python_anova.ipynb

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        "<a href=\"https://colab.research.google.com/github/c-susan/datasci_6_anova/blob/main/python_anova.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": [
        "# Loading in Packages"
      ],
      "metadata": {
        "id": "vjPqPtaIEmvu"
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      "cell_type": "code",
      "source": [
        "!pip install ucimlrepo"
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          "text": [
            "Collecting ucimlrepo\n",
            "  Downloading ucimlrepo-0.0.3-py3-none-any.whl (7.0 kB)\n",
            "Installing collected packages: ucimlrepo\n",
            "Successfully installed ucimlrepo-0.0.3\n"
          ]
        }
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      "source": [
        "## bringing in data\n",
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "##bring in packages for dataframe + stats\n",
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.formula.api import ols\n",
        "\n",
        "## for visualization\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Data Preparation**\n",
        "\n",
        "> Creating a subset of the data with only the variables of interest and checking that types are appropriate given the variable.\n",
        "\n",
        "**Question**: Does race and gender have a combined influence on the amount of days spent in the hospital (time_in_hospital)?\n",
        "\n",
        "**DV**: time_in_hospital\n",
        "\n",
        "**IV1**: race\n",
        "\n",
        "**IV2**: gender\n",
        "\n",
        "**H0 (Null Hypothesis)**: There is no significant combined influence of race and gender on the amount of days spent in the hospital.\n",
        "\n",
        "**H1 (Alternative Hypothesis)**: There is a significant combined influence of race and gender on the amount of days spent in the hospital."
      ],
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      "source": [
        "# fetching dataset\n",
        "diabetes_130_us_hospitals_for_years_1999_2008 = fetch_ucirepo(id=296)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = diabetes_130_us_hospitals_for_years_1999_2008.data.features\n",
        "y = diabetes_130_us_hospitals_for_years_1999_2008.data.targets"
      ],
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      "outputs": [
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/ucimlrepo/fetch.py:97: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv(data_url)\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(X)\n",
        "df.columns = df.columns.str.replace('-','_', regex=True)\n",
        "df.sample(5)"
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              "                  race  gender      age weight  admission_type_id  \\\n",
              "34336              NaN    Male  [50-60)    NaN                  2   \n",
              "36662         Hispanic    Male  [30-40)    NaN                  2   \n",
              "38945        Caucasian  Female  [80-90)    NaN                  6   \n",
              "5889   AfricanAmerican  Female  [60-70)    NaN                  1   \n",
              "15771        Caucasian    Male  [40-50)    NaN                  1   \n",
              "\n",
              "       discharge_disposition_id  admission_source_id  time_in_hospital  \\\n",
              "34336                         1                    1                 1   \n",
              "36662                         1                    1                 7   \n",
              "38945                         3                   17                 2   \n",
              "5889                          1                    7                 3   \n",
              "15771                         1                    7                 3   \n",
              "\n",
              "      payer_code       medical_specialty  ...  examide  citoglipton  insulin  \\\n",
              "34336         BC  Family/GeneralPractice  ...       No           No   Steady   \n",
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              "15771        NaN                     NaN  ...       No           No   Steady   \n",
              "\n",
              "       glyburide_metformin  glipizide_metformin  glimepiride_pioglitazone  \\\n",
              "34336                   No                   No                        No   \n",
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              "38945                   No                   No                        No   \n",
              "5889                    No                   No                        No   \n",
              "15771                   No                   No                        No   \n",
              "\n",
              "      metformin_rosiglitazone metformin_pioglitazone change  diabetesMed  \n",
              "34336                      No                     No     No          Yes  \n",
              "36662                      No                     No     No          Yes  \n",
              "38945                      No                     No     No          Yes  \n",
              "5889                       No                     No     Ch          Yes  \n",
              "15771                      No                     No     No          Yes  \n",
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              "        document.querySelector('#df-a210d6a9-6f62-4cf7-a197-64198f9e5bd3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mxZamBw4ddl",
        "outputId": "526871c6-1935-41af-da09-baf519b3081b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female             54708\n",
              "Male               47055\n",
              "Unknown/Invalid        3\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[(df['gender'].isin(['Female', 'Male']))]"
      ],
      "metadata": {
        "id": "cFK1Z9kGK2xC"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['race', 'gender', 'time_in_hospital']]\n",
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "muqykyEGP-ev",
        "outputId": "8a37d603-b1cb-45e7-fd4e-f44061303cc4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  race  gender  time_in_hospital\n",
              "65425        Caucasian    Male                 1\n",
              "74444              NaN    Male                 1\n",
              "95461        Caucasian  Female                 3\n",
              "7179   AfricanAmerican  Female                 5\n",
              "11688  AfricanAmerican  Female                 8"
            ],
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "</style>\n",
              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Assumption Checks**\n",
        "\n",
        "> Checking for normality and homogeneity of variance.\n",
        "\n"
      ],
      "metadata": {
        "id": "v7PYXLsU9onC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Main DV:', stats.shapiro(df['time_in_hospital']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tphRpDxrhXnz",
        "outputId": "65e8ea92-cbfc-4d38-bbd1-bcd15edbadbf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Main DV: ShapiroResult(statistic=0.8869178891181946, pvalue=0.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/stats/_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
            "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['time_in_hospital'], bins=20, edgecolor='k', alpha=0.8)\n",
        "plt.title('Histogram of Days in Hospital')\n",
        "plt.xlabel('Days in Hospital')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "128OL0oRhicD",
        "outputId": "6a82060c-c6ab-4d78-8577-d8193336d1f4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Checking for normality\n",
        "groups = df.groupby(['race', 'gender'])\n",
        "\n",
        "for (race_status, gender_status), group_df in groups:\n",
        "  if len(group_df['time_in_hospital']):\n",
        "      _, p_value = stats.shapiro(group_df['time_in_hospital'])\n",
        "\n",
        "      print(f\"Group ({race_status}, {gender_status}):\")\n",
        "      print(f\"P-value from Shapiro-Wilk Test: {p_value}\\n\")"
      ],
      "metadata": {
        "id": "wxZLTZK8bwVN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7babc5d3-dce2-453b-f421-b076ec6f792a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Group (AfricanAmerican, Female):\n",
            "P-value from Shapiro-Wilk Test: 0.0\n",
            "\n",
            "Group (AfricanAmerican, Male):\n",
            "P-value from Shapiro-Wilk Test: 0.0\n",
            "\n",
            "Group (Asian, Female):\n",
            "P-value from Shapiro-Wilk Test: 9.520779367313894e-17\n",
            "\n",
            "Group (Asian, Male):\n",
            "P-value from Shapiro-Wilk Test: 4.219604476332644e-16\n",
            "\n",
            "Group (Caucasian, Female):\n",
            "P-value from Shapiro-Wilk Test: 0.0\n",
            "\n",
            "Group (Caucasian, Male):\n",
            "P-value from Shapiro-Wilk Test: 0.0\n",
            "\n",
            "Group (Hispanic, Female):\n",
            "P-value from Shapiro-Wilk Test: 9.368428169951816e-30\n",
            "\n",
            "Group (Hispanic, Male):\n",
            "P-value from Shapiro-Wilk Test: 5.67417287622373e-27\n",
            "\n",
            "Group (Other, Female):\n",
            "P-value from Shapiro-Wilk Test: 2.508905647524163e-24\n",
            "\n",
            "Group (Other, Male):\n",
            "P-value from Shapiro-Wilk Test: 1.5322922575434318e-23\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/stats/_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.\n",
            "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Checking for homogeneity of variance\n",
        "stats.levene(\n",
        "    df['time_in_hospital'][df['race'] == 'AfricanAmerican'][df['gender'] == 'Female'],\n",
        "    df['time_in_hospital'][df['race'] == 'AfricanAmerican'][df['gender'] == 'Male'],\n",
        "    df['time_in_hospital'][df['race'] == 'Asian'][df['gender'] == 'Female'],\n",
        "    df['time_in_hospital'][df['race'] == 'Asian'][df['gender'] == 'Male'],\n",
        "    df['time_in_hospital'][df['race'] == 'Caucasian'][df['gender'] == 'Female'],\n",
        "    df['time_in_hospital'][df['race'] == 'Caucasian'][df['gender'] == 'Male'],\n",
        "    df['time_in_hospital'][df['race'] == 'Hispanic'][df['gender'] == 'Female'],\n",
        "    df['time_in_hospital'][df['race'] == 'Hispanic'][df['gender'] == 'Male'],\n",
        "    df['time_in_hospital'][df['race'] == 'Other'][df['gender'] == 'Female'],\n",
        "    df['time_in_hospital'][df['race'] == 'Other'][df['gender'] == 'Male'],\n",
        ")"
      ],
      "metadata": {
        "id": "lp--zb9CbwMr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66cf1e24-3524-4110-dbbd-047c98095211"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=4.212648053226347, pvalue=1.8106820990342793e-05)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Interpretations**\n",
        "\n",
        "**Shapiro-Wilk Test**\n",
        "+ The Shapiro-Wilk test for 'time_in_hospital' shows a p-value of 0.0, which is less than the significance level of 0.05. This means that the 'time_in_hospital' is not equally distributed. In addition, as seen in the histogram, 'Histogram of Days in Hospital', the graph has a right skew with more people having spent 1-4 days in the hospital compared to 10-14 days.\n",
        "+ The Shapiro-Wilk test for the independent variables, race and gender, shows all p-values less than 0.05, with 4 combinations having p-values of zero. This means that the null hypothesis is rejected and that race and gender has a significant combined infuence on the amount of days spent in the hospital.\n",
        "\n",
        "*It should be noted that the shapiro test may not be accurate as the dataset contains a sample size more than 5000 (N > 5000), as noted in the warnings shown below each shapiro test result.*\n",
        "\n",
        "**Levene**\n",
        "+ The Levene tests show a p-value of 1.8106820990342793e-05, which is less than the significance level of 0.05. This means that the homogeneity of variance is unequal."
      ],
      "metadata": {
        "id": "wwW0kCOdQ4mn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. ANOVA Analysis**\n",
        "\n",
        "\n",
        "> Performing 2-way anova analysis of the IV and DV variables.\n",
        "\n"
      ],
      "metadata": {
        "id": "yyoFdvsOLjeF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ols('time_in_hospital ~ C(race) * C(gender)', data=df).fit()\n",
        "anova_table = sm.stats.anova_lm(model, typ=2)\n",
        "print(anova_table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cdcQs8EN1jZ",
        "outputId": "1bcc0c00-decf-45f7-a351-132c3c19a7bd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          sum_sq       df          F        PR(>F)\n",
            "C(race)               541.291144      4.0  15.189971  2.025732e-12\n",
            "C(gender)             707.448149      1.0  79.410993  5.127034e-19\n",
            "C(race):C(gender)      94.442648      4.0   2.650295  3.143751e-02\n",
            "Residual           886254.590879  99482.0        NaN           NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Interpretations**\n",
        "\n",
        "+ Race has a significant influence on the days spent in the hospital (p-value is less than 0.05).\n",
        "+ Gender has a significant influence on the days spent in the hospital (p-value is less than 0.05).\n",
        "+ Race and gender has a significant combined influence on the days spent in the hospital (p-value is less than 0.05)."
      ],
      "metadata": {
        "id": "kuQ-EXZO_UTh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Challenges Faced**\n",
        "\n",
        "When conducting the anova analysis, the anova results showed NaN's in the 'C(race):C(gender)' row. Looking at this, I went back to perform value counts of each of the variables. The value counts for the gender column showed a value of 'Unkown/Invalid' with a frequency of 3. I have to remove the Unkown/Invalid' in order to perform the anova analysis."
      ],
      "metadata": {
        "id": "AZaCBckb3GZZ"
      }
    }
  ]
}