tsk_20181121_population_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook ended up more as an analysis than preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intitializing Scala interpreter ..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Spark Web UI available at http://localhost:4043\n",
       "SparkContext available as 'sc' (version = 2.3.2, master = local[*], app id = local-1549980138664)\n",
       "SparkSession available as 'spark'\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-02-12 15:02:16 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
      "2019-02-12 15:02:18 WARN  Utils:66 - Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n",
      "2019-02-12 15:02:18 WARN  Utils:66 - Service 'SparkUI' could not bind on port 4041. Attempting port 4042.\n",
      "2019-02-12 15:02:18 WARN  Utils:66 - Service 'SparkUI' could not bind on port 4042. Attempting port 4043.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "res0: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@602e2208\n"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%run \"src/scala/register_tables.scala\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%python\n",
    "import matplotlib.pyplot as plt\n",
    "from src.python.helpers import plotfig_magic\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import Image\n",
    "import tempfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===Prescription dataset===\n",
      "registering patients\n",
      "registering prescriptions\n",
      "registering drugs\n",
      "ok!\n",
      "Warning: This function will be deprecated soon\n",
      "Use register_elders() instead\n",
      "register_hospitalization() will later be used to register the hospitalized portion of the elders dataset\n",
      "===Hospitalization dataset===\n",
      "registering all\n",
      "registering patients\n",
      "registering prescriptions\n",
      "registering drugs\n",
      "ok!\n",
      "===NPR Elders===\n",
      "ok!\n",
      "+--------+--------------------------+-----------+\n",
      "|database|tableName                 |isTemporary|\n",
      "+--------+--------------------------+-----------+\n",
      "|        |elders                    |true       |\n",
      "|        |elders_drugs              |true       |\n",
      "|        |elders_patients           |true       |\n",
      "|        |elders_prescriptions      |true       |\n",
      "|        |npr_elders                |true       |\n",
      "|        |prescription_drugs        |true       |\n",
      "|        |prescription_patients     |true       |\n",
      "|        |prescription_prescriptions|true       |\n",
      "+--------+--------------------------+-----------+\n",
      "\n",
      "done!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "import spark.implicits._\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spark.implicits._\n",
    "register_tables(spark,\"spark-warehouse/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the hospitalization data set\n",
    " The following steps need to be implemented"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate the three types of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elders: org.apache.spark.sql.DataFrame = [id: string, birthyear: int ... 21 more fields]\n",
       "elders_with_diffdate: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, birthyear: int ... 21 more fields]\n",
       "elders_dated_with_death: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, birthyear: int ... 21 more fields]\n",
       "elders_dated_alive: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, birthyear: int ... 21 more fields]\n",
       "res4: org.apache.spark.sql.DataFrame = [id: string, birthyear: int ... 21 more fields]\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val elders = spark.sql(\"select * from elders\")\n",
    "val elders_with_diffdate = elders\n",
    "    .where(\"Diff_UtleveringDato is not null\")\n",
    "val elders_dated_with_death = elders\n",
    "    .where(\"timestamp is not null\")\n",
    "    .where(\"death_timestamp is not null\")\n",
    "val elders_dated_alive = elders\n",
    "    .where(\"timestamp is not null\")\n",
    "    .where(\"death_timestamp is null\")\n",
    "elders\n",
    "    /*.select(\n",
    "        \"id\"\n",
    "        ,\"birthyear\"\n",
    "        ,\"gender\"\n",
    "        ,\"timestamp\"\n",
    "        ,\"Diff_UtleveringDato\"\n",
    "        ,\"drugcode\"\n",
    "    )*/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We need prescriptions grouped by the patient ID, ordered by time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Small digression: to better know the population we are working with\n",
    "### Plot the age distribution of patients in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_patients: Long = 765431\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val total_patients = elders.select(\"id\").distinct.count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toAgeIn2012: Int => Int = <function1>\n",
       "simplifiedAge: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,IntegerType,Some(List(IntegerType)))\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val toAgeIn2012:Int=>Int = 2012-_\n",
    "val simplifiedAge = udf(toAgeIn2012)\n",
    "elders\n",
    "    .select(\"id\",\"birthyear\")\n",
    "    .distinct\n",
    "    .select(\"birthyear\")\n",
    "    .withColumn(\"age\",simplifiedAge('birthyear))\n",
    "    .select(\"age\").groupBy(\"age\").count.sort(asc(\"age\"))\n",
    "    .createOrReplaceTempView(\"agedistr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%python\n",
    "df = spark.sql(\"select * from agedistr where age > 60\").toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%python\n",
    "plt.clf()\n",
    "plt.title(\"Dataset population by age\")\n",
    "\n",
    "df.plot(x=\"age\",y=\"count\")\n",
    "retval=plotfig_magic()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We observe approximately the same numbers in these age groups from SSB\n",
    "![Norwegian population distribution](https://www.ssb.no/_image/343717/label/)\n",
    "Source: https://www.ssb.no/en/befolkning/nokkeltall/population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average prescriptions per patient in span of hospitalization dataset: 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "res7: Long = 78\n"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elders.count/total_patients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution in number of drugs received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "elders\n",
    "    .select(\"id\")\n",
    "    .groupBy(\"id\")\n",
    "    .count\n",
    "    .sort(desc(\"count\"))\n",
    "    .createOrReplaceTempView(\"t1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%python\n",
    "df = spark.sql(\"select * from t1\").toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%python\n",
    "plt.clf()\n",
    "plt.title(\"distribution of prescriptions by patient (from patient with most to least prescriptions)\")\n",
    "df[:4000].plot(x=\"id\",y=\"count\")\n",
    "retval=plotfig_magic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.23955253121572\n"
     ]
    }
   ],
   "source": [
    "%%python\n",
    "total_patients = 765640\n",
    "skipprior = 0\n",
    "skipafter = total_patients-0\n",
    "tsum = df[\"count\"][skipprior:skipafter].sum()\n",
    "mean = tsum/(skipafter-skipprior)\n",
    "print(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "spylon-kernel",
   "language": "scala",
   "name": "spylon-kernel"
  },
  "language_info": {
   "codemirror_mode": "text/x-scala",
   "file_extension": ".scala",
   "help_links": [
    {
     "text": "MetaKernel Magics",
     "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
    }
   ],
   "mimetype": "text/x-scala",
   "name": "scala",
   "pygments_lexer": "scala",
   "version": "0.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}