{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction of waiting time using R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In case you want to run R in Jupyter notebook: https://irkernel.github.io/installation/ (remember to type the commands in the R console)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: randomForest\n", "randomForest 4.6-12\n", "Type rfNews() to see new features/changes/bug fixes.\n" ] } ], "source": [ "require(randomForest)\n", "set.seed(11)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
Xsexboroughyear_entryref_fromref_toareaofcarewtimeentryage
139702 2 5601 2012 16 3 304 551 78.9083328
261 2 5101 2008 33 1 138 766 69.3944473
256217 1 5101 2013 21 1 115 39 0.9638889
7373 2 5101 2010 6 1 149 1 1.1222222
32944 2 5101 2010 6 1 138 2 83.2694473
499138 2 5101 2017 8 9 82 98 65.4416656
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllll}\n", " X & sex & borough & year\\_entry & ref\\_from & ref\\_to & areaofcare & wtime & entryage\\\\\n", "\\hline\n", "\t 139702 & 2 & 5601 & 2012 & 16 & 3 & 304 & 551 & 78.9083328\\\\\n", "\t 261 & 2 & 5101 & 2008 & 33 & 1 & 138 & 766 & 69.3944473\\\\\n", "\t 256217 & 1 & 5101 & 2013 & 21 & 1 & 115 & 39 & 0.9638889\\\\\n", "\t 7373 & 2 & 5101 & 2010 & 6 & 1 & 149 & 1 & 1.1222222\\\\\n", "\t 32944 & 2 & 5101 & 2010 & 6 & 1 & 138 & 2 & 83.2694473\\\\\n", "\t 499138 & 2 & 5101 & 2017 & 8 & 9 & 82 & 98 & 65.4416656\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "X | sex | borough | year_entry | ref_from | ref_to | areaofcare | wtime | entryage | \n", "|---|---|---|---|---|---|\n", "| 139702 | 2 | 5601 | 2012 | 16 | 3 | 304 | 551 | 78.9083328 | \n", "| 261 | 2 | 5101 | 2008 | 33 | 1 | 138 | 766 | 69.3944473 | \n", "| 256217 | 1 | 5101 | 2013 | 21 | 1 | 115 | 39 | 0.9638889 | \n", "| 7373 | 2 | 5101 | 2010 | 6 | 1 | 149 | 1 | 1.1222222 | \n", "| 32944 | 2 | 5101 | 2010 | 6 | 1 | 138 | 2 | 83.2694473 | \n", "| 499138 | 2 | 5101 | 2017 | 8 | 9 | 82 | 98 | 65.4416656 | \n", "\n", "\n" ], "text/plain": [ " X sex borough year_entry ref_from ref_to areaofcare wtime entryage \n", "1 139702 2 5601 2012 16 3 304 551 78.9083328\n", "2 261 2 5101 2008 33 1 138 766 69.3944473\n", "3 256217 1 5101 2013 21 1 115 39 0.9638889\n", "4 7373 2 5101 2010 6 1 149 1 1.1222222\n", "5 32944 2 5101 2010 6 1 138 2 83.2694473\n", "6 499138 2 5101 2017 8 9 82 98 65.4416656" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "valpoc <- read.table(\"valpos.csv\", header=TRUE, sep=\",\", stringsAsFactors=TRUE, quote = '\"')\n", "head(valpoc)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "valpoc$X <- NULL" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
sexboroughyear_entryref_fromref_toareaofcarewtimeentryage
2 5601 2012 16 3 304 551 78.9083328
2 5101 2008 33 1 138 766 69.3944473
1 5101 2013 21 1 115 39 0.9638889
2 5101 2010 6 1 149 1 1.1222222
2 5101 2010 6 1 138 2 83.2694473
2 5101 2017 8 9 82 98 65.4416656
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllll}\n", " sex & borough & year\\_entry & ref\\_from & ref\\_to & areaofcare & wtime & entryage\\\\\n", "\\hline\n", "\t 2 & 5601 & 2012 & 16 & 3 & 304 & 551 & 78.9083328\\\\\n", "\t 2 & 5101 & 2008 & 33 & 1 & 138 & 766 & 69.3944473\\\\\n", "\t 1 & 5101 & 2013 & 21 & 1 & 115 & 39 & 0.9638889\\\\\n", "\t 2 & 5101 & 2010 & 6 & 1 & 149 & 1 & 1.1222222\\\\\n", "\t 2 & 5101 & 2010 & 6 & 1 & 138 & 2 & 83.2694473\\\\\n", "\t 2 & 5101 & 2017 & 8 & 9 & 82 & 98 & 65.4416656\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "sex | borough | year_entry | ref_from | ref_to | areaofcare | wtime | entryage | \n", "|---|---|---|---|---|---|\n", "| 2 | 5601 | 2012 | 16 | 3 | 304 | 551 | 78.9083328 | \n", "| 2 | 5101 | 2008 | 33 | 1 | 138 | 766 | 69.3944473 | \n", "| 1 | 5101 | 2013 | 21 | 1 | 115 | 39 | 0.9638889 | \n", "| 2 | 5101 | 2010 | 6 | 1 | 149 | 1 | 1.1222222 | \n", "| 2 | 5101 | 2010 | 6 | 1 | 138 | 2 | 83.2694473 | \n", "| 2 | 5101 | 2017 | 8 | 9 | 82 | 98 | 65.4416656 | \n", "\n", "\n" ], "text/plain": [ " sex borough year_entry ref_from ref_to areaofcare wtime entryage \n", "1 2 5601 2012 16 3 304 551 78.9083328\n", "2 2 5101 2008 33 1 138 766 69.3944473\n", "3 1 5101 2013 21 1 115 39 0.9638889\n", "4 2 5101 2010 6 1 149 1 1.1222222\n", "5 2 5101 2010 6 1 138 2 83.2694473\n", "6 2 5101 2017 8 9 82 98 65.4416656" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(valpoc)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "valpoc = valpoc[complete.cases(valpoc), ]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#percentDataUsed=0.01\n", "#nobs = nrow(valpoc)\n", "#short=valpoc[sample(1:nobs,round(percentDataUsed*nobs)),]\n", "#write.csv(short, file = \"valpos.csv\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", " randomForest(formula = wtime ~ ., data = valpoc[valpoc$training == 1, -length(valpoc)], importance = TRUE, ntree = 300) \n", " Type of random forest: regression\n", " Number of trees: 300\n", "No. of variables tried at each split: 2\n", "\n", " Mean of squared residuals: 76910.86\n", " % Var explained: 19.89" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# I train with 75% of the data\n", "valpoc$training=0 \n", "valpoc$training[sample(1:nrow(valpoc),round(0.75*nrow(valpoc)))]=1\n", "\n", "# I use optimised random forest: mtry=2 and ntrees=300\n", "\n", "RF=randomForest(wtime~.,data=valpoc[valpoc$training==1,-length(valpoc)], importance=TRUE, ntree=300)\n", "\n", "RF" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
X.IncMSEIncNodePurity
sex 1259.524 8237060
entryage 6825.50969253962
ref_from13464.27442618177
borough17590.82420911597
year_entry35247.32343662451
ref_to57149.20619381669
areaofcare62277.91464876284
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & X.IncMSE & IncNodePurity\\\\\n", "\\hline\n", "\tsex & 1259.524 & 8237060 \\\\\n", "\tentryage & 6825.509 & 69253962 \\\\\n", "\tref\\_from & 13464.274 & 42618177 \\\\\n", "\tborough & 17590.824 & 20911597 \\\\\n", "\tyear\\_entry & 35247.323 & 43662451 \\\\\n", "\tref\\_to & 57149.206 & 19381669 \\\\\n", "\tareaofcare & 62277.914 & 64876284 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | X.IncMSE | IncNodePurity | \n", "|---|---|---|---|---|---|---|\n", "| sex | 1259.524 | 8237060 | \n", "| entryage | 6825.509 | 69253962 | \n", "| ref_from | 13464.274 | 42618177 | \n", "| borough | 17590.824 | 20911597 | \n", "| year_entry | 35247.323 | 43662451 | \n", "| ref_to | 57149.206 | 19381669 | \n", "| areaofcare | 62277.914 | 64876284 | \n", "\n", "\n" ], "text/plain": [ " X.IncMSE IncNodePurity\n", "sex 1259.524 8237060 \n", "entryage 6825.509 69253962 \n", "ref_from 13464.274 42618177 \n", "borough 17590.824 20911597 \n", "year_entry 35247.323 43662451 \n", "ref_to 57149.206 19381669 \n", "areaofcare 62277.914 64876284 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "VIL = data.frame(RF$importance)\n", "VIL <- VIL[order(VIL$X.IncMSE),]\n", "VIL" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 0.0202242497286564
    2. \n", "\t
  2. 0.109597586351281
    3. \n", "\t
  3. 0.21619661711611
    4. \n", "\t
  4. 0.282456866081855
    5. \n", "\t
  5. 0.565968258858352
    6. \n", "\t
  6. 0.917648046437007
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  7. 1
    8. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0.0202242497286564\n", "\\item 0.109597586351281\n", "\\item 0.21619661711611\n", "\\item 0.282456866081855\n", "\\item 0.565968258858352\n", "\\item 0.917648046437007\n", "\\item 1\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0.0202242497286564\n", "2. 0.109597586351281\n", "3. 0.21619661711611\n", "4. 0.282456866081855\n", "5. 0.565968258858352\n", "6. 0.917648046437007\n", "7. 1\n", "\n", "\n" ], "text/plain": [ "[1] 0.02022425 0.10959759 0.21619662 0.28245687 0.56596826 0.91764805 1.00000000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hola = VIL$X.IncMSE/max(VIL$X.IncMSE)\n", "hola" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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RD4Xgb5yz4M1BgJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(las=2)\n", "par(mar=c(5,12,4,2))\n", "barplot(hola, horiz=TRUE,xlab=\"VIL\",\n", " names.arg=c(\"Sex\", \"Age\",\"RefFrom\",\"Commune\",\"Year\",\"RefTo\",\"Specialty\"),\n", " col=\"darkorange\",cex.names=1.5,cex.axis=1.2,cex.lab=1.5)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "56.530557315428" ], "text/latex": [ "56.530557315428" ], "text/markdown": [ "56.530557315428" ], "text/plain": [ "[1] 56.53056" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# prediction\n", "pred <- data.frame(predict(RF, valpoc[valpoc$training==0,]))\n", "names(pred) <- \"prediction\"\n", "pred$real = valpoc$wtime[valpoc$training==0]\n", "pred$difference = pred$real-pred$prediction\n", "\n", "# normalised root-mean-square error\n", "\n", "RMSE=100*(sum((pred$real-pred$prediction)**2)/(sum(pred$real**2)))\n", "RMSE\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "195.795513719207" ], "text/latex": [ "195.795513719207" ], "text/markdown": [ "195.795513719207" ], "text/plain": [ "[1] 195.7955" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean(valpoc$wtime)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "example <- read.table(\"exampleWL.csv\", header=TRUE, sep=\",\", stringsAsFactors=TRUE, quote = '\"')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
XdateBirthsexspecialtyentryDateexitDatereason
0 22/11/1985 2 Endodoncia 31/12/2017 Celulitis y absceso de boca
1 01/02/1946 1 Periodoncia 31/12/2017 Periodontitis cronica
2 19/06/1937 2 Rehabilitación: Prótesis Removible 31/12/2017 03/01/2018 Otras afecciones especificadas de los dientes y de sus estructuras de sosten
3 13/04/1955 2 Neurocirugía 30/12/2017 Trastornos de disco lumbar y otros, con radiculopatia
4 15/07/1997 2 Endodoncia 30/12/2017 Celulitis y absceso de boca
5 22/08/2007 2 Dermatología 29/12/2017 Pitiriasis alba
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllll}\n", " X & dateBirth & sex & specialty & entryDate & exitDate & reason\\\\\n", "\\hline\n", "\t 0 & 22/11/1985 & 2 & Endodoncia & 31/12/2017 & & Celulitis y absceso de boca \\\\\n", "\t 1 & 01/02/1946 & 1 & Periodoncia & 31/12/2017 & & Periodontitis cronica \\\\\n", "\t 2 & 19/06/1937 & 2 & Rehabilitación: Prótesis Removible & 31/12/2017 & 03/01/2018 & Otras afecciones especificadas de los dientes y de sus estructuras de sosten\\\\\n", "\t 3 & 13/04/1955 & 2 & Neurocirugía & 30/12/2017 & & Trastornos de disco lumbar y otros, con radiculopatia \\\\\n", "\t 4 & 15/07/1997 & 2 & Endodoncia & 30/12/2017 & & Celulitis y absceso de boca \\\\\n", "\t 5 & 22/08/2007 & 2 & Dermatología & 29/12/2017 & & Pitiriasis alba \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "X | dateBirth | sex | specialty | entryDate | exitDate | reason | \n", "|---|---|---|---|---|---|\n", "| 0 | 22/11/1985 | 2 | Endodoncia | 31/12/2017 | | Celulitis y absceso de boca | \n", "| 1 | 01/02/1946 | 1 | Periodoncia | 31/12/2017 | | Periodontitis cronica | \n", "| 2 | 19/06/1937 | 2 | Rehabilitación: Prótesis Removible | 31/12/2017 | 03/01/2018 | Otras afecciones especificadas de los dientes y de sus estructuras de sosten | \n", "| 3 | 13/04/1955 | 2 | Neurocirugía | 30/12/2017 | | Trastornos de disco lumbar y otros, con radiculopatia | \n", "| 4 | 15/07/1997 | 2 | Endodoncia | 30/12/2017 | | Celulitis y absceso de boca | \n", "| 5 | 22/08/2007 | 2 | Dermatología | 29/12/2017 | | Pitiriasis alba | \n", "\n", "\n" ], "text/plain": [ " X dateBirth sex specialty entryDate exitDate \n", "1 0 22/11/1985 2 Endodoncia 31/12/2017 \n", "2 1 01/02/1946 1 Periodoncia 31/12/2017 \n", "3 2 19/06/1937 2 Rehabilitación: Prótesis Removible 31/12/2017 03/01/2018\n", "4 3 13/04/1955 2 Neurocirugía 30/12/2017 \n", "5 4 15/07/1997 2 Endodoncia 30/12/2017 \n", "6 5 22/08/2007 2 Dermatología 29/12/2017 \n", " reason \n", "1 Celulitis y absceso de boca \n", "2 Periodontitis cronica \n", "3 Otras afecciones especificadas de los dientes y de sus estructuras de sosten\n", "4 Trastornos de disco lumbar y otros, con radiculopatia \n", "5 Celulitis y absceso de boca \n", "6 Pitiriasis alba " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(example)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "percentDataUsed=0.01\n", "nobs = nrow(example)\n", "short=example[sample(1:nobs,round(percentDataUsed*nobs)),]\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "write.csv(short, file = \"shortExampleWL.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.3.2" } }, "nbformat": 4, "nbformat_minor": 2 }