analysis/measures_notebook.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acoustic-carnival",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import HTML\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "graduate-degree",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../output/measure_doac_rx_mechanical_valve.csv')\n",
    "df = df.dropna(axis=0)\n",
    "df = df[df['doac']>5]\n",
    "\n",
    "df.to_csv('../output/measures_dropped.csv')\n",
    "selected_stps = []\n",
    "for stp in df['stp'].unique():\n",
    "    subset = df[df['stp']==stp]\n",
    "    if subset['doac'].min() >5:\n",
    "        selected_stps.append(stp)\n",
    "\n",
    "\n",
    "stps = selected_stps[0:20]\n",
    "df[df['stp'].isin(stps)].to_csv('../output/check.csv')\n",
    "num_per_thousand = df['doac']/(df['population']/1000)\n",
    "df['rate'] = num_per_thousand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fallen-hawaii",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_percentiles(df, period_column=None, column=None, show_outer_percentiles=True):\n",
    "    \"\"\"For each period in `period_column`, compute percentiles across that\n",
    "    range.\n",
    "    Adds `percentile` column.\n",
    "    \"\"\"\n",
    "    deciles = np.arange(0.1, 1, 0.1)\n",
    "    bottom_percentiles = np.arange(0.01, 0.1, 0.01)\n",
    "    top_percentiles = np.arange(0.91, 1, 0.01)\n",
    "    if show_outer_percentiles:\n",
    "        quantiles = np.concatenate((deciles, bottom_percentiles, top_percentiles))\n",
    "    else:\n",
    "        quantiles = deciles\n",
    "    df = df.groupby(period_column)[column].quantile(quantiles).reset_index()\n",
    "    df = df.rename(index=str, columns={\"level_1\": \"percentile\"})\n",
    "    # create integer range of percentiles\n",
    "    df[\"percentile\"] = df[\"percentile\"].apply(lambda x: int(x * 100))\n",
    "    return df\n",
    "\n",
    "\n",
    "def interactive_deciles_chart(\n",
    "    df,\n",
    "    practices,\n",
    "    period_column=None,\n",
    "    column=None,\n",
    "    title=\"\",\n",
    "    ylabel=\"\", \n",
    "    ):\n",
    "    \"\"\"period_column must be dates / datetimes\n",
    "    \"\"\"\n",
    "    \n",
    "    original_df = df\n",
    "    \n",
    "    df = add_percentiles(\n",
    "        df,\n",
    "        period_column=period_column,\n",
    "        column=column,\n",
    "        show_outer_percentiles=False,\n",
    "    )\n",
    " \n",
    "    fig = go.Figure()\n",
    "\n",
    "    linestyles = {\n",
    "        \"decile\": {\"color\": \"blue\", \"dash\": \"dash\"},\n",
    "        \"median\": {\"color\": \"blue\", \"dash\": \"solid\"},\n",
    "        \"percentile\": {\"color\": \"blue\", \"dash\": \"dash\"},\n",
    "    }\n",
    "    \n",
    "\n",
    "    \n",
    "    for percentile in np.unique(df['percentile']):\n",
    "        df_subset = df[df['percentile'] == percentile]\n",
    "        if percentile == 50:\n",
    "            fig.add_trace(go.Scatter(x=df_subset[period_column], y=df_subset[column], line={\n",
    "                          \"color\": \"blue\", \"dash\": \"solid\", \"width\": 1.2}, name=\"median\", visible=True))\n",
    "            \n",
    "        else:\n",
    "            fig.add_trace(go.Scatter(x=df_subset[period_column], y=df_subset[column], line={\n",
    "                          \"color\": \"blue\", \"dash\": \"dash\", \"width\": 1}, name=f\"decile {int(percentile/10)}\", visible=True))\n",
    "\n",
    "    \n",
    "    for i, stp in enumerate(stps):\n",
    "        df_subset = original_df[original_df['stp']==stp]\n",
    "        fig.add_trace(go.Scatter(x=df_subset[period_column], y=df_subset[column], line={\n",
    "                          \"color\": \"red\", \"dash\": \"solid\", \"width\": 1}, name=f\"STP {i}\", visible=False))\n",
    "   \n",
    "    \n",
    "    buttons_list=[]\n",
    "    for i in range(len(stps)):\n",
    "        visible = [False]*len(stps)\n",
    "        visible[i] = True\n",
    "        \n",
    "        buttons_list.append(dict(args = [{'visible': [True]*9 + visible}],label=f\"STP {i}\",method=\"update\"))\n",
    "        \n",
    "\n",
    "    buttons_list.insert(0, dict(args = [{'visible':  [True]*9 + [False]*len(stps) }],label=\"All\",method=\"restyle\"))\n",
    "    \n",
    "    \n",
    "                \n",
    "     # Set title\n",
    "    fig.update_layout(\n",
    "        title_text=title,\n",
    "        hovermode='x',\n",
    "        title_x=0.5,\n",
    "        yaxis=dict(\n",
    "            range=[0, 170]\n",
    "        )\n",
    "\n",
    "\n",
    "    )\n",
    "\n",
    "    fig.update_yaxes(title=ylabel)\n",
    "    fig.update_xaxes(title=\"Date\")\n",
    "\n",
    "    # Add range slider\n",
    "    fig.update_layout(\n",
    "        xaxis=go.layout.XAxis(\n",
    "            rangeselector=dict(\n",
    "                buttons=list([\n",
    "                    dict(count=1,\n",
    "                         label=\"1m\",\n",
    "                         step=\"month\",\n",
    "                         stepmode=\"backward\"),\n",
    "                    dict(count=6,\n",
    "                         label=\"6m\",\n",
    "                         step=\"month\",\n",
    "                         stepmode=\"backward\"),\n",
    "\n",
    "                    dict(count=1,\n",
    "                         label=\"1y\",\n",
    "                         step=\"year\",\n",
    "                         stepmode=\"backward\"),\n",
    "                    dict(step=\"all\")\n",
    "                ])\n",
    "            ),\n",
    "            rangeslider=dict(\n",
    "                visible=True\n",
    "            ),\n",
    "            type=\"date\"\n",
    "        )\n",
    "        \n",
    "    \n",
    "    )\n",
    "    \n",
    "    fig.update_layout(\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            buttons=buttons_list,\n",
    "            direction=\"down\",\n",
    "            pad={\"r\": 10, \"t\": 10},\n",
    "           \n",
    "            x=1.0,\n",
    "            xanchor=\"left\",\n",
    "            y=1.1,\n",
    "            yanchor=\"top\"\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "\n",
    "    fig.update_layout(showlegend=False)\n",
    "    fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "martial-timothy",
   "metadata": {},
   "source": [
    "## DOAC Use in Patients with Mechanical Heart Valves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exotic-association",
   "metadata": {},
   "source": [
    "### Total Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "honey-gauge",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df = df.groupby(by='date')[['doac', 'population']].sum().reset_index()\n",
    "\n",
    "def plot_measures(df, title,column_to_plot, category=False, y_label='Rate per 1000', interactive=True):\n",
    "\n",
    "    if interactive:\n",
    "\n",
    "        fig = go.Figure()\n",
    "\n",
    "        if category:\n",
    "            for unique_category in df[category].unique():\n",
    "\n",
    "                df_subset = df[df[category] == unique_category]\n",
    "                fig.add_trace(go.Scatter(\n",
    "                    x=df_subset['date'], y=df_subset[column_to_plot], name=unique_category))\n",
    "\n",
    "        else:\n",
    "            fig.add_trace(go.Scatter(\n",
    "                x=df['date'], y=df[column_to_plot]))\n",
    "\n",
    "        # Set title\n",
    "        fig.update_layout(\n",
    "            title_text=title,\n",
    "            hovermode='x',\n",
    "            title_x=0.5,\n",
    "            yaxis=dict(\n",
    "            range=[0, 7000]\n",
    "        )\n",
    "\n",
    "\n",
    "        )\n",
    "\n",
    "        fig.update_yaxes(title=y_label)\n",
    "        fig.update_xaxes(title=\"Date\")\n",
    "\n",
    "        # Add range slider\n",
    "        fig.update_layout(\n",
    "            xaxis=go.layout.XAxis(\n",
    "                rangeselector=dict(\n",
    "                    buttons=list([\n",
    "                        dict(count=1,\n",
    "                            label=\"1m\",\n",
    "                            step=\"month\",\n",
    "                            stepmode=\"backward\"),\n",
    "                        dict(count=6,\n",
    "                            label=\"6m\",\n",
    "                            step=\"month\",\n",
    "                            stepmode=\"backward\"),\n",
    "\n",
    "                        dict(count=1,\n",
    "                            label=\"1y\",\n",
    "                            step=\"year\",\n",
    "                            stepmode=\"backward\"),\n",
    "                        dict(step=\"all\")\n",
    "                    ])\n",
    "                ),\n",
    "                rangeslider=dict(\n",
    "                    visible=True\n",
    "                ),\n",
    "                type=\"date\"\n",
    "            )\n",
    "        )\n",
    "\n",
    "        fig.show()\n",
    "\n",
    "    else:\n",
    "\n",
    "        if category:\n",
    "            for unique_category in df[category].unique():\n",
    "\n",
    "                df_subset = df[df[category] == unique_category]\n",
    "\n",
    "                plt.plot(df_subset['date'], df_subset[column_to_plot], marker='o')\n",
    "        else:\n",
    "            plt.plot(df['date'], df[column_to_plot], marker='o')\n",
    "\n",
    "        plt.ylabel(y_label)\n",
    "        plt.xlabel('Date')\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.title(title)\n",
    "\n",
    "        if category:\n",
    "            plt.legend(df[category].unique(), bbox_to_anchor=(\n",
    "                1.04, 1), loc=\"upper left\")\n",
    "\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "        plt.show()\n",
    "        plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "stretch-basket",
   "metadata": {},
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   "source": [
    "plot_measures(total_df, title='', column_to_plot='doac', category=False, y_label='Total', interactive=True)\n"
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   "source": [
    "### Rate per 1000 by STP"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "found-winning",
   "metadata": {},
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