inference_02_analysisR.ipynb

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  "metadata": {
    "colab": {
      "name": "inference_02_analysis2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXqIHxPDk9KC",
        "colab_type": "text"
      },
      "source": [
        "# BIDMC Datathon Question #1\n",
        "# English vs. Non-English Speaker MIMIC-III Cohort\n",
        "\n",
        "# Notebook 2: Exploratory Analysis\n",
        "\n",
        "In this notebook, we want to walk you through some basic steps on how to analyze the cohort which we generated in the first notebook. This notebook is meant to simply introduce a few first steps towards performing an exploratory analysis of the data as you begin building models as performing inference in your teams.\n",
        "\n",
        "This notebook is just a quick introduction to analyzing the cohort in R. It's up to you and your team to decide what and how you want to analyze the data! \n",
        "\n",
        "This is where our paths diverge! Looking forward to seeing where we all end up in the next 3 hours."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGoBGf3XGjIE",
        "colab_type": "text"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jq2Jm_3slPV4",
        "colab_type": "text"
      },
      "source": [
        "## Prerequisites\n",
        "\n",
        "- If you do not have a Gmail account, please create one at http://www.gmail.com. \n",
        "- If you have not yet signed the data use agreement (DUA) sent by the organizers, please do so now to get access to the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5J2gJ1qk8rW",
        "colab_type": "text"
      },
      "source": [
        "## Load libraries\n",
        "\n",
        "Run the following cells to install and import some libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVlGPvPO25Nj",
        "colab_type": "code",
        "outputId": "acbe4005-4e3b-4be6-d123-874b949bcf34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "install.packages(\"bigrquery\")\n",
        "install.packages(\"googledrive\")\n",
        "install.packages(\"R.utils\")\n",
        "install.packages(\"cowplot\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘bit’, ‘bit64’, ‘gargle’, ‘rapidjsonr’\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘R.oo’, ‘R.methodsS3’\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z60PpIfIeF4Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "suppressPackageStartupMessages({\n",
        "  library(tidyverse)   ## for easy DS in R\n",
        "  library(cowplot)     ## for plotting plots together\n",
        "  library(bigrquery)   ## for querying BigQuery\n",
        "  library(googledrive) ## for read/write Google Drive\n",
        "  library(httr)        ## for hacking bigrquery to run in Colab\n",
        "  library(R.utils)     ## \"\"\n",
        "  library(repr)        ## for modifying R plot dims in Colab\n",
        "})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rE6-FS4YQ1lx",
        "colab_type": "text"
      },
      "source": [
        "We also set a few global parameters for nicer plots."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHu7Iol3Q2HH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## specify default plot dimensions\n",
        "options(repr.plot.width=4, repr.plot.height=3, repr.plot.res = 300)\n",
        "\n",
        "## specify default ggplot2 theme\n",
        "theme_set(theme_bw())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_rx37IQOD4N",
        "colab_type": "text"
      },
      "source": [
        "## Connect to Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rG4rHtp7wHzd",
        "colab_type": "text"
      },
      "source": [
        "Connecting the Google Colab with Google Drive is relatively straightforward. First, we need to run the following hack to allow R access to Google Drive with the `googledrive` package from a Google Colab notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "x16V6S-0TdwU",
        "colab": {}
      },
      "source": [
        "## source: https://github.com/r-lib/httr/pull/634\n",
        "reassignInPackage(\"is_interactive\", pkgName = \"httr\", function() {return(TRUE)})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "az1VwBuqTz-D",
        "colab_type": "text"
      },
      "source": [
        "Next, authenticate your Google account by calling the following command. Follow the link and enter the authorization code in the dialog box."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHbYv66VN_gJ",
        "colab_type": "code",
        "outputId": "b8468bc3-5bae-4208-d0d0-195a567939fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "drive_auth(use_oob = TRUE)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Please point your browser to the following url: \n",
            "\n",
            "https://accounts.google.com/o/oauth2/auth?client_id=603366585132-0l3n5tr582q443rnomebdeeo0156b2bc.apps.googleusercontent.com&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&response_type=code\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Enter authorization code: 4/xAF2mwDEScNcMLyQeSOjpJAaD50tjjZLJe__irIav-kkn_A6FvG2Vfc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCNm8vmqE86u",
        "colab_type": "text"
      },
      "source": [
        "# Load data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5MIJdbDE8sh",
        "colab_type": "text"
      },
      "source": [
        "We will now load the dataset from Google Drive. This CSV file should have been created by going through the first notebook in this workshop."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KmahunmDFkMJ",
        "colab_type": "text"
      },
      "source": [
        "## Download from Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ge1mhWbwFpQV",
        "colab_type": "text"
      },
      "source": [
        "First, you can download the file from Google Drive using the `drive_download()` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsP3T4ncOHc6",
        "colab_type": "code",
        "outputId": "622aa835-7fa7-4f0b-8064-890a63c8f63e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "source": [
        "drive_download(\"dataset_datathon_28022020.csv\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File downloaded:\n",
            "  * dataset_datathon_28022020.csv\n",
            "Saved locally as:\n",
            "  * dataset_datathon_28022020.csv\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BDdYwP5GAsz",
        "colab_type": "text"
      },
      "source": [
        "Verify that the file is now available on our path."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhOOqrYBGA-Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "6ddc4030-f371-449e-c4f3-75224169785a"
      },
      "source": [
        "file.exists(\"dataset_datathon_28022020.csv\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] TRUE"
            ],
            "text/latex": "TRUE",
            "text/markdown": "TRUE",
            "text/html": [
              "TRUE"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sK_ZVphWFmsE",
        "colab_type": "text"
      },
      "source": [
        "## Read from File"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbPam2Q6FfOv",
        "colab_type": "text"
      },
      "source": [
        "Now that we have the CSV file downloaded, we can read the dataset into our Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFeN2d_hFe29",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "0bd26d08-5d76-4d23-b463-e28dc25d0763"
      },
      "source": [
        "cohort <- read_csv(\"dataset_datathon_28022020.csv\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parsed with column specification:\n",
            "cols(\n",
            "  .default = col_double(),\n",
            "  ethnicity = \u001b[31mcol_character()\u001b[39m,\n",
            "  marital_status = \u001b[31mcol_character()\u001b[39m,\n",
            "  religion = \u001b[31mcol_character()\u001b[39m,\n",
            "  insurance = \u001b[31mcol_character()\u001b[39m,\n",
            "  diagnosis = \u001b[31mcol_character()\u001b[39m,\n",
            "  admission_type = \u001b[31mcol_character()\u001b[39m,\n",
            "  admission_location = \u001b[31mcol_character()\u001b[39m,\n",
            "  hosp_admittime = \u001b[34mcol_datetime(format = \"\")\u001b[39m,\n",
            "  hosp_dischtime = \u001b[34mcol_datetime(format = \"\")\u001b[39m,\n",
            "  first_careunit = \u001b[31mcol_character()\u001b[39m,\n",
            "  icu_intime = \u001b[34mcol_datetime(format = \"\")\u001b[39m,\n",
            "  icu_outtime = \u001b[34mcol_datetime(format = \"\")\u001b[39m\n",
            ")\n",
            "\n",
            "See spec(...) for full column specifications.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_4pEnzjG3va",
        "colab_type": "text"
      },
      "source": [
        "# Exploratory analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9s8fJWb8G7Ve",
        "colab_type": "text"
      },
      "source": [
        "The first step in any data analysis should be **exploratory analysis** of the dataset. When performing an exploratory analysis, you should be checking high-level distributions of various covariates, marginal associations, and on the look out for potential messy data issues. \n",
        "\n",
        "We can start by just taking a look at the first few rows in the table."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KFjZNAMOgMt",
        "colab_type": "code",
        "outputId": "892584d8-90c4-423a-b20e-55b1f5158281",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        }
      },
      "source": [
        "head(cohort)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  subject_id hadm_id icustay_id english age  male ethnicity             \n",
              "1 109        189332  287547     1       25.1 0    BLACK/AFRICAN AMERICAN\n",
              "2 111        155897  249202     1       69.0 0    WHITE                 \n",
              "3 124        138376  256064     1       75.1 1    WHITE                 \n",
              "4 188        132401  246866     1       56.6 1    WHITE                 \n",
              "5 305        194340  217232     0       76.9 0    WHITE                 \n",
              "6 502        116367  209737     1       50.5 1    WHITE                 \n",
              "  marital_status religion          insurance ⋯ drug_abuse.y psychoses.y\n",
              "1 SINGLE         NOT SPECIFIED     Medicaid  ⋯ 0            0          \n",
              "2 MARRIED        PROTESTANT QUAKER Medicare  ⋯ 0            0          \n",
              "3 MARRIED        CATHOLIC          Medicare  ⋯ 0            0          \n",
              "4 MARRIED        CATHOLIC          Private   ⋯ 0            0          \n",
              "5 MARRIED        CATHOLIC          Medicare  ⋯ 0            0          \n",
              "6 MARRIED        PROTESTANT QUAKER Private   ⋯ 1            0          \n",
              "  depression.y elixhauser_vanwalraven.y elixhauser_sid29.y elixhauser_sid30.y\n",
              "1 0            28                       40                 46                \n",
              "2 0            18                       29                 33                \n",
              "3 0            21                       25                 35                \n",
              "4 0            24                       27                 35                \n",
              "5 0            23                       31                 37                \n",
              "6 0            13                       20                 19                \n",
              "  sepsis_3 sofa mechvent_event mechvent_free_days\n",
              "1 0        NA   0                     NA         \n",
              "2 0        NA   1              0.1850810         \n",
              "3 0        NA   1              2.2355208         \n",
              "4 0        NA   1              1.5448032         \n",
              "5 1         6   0                     NA         \n",
              "6 1        11   1              0.1132639         "
            ],
            "text/latex": "A tibble: 6 × 166\n\\begin{tabular}{lllllllllllllllllllll}\n subject\\_id & hadm\\_id & icustay\\_id & english & age & male & ethnicity & marital\\_status & religion & insurance & ⋯ & drug\\_abuse.y & psychoses.y & depression.y & elixhauser\\_vanwalraven.y & elixhauser\\_sid29.y & elixhauser\\_sid30.y & sepsis\\_3 & sofa & mechvent\\_event & mechvent\\_free\\_days\\\\\n <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t 109 & 189332 & 287547 & 1 & 25.1 & 0 & BLACK/AFRICAN AMERICAN & SINGLE  & NOT SPECIFIED     & Medicaid & ⋯ & 0 & 0 & 0 & 28 & 40 & 46 & 0 & NA & 0 &        NA\\\\\n\t 111 & 155897 & 249202 & 1 & 69.0 & 0 & WHITE                  & MARRIED & PROTESTANT QUAKER & Medicare & ⋯ & 0 & 0 & 0 & 18 & 29 & 33 & 0 & NA & 1 & 0.1850810\\\\\n\t 124 & 138376 & 256064 & 1 & 75.1 & 1 & WHITE                  & MARRIED & CATHOLIC          & Medicare & ⋯ & 0 & 0 & 0 & 21 & 25 & 35 & 0 & NA & 1 & 2.2355208\\\\\n\t 188 & 132401 & 246866 & 1 & 56.6 & 1 & WHITE                  & MARRIED & CATHOLIC          & Private  & ⋯ & 0 & 0 & 0 & 24 & 27 & 35 & 0 & NA & 1 & 1.5448032\\\\\n\t 305 & 194340 & 217232 & 0 & 76.9 & 0 & WHITE                  & MARRIED & CATHOLIC          & Medicare & ⋯ & 0 & 0 & 0 & 23 & 31 & 37 & 1 &  6 & 0 &        NA\\\\\n\t 502 & 116367 & 209737 & 1 & 50.5 & 1 & WHITE                  & MARRIED & PROTESTANT QUAKER & Private  & ⋯ & 1 & 0 & 0 & 13 & 20 & 19 & 1 & 11 & 1 & 0.1132639\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 166\n\n| subject_id &lt;dbl&gt; | hadm_id &lt;dbl&gt; | icustay_id &lt;dbl&gt; | english &lt;dbl&gt; | age &lt;dbl&gt; | male &lt;dbl&gt; | ethnicity &lt;chr&gt; | marital_status &lt;chr&gt; | religion &lt;chr&gt; | insurance &lt;chr&gt; | ⋯ ⋯ | drug_abuse.y &lt;dbl&gt; | psychoses.y &lt;dbl&gt; | depression.y &lt;dbl&gt; | elixhauser_vanwalraven.y &lt;dbl&gt; | elixhauser_sid29.y &lt;dbl&gt; | elixhauser_sid30.y &lt;dbl&gt; | sepsis_3 &lt;dbl&gt; | sofa &lt;dbl&gt; | mechvent_event &lt;dbl&gt; | mechvent_free_days &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 109 | 189332 | 287547 | 1 | 25.1 | 0 | BLACK/AFRICAN AMERICAN | SINGLE  | NOT SPECIFIED     | Medicaid | ⋯ | 0 | 0 | 0 | 28 | 40 | 46 | 0 | NA | 0 |        NA |\n| 111 | 155897 | 249202 | 1 | 69.0 | 0 | WHITE                  | MARRIED | PROTESTANT QUAKER | Medicare | ⋯ | 0 | 0 | 0 | 18 | 29 | 33 | 0 | NA | 1 | 0.1850810 |\n| 124 | 138376 | 256064 | 1 | 75.1 | 1 | WHITE                  | MARRIED | CATHOLIC          | Medicare | ⋯ | 0 | 0 | 0 | 21 | 25 | 35 | 0 | NA | 1 | 2.2355208 |\n| 188 | 132401 | 246866 | 1 | 56.6 | 1 | WHITE                  | MARRIED | CATHOLIC          | Private  | ⋯ | 0 | 0 | 0 | 24 | 27 | 35 | 0 | NA | 1 | 1.5448032 |\n| 305 | 194340 | 217232 | 0 | 76.9 | 0 | WHITE                  | MARRIED | CATHOLIC          | Medicare | ⋯ | 0 | 0 | 0 | 23 | 31 | 37 | 1 |  6 | 0 |        NA |\n| 502 | 116367 | 209737 | 1 | 50.5 | 1 | WHITE                  | MARRIED | PROTESTANT QUAKER | Private  | ⋯ | 1 | 0 | 0 | 13 | 20 | 19 | 1 | 11 | 1 | 0.1132639 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 6 × 166</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>subject_id</th><th scope=col>hadm_id</th><th scope=col>icustay_id</th><th scope=col>english</th><th scope=col>age</th><th scope=col>male</th><th scope=col>ethnicity</th><th scope=col>marital_status</th><th scope=col>religion</th><th scope=col>insurance</th><th scope=col>⋯</th><th scope=col>drug_abuse.y</th><th scope=col>psychoses.y</th><th scope=col>depression.y</th><th scope=col>elixhauser_vanwalraven.y</th><th scope=col>elixhauser_sid29.y</th><th scope=col>elixhauser_sid30.y</th><th scope=col>sepsis_3</th><th scope=col>sofa</th><th scope=col>mechvent_event</th><th scope=col>mechvent_free_days</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>109</td><td>189332</td><td>287547</td><td>1</td><td>25.1</td><td>0</td><td>BLACK/AFRICAN AMERICAN</td><td>SINGLE </td><td>NOT SPECIFIED    </td><td>Medicaid</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>28</td><td>40</td><td>46</td><td>0</td><td>NA</td><td>0</td><td>       NA</td></tr>\n",
              "\t<tr><td>111</td><td>155897</td><td>249202</td><td>1</td><td>69.0</td><td>0</td><td>WHITE                 </td><td>MARRIED</td><td>PROTESTANT QUAKER</td><td>Medicare</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>18</td><td>29</td><td>33</td><td>0</td><td>NA</td><td>1</td><td>0.1850810</td></tr>\n",
              "\t<tr><td>124</td><td>138376</td><td>256064</td><td>1</td><td>75.1</td><td>1</td><td>WHITE                 </td><td>MARRIED</td><td>CATHOLIC         </td><td>Medicare</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>21</td><td>25</td><td>35</td><td>0</td><td>NA</td><td>1</td><td>2.2355208</td></tr>\n",
              "\t<tr><td>188</td><td>132401</td><td>246866</td><td>1</td><td>56.6</td><td>1</td><td>WHITE                 </td><td>MARRIED</td><td>CATHOLIC         </td><td>Private </td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>24</td><td>27</td><td>35</td><td>0</td><td>NA</td><td>1</td><td>1.5448032</td></tr>\n",
              "\t<tr><td>305</td><td>194340</td><td>217232</td><td>0</td><td>76.9</td><td>0</td><td>WHITE                 </td><td>MARRIED</td><td>CATHOLIC         </td><td>Medicare</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>23</td><td>31</td><td>37</td><td>1</td><td> 6</td><td>0</td><td>       NA</td></tr>\n",
              "\t<tr><td>502</td><td>116367</td><td>209737</td><td>1</td><td>50.5</td><td>1</td><td>WHITE                 </td><td>MARRIED</td><td>PROTESTANT QUAKER</td><td>Private </td><td>⋯</td><td>1</td><td>0</td><td>0</td><td>13</td><td>20</td><td>19</td><td>1</td><td>11</td><td>1</td><td>0.1132639</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDQYIZ83MJhg",
        "colab_type": "text"
      },
      "source": [
        "Let's take a quick look at some of the primary columns in our dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1f1jijtHOZSd",
        "colab_type": "text"
      },
      "source": [
        "## Language"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0JQRcwMOer1",
        "colab_type": "text"
      },
      "source": [
        "We should start to analyze our cohort and find out if there is a significant relationship between end-of-life treatment between English and non-English speaking patients.\n",
        "\n",
        "Let's start by just taking a look at how many of the patients in our cohort were labeled as English speakers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sh0nFgtG5i-",
        "colab_type": "code",
        "outputId": "8803fd6c-5f38-496a-eaa0-88d14075a09f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "options(repr.plot.width = 4, repr.plot.height = 2)\n",
        "cohort %>%\n",
        "    ggplot(aes(x = factor(english))) + \n",
        "    geom_bar(color = 'black') + \n",
        "    coord_flip() + \n",
        "    xlab(\"English language\") + \n",
        "    ylab(\"hospital admissions\") + \n",
        "    ggtitle(\"Language across cohort\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q0sKdugTL/4033i6e+iSPYNdaqolNzj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\nrupszHaBsLmjotyfmLO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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 600,
              "height": 300
            },
            "text/plain": {
              "width": 600,
              "height": 300
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTwrbbFqO1E7",
        "colab_type": "text"
      },
      "source": [
        "It looks like most patients in our cohort spoke English.\n",
        "\n",
        "We can take a look at how different covariates or outcomes break down between the two groups. Below, we start by taking a look at the ventilation features in our table. After taking a look at this code, consider looking at other columns in our table."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GASU6FOjSZn9",
        "colab_type": "text"
      },
      "source": [
        "## Ventilation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbAGaXBkScLg",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at some of the ventilation characteristics across patients."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPibaSQKTX5j",
        "colab_type": "text"
      },
      "source": [
        "Let's see how many patients were on ventilation during their admission."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUcl2gLzPPBt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "5002dc65-1464-49ea-af1b-2859770bccc9"
      },
      "source": [
        "cohort %>%\n",
        "    dplyr::count(mechvent_event) %>%\n",
        "    dplyr::mutate(p = round(n / sum(n), 4))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  mechvent_event n    p     \n",
              "1 0               709 0.3103\n",
              "2 1              1576 0.6897"
            ],
            "text/latex": "A tibble: 2 × 3\n\\begin{tabular}{lll}\n mechvent\\_event & n & p\\\\\n <dbl> & <int> & <dbl>\\\\\n\\hline\n\t 0 &  709 & 0.3103\\\\\n\t 1 & 1576 & 0.6897\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 2 × 3\n\n| mechvent_event &lt;dbl&gt; | n &lt;int&gt; | p &lt;dbl&gt; |\n|---|---|---|\n| 0 |  709 | 0.3103 |\n| 1 | 1576 | 0.6897 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 2 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>mechvent_event</th><th scope=col>n</th><th scope=col>p</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>0</td><td> 709</td><td>0.3103</td></tr>\n",
              "\t<tr><td>1</td><td>1576</td><td>0.6897</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wnkya2l2PPTz",
        "colab_type": "text"
      },
      "source": [
        "It looks like most patients (almost 70%) were on ventilation.\n",
        "\n",
        "How do things look if we stratify by patient language?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lO6W40FkTXW1",
        "colab_type": "code",
        "outputId": "11425518-559f-49be-e263-2da50581c0d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "cohort %>%\n",
        "    dplyr::count(english, mechvent_event) %>%\n",
        "    dplyr::group_by(english) %>%\n",
        "    dplyr::mutate(p = round(n / sum(n), 4))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  english mechvent_event n    p     \n",
              "1 0       0                89 0.3410\n",
              "2 0       1               172 0.6590\n",
              "3 1       0               620 0.3063\n",
              "4 1       1              1404 0.6937"
            ],
            "text/latex": "A grouped\\_df: 4 × 4\n\\begin{tabular}{llll}\n english & mechvent\\_event & n & p\\\\\n <dbl> & <dbl> & <int> & <dbl>\\\\\n\\hline\n\t 0 & 0 &   89 & 0.3410\\\\\n\t 0 & 1 &  172 & 0.6590\\\\\n\t 1 & 0 &  620 & 0.3063\\\\\n\t 1 & 1 & 1404 & 0.6937\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA grouped_df: 4 × 4\n\n| english &lt;dbl&gt; | mechvent_event &lt;dbl&gt; | n &lt;int&gt; | p &lt;dbl&gt; |\n|---|---|---|---|\n| 0 | 0 |   89 | 0.3410 |\n| 0 | 1 |  172 | 0.6590 |\n| 1 | 0 |  620 | 0.3063 |\n| 1 | 1 | 1404 | 0.6937 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A grouped_df: 4 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>english</th><th scope=col>mechvent_event</th><th scope=col>n</th><th scope=col>p</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>0</td><td>0</td><td>  89</td><td>0.3410</td></tr>\n",
              "\t<tr><td>0</td><td>1</td><td> 172</td><td>0.6590</td></tr>\n",
              "\t<tr><td>1</td><td>0</td><td> 620</td><td>0.3063</td></tr>\n",
              "\t<tr><td>1</td><td>1</td><td>1404</td><td>0.6937</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avFt0KrIT6NW",
        "colab_type": "text"
      },
      "source": [
        "Interesting!\n",
        "\n",
        "It looks like there were proportionally fewer patients on mechanical ventilation in the non-english speaking subset of the cohort. \n",
        "\n",
        "We can also plot this to see it visually. Since the total number of patients in the english and non-english speaking cohorts is fairly different, we will plot the proportions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl7MwFMRUSVv",
        "colab_type": "code",
        "outputId": "11b65e20-dbe9-4b35-a7eb-14c46fd4eeaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "options(repr.plot.width = 4, repr.plot.height = 2)\n",
        "cohort %>%\n",
        "    ggplot(aes(x = factor(english), \n",
        "               fill = factor(mechvent_event))) + \n",
        "    geom_bar(position = \"fill\", color = 'black') + \n",
        "    coord_flip() + \n",
        "    xlab(\"English language\") + \n",
        "    scale_y_continuous(\"precent of patients\",  labels = scales::percent,\n",
        "                       expand = c(0, 0)) +\n",
        "    scale_fill_brewer(\"Mech\\nventilation\", palette = \"Pastel1\") + \n",
        "    ggtitle(\"Cohort proportion on ventilation\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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AAACAYk2aNIk+0rLJ40UpGzdujI6OtrOzk13NysoqODg4KCiovjka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\n09PTZrPZYrGUlJRs3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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 600,
              "height": 300
            },
            "text/plain": {
              "width": 600,
              "height": 300
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XyYJFWtWPtGj"
      },
      "source": [
        "We see a slight different between the populations. However, this **may be due to confounding** or **imbalance in other characteristics** of the populations.\n",
        "\n",
        "What other features in the table should we take a look at? Which do you think are most important?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_V8WFV3GROVk",
        "colab_type": "text"
      },
      "source": [
        "# Creating a \"Table 1\"\n",
        "\n",
        "In reports or papers for clinical trials or retrospective data studies, you'll often find a cohort summary table presented as _Table 1_. Table 1 describes the cohort's characteristics, such as age, sex, and ethnicity, stratified by the exposure under investigation. Often, the exposure groups are shown as two columns in the table (e.g. group A and group B). Creating these tables can be fairly tedious, especially when the list of potentially confounding covariates is long.\n",
        "\n",
        "The good news: there are plenty of packages for generating these tables automatically (in both Python _and_ R), so you only need to reshape your dataset, handle missing data (depending on the package) and call the corresponding functions in the respective Python and R libraries.\n",
        "\n",
        "The following 2 are examples of packages that can be used to generate a \"Table 1\" in Python and R:\n",
        "\n",
        "- Python: [Table One](https://pypi.org/project/tableone/)\n",
        "- R: [Table One](https://cran.r-project.org/web/packages/tableone/vignettes/introduction.html)\n",
        "\n",
        "In this section of the notebook, consider generating a \"Table 1\", analyze the results and include it in your final presentation. To do this, however, you first need to define the stratifying exposure or variable (e.g. English vs. non-English speaking) and define the set of covariates (rows) you want to display. \n",
        "\n",
        "Keep these things in mind while performing your analysis and consider creating Table 1 and performing exploratory analysis extensively *early on*!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuXH8qqLRPOg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}