{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f6aa857",
   "metadata": {},
   "source": [
    "# Exercise window functions: compute the cumulative number of cases across time, per diet group\n",
    "\n",
    "The variable `toevent` contains the time that patients where followed up. We want to calculate the number of events as a function of the follow-up time, separatedely for each diet group. We expect that, if the mediterranean diet has an effect, then over time there will be more cases appearing on the  control group in comparison to the other diet groups. \n",
    "\n",
    "Here is how to proceed:\n",
    "- Use a window function to compute the cumulative number of events for each diet group separatedly. As we are interested in the follow-up time, you need to sort the events by the follow-up time first (`toevent`), and then calculate the cumulative sum of events, separatedely per group.\n",
    "- Add the result as a new column called `'cumulative_event_count'`\n",
    "\n",
    "With your new awesome vectorization skills, these two steps  should take only one line!\n",
    "\n",
    "When ready, execute the code at the end, which has already code that creates a visualiation with the cumulative number of events per group, as a function of the time of follow-up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f9bc8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be11d54",
   "metadata": {},
   "source": [
    "### Load patient data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dfc3020",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient-id</th>\n",
       "      <th>location-id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>smoke</th>\n",
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       "      <th>p14</th>\n",
       "      <th>toevent</th>\n",
       "      <th>event</th>\n",
       "      <th>group</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
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       "      <td>25.92</td>\n",
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       "      <td>2</td>\n",
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       "      <td>68</td>\n",
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       "      <td>10</td>\n",
       "      <td>3.063655</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>66</td>\n",
       "      <td>Never</td>\n",
       "      <td>37.50</td>\n",
       "      <td>120</td>\n",
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       "      <td>5.590691</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>Never</td>\n",
       "      <td>29.26</td>\n",
       "      <td>93</td>\n",
       "      <td>0.628378</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>5.456537</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "      <td>Never</td>\n",
       "      <td>30.02</td>\n",
       "      <td>104</td>\n",
       "      <td>0.662420</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>9</td>\n",
       "      <td>2.746064</td>\n",
       "      <td>0</td>\n",
       "      <td>Control</td>\n",
       "      <td>Madrid</td>\n",
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       "    <tr>\n",
       "      <th>6240</th>\n",
       "      <td>1253</td>\n",
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       "      <td>79</td>\n",
       "      <td>Never</td>\n",
       "      <td>25.28</td>\n",
       "      <td>105</td>\n",
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       "      <td>27.10</td>\n",
       "      <td>104</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>5.067762</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
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       "      <td>Never</td>\n",
       "      <td>35.02</td>\n",
       "      <td>103</td>\n",
       "      <td>0.686667</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>1.993155</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Malaga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6243</th>\n",
       "      <td>1256</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Never</td>\n",
       "      <td>28.42</td>\n",
       "      <td>94</td>\n",
       "      <td>0.576687</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>2.039699</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
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       "    <tr>\n",
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       "      <td>Yes</td>\n",
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       "      <td>2.590007</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6245 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient-id  location-id     sex  age   smoke    bmi  waist       wth  \\\n",
       "0              1            1  Female   77   Never  25.92     94  0.657343   \n",
       "1              2            1  Female   68   Never  34.85    150  0.949367   \n",
       "2              3            1  Female   66   Never  37.50    120  0.750000   \n",
       "3              4            1  Female   77   Never  29.26     93  0.628378   \n",
       "4              5            1  Female   60   Never  30.02    104  0.662420   \n",
       "...          ...          ...     ...  ...     ...    ...    ...       ...   \n",
       "6240        1253            5    Male   79   Never  25.28    105  0.640244   \n",
       "6241        1254            5    Male   62  Former  27.10    104  0.594286   \n",
       "6242        1255            5  Female   65   Never  35.02    103  0.686667   \n",
       "6243        1256            5    Male   61   Never  28.42     94  0.576687   \n",
       "6244        1257            5    Male   58  Former  24.43     93  0.547059   \n",
       "\n",
       "      htn diab hyperchol famhist hormo  p14   toevent  event           group  \\\n",
       "0     Yes   No       Yes     Yes    No    9  5.538672      0   MedDiet + VOO   \n",
       "1     Yes   No       Yes     Yes   NaN   10  3.063655      0  MedDiet + Nuts   \n",
       "2     Yes  Yes        No      No    No    6  5.590691      0  MedDiet + Nuts   \n",
       "3     Yes  Yes        No      No    No    6  5.456537      0   MedDiet + VOO   \n",
       "4     Yes   No       Yes      No    No    9  2.746064      0         Control   \n",
       "...   ...  ...       ...     ...   ...  ...       ...    ...             ...   \n",
       "6240  Yes   No       Yes      No    No    8  5.828884      0   MedDiet + VOO   \n",
       "6241  Yes   No       Yes     Yes    No    9  5.067762      0  MedDiet + Nuts   \n",
       "6242  Yes   No       Yes      No    No   10  1.993155      0   MedDiet + VOO   \n",
       "6243  Yes  Yes        No      No    No    9  2.039699      0  MedDiet + Nuts   \n",
       "6244  Yes  Yes       Yes      No    No    9  2.590007      0  MedDiet + Nuts   \n",
       "\n",
       "        City  \n",
       "0     Madrid  \n",
       "1     Madrid  \n",
       "2     Madrid  \n",
       "3     Madrid  \n",
       "4     Madrid  \n",
       "...      ...  \n",
       "6240  Malaga  \n",
       "6241  Malaga  \n",
       "6242  Malaga  \n",
       "6243  Malaga  \n",
       "6244  Malaga  \n",
       "\n",
       "[6245 rows x 18 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('processed_data_predimed.csv')\n",
    "df['event'] = df['event'].map({'Yes': 1, 'No': 0})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db0d21f7-033e-48ca-9c90-81451af57003",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.640244</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>5.828884</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6241</th>\n",
       "      <td>1254</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>62</td>\n",
       "      <td>Former</td>\n",
       "      <td>27.10</td>\n",
       "      <td>104</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>5.067762</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6242</th>\n",
       "      <td>1255</td>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>Never</td>\n",
       "      <td>35.02</td>\n",
       "      <td>103</td>\n",
       "      <td>0.686667</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>1.993155</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6243</th>\n",
       "      <td>1256</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Never</td>\n",
       "      <td>28.42</td>\n",
       "      <td>94</td>\n",
       "      <td>0.576687</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>2.039699</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6244</th>\n",
       "      <td>1257</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>Former</td>\n",
       "      <td>24.43</td>\n",
       "      <td>93</td>\n",
       "      <td>0.547059</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>2.590007</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6245 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient-id  location-id     sex  age   smoke    bmi  waist       wth  \\\n",
       "0              1            1  Female   77   Never  25.92     94  0.657343   \n",
       "1              2            1  Female   68   Never  34.85    150  0.949367   \n",
       "2              3            1  Female   66   Never  37.50    120  0.750000   \n",
       "3              4            1  Female   77   Never  29.26     93  0.628378   \n",
       "4              5            1  Female   60   Never  30.02    104  0.662420   \n",
       "...          ...          ...     ...  ...     ...    ...    ...       ...   \n",
       "6240        1253            5    Male   79   Never  25.28    105  0.640244   \n",
       "6241        1254            5    Male   62  Former  27.10    104  0.594286   \n",
       "6242        1255            5  Female   65   Never  35.02    103  0.686667   \n",
       "6243        1256            5    Male   61   Never  28.42     94  0.576687   \n",
       "6244        1257            5    Male   58  Former  24.43     93  0.547059   \n",
       "\n",
       "      htn diab hyperchol famhist hormo  p14   toevent  event           group  \\\n",
       "0     Yes   No       Yes     Yes    No    9  5.538672      0   MedDiet + VOO   \n",
       "1     Yes   No       Yes     Yes   NaN   10  3.063655      0  MedDiet + Nuts   \n",
       "2     Yes  Yes        No      No    No    6  5.590691      0  MedDiet + Nuts   \n",
       "3     Yes  Yes        No      No    No    6  5.456537      0   MedDiet + VOO   \n",
       "4     Yes   No       Yes      No    No    9  2.746064      0         Control   \n",
       "...   ...  ...       ...     ...   ...  ...       ...    ...             ...   \n",
       "6240  Yes   No       Yes      No    No    8  5.828884      0   MedDiet + VOO   \n",
       "6241  Yes   No       Yes     Yes    No    9  5.067762      0  MedDiet + Nuts   \n",
       "6242  Yes   No       Yes      No    No   10  1.993155      0   MedDiet + VOO   \n",
       "6243  Yes  Yes        No      No    No    9  2.039699      0  MedDiet + Nuts   \n",
       "6244  Yes  Yes       Yes      No    No    9  2.590007      0  MedDiet + Nuts   \n",
       "\n",
       "        City  cumulative_event_count  \n",
       "0     Madrid                      73  \n",
       "1     Madrid                      35  \n",
       "2     Madrid                      61  \n",
       "3     Madrid                      73  \n",
       "4     Madrid                      50  \n",
       "...      ...                     ...  \n",
       "6240  Malaga                      74  \n",
       "6241  Malaga                      57  \n",
       "6242  Malaga                      27  \n",
       "6243  Malaga                      16  \n",
       "6244  Malaga                      27  \n",
       "\n",
       "[6245 rows x 19 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate cumulative number of cases across time, independently for each group\n",
    "df['cumulative_event_count'] = df.sort_values('toevent').groupby('group')['event'].cumsum()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de6ee1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df.sort_values('toevent'), x='toevent', y='cumulative_event_count', hue='group')\n",
    "plt.ylabel('Cumulative events')\n",
    "plt.xlabel('Years of follow up (variable `toevent`)')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8f7a0b7-1ae0-470c-a153-59dc8a6caa28",
   "metadata": {},
   "source": [
    "#### Optional exercise\n",
    "\n",
    "Redo the plot but with the cummulative *percentage* of cases. For that you need to divide the cummulative count by the total number of cases in each group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d63a56b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Control': 2016, 'MedDiet + Nuts': 2077, 'MedDiet + VOO': 2152}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we calculate the number of cases per group, and save them in a dictionary\n",
    "n_dict = df.groupby('group')['cumulative_event_count'].count().to_dict()\n",
    "n_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f77047f-09c0-4172-a51c-c67b3a75dd9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient-id</th>\n",
       "      <th>location-id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>smoke</th>\n",
       "      <th>bmi</th>\n",
       "      <th>waist</th>\n",
       "      <th>wth</th>\n",
       "      <th>htn</th>\n",
       "      <th>diab</th>\n",
       "      <th>hyperchol</th>\n",
       "      <th>famhist</th>\n",
       "      <th>hormo</th>\n",
       "      <th>p14</th>\n",
       "      <th>toevent</th>\n",
       "      <th>event</th>\n",
       "      <th>group</th>\n",
       "      <th>City</th>\n",
       "      <th>cumulative_event_count</th>\n",
       "      <th>N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>Never</td>\n",
       "      <td>25.92</td>\n",
       "      <td>94</td>\n",
       "      <td>0.657343</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>5.538672</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>73</td>\n",
       "      <td>2152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>68</td>\n",
       "      <td>Never</td>\n",
       "      <td>34.85</td>\n",
       "      <td>150</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>3.063655</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>35</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>66</td>\n",
       "      <td>Never</td>\n",
       "      <td>37.50</td>\n",
       "      <td>120</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>5.590691</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>61</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>Never</td>\n",
       "      <td>29.26</td>\n",
       "      <td>93</td>\n",
       "      <td>0.628378</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>5.456537</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>73</td>\n",
       "      <td>2152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "      <td>Never</td>\n",
       "      <td>30.02</td>\n",
       "      <td>104</td>\n",
       "      <td>0.662420</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>2.746064</td>\n",
       "      <td>0</td>\n",
       "      <td>Control</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>50</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6240</th>\n",
       "      <td>1253</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>79</td>\n",
       "      <td>Never</td>\n",
       "      <td>25.28</td>\n",
       "      <td>105</td>\n",
       "      <td>0.640244</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>5.828884</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>74</td>\n",
       "      <td>2152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6241</th>\n",
       "      <td>1254</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>62</td>\n",
       "      <td>Former</td>\n",
       "      <td>27.10</td>\n",
       "      <td>104</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>5.067762</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>57</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6242</th>\n",
       "      <td>1255</td>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>Never</td>\n",
       "      <td>35.02</td>\n",
       "      <td>103</td>\n",
       "      <td>0.686667</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>1.993155</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + VOO</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>27</td>\n",
       "      <td>2152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6243</th>\n",
       "      <td>1256</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Never</td>\n",
       "      <td>28.42</td>\n",
       "      <td>94</td>\n",
       "      <td>0.576687</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>2.039699</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>16</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6244</th>\n",
       "      <td>1257</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>Former</td>\n",
       "      <td>24.43</td>\n",
       "      <td>93</td>\n",
       "      <td>0.547059</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>2.590007</td>\n",
       "      <td>0</td>\n",
       "      <td>MedDiet + Nuts</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>27</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6245 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient-id  location-id     sex  age   smoke    bmi  waist       wth  \\\n",
       "0              1            1  Female   77   Never  25.92     94  0.657343   \n",
       "1              2            1  Female   68   Never  34.85    150  0.949367   \n",
       "2              3            1  Female   66   Never  37.50    120  0.750000   \n",
       "3              4            1  Female   77   Never  29.26     93  0.628378   \n",
       "4              5            1  Female   60   Never  30.02    104  0.662420   \n",
       "...          ...          ...     ...  ...     ...    ...    ...       ...   \n",
       "6240        1253            5    Male   79   Never  25.28    105  0.640244   \n",
       "6241        1254            5    Male   62  Former  27.10    104  0.594286   \n",
       "6242        1255            5  Female   65   Never  35.02    103  0.686667   \n",
       "6243        1256            5    Male   61   Never  28.42     94  0.576687   \n",
       "6244        1257            5    Male   58  Former  24.43     93  0.547059   \n",
       "\n",
       "      htn diab hyperchol famhist hormo  p14   toevent  event           group  \\\n",
       "0     Yes   No       Yes     Yes    No    9  5.538672      0   MedDiet + VOO   \n",
       "1     Yes   No       Yes     Yes   NaN   10  3.063655      0  MedDiet + Nuts   \n",
       "2     Yes  Yes        No      No    No    6  5.590691      0  MedDiet + Nuts   \n",
       "3     Yes  Yes        No      No    No    6  5.456537      0   MedDiet + VOO   \n",
       "4     Yes   No       Yes      No    No    9  2.746064      0         Control   \n",
       "...   ...  ...       ...     ...   ...  ...       ...    ...             ...   \n",
       "6240  Yes   No       Yes      No    No    8  5.828884      0   MedDiet + VOO   \n",
       "6241  Yes   No       Yes     Yes    No    9  5.067762      0  MedDiet + Nuts   \n",
       "6242  Yes   No       Yes      No    No   10  1.993155      0   MedDiet + VOO   \n",
       "6243  Yes  Yes        No      No    No    9  2.039699      0  MedDiet + Nuts   \n",
       "6244  Yes  Yes       Yes      No    No    9  2.590007      0  MedDiet + Nuts   \n",
       "\n",
       "        City  cumulative_event_count     N  \n",
       "0     Madrid                      73  2152  \n",
       "1     Madrid                      35  2077  \n",
       "2     Madrid                      61  2077  \n",
       "3     Madrid                      73  2152  \n",
       "4     Madrid                      50  2016  \n",
       "...      ...                     ...   ...  \n",
       "6240  Malaga                      74  2152  \n",
       "6241  Malaga                      57  2077  \n",
       "6242  Malaga                      27  2152  \n",
       "6243  Malaga                      16  2077  \n",
       "6244  Malaga                      27  2077  \n",
       "\n",
       "[6245 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we assign now the N to eaach group, to later divide it in vector form\n",
    "df['N'] = df['group'].map(n_dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71084abd-2f8c-4d02-8fd6-0be5ae130882",
   "metadata": {},
   "outputs": [],
   "source": [
    "# divide, vectorized\n",
    "df['cumulative_incidence'] = df['cumulative_event_count'] / df['N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "16b80647-d24f-4d3b-b602-2270e611d398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df.sort_values('toevent'), x='toevent', y='cumulative_incidence', hue='group')\n",
    "plt.ylabel('Cumulative incidence')\n",
    "plt.xlabel('Years of follow up')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffd0e784-ab11-4073-a14a-038ef87c5464",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}