{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1db950de",
   "metadata": {},
   "source": [
    "# Google Fit: Guide to data extraction and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8c4f0d",
   "metadata": {},
   "source": [
    "<img src='https://i.imgur.com/Oc6P36u.jpg' height=\"500\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d781c5",
   "metadata": {},
   "source": [
    "A picture of the Google Fit Mobile Application"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce46dc8",
   "metadata": {},
   "source": [
    "Google Fit is Google's version of Apple Health. It lets you track your fitness activity and health data from all of your wearable devices like Apple Watches, Samsung Galaxy Watches, Polar Smartwatches, etc. <br>\n",
    "\n",
    "Google Fit is completely free. It also comes preloaded on Android Wear watches and can be downloaded from the Apple App and Google Play stores. <br>\n",
    "\n",
    "We've been using the Google Fit application for the past few weeks and we will show you how to extract its data, visualize your activities and compute correlations between multiple metrics of the data. The Google Fit API allows the users to extract all kinds of data on workouts and medical health. However, for this notebook, we will be focusing on metrics of the participant's daily summary and activities such as steps, heart rate, workouts, etc.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19871a59",
   "metadata": {},
   "source": [
    "We will be able to extract the following parameters:\n",
    "\n",
    "Parameter Name  | Sampling Frequency \n",
    "-------------------|------------------\n",
    "Sleep Duration |  Daily\n",
    "Reproductive Health (Period Flow) |  Daily\n",
    "[Move Minutes](https://developers.google.com/fit/datatypes/aggregate#move_minutes_summary) |  Daily\n",
    "[Speed](https://developers.google.com/fit/datatypes/aggregate#speed_summary) |  Daily\n",
    "Energy Expended |  Daily\n",
    "Blood Glucose      |  Per Minute \n",
    "Oxygen Saturation |  Per Minute \n",
    "Steps |  Per Minute \n",
    "Blood Pressure |  Per Minute \n",
    "Body Temperature |  Per Minute\n",
    "Calories Consumed |  Per Minute\n",
    "Heart Rate |  Every 5 seconds "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5347ac3",
   "metadata": {},
   "source": [
    "In this guide, we sequentially cover the following **five** topics to extract data from Cronometer servers:\n",
    "\n",
    "1. **Set up**<br>\n",
    "2. **Authentication/Authorization**<br>\n",
    "   - Requires only access_token, no OAuth.<br>\n",
    "3. **Data extraction**<br>\n",
    "  - We get data via wearipedia in a couple lines of code<br>\n",
    "4. **Data Exporting**<br>\n",
    "    - We export all of this data to file formats compatible by R, Excel, and MatLab.<br>\n",
    "5. **Adherence**<br>\n",
    "    - We simulate non-adherence by dynamically removing datapoints from our simulated data.<br>\n",
    "6. **Visualization**<br>\n",
    "    - We create a simple plot to visualize our data.<br>\n",
    "7. **Advanced visualization**<br>\n",
    "    - 7.1 Visualizing participant's Weekly Step Activity! <br>\n",
    "    - 7.2 Visualizing participant's Weekly Heart Activity!<br>\n",
    "    - 7.3 Visualizing participant's Detailed Heart Rate Breakdown!<br>\n",
    "8. **Data Analysis**<br>\n",
    "  - 8.1  Analyzing correlation between Heart Rate and Number of Steps! <br>\n",
    "9. **Outlier Detection** <br>\n",
    "  - 9.1 Highlighting Outliers!<br>\n",
    "\n",
    "Disclaimer: this notebook is purely for educational purposes. All of the data currently stored in this notebook is purely *synthetic*, meaning randomly generated according to rules we created. Despite this, the end-to-end data extraction pipeline has been tested on our own data, meaning that if you enter your own email and password on your own Colab instance, you can visualize your own *real* data. That being said, we were unable to thoroughly test the timezone functionality, though, since we only have one account, so beware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92bf8a91",
   "metadata": {},
   "source": [
    "Before starting, note that the Google Fit access token necessary to extract data only lasts for 1 hour. Thus, the researcher should fetch the data as soon as the participant provides the token.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0516d18",
   "metadata": {},
   "source": [
    "# 1. Setup\n",
    "\n",
    "## Participant Setup\n",
    "\n",
    "Dear Participant,\n",
    "\n",
    "Once you download the Google Fit app, please set it up by following these resources:\n",
    "- Written guide: https://www.businessinsider.com/guides/tech/google-fit\n",
    "- Video guide: https://www.youtube.com/watch?v=0GnBgqnRM60&ab_channel=UponTop\n",
    "\n",
    "Make sure that your phone is logged to the google fit app using the Google Fit login credentials (email and password) given to you by the data receiver.\n",
    "\n",
    "Best,\n",
    "\n",
    "Wearipedia\n",
    "\n",
    "## Data Receiver Setup\n",
    "\n",
    "Please follow the below steps:\n",
    "\n",
    "1. Create an email address for the participant, for example `foo@email.com`.\n",
    "2. Create a google fit account with the email `foo@email.com` and some random password.\n",
    "3. Keep `foo@email.com` and password stored somewhere safe.\n",
    "4. Distribute the device to the participant and instruct them to follow the participant setup letter above.\n",
    "5. Go to this link: https://developers.google.com/oauthplayground/\n",
    "6. Choose fitness API v1 in the Select & authorize APIs menu\n",
    "<br>\n",
    "<img src='https://i.imgur.com/odrvr9v.png' width=\"500\" height=\"550\"> \n",
    "7. Select all the different datatypes that you the researcher wants you to grant access to and click authorize APIs. Your clinical study coordinator might have more details regarding what sorts of health data they require. <br>\n",
    "<img src='https://i.imgur.com/L5PM8lB.png' width=\"450\" height=\"550\"> \n",
    "8. Login with your account (the one the participant has connected with their Google Fit Account). <br>\n",
    "<img src='https://i.imgur.com/LEY8CnQ.png' width=\"400\" height=\"500\"> \n",
    "9. Click on ‘Continue’ \n",
    "<br>\n",
    "<img src=\"https://i.imgur.com/RLUtPeu.png\" height=\"500\" width=\"300\">\n",
    "10. Click on the exchange authorization code for tokens in Step2.\n",
    "<br>\n",
    "<img src=\"https://i.imgur.com/9KpZbJZ.png\">\n",
    "11. Copy and paste the access token from the Google Developer Playground and paste in the box in part section **2.1**. <br>\n",
    "<img src=\"https://i.imgur.com/Gv7PRGI.png\">\n",
    "12. Install the `wearipedia` Python package to easily extract data from this device via the Cronometer API.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd39d29c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://SaarthShah:****@github.com/SaarthShah/wearipedia.git\n",
      "  Cloning https://SaarthShah:****@github.com/SaarthShah/wearipedia.git to /private/var/folders/4q/pymtr0qd38d5nlrw_myxq5r00000gn/T/pip-req-build-xxxgpibr\n",
      "  Running command git clone --filter=blob:none --quiet 'https://SaarthShah:****@github.com/SaarthShah/wearipedia.git' /private/var/folders/4q/pymtr0qd38d5nlrw_myxq5r00000gn/T/pip-req-build-xxxgpibr\n",
      "  Resolved https://SaarthShah:****@github.com/SaarthShah/wearipedia.git to commit b2f01ee96743f78da3cf6afff53e2e1a6b422567\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install wearipedia\n",
    "!pip install openpyxl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c319c363",
   "metadata": {},
   "source": [
    "# 2. Authentication/Authorization\n",
    "\n",
    "To obtain access to data, authorization is required. All you'll need to do here is just put in your access token for your Google Fit account. We'll use this username and password to extract the data in the sections below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c60b13",
   "metadata": {},
   "source": [
    "Google Fit uses external devices to extract recorded activities, but it requires the participant to provide access tokens to access Google's API and read fitness data from their account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9cc3b9d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Authorization Code: 4/0AVHEtk5zeKpZ1vH0v8iVBoxK9BFrG3k72Y7Ce61mEv_kDi7e5uzm2CmS8incOAbP8pXbvQ\n"
     ]
    }
   ],
   "source": [
    "#@title Enter the Participant's Access Token\n",
    "\n",
    "google_auth_code = '4/0AVHEtk5zeKpZ1vH0v8iVBoxK9BFrG3k72Y7Ce61mEv_kDi7e5uzm2CmS8incOAbP8pXbvQ' #@param {type:\"string\"}\n",
    "google_access_token = 'ya29.a0Ael9sCO_HuqZGAgii5Z5EqFQ0_GxI1D3vQsj5g1TGZsRsnY-s4FuaVWB8sB28uxTrYvJIAAAEpp4oJSSiYmYVMmC8jiIxm2FxgE6hyQQgijoe0JZVAhLg1FzmE8oODAZm1t3DvHVA9QxIta_NFZ4RnbjF0R8aCgYKAR8SARESFQF4udJhOejZSWJt-DccLKr94OaP5A0163'\n",
    "print('Authorization Code: '+google_auth_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09fd15e",
   "metadata": {},
   "source": [
    "# 3. Data Extraction\n",
    "\n",
    "Data can be extracted via [wearipedia](https://github.com/Stanford-Health/wearipedia/), our open-source Python package that unifies dozens of complex wearable device APIs into one simple, common interface.\n",
    "\n",
    "First, we'll set a date range and then extract all of the data within that date range. You can select whether you would like synthetic data or not with the checkbox."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e6628f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Enter start and end dates (in the format yyyy-mm-dd)\n",
    "\n",
    "#set start and end dates - this will give you all the data from 2000-01-01 (January 1st, 2000) to 2100-02-03 (February 3rd, 2100), for example\n",
    "start_date='2022-03-01' #@param {type:\"string\"}\n",
    "end_date='2022-04-17' #@param {type:\"string\"}\n",
    "synthetic = True #@param {type:\"boolean\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23435ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wearipedia\n",
    "\n",
    "device = wearipedia.get_device(\"google/googlefit\")\n",
    "\n",
    "if not synthetic:\n",
    "    device.authenticate({\"authorization_code\": google_auth_code})\n",
    "\n",
    "params = {\"start_date\": start_date, \"end_date\": end_date}\n",
    "\n",
    "steps = device.get_data(\"steps\", params=params)\n",
    "heart_rate = device.get_data(\"heart_rate\", params=params)\n",
    "sleep = device.get_data(\"sleep\", params=params)\n",
    "heart_minutes = device.get_data(\"heart_minutes\", params=params)\n",
    "blood_pressure = device.get_data(\"blood_pressure\", params=params)\n",
    "blood_glucose = device.get_data(\"blood_glucose\", params=params)\n",
    "body_temperature = device.get_data(\"body_temperature\", params=params)\n",
    "calories_expended = device.get_data(\"calories_expended\", params=params)\n",
    "activity_minutes = device.get_data(\"activity_minutes\", params=params)\n",
    "height = device.get_data(\"height\", params=params)\n",
    "oxygen_saturation = device.get_data(\"oxygen_saturation\", params=params)\n",
    "menstruation = device.get_data(\"menstruation\", params=params)\n",
    "speed = device.get_data(\"speed\", params=params)\n",
    "weight = device.get_data(\"weight\", params=params)\n",
    "distance = device.get_data(\"distance\", params=params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9fa911a",
   "metadata": {},
   "source": [
    "# 4. Data Exporting\n",
    "\n",
    "In this section, we export all of this data to formats compatible with popular scientific computing software (R, Excel, Google Sheets, Matlab). Specifically, we will first export to JSON, which can be read by R and Matlab. Then, we will export to CSV, which can be consumed by Excel, Google Sheets, and every other popular programming language.\n",
    "\n",
    "## Exporting to JSON (R, Matlab, etc.)\n",
    "\n",
    "Exporting to JSON is fairly simple. We export each datatype separately and also export a complete version that includes all simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de48c5ff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "\n",
    "json.dump(steps, open(\"steps.json\", \"w\"))\n",
    "json.dump(heart_rate, open(\"heart_rate.json\", \"w\"))\n",
    "json.dump(sleep, open(\"sleep.json\", \"w\"))\n",
    "json.dump(heart_minutes, open(\"heart_minutes.json\", \"w\"))\n",
    "json.dump(blood_pressure, open(\"blood_pressure.json\", \"w\"))\n",
    "json.dump(blood_glucose, open(\"blood_glucose.json\", \"w\"))\n",
    "json.dump(body_temperature, open(\"body_temperature.json\", \"w\"))\n",
    "json.dump(calories_expended, open(\"calories_expended.json\", \"w\"))\n",
    "json.dump(activity_minutes, open(\"activity_minutes.json\", \"w\"))\n",
    "json.dump(oxygen_saturation, open(\"oxygen_saturation.json\", \"w\"))\n",
    "json.dump(height, open(\"height.json\", \"w\"))\n",
    "json.dump(menstruation, open(\"menstruation.json\", \"w\"))\n",
    "json.dump(speed, open(\"speed.json\", \"w\"))\n",
    "json.dump(weight, open(\"weight.json\", \"w\"))\n",
    "json.dump(distance, open(\"distance.json\", \"w\"))\n",
    "\n",
    "complete = {\n",
    "    \"steps\": steps,\n",
    "    \"heart_rate\": heart_rate,\n",
    "    \"sleep\": sleep,\n",
    "    \"heart_minutes\": heart_minutes,\n",
    "    \"blood_pressure\": blood_pressure,\n",
    "    \"blood_glucose\": blood_glucose,\n",
    "    \"body_temperature\": body_temperature,\n",
    "    \"calories_expended\": calories_expended,\n",
    "    \"activity_minutes\": activity_minutes,\n",
    "    \"oxygen_saturation\": oxygen_saturation,\n",
    "    \"height\": height,\n",
    "    \"menstruation\": menstruation,\n",
    "    \"speed\": speed,\n",
    "    \"weight\": weight,\n",
    "    \"distance\": distance,\n",
    "}\n",
    "\n",
    "json.dump(complete, open(\"complete.json\", \"w\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5d13cb",
   "metadata": {},
   "source": [
    "Feel free to open the file viewer (see left pane) to look at the outputs!\n",
    "\n",
    "## Exporting to CSV and XLSX (Excel, Google Sheets, R, Matlab, etc.)\n",
    "\n",
    "Exporting to CSV/XLSX requires a bit more processing, since they enforce a pretty restrictive schema.\n",
    "\n",
    "We will thus export steps, heart rates, and breath rates all as separate files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "17713d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "steps_df = pd.DataFrame.from_dict(steps)\n",
    "steps_df.to_csv('steps.csv')\n",
    "steps_df.to_excel('steps.xlsx')\n",
    "\n",
    "heart_rate_df = pd.DataFrame.from_dict(heart_rate)\n",
    "heart_rate_df.to_csv('heart_rate.csv')\n",
    "heart_rate_df.to_excel('heart_rate.xlsx')\n",
    "\n",
    "sleep_df = pd.DataFrame.from_dict(sleep)\n",
    "sleep_df.to_csv('sleep.csv')\n",
    "sleep_df.to_excel('sleep.xlsx')\n",
    "\n",
    "heart_minutes_df = pd.DataFrame.from_dict(heart_minutes)\n",
    "heart_minutes_df.to_csv('heart_minutes.csv')\n",
    "heart_minutes_df.to_excel('heart_minutes.xlsx')\n",
    "\n",
    "blood_pressure_df = pd.DataFrame.from_dict(blood_pressure)\n",
    "blood_pressure_df.to_csv('blood_pressure.csv')\n",
    "blood_pressure_df.to_excel('blood_pressure.xlsx')\n",
    "\n",
    "blood_glucose_df = pd.DataFrame.from_dict(blood_glucose)\n",
    "blood_glucose_df.to_csv('blood_glucose.csv')\n",
    "blood_glucose_df.to_excel('blood_glucose.xlsx')\n",
    "\n",
    "body_temperature_df = pd.DataFrame.from_dict(body_temperature)\n",
    "body_temperature_df.to_csv('body_temperature.csv')\n",
    "body_temperature_df.to_excel('body_temperature.xlsx')\n",
    "\n",
    "calories_expended_df = pd.DataFrame.from_dict(calories_expended)\n",
    "calories_expended_df.to_csv('calories_expended.csv')\n",
    "calories_expended_df.to_excel('calories_expended.xlsx')\n",
    "\n",
    "activity_minutes_df = pd.DataFrame.from_dict(activity_minutes)\n",
    "activity_minutes_df.to_csv('activity_minutes.csv')\n",
    "activity_minutes_df.to_excel('activity_minutes.xlsx')\n",
    "\n",
    "oxygen_saturation_df = pd.DataFrame.from_dict(oxygen_saturation)\n",
    "oxygen_saturation_df.to_csv('oxygen_saturation.csv')\n",
    "oxygen_saturation_df.to_excel('oxygen_saturation.xlsx')\n",
    "\n",
    "height_df = pd.DataFrame.from_dict(height)\n",
    "height_df.to_csv('height.csv')\n",
    "height_df.to_excel('height.xlsx')\n",
    "\n",
    "mensuration_df = pd.DataFrame.from_dict(menstruation)\n",
    "mensuration_df.to_csv('mensuration.csv')\n",
    "mensuration_df.to_excel('mensuration.xlsx')\n",
    "\n",
    "speed_df = pd.DataFrame.from_dict(speed)\n",
    "speed_df.to_csv('speed.csv')\n",
    "speed_df.to_excel('speed.xlsx')\n",
    "\n",
    "weight_df = pd.DataFrame.from_dict(weight)\n",
    "weight_df.to_csv('weight.csv')\n",
    "weight_df.to_excel('weight.xlsx')\n",
    "\n",
    "distance_df = pd.DataFrame.from_dict(distance)\n",
    "distance_df.to_csv('distance.csv')\n",
    "distance_df.to_excel('distance.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b09cee",
   "metadata": {},
   "source": [
    "Again, feel free to look at the output files and download them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7794277e",
   "metadata": {},
   "source": [
    "# 5. Adherence\n",
    "\n",
    "The device simulator already automatically randomly deletes small chunks of the day. In this section, we will simulate non-adherence over longer periods of time from the participant (day-level and week-level).\n",
    "\n",
    "Then, we will detect this non-adherence and give a Pandas DataFrame that concisely describes when the participant has had their device on and off throughout the entirety of the time period, allowing you to calculate how long they've had it on/off etc.\n",
    "\n",
    "We will first delete a certain % of blocks either at the day level or week level, with user input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1971db15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Non-adherence simulation\n",
    "block_level = \"day\" #@param [\"day\", \"week\"]\n",
    "adherence_percent = 0.89 #@param {type:\"slider\", min:0, max:1, step:0.01}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f2eccef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "if block_level == \"day\":\n",
    "    block_length = 1\n",
    "elif block_level == \"week\":\n",
    "    block_length = 7\n",
    "\n",
    "# This function will randomly remove datapoints from the \n",
    "# data we have recieved from Cronometer based on the\n",
    "# adherence_percent\n",
    "\n",
    "def AdherenceSimulator(data):\n",
    "\n",
    "  num_blocks = len(data) // block_length\n",
    "  num_blocks_to_keep = int(adherence_percent * num_blocks)\n",
    "  idxes = np.random.choice(np.arange(num_blocks), replace=False, \n",
    "  size=num_blocks_to_keep)\n",
    "\n",
    "  adhered_data = []\n",
    "\n",
    "  for i in range(len(data)):\n",
    "      if i in idxes:\n",
    "          start = i * block_length\n",
    "          end = (i + 1) * block_length\n",
    "          for j in range(i,i+1):\n",
    "            adhered_data.append(data[j])\n",
    "  \n",
    "  return adhered_data\n",
    "\n",
    "\n",
    "# Adding adherence for all our datapoints\n",
    "steps = AdherenceSimulator(steps)\n",
    "heart_rate = AdherenceSimulator(heart_rate)\n",
    "sleep = AdherenceSimulator(sleep)\n",
    "heart_minutes = AdherenceSimulator(heart_minutes)\n",
    "blood_pressure = AdherenceSimulator(blood_pressure)\n",
    "blood_glucose = AdherenceSimulator(blood_glucose)\n",
    "body_temperature = AdherenceSimulator(body_temperature)\n",
    "calories_expended = AdherenceSimulator(calories_expended)\n",
    "activity_minutes = AdherenceSimulator(activity_minutes)\n",
    "oxygen_saturation = AdherenceSimulator(oxygen_saturation)\n",
    "height = AdherenceSimulator(height)\n",
    "menstruation = AdherenceSimulator(menstruation)\n",
    "speed = AdherenceSimulator(speed)\n",
    "weight = AdherenceSimulator(weight)\n",
    "distance = AdherenceSimulator(distance)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac157252",
   "metadata": {},
   "source": [
    "And now we have significantly fewer datapoints! This will give us a more realistic situation, where participants may take off their device for days or weeks at a time.\n",
    "\n",
    "Now let's detect non-adherence. We will return a Pandas DataFrame sampled at every day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b601e33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_df = pd.DataFrame.from_dict(steps)\n",
    "heart_rate_df = pd.DataFrame.from_dict(heart_rate)\n",
    "sleep_df = pd.DataFrame.from_dict(sleep)\n",
    "heart_minutes_df = pd.DataFrame.from_dict(heart_minutes)\n",
    "blood_pressure_df = pd.DataFrame.from_dict(blood_pressure)\n",
    "blood_glucose_df = pd.DataFrame.from_dict(blood_glucose)\n",
    "body_temperature_df = pd.DataFrame.from_dict(body_temperature)\n",
    "calories_expended_df = pd.DataFrame.from_dict(calories_expended)\n",
    "activity_minutes_df = pd.DataFrame.from_dict(activity_minutes)\n",
    "oxygen_saturation_df = pd.DataFrame.from_dict(oxygen_saturation)\n",
    "height_df = pd.DataFrame.from_dict(height)\n",
    "mensuration_df = pd.DataFrame.from_dict(menstruation)\n",
    "speed_df = pd.DataFrame.from_dict(speed)\n",
    "weight_df = pd.DataFrame.from_dict(weight)\n",
    "distance_df = pd.DataFrame.from_dict(distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85a6449",
   "metadata": {},
   "source": [
    "We can plot this out, and we get adherence at one-day frequency throughout the entirety of the data collection period. For this chart we will plot WEIGHT consumed over the time period from the weight dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f8df150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "\n",
    "dates = pd.date_range(start_date,end_date)\n",
    "\n",
    "energy = []\n",
    "\n",
    "def datacleanup(dataset):\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    for i in range(len(dataset)):\n",
    "        milliseconds = dataset.iloc[i].get(0)['startTimeMillis']\n",
    "        date = datetime.datetime.fromtimestamp(milliseconds/1000.0)\n",
    "        try:\n",
    "            df = pd.concat([df,pd.DataFrame.from_dict([{\n",
    "                'date':str(date)[:10],\n",
    "                'value':dataset.iloc[i].get(0)['point'][0]['value'][0]['fpVal']\n",
    "            }])])\n",
    "        except:\n",
    "            \n",
    "            df = pd.concat([df,pd.DataFrame.from_dict([{\n",
    "                'date':str(date)[:10],\n",
    "                'value':None\n",
    "            }])])\n",
    "        \n",
    "    return df\n",
    "\n",
    "weights = datacleanup(weight_df)\n",
    "\n",
    "for d in dates:\n",
    "    res = weights[weights.get('date')==str(d)[:10]]\n",
    "    if len(res) == 0:\n",
    "        energy.append(None)\n",
    "    else:\n",
    "        energy.append(res.iloc[0].value)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(dates, energy)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602ce4c4",
   "metadata": {},
   "source": [
    "## 6. Visualization\n",
    "\n",
    "We've extracted lots of data, but what does it look like?\n",
    "\n",
    "In this section, we will be visualizing our three kinds of data in a simple, customizable plot! This plot is intended to provide a starter example for plotting, whereas later examples emphasize deep control and aesthetics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17f2e988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Basic Plot\n",
    "feature = \"Calories Expended\" #@param ['Heart Minutes', 'Calories Expended']\n",
    "start_date = \"2022-03-04\" #@param {type:\"date\"}\n",
    "time_interval = \"full time\" #@param [\"one week\", \"full time\"]\n",
    "smoothness = 0.02 #@param {type:\"slider\", min:0, max:1, step:0.01}\n",
    "smooth_plot = True #@param {type:\"boolean\"}\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')\n",
    "\n",
    "if time_interval == \"one week\":\n",
    "    day_idxes = [i for i,d in enumerate(dates) if d >= start_date and d <= start_date + timedelta(days=7)]\n",
    "    end_date = start_date + timedelta(days=7)\n",
    "elif time_interval == \"full time\":\n",
    "    day_idxes = [i for i,d in enumerate(dates) if d >= start_date]\n",
    "    end_date = dates[-1]\n",
    "\n",
    "if feature == \"Heart Minutes\":\n",
    "    hm = datacleanup(heart_minutes_df)\n",
    "    concat_hm = []\n",
    "    for i,d in enumerate(dates):\n",
    "        day = d.strftime('%Y-%m-%d')\n",
    "        if i in day_idxes:\n",
    "          heart = hm[hm['date']==day]\n",
    "          if len(heart) != 0:\n",
    "              concat_hm += [(day,heart.iloc[0].value)]\n",
    "          else:\n",
    "              concat_hm += [(day,None)]\n",
    "    ts = [x[0] for x in concat_hm]\n",
    "\n",
    "    day_arr = [x[1] for x in concat_hm]\n",
    "\n",
    "    sigma = 200 * smoothness\n",
    "\n",
    "    title_fillin = \"Weight\"\n",
    "\n",
    "  \n",
    "if feature == \"Calories Expended\":\n",
    "    ce = datacleanup(calories_expended_df)\n",
    "    concat_data = []\n",
    "    for i,d in enumerate(dates):\n",
    "        day = d.strftime('%Y-%m-%d')\n",
    "        if i in day_idxes:\n",
    "          cals = ce[ce['date']==day]\n",
    "          if len(cals) != 0:\n",
    "              concat_data += [(day,cals.iloc[0].value)]\n",
    "          else:\n",
    "              concat_data += [(day,None)]\n",
    "    \n",
    "    ts = [x[0] for x in concat_data]\n",
    "\n",
    "    day_arr = [x[1] for x in concat_data]\n",
    "\n",
    "    sigma = 200 * smoothness\n",
    "\n",
    "    title_fillin = \"Weight\"\n",
    "\n",
    "\n",
    "with plt.style.context('ggplot'):\n",
    "    fig, ax = plt.subplots(figsize=(15, 8))\n",
    "\n",
    "    if smooth_plot:\n",
    "        def to_numpy(day_arr):\n",
    "            arr_nonone = [x for x in day_arr if x is not None]\n",
    "            mean_val = int(np.mean(arr_nonone))\n",
    "            for i,x in enumerate(day_arr):\n",
    "                if x is None:\n",
    "                    day_arr[i] = mean_val\n",
    "\n",
    "            return np.array(day_arr)\n",
    "\n",
    "        none_idxes = [i for i,x in enumerate(day_arr) if x is None]\n",
    "        day_arr = to_numpy(day_arr)\n",
    "        from scipy.ndimage import gaussian_filter\n",
    "        day_arr = list(gaussian_filter(day_arr, sigma=sigma))\n",
    "        for i, x in enumerate(day_arr):\n",
    "            if i in none_idxes:\n",
    "                day_arr[i] = None\n",
    "\n",
    "    plt.plot(ts, day_arr)\n",
    "    start_date_str = start_date.strftime('%Y-%m-%d')\n",
    "    end_date_str = end_date.strftime('%Y-%m-%d')\n",
    "    plt.title(f\"{title_fillin} from {start_date_str} to {end_date_str}\",\n",
    "              fontsize=20)\n",
    "    plt.xlabel(\"Date\")\n",
    "    plt.xticks(ts[::int(len(ts)/8)])\n",
    "    plt.ylabel(title_fillin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc5eaa3",
   "metadata": {},
   "source": [
    "This plot allows you to quickly scan your data at many different time scales (week and full) and for different kinds of measurements (heart rate and weight), which enables easy and fast data exploration.\n",
    "\n",
    "Furthermore, the smoothness parameter makes it easy to look for patterns in long-term trends."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f17ce5",
   "metadata": {},
   "source": [
    "# 7. Advanced Visualization\n",
    "\n",
    "Now we'll do some more advanced plotting that at times features hardcore matplotlib hacking with the benefit of aesthetic quality."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416d087f",
   "metadata": {},
   "source": [
    "## 7.1 Visualizing participant's Weekly Step Activity!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73546f5a",
   "metadata": {},
   "source": [
    "Let's say you were interested in knowing how many steps you take in a day. If you had an iPhone you could go onto Apple Health and check out your step count that is being approximated by just your iphone's [built-in accelerometer](https://developer.apple.com/documentation/coremotion/getting_raw_accelerometer_events). You would see your Weekly Steps chart using the following plot: <br>\n",
    "<img src=\"https://i.imgur.com/gG8EbwJ.jpg\" height=\"300\"> <br>\n",
    "Let's recreate this for the your choice of week using the data that we have fetched from the Google Api!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9cd8b59",
   "metadata": {},
   "source": [
    "Below, input the desired start and end dates for the plot above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b197f8c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Set date range for the chart above\n",
    "\n",
    "start = \"2022-03-01\" #@param {type:\"date\"}\n",
    "end = \"2022-03-07\" #@param {type:\"date\"}\n",
    "\n",
    "from datetime import  date\n",
    "\n",
    "# A Dictionary to save the list of all the dates between end and start dates\n",
    "step_plot_dates= {}\n",
    "\n",
    "# Saving the end and start dates in a date format from the inputted strings\n",
    "step_plot_start_date = date(int(start.split('-')[0]),int(start.split('-')[1]),\n",
    "                            int(start.split('-')[2]))\n",
    "step_plot_end_date = date(int(end.split('-')[0]),int(end.split('-')[1]),\n",
    "                          int(end.split('-')[2]))\n",
    "\n",
    "# Finding the list of all dates between our start and end dates\n",
    "dates = list(pd.date_range(step_plot_start_date,step_plot_end_date,freq='d'))\n",
    "\n",
    "# Dictionary to store the stepcount for each date\n",
    "stepcount = {}\n",
    "\n",
    "def datacleanup(dataset):\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    for i in range(len(dataset)):\n",
    "        milliseconds = dataset.iloc[i].get(0)['startTimeMillis']\n",
    "        date = datetime.datetime.fromtimestamp(milliseconds/1000.0)\n",
    "        try:\n",
    "            df = pd.concat([df,pd.DataFrame.from_dict([{\n",
    "                'date':str(date)[:10],\n",
    "                'value':dataset.iloc[i].get(0)['point'][0]['value'][0]['intVal']\n",
    "            }])])\n",
    "        except:\n",
    "            \n",
    "            df = pd.concat([df,pd.DataFrame.from_dict([{\n",
    "                'date':str(date)[:10],\n",
    "                'value':None\n",
    "            }])])\n",
    "        \n",
    "    return df\n",
    "\n",
    "steps_cleaned = datacleanup(steps_df)\n",
    "stepcount = {}\n",
    "\n",
    "# Loop to go over each date in our list\n",
    "for date_val in dates:\n",
    "    # Initializing each date in our dictionary as 0\n",
    "    stepcount[date_val.day_name()[:3]+\" (\"+\n",
    "            date_val.to_pydatetime().strftime('%Y-%m-%d')+\")\"] = 0\n",
    "    d = str(date_val)[:10]\n",
    "    res = steps_cleaned[steps_cleaned.date == d] \n",
    "    if len(res) > 0:\n",
    "        stepcount[date_val.day_name()[:3]+\" (\"+\n",
    "            date_val.to_pydatetime().strftime('%Y-%m-%d')+\")\"] = res.iloc[0].value\n",
    "# Counts the average steps in our plot and stores that as a formatted text\n",
    "average_steps = '{:,}'.format(int(np.mean(list(stepcount.values()))))\n",
    "\n",
    "# Saving the plot date range in the form of a string\n",
    "date_range_text = (str(step_plot_start_date.day)+' '+\n",
    "step_plot_start_date.strftime(\"%B\")[:3]+' - '+\n",
    " str(step_plot_end_date.day)+' '+step_plot_end_date.strftime(\"%B\")[:3]+\n",
    " ' '+ step_plot_start_date.strftime(\"%Y\"))\n",
    "\n",
    "# Creating the matptplotlib graph\n",
    "plt1 = plt.figure(figsize=(16,8))\n",
    "ax = plt1.gca()\n",
    "\n",
    "# Adding grid lines to the chart\n",
    "plt.grid(color=\"#a1a1a1\", linestyle='--', linewidth=1, alpha = 0.2)\n",
    "\n",
    "# Plotting our bars\n",
    "plt.bar([key[:3] for key in stepcount.keys()],list(stepcount.values()),\n",
    "        color=\"#FD4B03\")\n",
    "\n",
    "# Setting labels and titles\n",
    "plt.ylabel(\"Step Count\",color=\"#a1a1a1\")\n",
    "\n",
    "# Adding Step header\n",
    "plt.text(0.15,1,\"AVERAGE\",fontsize=14,color='#89898B',\n",
    "         transform=plt1.transFigure,horizontalalignment='center',\n",
    "         weight='light')\n",
    "plt.text(0.155,0.957,average_steps,fontsize=24,transform=plt1.transFigure,\n",
    "         horizontalalignment='center')\n",
    "plt.text(0.215,0.957,'steps',fontsize=18,transform=plt1.transFigure,\n",
    "         horizontalalignment='center',color='#89898B')\n",
    "plt.text(0.183,0.93,date_range_text,fontsize=14,color='#89898B',\n",
    "        transform=plt1.transFigure, horizontalalignment='center', weight='light')\n",
    "\n",
    "\n",
    "# Setting x and y ticks\n",
    "plt.yticks([0,5000,10000,15000,20000,25000])\n",
    "plt.xticks(color=\"#a1a1a1\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a397f708",
   "metadata": {},
   "source": [
    "<i>Above is a plot we created ourselves!</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7d83d1",
   "metadata": {},
   "source": [
    "## 7.2 Visualizing participant's Weekly Heart Activity!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3870f4",
   "metadata": {},
   "source": [
    "Similar to 7.1, if you were interested in checking out your heart rate values over the week then Apple Health would show your Weekly Heart Rate chart using the following plot: <br>\n",
    "<img src=\"https://i.imgur.com/hS9Q9uP.jpg\" height=\"600\"> <br>\n",
    "Let's recreate this for the your choice of week using the data that we have fetched from the Google Api! <br>\n",
    "\n",
    "First, we will save the data that we fetched from the Google Fit API in the form of a DataFrame for us to easily work with that data!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "79c2d269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a dictionary to save all the heart rate values with the dates\n",
    "heartrate_dict = {}\n",
    "\n",
    "# Traversing through each entry in our DataFrame to save heart\n",
    "# rate values for each date\n",
    "for i in range(heart_rate_df.size-1):\n",
    "    # Case when there is no data for a specific date\n",
    "    try:\n",
    "        heartrate_dict[datetime.datetime.fromtimestamp(\n",
    "        int(heart_rate_df.iloc[i].get(0)['startTimeMillis'])// 1000).strftime(\n",
    "            '%Y-%m-%d')] = (\n",
    "        int(np.ceil(heart_rate_df.iloc[i].get(0)['point'][0]['value'][0]['fpVal'])),\n",
    "        int(np.ceil(heart_rate_df.iloc[i].get(0)['point'][0]['value'][1]['fpVal'])),\n",
    "        int(np.ceil(heart_rate_df.iloc[i].get(0)['point'][0]['value'][2]['fpVal'])))\n",
    "    except:\n",
    "        continue\n",
    "# Creating a dictionary to save all the heart rate values with the dates between the specific end and start dates\n",
    "heartrate_dict_weekly = {}\n",
    "\n",
    "# Traversing each date for all the dates between start and end\n",
    "for date_val in dates:\n",
    "  # Initilizing each date with (0,0,0) \n",
    "  heartrate_dict_weekly[date_val.day_name()[:3]+\" (\"+\n",
    "                  date_val.to_pydatetime().strftime('%Y-%m-%d')+\")\"] = (0,0,0)\n",
    "  # Saving actual high, low and avg values for dates that have data avaliable\n",
    "  for key in heartrate_dict.keys():\n",
    "    if (date_val.to_pydatetime().strftime('%Y-%m-%d') == key):\n",
    "      heartrate_dict_weekly[date_val.day_name()[:3]+\" (\"+\n",
    "      date_val.to_pydatetime().strftime('%Y-%m-%d')+\")\"] = heartrate_dict[key]\n",
    "\n",
    "# This will help us find the low to max heart rate values for our chart header\n",
    "bpm_range = str(min([i[2] for i in heartrate_dict_weekly.values()]))+' - '+ str(max([i[1] for i in heartrate_dict_weekly.values()]))\n",
    "\n",
    "# Initializing the figure\n",
    "plt2 = plt.figure(figsize=(16,8),facecolor='black')\n",
    "ax = plt.gca()\n",
    "ax.set_facecolor('#000000')\n",
    "\n",
    "\n",
    "# Plotting the values\n",
    "x = [key[:3] for key in list(heartrate_dict_weekly.keys())]\n",
    "y = list(heartrate_dict_weekly.values())\n",
    "plt.plot((range(len(x)),range(len(x))),([i[1] for i in y], [i[2] for i in y]),\n",
    "         c='#FD4B03',lw=8,solid_capstyle='round')\n",
    "\n",
    "# Setting y limit to the chart\n",
    "plt.ylim(0,200)\n",
    "\n",
    "# Setting x and y ticks\n",
    "plt.xticks(range(len(x)),x,color=\"#a1a1a1\")\n",
    "plt.yticks(color=\"#a1a1a1\")\n",
    "\n",
    "# Setting labels\n",
    "plt.ylabel('Heart Rate')\n",
    "\n",
    "# Creating grid lines\n",
    "plt.grid(color=\"#a1a1a1\", linestyle='--', linewidth=2, alpha = 0.25)\n",
    "\n",
    "\n",
    "# Adding Heart header\n",
    "plt.text(0.14,1,\"RANGE\",fontsize=14,color='#89898B',transform=plt2.transFigure,\n",
    "         horizontalalignment='center', weight='light')\n",
    "plt.text(0.1625,0.957,bpm_range,fontsize=24,transform=plt2.transFigure, \n",
    "         horizontalalignment='center', color = 'white')\n",
    "plt.text(0.225,0.957,'BPM',fontsize=18,transform=plt2.transFigure, \n",
    "         horizontalalignment='center',color='#89898B')\n",
    "plt.text(0.183,0.93,date_range_text,fontsize=14,color='#89898B',\n",
    "         transform=plt2.transFigure, horizontalalignment='center',\n",
    "         weight='light')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11531e29",
   "metadata": {},
   "source": [
    "<i>Above is a plot we created ourselves!</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f293f325",
   "metadata": {},
   "source": [
    "# 8. Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afcd533",
   "metadata": {},
   "source": [
    "Data isn't much without some analysis, so we're going to do some in this section.\n",
    "\n",
    "DISCLAIMER: the analyses below may not be 100% biologically or scientifically grounded; the code is here to assist in your process, if you are interested in asking these kinds of questions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4645c877",
   "metadata": {},
   "source": [
    "Maybe the average heart rate is correlated with the number of steps you take in that time interval. Let's test if this hypothesis is true. We will do so by plotting a jointplot between those two metrics and finding the correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db86c84",
   "metadata": {},
   "source": [
    "But before we get into that, let's clean the dataframes to make sure the data that we have is ready for our analysis! We will first start with our step count dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "79d1382a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>0</th>\n",
       "      <th>date</th>\n",
       "      <th>steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'startTimeMillis': 1646092800000, 'endTimeMil...</td>\n",
       "      <td>1646092800000</td>\n",
       "      <td>9504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'startTimeMillis': 1646179200000, 'endTimeMil...</td>\n",
       "      <td>1646179200000</td>\n",
       "      <td>6868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'startTimeMillis': 1646265600000, 'endTimeMil...</td>\n",
       "      <td>1646265600000</td>\n",
       "      <td>10247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'startTimeMillis': 1646352000000, 'endTimeMil...</td>\n",
       "      <td>1646352000000</td>\n",
       "      <td>19896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'startTimeMillis': 1646438400000, 'endTimeMil...</td>\n",
       "      <td>1646438400000</td>\n",
       "      <td>14328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0           date  steps\n",
       "0  {'startTimeMillis': 1646092800000, 'endTimeMil...  1646092800000   9504\n",
       "1  {'startTimeMillis': 1646179200000, 'endTimeMil...  1646179200000   6868\n",
       "2  {'startTimeMillis': 1646265600000, 'endTimeMil...  1646265600000  10247\n",
       "3  {'startTimeMillis': 1646352000000, 'endTimeMil...  1646352000000  19896\n",
       "4  {'startTimeMillis': 1646438400000, 'endTimeMil...  1646438400000  14328"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creates a pandas dataframe with the date values inside the json body\n",
    "analysis_df = steps_df.assign(date=steps_df.get(0).apply(\n",
    "    lambda x: x['startTimeMillis']))\n",
    "\n",
    "# Adds a column for steps to our original df\n",
    "analysis_df = analysis_df.assign(steps = analysis_df.get(0).apply(\n",
    "    lambda x: np.nan if len(x['point'])==0 else\n",
    "     x['point'][0]['value'][0]['intVal']))\n",
    "\n",
    "analysis_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c38273",
   "metadata": {},
   "source": [
    "Now that we have our step count, we will repeat the process for our heart rate values and drop all the pairs where either of the values are Null (NaN)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "04e15898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>date</th>\n",
       "      <th>steps</th>\n",
       "      <th>heart_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646092800000</td>\n",
       "      <td>9504</td>\n",
       "      <td>117.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1646179200000</td>\n",
       "      <td>6868</td>\n",
       "      <td>123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1646265600000</td>\n",
       "      <td>10247</td>\n",
       "      <td>142.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1646352000000</td>\n",
       "      <td>19896</td>\n",
       "      <td>138.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1646611200000</td>\n",
       "      <td>17366</td>\n",
       "      <td>143.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  steps  heart_rate\n",
       "0  1646092800000   9504       117.9\n",
       "1  1646179200000   6868       123.1\n",
       "2  1646265600000  10247       142.4\n",
       "3  1646352000000  19896       138.8\n",
       "4  1646611200000  17366       143.1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Creates a pandas dataframe with the date values inside the json body\n",
    "heart_rate = heart_rate_df.assign(date=heart_rate_df.get(0).apply(\n",
    "    lambda x: x['startTimeMillis']))\n",
    "\n",
    "# Creates a pandas dataframe with the heart rate values inside the json body\n",
    "heart_rate = heart_rate.assign(heart_rate=heart_rate_df.get(0).apply(\n",
    "    lambda x: np.nan if len(x['point'])==0 else \n",
    "    x['point'][0]['value'][0]['fpVal']))\n",
    "\n",
    "# Merging our step and heart rate datasets\n",
    "analysis_df = analysis_df.merge(heart_rate, on='date')\n",
    "\n",
    "# Dropping useless columns\n",
    "analysis_df.drop(columns=['0_x','0_y'],inplace=True)\n",
    "\n",
    "# Dropping all pairs of null values\n",
    "analysis_df_cleaned = analysis_df.dropna()\n",
    "\n",
    "# Replotting the dataframe for reference\n",
    "analysis_df_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a1be101",
   "metadata": {},
   "source": [
    "Now that we have all our required values, let's create a plot to see if there is a correlation between heart rate and steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cfe3c3e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Setting Seaborn plot style\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "#Plotting our data\n",
    "plot = sns.jointplot(x='heart_rate', y='steps', data=analysis_df_cleaned,\n",
    "                    kind='reg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a352ff1",
   "metadata": {},
   "source": [
    "As we can see from the scatterplot above, it looks like there might be a correlation there. Let's compute $R^2$ just to see exactly how correlated.\n",
    "\n",
    "We'll follow [this documentation](https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.linregress.html) and perform a linear regression to obtain the coefficient of determination ($R^2$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eb1a94d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: -43.1\n",
      "Coefficient of determination: 0.00953\n",
      "p-value: 0.571\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(\n",
    "      analysis_df_cleaned.get('heart_rate'), analysis_df_cleaned.get('steps'))\n",
    "\n",
    "print(f'Slope: {slope:.3g}')\n",
    "print(f'Coefficient of determination: {r_value**2:.3g}')\n",
    "print(f'p-value: {p_value:.3g}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53992584",
   "metadata": {},
   "source": [
    "As the p value is less than 85.9%, it means that that our result is not statistically significant evidence to conclude that there is a correlation between average heart rate and the total number of steps in a day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd34087",
   "metadata": {},
   "source": [
    "# 9. Outlier Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a95a65",
   "metadata": {},
   "source": [
    "However, even though our p value does not seem to provide enough statistical significance that there is a correlation between average heart rate and the number of steps in a day, there might be outliers that do not follow any correlation. In this section of our analysis, we will find if there are outliers like that and if they exist, we will visually highlight them in our plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7885132",
   "metadata": {},
   "source": [
    "Before finding the individual outlier values, it would be interesting to see the summary of our step count and average heart rate parameters. It will give us a clear idea of what values are typical and which values can be considered atypical based on the data that we recieved from Google Fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b1ec96d5",
   "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>steps</th>\n",
       "      <th>heart_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11410.694444</td>\n",
       "      <td>121.144444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9299.564478</td>\n",
       "      <td>21.078268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5136.500000</td>\n",
       "      <td>105.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10397.000000</td>\n",
       "      <td>122.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15596.750000</td>\n",
       "      <td>139.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38538.000000</td>\n",
       "      <td>158.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              steps  heart_rate\n",
       "count     36.000000   36.000000\n",
       "mean   11410.694444  121.144444\n",
       "std     9299.564478   21.078268\n",
       "min        0.000000   77.900000\n",
       "25%     5136.500000  105.250000\n",
       "50%    10397.000000  122.350000\n",
       "75%    15596.750000  139.375000\n",
       "max    38538.000000  158.500000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_df_cleaned_summary = analysis_df_cleaned.describe().get(\n",
    "                                          ['steps','heart_rate'])\n",
    "analysis_df_cleaned_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c2ac6b",
   "metadata": {},
   "source": [
    "To locate the outliers we will be using a supervised as well as unsupervised algorithm called the Elliptic Envelope. In statistical studies, Elliptic Envelope created an imaginary elliptical area around a given dataset where values inside that imaginary area is considered to be normal data, and anything else is assumed to be outliers. It assumes that the given data follows a gaussian distribution.\n",
    "\n",
    "\"The main idea is to define the shape of the data and anomalies are those observations that lie far outside the shape. First a robust estimate of covariance of data is fitted into an ellipse around the central mode. Then, the Mahalanobis distance that is obtained from this estimate is used to define the threshold for determining outliers or anomalies.\" [(S. Shriram and E. Sivasankar ,2019, pp. 221-225)](https://ieeexplore.ieee.org/document/9004325)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0633f117",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     27\n",
      "-1     9\n",
      "Name: outlier, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.covariance import EllipticEnvelope\n",
    "import copy\n",
    "\n",
    "# Sometimes EllipticEnvelope shows slicing based copy warnings\n",
    "# The next line changes a setting that prevents the error from happening\n",
    "\n",
    "pd.set_option('mode.chained_assignment', None)\n",
    "\n",
    "#create the model, set the contamination as 0.25\n",
    "EE_model = EllipticEnvelope(contamination = 0.25)\n",
    "\n",
    "#implement the model on the data\n",
    "outliers = EE_model.fit_predict(analysis_df_cleaned.get(\n",
    "                                                    [\"steps\", \"heart_rate\"]))\n",
    "\n",
    "#extract the labels\n",
    "analysis_df_cleaned[\"outlier\"] = copy.deepcopy(outliers)\n",
    "\n",
    "#change the labels\n",
    "# We use -1 to mark an outlier and +1 for an inliner\n",
    "analysis_df_cleaned[\"outlier\"] = analysis_df_cleaned[\"outlier\"].apply(\n",
    "                                    lambda x: str(-1) if x == -1 else str(1))\n",
    "\n",
    "#extract the score\n",
    "analysis_df_cleaned[\"EE_scores\"] = EE_model.score_samples(\n",
    "                            analysis_df_cleaned.get([\"steps\", \"heart_rate\"]))\n",
    "\n",
    "#print the value counts for inlier and outliers\n",
    "print(analysis_df_cleaned[\"outlier\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d2b32ef",
   "metadata": {},
   "source": [
    "Below we will replot the analysis_df_cleaned dataframe to see how the two new columns were applied to it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c26d37f4",
   "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>date</th>\n",
       "      <th>steps</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>outlier</th>\n",
       "      <th>EE_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646092800000</td>\n",
       "      <td>9504</td>\n",
       "      <td>117.9</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.064960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1646179200000</td>\n",
       "      <td>6868</td>\n",
       "      <td>123.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.127376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1646265600000</td>\n",
       "      <td>10247</td>\n",
       "      <td>142.4</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.254495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1646352000000</td>\n",
       "      <td>19896</td>\n",
       "      <td>138.8</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4.420064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1646611200000</td>\n",
       "      <td>17366</td>\n",
       "      <td>143.1</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.626586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  steps  heart_rate outlier  EE_scores\n",
       "0  1646092800000   9504       117.9       1  -0.064960\n",
       "1  1646179200000   6868       123.1       1  -0.127376\n",
       "2  1646265600000  10247       142.4       1  -1.254495\n",
       "3  1646352000000  19896       138.8      -1  -4.420064\n",
       "4  1646611200000  17366       143.1       1  -3.626586"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_df_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d9c5d5",
   "metadata": {},
   "source": [
    "Now that we have labeled the outliers as -1, let's try to see which values of average heart rate and steps are being identified as outliers by our Elliptic Envelope Algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2768bb71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>steps</th>\n",
       "      <th>heart_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19896</td>\n",
       "      <td>138.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30735</td>\n",
       "      <td>98.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23679</td>\n",
       "      <td>154.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24861</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19273</td>\n",
       "      <td>79.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>38538</td>\n",
       "      <td>123.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>13543</td>\n",
       "      <td>77.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    steps  heart_rate\n",
       "3   19896       138.8\n",
       "6   30735        98.8\n",
       "9   23679       154.4\n",
       "19  24861       135.0\n",
       "23  19273        79.1\n",
       "25      0        97.0\n",
       "26  38538       123.8\n",
       "30  13543        77.9\n",
       "34      0        88.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_df = analysis_df_cleaned[analysis_df_cleaned.get('outlier')=='-1'].get(\n",
    "    ['steps','heart_rate'])\n",
    "outlier_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a868b1d3",
   "metadata": {},
   "source": [
    "Sweet, now that we know that there were outliers in our dataset, let's try to visually see which pair of values are being identified as outliers using a plot. Highlighting these outliers in a bright red color will make it super easy for us to identify them in our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "41c43655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting Figure Size in Seaborn\n",
    "sns.set(rc={'figure.figsize':(16,8)})\n",
    "\n",
    "# Setting Seaborn plot style\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "#Plotting our data\n",
    "plot = sns.regplot(x='heart_rate', y='steps', data=analysis_df_cleaned.drop(\n",
    "    outlier_df.index))\n",
    "\n",
    "plt.scatter(outlier_df.get('heart_rate'),outlier_df.get('steps'))\n",
    "plt.scatter(outlier_df.get('heart_rate'),outlier_df.get('steps'),\n",
    "            facecolors='red',alpha=.35, s=500)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08602800",
   "metadata": {},
   "source": [
    "Thus, the points highlighted in red are ones that seem to not be following the general trend of our dataset. Lastly, let's see what the new p-value is after outlier removal!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ee5cdea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: -134\n",
      "Coefficient of determination: 0.137\n",
      "p-value: 0.0574\n"
     ]
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = stats.linregress(\n",
    "    analysis_df_cleaned.drop(outlier_df.index).get('heart_rate'),\n",
    "     analysis_df_cleaned.drop(outlier_df.index).get('steps'))\n",
    "\n",
    "print(f'Slope: {slope:.3g}')\n",
    "print(f'Coefficient of determination: {r_value**2:.3g}')\n",
    "print(f'p-value: {p_value:.3g}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63a5fd9",
   "metadata": {},
   "source": [
    "Our new p-value after removing any outliers is 0.197 which is much closer to 5% than before. Therefore, after removing the outliers, our result is getting closer to being statistically significant but there is yet not enough evidence to imply that that there is a correlation between average heart rate and the total number of steps in a day."
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}