exploring_notebooks_fitbit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example of using Open Humans notebooks for fitbit data analysis\n",
    "\n",
    "## Getting started\n",
    "\n",
    "In this notebook we want to plot some of our own `fitbit` data. If you want to run this notebook on your personal data in the [*Open Humans Notebooks*](https://notebooks.openhumans.org) you need to have [*Fitbit*](https://www.openhumans.org/activity/fitbit-connection) data in your Open Humans account."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize packages\n",
    "We'll need some Python packages to get our personal data out of Open Humans and to play around with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os, requests, json\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ohapi import api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now start by getting a list of all the data we have on Open Humans. To do this we need our `access_token`. Using the `os` library we can access it from the environment variables which are set in our notebooks using `os.environ.get('OH_ACCESS_TOKEN')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "user = api.exchange_oauth2_member(os.environ.get('OH_ACCESS_TOKEN'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a `json` object that contains all the details about our user account. We can look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not for the published notebook ;-)\n"
     ]
    }
   ],
   "source": [
    "#user #commented out to not leak all download links ;)\n",
    "#os.environ.get(\"OH_ACCESS_TOKEN\")\n",
    "print(\"not for the published notebook ;-)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amongst the keys we see that `user[\"data\"]` gives us a list with all data files. Iterating over it gives us the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\tfitbit-data.json\n",
      "1\tRunkeeper-activity-data-2017.json\n",
      "2\tssr_4471.zip\n",
      "3\tssr_4471.taxonomy.json\n",
      "4\tssr_4471.zip\n",
      "5\tssr_4471.taxonomy.json\n",
      "6\tBG85_2.vcf\n",
      "7\tfitbit-data.json\n",
      "8\t14286491_1ebadbb79c1d1db421e4b55751270fb567133e1b.zip\n",
      "9\tjawbone-moves-data.json\n",
      "10\tRunkeeper-activity-data-2018.json\n",
      "11\tRunkeeper-activity-data-2017.json\n",
      "12\topenhumans-bosc2018.pdf\n",
      "13\tRunkeeper-activity-data-2018.json\n",
      "14\t1.json\n",
      "15\thealthkit-export_2015-11-01_2015-11-30_1511288864.58225.json\n",
      "16\thealthkit-export_2015-12-01_2015-12-31_1511288864.58225.json\n",
      "17\thealthkit-export_2015-12-31_2016-01-31_1511297617.26078.json\n",
      "18\thealthkit-export_2016-02-01_2016-02-29_1511297617.26078.json\n",
      "19\thealthkit-export_2016-03-01_2016-03-31_1511297617.26078.json\n",
      "20\thealthkit-export_2016-04-01_2016-04-30_1511297617.26078.json\n",
      "21\thealthkit-export_2016-05-01_2016-05-31_1511297617.26078.json\n",
      "22\thealthkit-export_2016-06-01_2016-06-30_1511297617.26078.json\n",
      "23\thealthkit-export_2016-07-01_2016-07-31_1511297617.26078.json\n",
      "24\thealthkit-export_2016-08-01_2016-08-31_1511297617.26078.json\n",
      "25\thealthkit-export_2016-09-01_2016-09-30_1511297617.26078.json\n",
      "26\thealthkit-export_2016-10-01_2016-10-31_1511297617.26078.json\n",
      "27\thealthkit-export_2016-11-01_2016-11-30_1511297617.26078.json\n",
      "28\thealthkit-export_2016-12-01_2016-12-31_1511297617.26078.json\n",
      "29\thealthkit-export_2017-01-01_2017-01-31_1511297617.26078.json\n",
      "30\thealthkit-export_2017-02-01_2017-02-28_1511297617.26078.json\n",
      "31\thealthkit-export_2017-03-01_2017-03-31_1511297617.26078.json\n",
      "32\thealthkit-export_2017-04-01_2017-04-30_1511297617.26078.json\n",
      "33\thealthkit-export_2017-05-01_2017-05-31_1511297617.26078.json\n",
      "34\thealthkit-export_2017-06-01_2017-06-30_1511297617.26078.json\n",
      "35\thealthkit-export_2017-07-01_2017-07-31_1511297617.26078.json\n",
      "36\thealthkit-export_2017-08-01_2017-08-31_1511297617.26078.json\n",
      "37\thealthkit-export_2017-09-01_2017-09-30_1511297617.26078.json\n",
      "38\thealthkit-export_2017-10-01_2017-10-31_1511297617.26078.json\n",
      "39\thealthkit-export_2017-11-01_2017-11-21_1511297617.26078.json\n",
      "40\thealthkit-export_2017-11-21_2018-01-03_1515014347.27459.json\n",
      "41\tuBiome-fastq-1.zip\n",
      "42\ttaxonomy-1.json\n",
      "43\tuBiome-fastq-2.zip\n",
      "44\ttaxonomy-2.json\n",
      "45\trescuetime.json\n",
      "46\thealthkit-export_2018-01-03_2018-02-28_1526679272.60943.json\n",
      "47\thealthkit-export_2018-03-01_2018-03-31_1526679272.60943.json\n",
      "48\thealthkit-export_2018-04-01_2018-04-30_1526679272.60943.json\n",
      "49\thealthkit-export_2018-05-01_2018-05-18_1526679272.60943.json\n",
      "50\tftdna-u7u9yzcj.csv\n",
      "51\tfitbit-data.json\n",
      "52\texample_data1.txt\n",
      "53\t23andMe-genotyping.txt\n",
      "54\t23andMe-genotyping.vcf.bz2\n",
      "55\tmoves-storyline-data.json\n",
      "56\ttakeout-20180520T235741Z-001.zip\n",
      "57\tConsent-text.pdf\n",
      "58\trescuetime.json\n",
      "59\tmoves-storyline-data.json\n"
     ]
    }
   ],
   "source": [
    "for i,f in enumerate(user['data']):\n",
    "    print(\"{}\\t{}\".format(i,f['basename']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's iterate again and grab ouy fitbit data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Fitbit data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for f in user['data']:\n",
    "    if f['source'] == \"direct-sharing-102\":\n",
    "        fitbit_download_url = f['download_url']\n",
    "        break\n",
    "if not fitbit_download_url: \n",
    "    print(\"NO FITBIT DATA FOUND!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To download the data we can use `requests` and subsequently convert the downloaded content into a `JSON` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['sleep-start-time', 'awake-minutes', 'tracker-minutes-very-active', 'profile', 'minutes-to-sleep', 'tracker-floors', 'sleep-minutes', 'sleep-awakenings', 'tracker-minutes-lightly-active', 'tracker-calories', 'activities-overview', 'weight-log', 'time-in-bed', 'tracker-distance', 'sleep-minutes-after-wakeup', 'tracker-minutes-fairly-active', 'tracker-elevation', 'weight', 'heart', 'tracker-steps', 'sleep-efficiency', 'tracker-minutes-sedentary', 'tracker-activity-calories'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitbit_temp = requests.get(fitbit_download_url).content\n",
    "fitbit = json.loads(fitbit_temp)\n",
    "fitbit.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The keys give us an idea what data is in our fitbit store. Let's have a quick look at the step counts. To do this we convert our JSON file into a `pandas` dataframe which can then be used for plotting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Fitbit steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f17cdc08b00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17cdc08e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dates = []\n",
    "steps = []\n",
    "for year in fitbit['tracker-steps']:\n",
    "    for entry in fitbit['tracker-steps'][year]['activities-tracker-steps']:\n",
    "        dates.append(datetime.datetime.strptime(entry['dateTime'], '%Y-%m-%d'))\n",
    "        steps.append(int(entry['value']))\n",
    "fitbit_df = pd.DataFrame(data = {'date': dates, \n",
    "                          'steps': steps})\n",
    "fitbit_df.sort_values(by=['date'],inplace=True)\n",
    "fitbit_df.index = fitbit_df[\"date\"]\n",
    "\n",
    "fitbit_df = fitbit_df.groupby(fitbit_df.index.date).sum()\n",
    "fitbit_df.index = pd.to_datetime(fitbit_df.index)\n",
    "fitbit_dfr = fitbit_df.rolling('60d').mean()\n",
    "fitbit_pt = fitbit_dfr.plot(y=['steps'],figsize=(15,10),fontsize=14)\n",
    "fitbit_pt.legend([\"Fitbit\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All days with zero steps are most likely these days in which no fitbit tracker was worn at all and are thus not super Interesting. Nevertheless we can see some variation along the time, with a somewhat repeating pattern. \n",
    "\n",
    "Let's also have a look at our step histogram, i.e. on how many days did we run a certain amount of steps. But let's first remove all the days with zero steps, because otherwise we'll not be able to see much due to the overwhelming number of days with no data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f17cc263e80>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17cc2632e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitbit_df = fitbit_df.replace(0, np.nan)\n",
    "fitbit_df.hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the daily step counts appear to be largely follow a normal distribution, if a somewhat right-skewed one. More intersting though are the gaps and peaks we see: Peaks can be seen at step count intervals that are just above the limits at which Fitbit gives out achievements/awards! \n",
    "\n",
    "E.g. Fitbit hands out stickers for getting 10,000 steps. Consequentially we can see much more daily step counts than expected that fall in the range of `10,000-10,999 steps`, while step counts of `9,000-9,999` are suspiciously absent. \n",
    "\n",
    "And we can see the same effect at the other award-giving thresholds: 5,000 steps, 15,000 steps, 20,000 steps and even somewhat at 35,000 steps! So it seems that gamification works for me!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resting Heart Rates\n",
    "Let's now also quickly dive into the resting heart rate data. If you're wearing a sufficiently modern `Fitbit` tracker you will have this data as well. \n",
    "\n",
    "In a first step we export the heart-rate data from our complete `fitbit`-json object. The data here is structured a bit differently, but overall the approach is the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dates = []\n",
    "resting_hr = []\n",
    "\n",
    "for month_year in fitbit['heart']:\n",
    "    for day in (fitbit['heart'][month_year]['activities-heart']):\n",
    "        if \"restingHeartRate\" in day['value'].keys():\n",
    "            dates.append(datetime.datetime.strptime(day['dateTime'], '%Y-%m-%d'))\n",
    "            resting_hr.append(day['value']['restingHeartRate'])\n",
    "\n",
    "fitbit_df_hr = pd.DataFrame(data = {'date': dates, \n",
    "                          'resting_hr': resting_hr})\n",
    "fitbit_df_hr.sort_values(by=['date'],inplace=True)\n",
    "fitbit_df_hr.index = fitbit_df_hr[\"date\"]\n",
    "\n",
    "fitbit_df_hr = fitbit_df_hr.groupby(fitbit_df_hr.index.date).sum()\n",
    "fitbit_df_hr.index = pd.to_datetime(fitbit_df_hr.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now also plot a histogram of the heart-rate, along with some basic statistics: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average measured resting heart rate was: 60.67099567099567 with a standard deviation of 4.4439120514499875\n",
      "The median measured resting heart rate was: 60.0\n",
      "\n",
      "\n",
      "The maximum measured resting heart rate was: 76\n",
      "The minimal measured resting heart rate was: 52\n",
      "\n",
      "\n",
      "And here's the resting heart rate distribution:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f17cc145a20>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17cdbfcf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('The average measured resting heart rate was: {} with a standard deviation of {}'.format(\n",
    "    fitbit_df_hr['resting_hr'].mean(),\n",
    "    fitbit_df_hr['resting_hr'].std()))\n",
    "print('The median measured resting heart rate was: {}'.format(fitbit_df_hr['resting_hr'].median()))\n",
    "print(\"\\n\")\n",
    "print('The maximum measured resting heart rate was: {}'.format(fitbit_df_hr['resting_hr'].max()))\n",
    "print('The minimal measured resting heart rate was: {}'.format(fitbit_df_hr['resting_hr'].min()))\n",
    "print(\"\\n\")\n",
    "print(\"And here's the resting heart rate distribution:\")\n",
    "fitbit_df_hr.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now also merge the `step` data along with the `resting heart rate` data and see whether there is any correlation between the two:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f17c9719630>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17cd66a5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitbit_df_merged = fitbit_df_hr.reset_index().merge(fitbit_df.reset_index())\n",
    "scatter_pt = fitbit_df_merged.plot.scatter(\"steps\", \"resting_hr\",figsize=(15,10),fontsize=14)\n",
    "scatter_pt.set_xlabel(\"Steps\")\n",
    "scatter_pt.set_ylabel(\"Resting Heartrate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alas, this doesn't seem to be the case, though it is hard to say as most data points fall inside a rather narrow band of step counts and heart rate. How will you now explore your own data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Elevation/Floors\n",
    "Yes, the same approach also works for your elevation/floor climbing tracking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f17cd78ce48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17c9675828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dates = []\n",
    "elevation = []\n",
    "for year in fitbit['tracker-elevation']:\n",
    "    for entry in fitbit['tracker-elevation'][year]['activities-tracker-elevation']:\n",
    "        dates.append(datetime.datetime.strptime(entry['dateTime'], '%Y-%m-%d'))\n",
    "        elevation.append(int(entry['value']))\n",
    "fitbit_df_elevation = pd.DataFrame(data = {'date': dates, \n",
    "                          'elevation': elevation})\n",
    "fitbit_df_elevation.sort_values(by=['date'],inplace=True)\n",
    "fitbit_df_elevation.index = fitbit_df_elevation[\"date\"]\n",
    "\n",
    "fitbit_df_elevation = fitbit_df_elevation.groupby(fitbit_df_elevation.index.date).sum()\n",
    "fitbit_df_elevation.index = pd.to_datetime(fitbit_df_elevation.index)\n",
    "fitbit_dfr_elevation = fitbit_df_elevation.rolling('60d').mean()\n",
    "fitbit_dfr_elevation = fitbit_dfr_elevation.plot(y=['elevation'],figsize=(15,10),fontsize=14)\n",
    "fitbit_pt.legend([\"Fitbit\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f17c9682d68>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17cc1f4b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitbit_df_elevation = fitbit_df_elevation.replace(0, np.nan)\n",
    "fitbit_df_elevation.hist(bins=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}