Farm_Yield_Using_R.ipynb

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      "gpuType": "T4",
      "authorship_tag": "ABX9TyOui+uzt8Kc1VPnz/bTqsfg",
      "include_colab_link": true
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Sankytanky100/Data_Science-Projects/blob/main/Farm_Yield_Using_R.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "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": "39f609f5-4305-4703-9d19-a77fe14bf148"
      },
      "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": "1910be84-3e27-4b72-b7d5-f2e6b854c632"
      },
      "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"
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      "metadata": {
        "id": "pLDysHHF56wR"
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      "execution_count": 3,
      "outputs": []
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        "%%R\n",
        "install.packages(\"cobalt\")\n",
        "library(cobalt)\n"
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        "id": "AIJvfBVgFTe0",
        "outputId": "01862a20-9c9f-4172-b27e-02b3ce5cad27"
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      "execution_count": 4,
      "outputs": [
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            "\n",
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    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "install.packages(\"WeightIt\")\n",
        "library(WeightIt)\n",
        "install.packages(\"lmtest\")\n",
        "library(lmtest)\n",
        "install.packages(\"sandwich\")\n",
        "library(sandwich)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "RXJ_O-_AF1U6",
        "outputId": "bc7bef44-1842-4c4c-a5b8-48fb908d8d59"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "\t‘/tmp/RtmpbDXCFp/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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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()  # Select the \"farms.csv\" file to upload\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "xVd1Jlqr6Q5U",
        "outputId": "5258f98f-67d5-4e6c-c1b0-7d7d95d61bd8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e019aacd-8dae-4460-b2c6-e8cae5c52aad\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e019aacd-8dae-4460-b2c6-e8cae5c52aad\">\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 farms.csv to farms (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Task 1: Load file as dataframe\n",
        "farm_df <- read.csv(\"farms.csv\")"
      ],
      "metadata": {
        "id": "e7cywYGeG1Po"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Task 2: Inspect dataframe\n",
        "head(farm_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BduQv4vLG13g",
        "outputId": "1f2c85c6-2194-4577-86d9-c1334210725e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  total_yield cover_10 region total_avg age_avg experience_avg insurance_avg\n",
            "1    61.33234        0  South     0.305    57.2           21.6     0.2543968\n",
            "2    47.47099        1  South     0.208    61.2           24.9     0.6386207\n",
            "3    46.27485        0  South     0.307    61.0           24.5     0.4223478\n",
            "4    81.77405        0  South     0.254    58.8           23.9     1.0557846\n",
            "5    81.04827        0  South     0.117    58.0           23.9     0.2556000\n",
            "6    58.26087        0  South     0.292    62.5           23.6     0.6138043\n",
            "  easement_p conservation_till_avg fertilizer_per_area\n",
            "1  1.8867925                   145            20.26741\n",
            "2  1.5439430                   553            74.62687\n",
            "3  0.8032129                    63            15.08367\n",
            "4  1.1844332                   391            60.03353\n",
            "5  0.5053341                    27            22.93760\n",
            "6  2.3454158                   579            25.26148\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Task 3: Balance plot for the average age\n",
        "library(cobalt)\n",
        "bal.plot(\n",
        "  x = cover_10 ~ age_avg, #formula\n",
        "  data = farm_df, #dataset\n",
        "  var.name = \"age_avg\", #variable\n",
        "  colors = c(\"#E69F00\", \"#009E73\") #set fill colors\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "annKG6GpG4Sv",
        "outputId": "e464ffe4-5d53-43dc-dcad-be8bd9bb7b73"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Balance plot for geographic region\n",
        "bal.plot(\n",
        "  x = cover_10 ~ region, # formula\n",
        "  data = farm_df,        # dataset\n",
        "  var.name = \"region\",   # variable\n",
        "  colors = c(\"#E69F00\", \"#009E73\") # set fill colors\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "fs3fJBGWHeLb",
        "outputId": "d1121ac0-3cdb-44be-dbe0-12ee2b200a34"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Balance table to show SMD and variance ratio between groups\n",
        "bal.tab(\n",
        "  cover_10 ~ region + total_avg + age_avg + experience_avg + insurance_avg + easement_p + conservation_till_avg + fertilizer_per_area,\n",
        "  data = farm_df,         # dataset\n",
        "  binary = \"std\",         # SMD for binary\n",
        "  disp.v.ratio = TRUE     # show variance ratios\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWc7JP4YHgMa",
        "outputId": "e51a1b2a-4407-478a-e725-97017064e876"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Note: `s.d.denom` not specified; assuming \"pooled\".\n",
            "Balance Measures\n",
            "                         Type Diff.Un V.Ratio.Un\n",
            "region_North Central   Binary  0.0627          .\n",
            "region_Northeast       Binary  0.5896          .\n",
            "region_South           Binary -0.1454          .\n",
            "region_West            Binary -0.4488          .\n",
            "total_avg             Contin. -0.4775     0.2499\n",
            "age_avg               Contin. -0.4668     1.4521\n",
            "experience_avg        Contin.  0.1226     0.7960\n",
            "insurance_avg         Contin. -0.4963     0.4423\n",
            "easement_p            Contin.  0.2982     1.1288\n",
            "conservation_till_avg Contin. -0.3670     0.6057\n",
            "fertilizer_per_area   Contin.  0.6299     1.5627\n",
            "\n",
            "Sample sizes\n",
            "    Control Treated\n",
            "All    1048     529\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Calculate IPTW weights with initial propensity score model\n",
        "farm_iptw <- weightit(\n",
        "  cover_10 ~ region + total_avg + insurance_avg + fertilizer_per_area,\n",
        "  data = farm_df,         # dataset\n",
        "  method = \"ps\",          # use IPTW\n",
        "  estimand = \"ATE\"        # IPTW formulas for the ATE\n",
        ")\n"
      ],
      "metadata": {
        "id": "xSqaIAe8Hh-1"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Love plot with threshold lines to show SMD balance before and after weighting\n",
        "love.plot(\n",
        "  farm_iptw,              # weightit model\n",
        "  binary = \"std\",         # use SMD for binary\n",
        "  thresholds = c(m = 0.1), # guidelines\n",
        "  colors = c(\"#E69F00\", \"#009E73\") # set fill colors\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "WvUVQK5IHkA5",
        "outputId": "703a392c-1e70-4eaf-f51e-433c83d481ff"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Re-calculate IPTW weights with updated propensity score model\n",
        "farm_iptw2 <- weightit(\n",
        "  cover_10 ~ region + total_avg + insurance_avg + age_avg + experience_avg + easement_p + conservation_till_avg,\n",
        "  data = farm_df,         # dataset\n",
        "  method = \"ps\",          # IPTW weights\n",
        "  estimand = \"ATE\"        # IPTW for ATE\n",
        ")\n"
      ],
      "metadata": {
        "id": "tBMOI9YiHlZw"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Plot Love plot of SMDs with threshold lines to show balance before and after IPTW procedure\n",
        "love.plot(\n",
        "  farm_iptw2,             # weightit model\n",
        "  binary = \"std\",         # use SMD for binary\n",
        "  thresholds = c(m = 0.1), # guidelines\n",
        "  colors = c(\"#E69F00\", \"#009E73\") # set fill colors\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "go70PJ2EHmpg",
        "outputId": "1476ae69-bb8b-4ccc-eb43-83c9d158965f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Balance plot of propensity scores before and after weighting\n",
        "bal.plot(\n",
        "  x = farm_iptw2,         # model\n",
        "  var.name = \"prop.score\", # propensity scores\n",
        "  which = \"both\",         # before and after\n",
        "  colors = c(\"#E69F00\", \"#009E73\") # set fill colors\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "Lqxdc4ghHoCB",
        "outputId": "fc621b52-10d0-4259-dfd5-a182fe6c6a66"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Fit outcome regression model\n",
        "yield_mod <- glm(\n",
        "  formula = total_yield ~ cover_10 + region + total_avg + insurance_avg + age_avg + experience_avg + insurance_avg + easement_p + conservation_till_avg,\n",
        "  data = farm_df,         # dataset\n",
        "  weights = farm_iptw2$weights # IPTW weights\n",
        ")\n"
      ],
      "metadata": {
        "id": "fbyiIuIsHpS_"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "\n",
        "# Estimate regression coefficients for weighted outcome model with robust standard errors\n",
        "coeftest(\n",
        "  yield_mod,              # outcome model\n",
        "  vcov. = vcovHC          # robust standard errors\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzKMGCBbHq2z",
        "outputId": "dfd52a3c-1057-490c-b1ff-64903e0aad24"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "z test of coefficients:\n",
            "\n",
            "                         Estimate  Std. Error z value  Pr(>|z|)    \n",
            "(Intercept)           111.0182307  15.4101202  7.2042 5.837e-13 ***\n",
            "cover_10                3.5768404   1.0373046  3.4482 0.0005643 ***\n",
            "regionNortheast        -7.8566450   2.4221267 -3.2437 0.0011799 ** \n",
            "regionSouth            -3.6146891   1.4750675 -2.4505 0.0142648 *  \n",
            "regionWest             18.5398103   2.9171803  6.3554 2.079e-10 ***\n",
            "total_avg             -18.1552947   2.1255363 -8.5415 < 2.2e-16 ***\n",
            "insurance_avg           6.2900784   2.2013023  2.8574 0.0042708 ** \n",
            "age_avg                -1.7556714   0.2890145 -6.0747 1.242e-09 ***\n",
            "experience_avg          2.1238056   0.3191488  6.6546 2.841e-11 ***\n",
            "easement_p              0.2464213   0.1659482  1.4849 0.1375627    \n",
            "conservation_till_avg  -0.0048407   0.0016546 -2.9256 0.0034379 ** \n",
            "---\n",
            "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Results Interpretation:\n",
        "Our estimate of the ATE is about 3.58. We estimate that when counties have at least 10% of farms with cover crops, the average total yield of wheat increases by about 3.58 bushels per acre.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "SgULlh2JHsuq",
        "outputId": "77ac0fcb-95de-480f-cd84-41367bf291f6"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-27-c0545c03b996>, line 2)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-27-c0545c03b996>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    Our estimate of the ATE is about 3.58. We estimate that when counties have at least 10% of farms with cover crops, the average total yield of wheat increases by about 3.58 bushels per acre.\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    }
  ]
}