spring-sleep-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spring Sleep Analysis \n",
    "#### Studying springtime sleep habits though analysis of my Fitbit sleep data\n",
    "\n",
    "In this notebook, I will study my spring quarter sleep habits with data extracted from my Fitbit Charge HR tracker. The data was obtained through use of Fitbit's API; detailed information about this can be found in `README.md` and `fitbit.py`. Each day's sleep data is stored in the /logs/ directory, in the format yyyy-mm-dd.json, which is the output of the `fitbit.py` script."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "Let's begin with importing what we need and configuring plots for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "import datetime\n",
    "import scipy.stats\n",
    "\n",
    "matplotlib.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = [12.0, 8.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we dive in, we must first understand the shape of the data. Refer to the [Fitbit documentation](https://dev.fitbit.com/docs/sleep/) for detailed information on the format of the log files. As a quick overview, each day contains two top-level attributes, `sleep` and `summary`:\n",
    " - `sleep` is a list of one or more entries for different sleep events in a day (e.g., a nap and then one's main sleep). Each entry has several attributes, like `isMainSleep`, `minutesAsleep`, and `minuteData`. `minuteData` contains minute-by-minute sleep data, where each minute has an identifier, `1`, `2`, or `3`, to denote asleep, restless, or awake.\n",
    " - `summary` contains summary statistics. It includes the number of sleep events (`totalSleepRecords`) and the total time in bed or asleep (`totalTimeInBed` and `totalMinutesAsleep`).\n",
    "\n",
    "We can run a couple of simple commands to illustrate this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['summary', 'sleep']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('logs/2016-07-01.json') as f:\n",
    "    sample_data = json.loads(f.read())\n",
    "list(sample_data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['totalMinutesAsleep', 'totalTimeInBed', 'totalSleepRecords']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sample_data['summary'].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['restlessCount',\n",
       " 'awakeCount',\n",
       " 'restlessDuration',\n",
       " 'logId',\n",
       " 'dateOfSleep',\n",
       " 'duration',\n",
       " 'timeInBed',\n",
       " 'startTime',\n",
       " 'minutesAsleep',\n",
       " 'awakeningsCount',\n",
       " 'efficiency',\n",
       " 'awakeDuration',\n",
       " 'minuteData',\n",
       " 'minutesAfterWakeup',\n",
       " 'isMainSleep',\n",
       " 'minutesToFallAsleep',\n",
       " 'minutesAwake']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sample_data['sleep'][0].keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Spent in Bed\n",
    "How long was I in bed each night? I restrict my analysis to the spring academic term at my school (March 29th to June 10th). \n",
    "\n",
    "(I am choosing to study `totalTimeInBed`, rather than `totalMinutesAsleep`, for a couple of reasons. I generally fall asleep quickly and sleep well; I rarely get up in the middle of the night; and a quick look at the distributions shows them as being very similar. I choose to study the dataset that counts time I was 'restless.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5/bzrG+i0W76fC5J6vmkMBijMz6OzdQGF+iHS5y8E8rp333338O+XLl3CpUuXwi2kFxYW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ZNPPomTJ0/iDW94A1IB3/GTEzeyYJZW3KUdR0Af+KL37dWwIT8ZU4NKqf2FstsR/2KPSzt6Xba0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8c293d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dates = pd.date_range('2016-03-29', '2016-06-10')\n",
    "time_in_bed = []\n",
    "\n",
    "for date in dates:\n",
    "    fname = 'logs/' + date.strftime('%Y-%m-%d') + '.json'\n",
    "    with open(fname) as f:\n",
    "        date_data = json.loads(f.read())\n",
    "        \n",
    "        time_in_bed.append(date_data['summary']['totalTimeInBed'] / 60.0)\n",
    "        \n",
    "df = pd.DataFrame(time_in_bed, index = dates)\n",
    "df.columns = ['bed']\n",
    "\n",
    "df.plot()\n",
    "plt.ylabel('Hours in Bed');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As interesting as this looks, there isn't a whole lot to take from it on the surface. The vague periodicity of the graph sugggests that we look at different days of the week, so we'll do that soon. Let's first look at the distribution of how long I was in bed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bed</th>\n",
       "      <td>74.0</td>\n",
       "      <td>7.154955</td>\n",
       "      <td>1.786836</td>\n",
       "      <td>3.416667</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>7.141667</td>\n",
       "      <td>8.283333</td>\n",
       "      <td>10.816667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count      mean       std       min       25%       50%       75%  \\\n",
       "bed   74.0  7.154955  1.786836  3.416667  5.916667  7.141667  8.283333   \n",
       "\n",
       "           max  \n",
       "bed  10.816667  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/+tWvdr598cUXb3dbbW1tnHXWWdlPBQAA3dx7hvQvf/nLiIi44oor4nvf+95uHwgAAMpB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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8d2f970>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(bins = 8, range = (3, 11))\n",
    "\n",
    "plt.xlim(3, 11)\n",
    "plt.xticks(range(3, 11))\n",
    "plt.xlabel('Hours in Bed')\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As far as behavioral data goes, this is reasonably well-behaved. Notice that this distribution varies quite a bit, with a standard deviation of nearly two hours. (Anyone familiar with the sleep habits of a college student will not be surprised.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sleep Paterns by Day of Week\n",
    "\n",
    "Let's now look at the different days of the week. Did I sleep more on weekends? (I certainly hope so.) What nights were the worst? We can add another identifier denoting the day of the week, and then group the days based on whether they were weekdays or weekends. We'll then look at the distriutions and compare them with a t-test.\n",
    "\n",
    "Note that a sleep record for a certain day (e.g., Friday) contains data from the previous night (in that case, Thursday night). Also, `pandas` numbers days as 0 = Monday, ... ,  6 = Sunday. Altogether, this means weekdays are labeled 0 through 4, and weekends are labeled 5 and 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bed</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-29</th>\n",
       "      <td>6.850000</td>\n",
       "      <td>1</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-30</th>\n",
       "      <td>6.683333</td>\n",
       "      <td>2</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>5.916667</td>\n",
       "      <td>3</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>9.066667</td>\n",
       "      <td>4</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-02</th>\n",
       "      <td>8.816667</td>\n",
       "      <td>5</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 bed  day_of_week day_type\n",
       "2016-03-29  6.850000            1  Weekday\n",
       "2016-03-30  6.683333            2  Weekday\n",
       "2016-03-31  5.916667            3  Weekday\n",
       "2016-04-01  9.066667            4  Weekday\n",
       "2016-04-02  8.816667            5  Weekend"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day_of_week'] = df.index.weekday\n",
    "df['day_type'] = df['day_of_week'].apply(lambda x: 'Weekend' if x >= 5 else 'Weekday')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TlUqlx++5adOmHr/n73P++eentbU1N954Y3bcccfccsstf9C3D1BPnPgDFKCt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\nQPgDAEABhD8AABTg/wNaBZnoPo8oGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8d85890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = 'bed', by = 'day_type', positions = [2, 1], \n",
    "           vert = False, widths = 0.5)\n",
    "plt.xlabel('Hours in Bed')\n",
    "plt.suptitle('')\n",
    "plt.title('');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.2541604345082797, pvalue=0.0017337202641399434)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group dataframe by weekday vs. weekend\n",
    "df_weekdays = df[df.day_of_week < 5]\n",
    "df_weekend = df[df.day_of_week >= 5]\n",
    "\n",
    "scipy.stats.ttest_ind(df_weekdays['bed'], df_weekend['bed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A low p-value would suggest I sleep statistically significantly more on weekends than on weekdays. The two boxplots illustrate this observation. I'm not at all surprised by this, but it's always cool to have data to back up your intuition.\n",
    "\n",
    "What about individual days? We can consider each night of the week separately, and look at the distributions there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o0KGmy3IFcwwDAADgZMYDuiRlZWUpKyvLdBmuY45hAAAAnMz4SaIAAAAA/o2ADgAAAFiE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\nEQI6AAAAYBECOgAAAGCR/wdL72HKaTLQ7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8beae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a label for day name, to make the boxplot more readable\n",
    "days = {0: 'Monday', 1: 'Tuesday', 2: 'Wednesday', 3: 'Thursday', 4: 'Friday', \n",
    "        5: 'Saturday', 6: 'Sunday'}\n",
    "df['day_name'] = df['day_of_week'].apply(lambda x: days[x])\n",
    "\n",
    "df.boxplot(column = 'bed', by = 'day_name', positions = [5, 1, 6, 7, 4, 2, 3])\n",
    "\n",
    "# Configure title and axes\n",
    "plt.suptitle('')\n",
    "plt.title('')\n",
    "plt.ylabel('Hours in Bed')\n",
    "plt.xlabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very illustrative. Tuesdays, Thursdays, and Fridays were the days on which I got the least amount of sleep the previous night. Looking back on my quarter, I can find explanations for all of these -- Monday nights were extremely busy, and I could not start work until pretty late; Thursday mornings, I had to wake up earlier than usual to go to work, impacting my sleep the night before; and on Fridays, I had a quantum mechanics problem set due, which was too often put off to the night before. I find it extremely interesting to see my academic habits reflected in my sleep habits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bedtimes\n",
    "\n",
    "I've learned quite a bit about how much I sleep. What about _when_ I sleep? Let's create a new dataset; this one will have columns for bedtime (formally defined as the start of any sleep event, including naps), for the time I was in bed (as before), and for whether or not it was a 'main sleep' (the longest sleep event of a 24-hour period, distinguishing regular sleep from naps).\n",
    "\n",
    "Let's look at my sleep duration versus my bedtime. While the next two code blocks are pretty large, their purposes are straightforward: we want to create the dataset described above, then plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bedtimes = []\n",
    "\n",
    "# Read data into list\n",
    "for date in dates:\n",
    "    fname = 'logs/' + date.strftime('%Y-%m-%d') + '.json'\n",
    "    with open(fname) as f:\n",
    "        date_data = json.loads(f.read())\n",
    "        \n",
    "        # Note that sleep_event['startTime'][11:16] gets the hh:mm characters\n",
    "        # from the start of a sleep event; it is then converted to a datetime\n",
    "        for sleep_event in date_data['sleep']:\n",
    "            bedtimes.append((pd.to_datetime(sleep_event['startTime'][11:16]), \n",
    "                             sleep_event['timeInBed'] / 60.0,\n",
    "                             sleep_event['isMainSleep']))\n",
    "            \n",
    "# Convert to dataframe, and make 'bedtime' a float (e.g., 5:30 -> 5.5)\n",
    "df = pd.DataFrame(bedtimes, columns = ['bedtime', 'duration', 'main'])\n",
    "df['bedtime'] = df['bedtime'].dt.hour + df['bedtime'].dt.minute / 60.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dDhW9ezfF8IiIiIgogtRqNe6//35069YNDocDWVlZGDBgALZu3Yp+/fohIyMD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\nAGAxhHwAAADAYgj5AAAAgMX8f3rr/9QLYoRbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9dae230>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make first plot: scatterplot of bedtime vs. duration, colored by main sleep\n",
    "ax = df[df.main == True].plot.scatter(x = 'bedtime', y = 'duration', \n",
    "                                      color = 'Red', s = 100, \n",
    "                                      label = 'Main Sleep')\n",
    "\n",
    "df[df.main == False].plot.scatter(x = 'bedtime', y = 'duration', \n",
    "                                  color = 'SlateBlue', ax = ax, s = 100, \n",
    "                                  label = 'Secondary')\n",
    "\n",
    "# List of times to use for labels\n",
    "times = [str(2 * h) + ':00' for h in range(12)]\n",
    "\n",
    "# Configure legend, x-axis, labels\n",
    "plt.legend(scatterpoints = 1, markerfirst = False)\n",
    "plt.xticks(range(0, 24, 2), times)\n",
    "plt.xlim(0, 24)\n",
    "plt.xlabel('Bedtime')\n",
    "plt.ylabel('Hours in Bed');\n",
    "\n",
    "# Overlay a histogram of bedtimes on the same plot, using a secondary y-axis\n",
    "ax2 = ax.twinx()\n",
    "df['bedtime'].map(lambda x: int(x)).plot.hist(bins = range(24), \n",
    "                                              color = 'MediumSeaGreen', \n",
    "                                              alpha = 0.3, grid = False)\n",
    "\n",
    "# Configure secondary y-axis\n",
    "plt.yticks(range(0, 28, 4))\n",
    "plt.ylim(0, 28);"
   ]
  },
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   "source": [
    "There is a clear negative correlation between my bedtime and my time spent in bed; it's no surprise that the later I go to bed, the less sleep I get.\n",
    "\n",
    "But some other things can be read from this plot -- I most frequently went to bed between 2:00 and 3:00 am, for instance, followed by between 1:00 and 2:00, then 3:00 to 4:00. I rarely slept before 1:00, and even less so before midnight. I didn't take very many naps (note also that not every secondary sleep event is a nap), but when I did, they were around two or three hours in length."
   ]
  },
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   "source": [
    "## Final Remarks\n",
    "Data is great, but its purpose is ultimately to give insight into a larger picture. What can I learn from this dive into my sleep habits?\n",
    "\n",
    "Clearly, **my sleep durations vary a lot.** We noticed a standard deviation of nearly two hours on the distribution of sleep durations. I sleep wildly different amounts each night; it would likely be healthier for me to work towards sleeping more consistently.\n",
    "\n",
    "From the section \"Sleep Patterns by Day of Week,\" we notice that **my workload is reflected in my sleep habits**. I slept noticably less on nights where I had something due the following day. The solution, which is much easier said than done, is to start work earlier -- every student's goal. Similarly, **I sleep more on weekends.** The immediate explanation for this observation is that I'm trying to make up for getting less sleep during the week. But this idea of \"catching up on sleep\" has been [often](https://sleepfoundation.org/sleep-news/you-cant-catch-sleep) [debated](http://www.scientificamerican.com/article/fact-or-fiction-can-you-catch-up-on-sleep/) by scientists, and it is unclear whether this is actually possible. \n",
    "\n",
    "**Future directions** for this project include analyzing and comparing sleep data from fall, winter, and spring terms. Each quarter was very different, posing its own set of challenges; can I see this reflected in my sleep patterns? Similarly, I could consider how these patterns change during summer. Finally, I might also consider making use of the nightly, minute-by-minute data. "
   ]
  }
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