Enhanced_Recovery_After_Surgery_Using_R.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMs2Sg40hnYAcgu1vLCVzw6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sankytanky100/Data_Science-Projects/blob/main/Enhanced_Recovery_After_Surgery_Using_R.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Evaluating the Impact of Cover Crops on Farm Yield Using Propensity Score Weighting and Regression Analysis**"
      ],
      "metadata": {
        "id": "dXqMTh0aIvjO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rpy2\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZeU9OefFO5O",
        "outputId": "0ca3a5c4-1194-4356-d382-4d7125473066"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rpy2 in /usr/local/lib/python3.10/dist-packages (3.4.2)\n",
            "Requirement already satisfied: cffi>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from rpy2) (1.17.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from rpy2) (3.1.4)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from rpy2) (2024.2)\n",
            "Requirement already satisfied: tzlocal in /usr/local/lib/python3.10/dist-packages (from rpy2) (5.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.10.0->rpy2) (2.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->rpy2) (3.0.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhx7DdgTGT1f",
        "outputId": "941eaab4-c973-4146-9206-73547727a9c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "r-base is already the newest version (4.4.2-1.2204.0).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!apt-get install -y r-base"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext rpy2.ipython\n",
        "\n"
      ],
      "metadata": {
        "id": "pLDysHHF56wR"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "%%R\n",
        "install.packages(\"cobalt\")\n",
        "library(cobalt)\n",
        "install.packages(\"WeightIt\")\n",
        "library(WeightIt)\n",
        "install.packages(\"lmtest\")\n",
        "library(lmtest)\n",
        "install.packages(\"sandwich\")\n",
        "library(sandwich)\n",
        "install.packages(\"rddtools\", dependencies = TRUE)\n",
        "library(rddtools)\n",
        "install.packages(\"dplyr\")\n",
        "library(dplyr)\n",
        "install.packages(\"ggplot2\")\n",
        "library(ggplot2)\n",
        "install.packages(\"rdd\")\n",
        "library(rdd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXJ_O-_AF1U6",
        "outputId": "1fed2c52-0b1f-4eeb-9fc0-4472688ce898"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
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            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: downloaded 208 KB\n",
            "\n",
            "\n",
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            "\n",
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            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The downloaded source packages are in\n",
            "\t‘/tmp/RtmprnquMJ/downloaded_packages’\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: AER\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: car\n",
            "\n",
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            "\n",
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            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: np\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Nonparametric Kernel Methods for Mixed Datatypes (version 0.60-17)\n",
            "[vignette(\"np_faq\",package=\"np\") provides answers to frequently asked questions]\n",
            "[vignette(\"np\",package=\"np\") an overview]\n",
            "[vignette(\"entropy_np\",package=\"np\") an overview of entropy-based methods]\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n",
            "Please consider citing R and rddtools,\n",
            "citation()\n",
            "citation(\"rddtools\")\n",
            "\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’\n",
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            "\n",
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            "\n",
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            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The downloaded source packages are in\n",
            "\t‘/tmp/RtmprnquMJ/downloaded_packages’\n",
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            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n",
            "Attaching package: ‘dplyr’\n",
            "\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following object is masked from ‘package:car’:\n",
            "\n",
            "    recode\n",
            "\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following objects are masked from ‘package:stats’:\n",
            "\n",
            "    filter, lag\n",
            "\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following objects are masked from ‘package:base’:\n",
            "\n",
            "    intersect, setdiff, setequal, union\n",
            "\n",
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            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’\n",
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            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: downloaded 3.4 MB\n",
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            "\t‘/tmp/RtmprnquMJ/downloaded_packages’\n",
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            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’\n",
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            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/rdd_0.57.tar.gz'\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "xVd1Jlqr6Q5U",
        "outputId": "1e33481e-4150-4d8a-a0a3-79b63ab0d9d0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b4deae5f-e818-48da-9801-3b5ad95f2eec\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b4deae5f-e818-48da-9801-3b5ad95f2eec\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving eras_df.csv to eras_df.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "\n",
        "# Read in data\n",
        "eras <- read.csv(\"eras_df.csv\")\n",
        "\n",
        "head(eras)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n383SHv3LDJ1",
        "outputId": "1e5b1713-be1d-4595-9493-fd40a43d05c2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  year   hospital      los\n",
            "1 2010 Hospital A 4.931446\n",
            "2 2010 Hospital B 4.481862\n",
            "3 2011 Hospital A 4.591706\n",
            "4 2011 Hospital B 4.350371\n",
            "5 2012 Hospital A 4.733123\n",
            "6 2012 Hospital B 4.177808\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Line plot for Hospital B\n",
        "eras %>%\n",
        "  filter(hospital == \"Hospital B\") %>%  # Filter to only Hospital B\n",
        "  ggplot(aes(x = year, y = los)) +      # LOS over time\n",
        "  geom_line() +                         # Add line plot\n",
        "  geom_vline(xintercept = 2014, linetype = \"dashed\") + # Vertical line at 2014\n",
        "  scale_x_continuous(breaks = c(2010:2019))           # Set x-axis scale\n",
        "\n"
      ],
      "metadata": {
        "id": "e7cywYGeG1Po",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "17424ab9-2774-40a0-a1a5-f2d14befb63b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Line plot for both hospitals\n",
        "ggplot(\n",
        "  data = eras,\n",
        "  aes(x = year, y = los, color = hospital, linetype = hospital)\n",
        ") +\n",
        "  geom_line() +                                        # Add line plot\n",
        "  geom_vline(xintercept = 2014, linetype = \"dashed\") + # Vertical line at 2014\n",
        "  scale_x_continuous(breaks = c(2010:2019))           # Set x-axis scale\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "uue_MmmiLk41",
        "outputId": "2e35c965-224e-4f1e-e8cd-385432b3b2cd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Restrict data to 2014-2015\n",
        "eras2 <- eras %>% filter(year == 2014 | year == 2015)\n",
        "\n",
        "# View restricted dataset\n",
        "eras2\n"
      ],
      "metadata": {
        "id": "0F7cFNQxLmCn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1658e1f4-90f8-4576-ba90-d8d43b33021d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  year   hospital      los\n",
            "1 2014 Hospital A 4.464623\n",
            "2 2014 Hospital B 4.026384\n",
            "3 2015 Hospital A 3.705750\n",
            "4 2015 Hospital B 2.472485\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Calculate differences within hospitals\n",
        "Bdiff <- eras2$los[4] - eras2$los[2]\n",
        "Adiff <- eras2$los[3] - eras2$los[1]\n",
        "\n",
        "# Print differences\n",
        "Bdiff\n",
        "Adiff\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4sit8MWBLoM2",
        "outputId": "c8a92e9f-7384-4b4c-8c6e-8a174a62dea8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] -0.7588729\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Calculate the difference in differences\n",
        "DID <- Bdiff - Adiff\n",
        "\n",
        "# Print DID\n",
        "DID\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwkT-F_cMVvz",
        "outputId": "9d37da07-9cfa-482f-efd3-28e39f296d1c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] -0.7950255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Create indicators\n",
        "eras2$treat <- ifelse(eras2$hospital == \"Hospital B\", 1, 0)\n",
        "eras2$time <- ifelse(eras2$year == 2015, 1, 0)\n",
        "\n",
        "# View dataset with indicators\n",
        "head(eras2)\n"
      ],
      "metadata": {
        "id": "fTuHT4bAMXoz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dac0747-9254-4683-9ec8-98144a0e76d1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  year   hospital      los treat time\n",
            "1 2014 Hospital A 4.464623     0    0\n",
            "2 2014 Hospital B 4.026384     1    0\n",
            "3 2015 Hospital A 3.705750     0    1\n",
            "4 2015 Hospital B 2.472485     1    1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Fit linear model with interaction between treat and time\n",
        "hospital_model <- lm(los ~ treat * time, data = eras2)\n",
        "\n",
        "# View model summary\n",
        "summary(hospital_model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ekfyp_ThMXX4",
        "outputId": "65761b0b-a3e4-4da5-a7d8-a1cd54fceb3b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call:\n",
            "lm(formula = los ~ treat * time, data = eras2)\n",
            "\n",
            "Residuals:\n",
            "ALL 4 residuals are 0: no residual degrees of freedom!\n",
            "\n",
            "Coefficients:\n",
            "            Estimate Std. Error t value Pr(>|t|)\n",
            "(Intercept)   4.4646        NaN     NaN      NaN\n",
            "treat        -0.4382        NaN     NaN      NaN\n",
            "time         -0.7589        NaN     NaN      NaN\n",
            "treat:time   -0.7950        NaN     NaN      NaN\n",
            "\n",
            "Residual standard error: NaN on 0 degrees of freedom\n",
            "Multiple R-squared:      1,\tAdjusted R-squared:    NaN \n",
            "F-statistic:   NaN on 3 and 0 DF,  p-value: NA\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Print ATT\n",
        "ATT <- hospital_model$coefficients[4]\n",
        "\n",
        "# View ATT\n",
        "ATT\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJ-WxOvn4MD0",
        "outputId": "aacb67d2-c224-467a-8fc6-a00acfd04883"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "treat:time \n",
            "-0.7950255 \n"
          ]
        }
      ]
    }
  ]
}