Air_Quality_Using_R.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyNiF/KDSK66keJh3pQAUPTD",
      "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/Air_Quality_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": "f8319d5e-c702-49d0-a4e1-464b2322e286"
      },
      "execution_count": 41,
      "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": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhx7DdgTGT1f",
        "outputId": "07c75ddc-a14a-4f93-9d3a-f09d7580eee0"
      },
      "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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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "e4fababf-40f5-45ff-d114-ace078373c48"
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      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rpy2.ipython extension is already loaded. To reload it, use:\n",
            "  %reload_ext rpy2.ipython\n"
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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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXJ_O-_AF1U6",
        "outputId": "1083037f-7fa6-4600-9e28-f506d3fe4dac"
      },
      "execution_count": 56,
      "outputs": [
        {
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          "name": "stderr",
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            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: downloaded 1.3 MB\n",
            "\n",
            "\n",
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            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The downloaded source packages are in\n",
            "\t‘/tmp/RtmpV8oAC8/downloaded_packages’\n",
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            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/rddtools_1.6.0.tar.gz'\n",
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            "\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": "a92980c9-390b-44f3-99af-6f2259c4842b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-98c83f43-2d4c-49de-844f-b68302e686f8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-98c83f43-2d4c-49de-844f-b68302e686f8\">\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 air_data.csv to air_data (2).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "\n",
        "# Load ggplot2 library\n",
        "library(ggplot2)\n",
        "\n",
        "# Import dataset\n",
        "air_data <- read.csv(\"air_data.csv\")\n",
        "\n",
        "head(air_data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n383SHv3LDJ1",
        "outputId": "71c5e4c0-062a-47a7-d5b1-b69c0de09c56"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  id    watts            group      aqi\n",
            "1  1 603.7698 Emissions Device 110.0132\n",
            "2  2 546.2823        No Device 152.8377\n",
            "3  3 534.8220        No Device 144.6049\n",
            "4  4 641.5463 Emissions Device 111.7767\n",
            "5  5 531.4000        No Device 139.7165\n",
            "6  6 625.7948 Emissions Device 111.1061\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Create scatter plot with watts as forcing variable and AQI as outcome\n",
        "air_scatter <- ggplot(air_data, aes(x = watts, y = aqi)) +\n",
        "  geom_point() +\n",
        "  labs(\n",
        "    title = \"Scatter Plot: Watts vs AQI\",\n",
        "    x = \"Watts (forcing variable)\",\n",
        "    y = \"AQI (outcome)\"\n",
        "  )\n"
      ],
      "metadata": {
        "id": "e7cywYGeG1Po"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Print the scatter plot\n",
        "print(air_scatter)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "uue_MmmiLk41",
        "outputId": "290dc259-4c47-473c-f094-9ee0ef4a4d73"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Add best-fit lines to the scatter plot\n",
        "air_scatter2 <- air_scatter +\n",
        "  geom_smooth(method = \"lm\", se = FALSE, color = \"blue\") +\n",
        "  labs(\n",
        "    title = \"Scatter Plot with Best-Fit Line: Watts vs AQI\"\n",
        "  )\n"
      ],
      "metadata": {
        "id": "0F7cFNQxLmCn"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Print the scatter plot with best-fit line\n",
        "print(air_scatter2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "4sit8MWBLoM2",
        "outputId": "2ae2f054-7ceb-4d65-8ff7-9f31b62d480f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "`geom_smooth()` using formula = 'y ~ x'\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Calculate the IK bandwidth\n",
        "air_ik_bw <- IKbandwidth(\n",
        "  X = air_data$watts, # forcing variable\n",
        "  Y = air_data$aqi,   # outcome variable\n",
        "  cutpoint = 600      # cutpoint\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwkT-F_cMVvz",
        "outputId": "b6bc5a7b-c7c6-49b2-c1ee-bd29a62fbbf1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Error in IKbandwidth(X = air_data$watts, Y = air_data$aqi, cutpoint = 600) : \n",
            "  could not find function \"IKbandwidth\"\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Error in IKbandwidth(X = air_data$watts, Y = air_data$aqi, cutpoint = 600) : \n",
            "  could not find function \"IKbandwidth\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Fit local linear regression model\n",
        "air_rdd <- RDestimate(\n",
        "  formula = aqi ~ watts,  # formula for outcome model\n",
        "  data = air_data,        # dataset\n",
        "  cutpoint = 600,         # cutpoint\n",
        "  bw = air_ik_bw          # bandwidth\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTuHT4bAMXoz",
        "outputId": "84df4b25-5348-46b4-f5b4-f22cdfb16358"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Error in RDestimate(formula = aqi ~ watts, data = air_data, cutpoint = 600,  : \n",
            "  could not find function \"RDestimate\"\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Error in RDestimate(formula = aqi ~ watts, data = air_data, cutpoint = 600,  : \n",
            "  could not find function \"RDestimate\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Print results of local linear regression\n",
        "print(air_rdd)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ekfyp_ThMXX4",
        "outputId": "2ce8d559-8307-4a56-fb1f-163d8e84069a"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Error in (function (expr, envir = parent.frame(), enclos = if (is.list(envir) ||  : \n",
            "  object 'air_rdd' not found\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Error in (function (expr, envir = parent.frame(), enclos = if (is.list(envir) ||  : \n",
            "  object 'air_rdd' not found\n"
          ]
        }
      ]
    }
  ]
}