ 1 │ 1 │ 6 │ 'A' │\n", - "│ 2 │ 1 │ 14 │ 'A' │\n", - "│ 3 │ 1 │ 21 │ 'D' │\n", - "│ 4 │ 1 │ 24 │ 'A' │\n", - "│ 5 │ 1 │ 26 │ 'B' │\n", - "│ 6 │ 1 │ 31 │ 'C' │\n", - "│ 7 │ 1 │ 32 │ 'B' │\n", - "│ 8 │ 1 │ 37 │ 'C' │\n", - "│ 9 │ 1 │ 41 │ 'D' │\n", - "│ 10 │ 1 │ 48 │ 'C' │\n", - "│ 11 │ 1 │ 53 │ 'B' │\n", - "⋮\n", - "│ 1013 │ 64 │ 4561 │ 'C' │\n", - "│ 1014 │ 64 │ 4569 │ 'C' │\n", - "│ 1015 │ 64 │ 4570 │ 'E' │\n", - "│ 1016 │ 64 │ 4574 │ 'C' │\n", - "│ 1017 │ 64 │ 4579 │ 'D' │\n", - "│ 1018 │ 64 │ 4584 │ 'C' │\n", - "│ 1019 │ 64 │ 4588 │ 'A' │\n", - "│ 1020 │ 64 │ 4590 │ 'C' │\n", - "│ 1021 │ 64 │ 4592 │ 'D' │\n", - "│ 1022 │ 64 │ 4599 │ 'B' │\n", - "│ 1023 │ 64 │ 4601 │ 'B' │\n", - "│ 1024 │ 64 │ 4603 │ 'C' │" + "8×3 DataFrames.DataFrame\n", + "│ Row │ subject │ time │ event │\n", + "├─────┼─────────┼──────┼───────┤\n", + "│ 1 │ 1 │ 5 │ 'C' │\n", + "│ 2 │ 1 │ 7 │ 'B' │\n", + "│ 3 │ 1 │ 15 │ 'D' │\n", + "│ 4 │ 1 │ 20 │ 'B' │\n", + "│ 5 │ 1 │ 24 │ 'C' │\n", + "│ 6 │ 1 │ 32 │ 'C' │\n", + "│ 7 │ 1 │ 40 │ 'C' │\n", + "│ 8 │ 1 │ 45 │ 'C' │" ] }, - "execution_count": 25, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "exportdata = DataFrame(\n", + "longitudinal = DataFrame(\n", " subject = sort(rand(1:64, 1024)),\n", " time = cumsum(rand(1:8, 1024)),\n", " event = rand(['A', 'B', 'B', 'C', 'C', 'C', 'C', 'D', 'D', 'E'], 1024)\n", - ")" + ");\n", + "longitudinal[1:8, :]" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Notice that the time is increasing across all subjects. Instead we want to be the cumulative sum strictly within each subject. To accomplish this we use logical indexing to pull the last time from each subject and subtract it from the times of the next subject." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "First find the logic index of the row before the subject changed." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "data": { "text/html": [ - "
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" + ], + "text/plain": [ + "8×6 DataFrames.DataFrame\n", + "│ Row │ subject │ time │ event │ before │ after │ shift │\n", + "├─────┼─────────┼──────┼───────┼────────┼───────┼───────┤\n", + "│ 1 │ 1 │ 5 │ 'C' │ false │ false │ 0 │\n", + "│ 2 │ 1 │ 7 │ 'B' │ false │ false │ 0 │\n", + "│ 3 │ 1 │ 15 │ 'D' │ false │ false │ 0 │\n", + "│ 4 │ 1 │ 20 │ 'B' │ false │ false │ 0 │\n", + "│ 5 │ 1 │ 24 │ 'C' │ false │ false │ 0 │\n", + "│ 6 │ 1 │ 32 │ 'C' │ false │ false │ 0 │\n", + "│ 7 │ 1 │ 40 │ 'C' │ false │ false │ 0 │\n", + "│ 8 │ 1 │ 45 │ 'C' │ false │ false │ 0 │" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "longitudinal[:time] = longitudinal[:time] - longitudinal[:shift];\n", + "longitudinal[1:8, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can now export the data, choosing the first 3 columns." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "writetable(\"longitudinal.csv\", longitudinal[[:subject, :time, :event]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contingency Data\n", + "\n", + "The goal is to generate four columns of dimensional classifiers, as two pairs of dependent dimensions. We start by initializing the two distributions to sample:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distributionone = [\n", + " [\"A\" -1],\n", + " [\"A\" -1],\n", + " [\"A\" -1],\n", + " [\"A\" 0],\n", + " [\"A\" 0],\n", + " [\"A\" 1],\n", + " [\"B\" -1],\n", + " [\"B\" 0],\n", + " [\"B\" 0],\n", + " [\"B\" 1],\n", + " [\"B\" 1],\n", + " [\"B\" 1]\n", + "];\n", + "distributiontwo = [\n", + " [\"C\" -2],\n", + " [\"C\" -1],\n", + " [\"C\" -1],\n", + " [\"C\" 0],\n", + " [\"C\" 0],\n", + " [\"C\" 0],\n", + " [\"C\" 1],\n", + " [\"C\" 1],\n", + " [\"C\" 1],\n", + " [\"C\" 1],\n", + " [\"C\" 2],\n", + " [\"C\" 2],\n", + " [\"C\" 2],\n", + " [\"C\" 2],\n", + " [\"C\" 2],\n", + " [\"D\" -2],\n", + " [\"D\" -1],\n", + " [\"D\" -1],\n", + " [\"D\" 0],\n", + " [\"D\" 0],\n", + " [\"D\" 0],\n", + " [\"D\" 1],\n", + " [\"D\" 1],\n", + " [\"D\" 2],\n", + " [\"E\" -2],\n", + " [\"E\" -2],\n", + " [\"E\" -2],\n", + " [\"E\" -1],\n", + " [\"E\" -1],\n", + " [\"E\" 0],\n", + " [\"E\" 1],\n", + " [\"E\" 1],\n", + " [\"E\" 2],\n", + " [\"E\" 2],\n", + " [\"E\" 2],\n", + " [\"F\" 2],\n", + " [\"F\" 1],\n", + " [\"F\" 1],\n", + " [\"F\" 0],\n", + " [\"F\" 0],\n", + " [\"F\" 0],\n", + " [\"F\" -1],\n", + " [\"F\" -1],\n", + " [\"F\" -1],\n", + " [\"F\" -1],\n", + " [\"F\" -2],\n", + " [\"F\" -2],\n", + " [\"F\" -2],\n", + " [\"F\" -2],\n", + " [\"F\" -2]\n", + "];" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then generate another 1024 samples to populate a data frame." + ] + }, + { + "cell_type": "code", + "execution_count": 53, "metadata": { "collapsed": false }, @@ -412,64 +516,109 @@ { "data": { "text/html": [ - "
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" ], "text/plain": [ - "1024×6 DataFrames.DataFrame\n", - "│ Row │ subject │ time │ event │ before │ after │ shift │\n", - "├──────┼─────────┼──────┼───────┼────────┼───────┼───────┤\n", - "│ 1 │ 1 │ 6 │ 'A' │ false │ false │ 0 │\n", - "│ 2 │ 1 │ 14 │ 'A' │ false │ false │ 0 │\n", - "│ 3 │ 1 │ 21 │ 'D' │ false │ false │ 0 │\n", - "│ 4 │ 1 │ 24 │ 'A' │ false │ false │ 0 │\n", - "│ 5 │ 1 │ 26 │ 'B' │ false │ false │ 0 │\n", - "│ 6 │ 1 │ 31 │ 'C' │ false │ false │ 0 │\n", - "│ 7 │ 1 │ 32 │ 'B' │ false │ false │ 0 │\n", - "│ 8 │ 1 │ 37 │ 'C' │ false │ false │ 0 │\n", - "│ 9 │ 1 │ 41 │ 'D' │ false │ false │ 0 │\n", - "│ 10 │ 1 │ 48 │ 'C' │ false │ false │ 0 │\n", - "│ 11 │ 1 │ 53 │ 'B' │ false │ false │ 0 │\n", - "⋮\n", - "│ 1013 │ 64 │ 30 │ 'C' │ false │ false │ 4531 │\n", - "│ 1014 │ 64 │ 38 │ 'C' │ false │ false │ 4531 │\n", - "│ 1015 │ 64 │ 39 │ 'E' │ false │ false │ 4531 │\n", - "│ 1016 │ 64 │ 43 │ 'C' │ false │ false │ 4531 │\n", - "│ 1017 │ 64 │ 48 │ 'D' │ false │ false │ 4531 │\n", - "│ 1018 │ 64 │ 53 │ 'C' │ false │ false │ 4531 │\n", - "│ 1019 │ 64 │ 57 │ 'A' │ false │ false │ 4531 │\n", - "│ 1020 │ 64 │ 59 │ 'C' │ false │ false │ 4531 │\n", - "│ 1021 │ 64 │ 61 │ 'D' │ false │ false │ 4531 │\n", - "│ 1022 │ 64 │ 68 │ 'B' │ false │ false │ 4531 │\n", - "│ 1023 │ 64 │ 70 │ 'B' │ false │ false │ 4531 │\n", - "│ 1024 │ 64 │ 72 │ 'C' │ false │ false │ 4531 │" + "8×3 DataFrames.DataFrame\n", + "│ Row │ sample │ preonetwo │ prethreefour │\n", + "├─────┼────────┼─────────────┼──────────────┤\n", + "│ 1 │ 1 │ Any[\"B\" 0] │ Any[\"C\" 0] │\n", + "│ 2 │ 2 │ Any[\"B\" 0] │ Any[\"C\" 0] │\n", + "│ 3 │ 3 │ Any[\"B\" 1] │ Any[\"C\" -1] │\n", + "│ 4 │ 4 │ Any[\"B\" -1] │ Any[\"D\" -1] │\n", + "│ 5 │ 5 │ Any[\"B\" 1] │ Any[\"F\" 0] │\n", + "│ 6 │ 6 │ Any[\"B\" 0] │ Any[\"F\" -2] │\n", + "│ 7 │ 7 │ Any[\"B\" 1] │ Any[\"C\" -1] │\n", + "│ 8 │ 8 │ Any[\"B\" -1] │ Any[\"C\" 1] │" ] }, - "execution_count": 31, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "exportdata[:time] = exportdata[:time] - exportdata[:shift];\n", - "exportdata" + "contingency = DataFrame(\n", + " sample = 1:1024,\n", + " preonetwo = rand(distributionone, 1024),\n", + " prethreefour = rand(distributiontwo, 1024)\n", + ");\n", + "contingency[1:8, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now export the data, choosing the first 3 columns." + "Next we extract the individual column elements into dimension columns." ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 54, "metadata": { "collapsed": false }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "8×7 DataFrames.DataFrame\n", + "│ Row │ sample │ preonetwo │ prethreefour │ dimensionone │ dimensiontwo │\n", + "├─────┼────────┼─────────────┼──────────────┼──────────────┼──────────────┤\n", + "│ 1 │ 1 │ Any[\"B\" 0] │ Any[\"C\" 0] │ \"B\" │ 0 │\n", + "│ 2 │ 2 │ Any[\"B\" 0] │ Any[\"C\" 0] │ \"B\" │ 0 │\n", + "│ 3 │ 3 │ Any[\"B\" 1] │ Any[\"C\" -1] │ \"B\" │ 1 │\n", + "│ 4 │ 4 │ Any[\"B\" -1] │ Any[\"D\" -1] │ \"B\" │ -1 │\n", + "│ 5 │ 5 │ Any[\"B\" 1] │ Any[\"F\" 0] │ \"B\" │ 1 │\n", + "│ 6 │ 6 │ Any[\"B\" 0] │ Any[\"F\" -2] │ \"B\" │ 0 │\n", + "│ 7 │ 7 │ Any[\"B\" 1] │ Any[\"C\" -1] │ \"B\" │ 1 │\n", + "│ 8 │ 8 │ Any[\"B\" -1] │ Any[\"C\" 1] │ \"B\" │ -1 │\n", + "\n", + "│ Row │ dimensionthree │ dimensionfour │\n", + "├─────┼────────────────┼───────────────┤\n", + "│ 1 │ \"C\" │ 0 │\n", + "│ 2 │ \"C\" │ 0 │\n", + "│ 3 │ \"C\" │ -1 │\n", + "│ 4 │ \"D\" │ -1 │\n", + "│ 5 │ \"F\" │ 0 │\n", + "│ 6 │ \"F\" │ -2 │\n", + "│ 7 │ \"C\" │ -1 │\n", + "│ 8 │ \"C\" │ 1 │" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "contingency[:dimensionone] = vcat(contingency[:, :preonetwo]...)[:, 1];\n", + "contingency[:dimensiontwo] = vcat(contingency[:, :preonetwo]...)[:, 2];\n", + "contingency[:dimensionthree] = vcat(contingency[:, :prethreefour]...)[:, 1];\n", + "contingency[:dimensionfour] = vcat(contingency[:, :prethreefour]...)[:, 2];\n", + "contingency[1:8, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we export our contingency table data." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "writetable(\"export.csv\", exportdata[[:subject, :time, :event]])" + "writetable(\"contingency.csv\", contingency[[:sample, :dimensionone, :dimensiontwo, :dimensionthree, :dimensionfour]])" ] } ],