diff --git a/fitbit_data_complier/README.md b/fitbit_data_complier/README.md new file mode 100644 index 0000000..604862a --- /dev/null +++ b/fitbit_data_complier/README.md @@ -0,0 +1,14 @@ +# Fitbit Data Compiler + +## About +This script compiles all the personal files that has been collected by your fitbit. To get your own data, follow this link [here](https://help.fitbit.com/articles/en_US/Help_article/1133). + +The script is very basic, and just provides the avenue for people with fitbits to compile all the files into a single dataframe. + +## Why is this useful? +This is useful for those who own a fitbit, or have a fitbit API and would like to perform analysis and play around with their own data. + +## What do I need? +In order to run this script, you would need to download [Anaconda](https://www.anaconda.com/) and run Jupyter notebook on it. + +Alternatively, you could also upload the script along with your fitbit data and run it on Google Colab - completely on the cloud. \ No newline at end of file diff --git a/fitbit_data_complier/cleaning.ipynb b/fitbit_data_complier/cleaning.ipynb new file mode 100644 index 0000000..831ef75 --- /dev/null +++ b/fitbit_data_complier/cleaning.ipynb @@ -0,0 +1,1468 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7FhZpGJqZBhr" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import matplotlib.mlab as mlab" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SqIFi2EL2mco" + }, + "source": [ + "# In directory" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "-XCICwcG6F7-", + "outputId": "c2e456b1-59a8-494c-d28d-722ba3a411c5" + }, + "outputs": [], + "source": [ + "#os.listdir('json')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PBC4v-GG2rAk" + }, + "source": [ + "# Heart data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "QD_DpYaUXxTU" + }, + "outputs": [], + "source": [ + "inDirHeart = [i for i in os.listdir('json') if i.startswith('heart_rate')]\n", + "\n", + "dfHeart = pd.DataFrame()\n", + "\n", + "for i in inDirHeart: \n", + " data = pd.read_json('./json/' + i)\n", + " dfHeart = dfHeart.append(data, ignore_index=True)\n", + "\n", + "bpm = []\n", + "days = []\n", + "for n in range(len(dfHeart.index)):\n", + " val = dfHeart.value[n]['bpm']\n", + " bpm.append(val)\n", + " \n", + " day = dfHeart.dateTime[n].dayofweek\n", + " days.append(day)\n", + "\n", + "dfHeart['bpm'] = bpm\n", + "dfHeart['day'] = days\n", + "\n", + "date = []\n", + "for i in range(len(dfHeart.index)):\n", + " val = dfHeart.loc[i].dateTime.date()\n", + " date.append(val)\n", + "\n", + "dfHeart['date'] = date\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "XjLpMS5lWM_V", + "outputId": "8e399812-416a-400a-e039-57c5d795078e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateTimevaluebpmdaydate
2985672019-08-20 00:00:09{'bpm': 67, 'confidence': 2}6712019-08-20
2985682019-08-20 00:00:24{'bpm': 67, 'confidence': 2}6712019-08-20
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2985702019-08-20 00:00:34{'bpm': 69, 'confidence': 2}6912019-08-20
2985712019-08-20 00:00:49{'bpm': 69, 'confidence': 2}6912019-08-20
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" + ], + "text/plain": [ + " dateTime value bpm day date\n", + "298567 2019-08-20 00:00:09 {'bpm': 67, 'confidence': 2} 67 1 2019-08-20\n", + "298568 2019-08-20 00:00:24 {'bpm': 67, 'confidence': 2} 67 1 2019-08-20\n", + "298569 2019-08-20 00:00:29 {'bpm': 68, 'confidence': 2} 68 1 2019-08-20\n", + "298570 2019-08-20 00:00:34 {'bpm': 69, 'confidence': 2} 69 1 2019-08-20\n", + "298571 2019-08-20 00:00:49 {'bpm': 69, 'confidence': 2} 69 1 2019-08-20" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfHeart.head()\n", + "\n", + "dfHeart.groupby('date').agg('mean')\n", + "\n", + "dfHeart.date = pd.to_datetime(dfHeart.date)\n", + "\n", + "dfHeart.loc[dfHeart.date == '2019-08-20'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.time(23, 0, 44)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfHeart.loc[5].dateTime.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 366 + }, + "colab_type": "code", + "id": "x8Zqir2VUIDH", + "outputId": "40f9ddf9-b489-4f5c-9b68-0d1ed632e561" + }, + "outputs": [], + "source": [ + "dfHeart['hour'] = dfHeart.dateTime.dt.hour" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gibkD9yc2igO" + }, + "source": [ + "# Sleep data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "FF1p_FuZO1xO", + "outputId": "fbd97044-2d4c-444c-c3f1-1fa62b604efd" + }, + "outputs": [], + "source": [ + "inDirSleep = [i for i in os.listdir('json') if i.startswith('sleep')]\n", + "\n", + "dfSleep = pd.DataFrame()\n", + "\n", + "for i in inDirSleep: \n", + " data = pd.read_json('./json/' + i)\n", + " dfSleep = dfSleep.append(data, ignore_index=True)\n", + "\n", + "dfSleep['date'] = pd.to_datetime(dfSleep.dateOfSleep)\n", + "\n", + "daysSleep = []\n", + "\n", + "for n in range(len(dfSleep.index)):\n", + " \n", + " day = dfSleep.date[n].dayofweek\n", + " daysSleep.append(day)\n", + " \n", + "dfSleep['day2'] = daysSleep\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BO6W_q2aqFtg" + }, + "source": [ + "Why startTime instead of minutesAsleep? Erratic sleep patterns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "colab_type": "code", + "id": "ziKND6_Gow10", + "outputId": "02900e10-e9b4-4bd7-b967-1cbaeab973ea" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "66" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfSleep.startTime = pd.to_datetime(dfSleep.startTime)\n", + "\n", + "nap = []\n", + "for n in range(len(dfSleep.index)):\n", + " if dfSleep.startTime[n].hour > 7 and dfSleep.startTime[n].hour < 21 and dfSleep.minutesAsleep[n] <= 300 :\n", + " val = 1\n", + " else:\n", + " val = 0\n", + " nap.append(val)\n", + " \n", + "dfSleep['nap'] = nap\n", + "nap.count(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hBvr0ZaGm2ab" + }, + "outputs": [], + "source": [ + "lengthOfNap = []\n", + "for t in range(len(dfSleep.index)):\n", + " if dfSleep.nap[t] == 1:\n", + " length = dfSleep.minutesAsleep[t]\n", + " else:\n", + " length = 0\n", + " lengthOfNap.append(length)\n", + " \n", + "dfSleep['lengthOfNap'] = lengthOfNap\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "v-EugeC2i__J" + }, + "outputs": [], + "source": [ + "dfSleep = dfSleep.drop_duplicates(['logId'], keep='last')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateOfSleepdurationefficiencyendTimeinfoCodelevelslogIdminutesAfterWakeupminutesAsleepminutesAwakeminutesToFallAsleepstartTimetimeInBedtypedateday2naplengthOfNap
02019-08-2533660000932019-08-25T12:41:00.0001{'summary': {'restless': {'count': 9, 'minutes...2360634116945164102019-08-25 03:19:30561classic2019-08-25600
12019-08-246540000972019-08-24T20:47:30.0002{'summary': {'restless': {'count': 2, 'minutes...236063411680106302019-08-24 18:58:30109classic2019-08-2451106
22019-08-2430480000932019-08-24T09:35:30.0000{'summary': {'deep': {'count': 5, 'minutes': 1...2360634116614367202019-08-24 01:07:30508stages2019-08-24500
32019-08-235040000922019-08-23T18:59:30.0002{'summary': {'restless': {'count': 3, 'minutes...23606341165077702019-08-23 17:35:0084classic2019-08-234177
42019-08-2318180000922019-08-23T07:38:30.0000{'summary': {'deep': {'count': 4, 'minutes': 4...2357918539422584502019-08-23 02:35:00303stages2019-08-23400
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dateTimevaluedate
02019-08-20 00:00:000.952019-08-20
12019-08-20 00:01:000.952019-08-20
22019-08-20 00:02:000.952019-08-20
32019-08-20 00:03:000.952019-08-20
42019-08-20 00:04:000.952019-08-20
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" + ], + "text/plain": [ + " dateTime value date\n", + "0 2019-08-20 00:00:00 0.95 2019-08-20\n", + "1 2019-08-20 00:01:00 0.95 2019-08-20\n", + "2 2019-08-20 00:02:00 0.95 2019-08-20\n", + "3 2019-08-20 00:03:00 0.95 2019-08-20\n", + "4 2019-08-20 00:04:00 0.95 2019-08-20" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfCalories = pd.DataFrame()\n", + "\n", + "for i in inDirCalories: \n", + " data = pd.read_json('./json/' + i)\n", + " dfCalories = dfCalories.append(data, ignore_index=True)\n", + "\n", + "date = []\n", + "for i in range(len(dfCalories.index)):\n", + " val = dfCalories.loc[i].dateTime.date()\n", + " date.append(val)\n", + "\n", + "dfCalories['date'] = date\n", + "\n", + " \n", + "dfCalories.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "innRsMyG6DVn" + }, + "source": [ + "# Active minutes" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 66 + }, + "colab_type": "code", + "id": "AEQgAkV36G8v", + "outputId": "cd32176c-88db-4947-e7cf-edf8b6064347" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['very_active_minutes-2019-06-21.json',\n", + " 'very_active_minutes-2019-07-21.json',\n", + " 'very_active_minutes-2019-08-20.json']" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inDirActive = [i for i in os.listdir('json') if i.startswith('very')]\n", + "inDirActive" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 191 + }, + "colab_type": "code", + "id": "jTM2Grp86SI0", + "outputId": "94098a9e-20ef-40d7-8060-f395782215be" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateTimevalue
02019-06-210
12019-06-220
22019-06-2320
32019-06-2466
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