bellabeat-case-study-gdapc.ipynb

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    "# About a company\n",
    "\n",
    "![](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTnml1r1XbvYLIMQQpe7XVVbUnS5_rjJ8brAw&usqp=CAU)\n",
    "\n",
    "Bellabeat, a high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Urška Sršen, cofounder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company\n",
    "\n",
    "# Questions for the analysis\n",
    "\n",
    "1. What are some trends in smart device usage? \n",
    "2. How could these trends apply to Bellabeat customers? \n",
    "3. How could these trends help influence Bellabeat marketing strategy\n",
    "\n",
    "# Business task\n",
    "\n",
    "Identify potential opportunities for growth and recommendations for the Bellabeat marketing strategy improvement based on trends in smart device usage."
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "# Loading packages"
   ]
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  {
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      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.3     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mforcats  \u001b[39m 1.0.0     \u001b[32m✔\u001b[39m \u001b[34mstringr  \u001b[39m 1.5.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2  \u001b[39m 3.4.4     \u001b[32m✔\u001b[39m \u001b[34mtibble   \u001b[39m 3.2.1\n",
      "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.2     \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n"
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   "source": [
    "library(tidyverse) \n",
    "library(dplyr)"
   ]
  },
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   "source": [
    "# Importing Datasets\n",
    "\n",
    "For this project I am importing required datasets only from FitBit Fitness Tracker [Data](https://www.kaggle.com/arashnic/fitbit)."
   ]
  },
  {
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     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m940\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m15\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m  (1): ActivityDate\n",
      "\u001b[32mdbl\u001b[39m (14): Id, TotalSteps, TotalDistance, TrackerDistance, LoggedActivitiesDi...\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m2483658\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m3\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): Time\n",
      "\u001b[32mdbl\u001b[39m (2): Id, Value\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m413\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m5\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): SleepDay\n",
      "\u001b[32mdbl\u001b[39m (4): Id, TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m67\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m8\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (1): Date\n",
      "\u001b[32mdbl\u001b[39m (6): Id, WeightKg, WeightPounds, Fat, BMI, LogId\n",
      "\u001b[33mlgl\u001b[39m (1): IsManualReport\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    }
   ],
   "source": [
    "daily_activity <- read_csv(\"/kaggle/input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv\")\n",
    "heartrate_seconds <- read_csv(\"/kaggle/input/fitbit/Fitabase Data 4.12.16-5.12.16/heartrate_seconds_merged.csv\")\n",
    "sleepDay <- read_csv(\"/kaggle/input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv\")\n",
    "weightLog <- read_csv(\"/kaggle/input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv\")"
   ]
  },
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    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 15</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>ActivityDate</th><th scope=col>TotalSteps</th><th scope=col>TotalDistance</th><th scope=col>TrackerDistance</th><th scope=col>LoggedActivitiesDistance</th><th scope=col>VeryActiveDistance</th><th scope=col>ModeratelyActiveDistance</th><th scope=col>LightActiveDistance</th><th scope=col>SedentaryActiveDistance</th><th scope=col>VeryActiveMinutes</th><th scope=col>FairlyActiveMinutes</th><th scope=col>LightlyActiveMinutes</th><th scope=col>SedentaryMinutes</th><th scope=col>Calories</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>4/12/2016</td><td>13162</td><td>8.50</td><td>8.50</td><td>0</td><td>1.88</td><td>0.55</td><td>6.06</td><td>0</td><td>25</td><td>13</td><td>328</td><td> 728</td><td>1985</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/13/2016</td><td>10735</td><td>6.97</td><td>6.97</td><td>0</td><td>1.57</td><td>0.69</td><td>4.71</td><td>0</td><td>21</td><td>19</td><td>217</td><td> 776</td><td>1797</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/14/2016</td><td>10460</td><td>6.74</td><td>6.74</td><td>0</td><td>2.44</td><td>0.40</td><td>3.91</td><td>0</td><td>30</td><td>11</td><td>181</td><td>1218</td><td>1776</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/15/2016</td><td> 9762</td><td>6.28</td><td>6.28</td><td>0</td><td>2.14</td><td>1.26</td><td>2.83</td><td>0</td><td>29</td><td>34</td><td>209</td><td> 726</td><td>1745</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/16/2016</td><td>12669</td><td>8.16</td><td>8.16</td><td>0</td><td>2.71</td><td>0.41</td><td>5.04</td><td>0</td><td>36</td><td>10</td><td>221</td><td> 773</td><td>1863</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/17/2016</td><td> 9705</td><td>6.48</td><td>6.48</td><td>0</td><td>3.19</td><td>0.78</td><td>2.51</td><td>0</td><td>38</td><td>20</td><td>164</td><td> 539</td><td>1728</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 15\n",
       "\\begin{tabular}{lllllllllllllll}\n",
       " Id & ActivityDate & TotalSteps & TotalDistance & TrackerDistance & LoggedActivitiesDistance & VeryActiveDistance & ModeratelyActiveDistance & LightActiveDistance & SedentaryActiveDistance & VeryActiveMinutes & FairlyActiveMinutes & LightlyActiveMinutes & SedentaryMinutes & Calories\\\\\n",
       " <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 4/12/2016 & 13162 & 8.50 & 8.50 & 0 & 1.88 & 0.55 & 6.06 & 0 & 25 & 13 & 328 &  728 & 1985\\\\\n",
       "\t 1503960366 & 4/13/2016 & 10735 & 6.97 & 6.97 & 0 & 1.57 & 0.69 & 4.71 & 0 & 21 & 19 & 217 &  776 & 1797\\\\\n",
       "\t 1503960366 & 4/14/2016 & 10460 & 6.74 & 6.74 & 0 & 2.44 & 0.40 & 3.91 & 0 & 30 & 11 & 181 & 1218 & 1776\\\\\n",
       "\t 1503960366 & 4/15/2016 &  9762 & 6.28 & 6.28 & 0 & 2.14 & 1.26 & 2.83 & 0 & 29 & 34 & 209 &  726 & 1745\\\\\n",
       "\t 1503960366 & 4/16/2016 & 12669 & 8.16 & 8.16 & 0 & 2.71 & 0.41 & 5.04 & 0 & 36 & 10 & 221 &  773 & 1863\\\\\n",
       "\t 1503960366 & 4/17/2016 &  9705 & 6.48 & 6.48 & 0 & 3.19 & 0.78 & 2.51 & 0 & 38 & 20 & 164 &  539 & 1728\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 15\n",
       "\n",
       "| Id &lt;dbl&gt; | ActivityDate &lt;chr&gt; | TotalSteps &lt;dbl&gt; | TotalDistance &lt;dbl&gt; | TrackerDistance &lt;dbl&gt; | LoggedActivitiesDistance &lt;dbl&gt; | VeryActiveDistance &lt;dbl&gt; | ModeratelyActiveDistance &lt;dbl&gt; | LightActiveDistance &lt;dbl&gt; | SedentaryActiveDistance &lt;dbl&gt; | VeryActiveMinutes &lt;dbl&gt; | FairlyActiveMinutes &lt;dbl&gt; | LightlyActiveMinutes &lt;dbl&gt; | SedentaryMinutes &lt;dbl&gt; | Calories &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1503960366 | 4/12/2016 | 13162 | 8.50 | 8.50 | 0 | 1.88 | 0.55 | 6.06 | 0 | 25 | 13 | 328 |  728 | 1985 |\n",
       "| 1503960366 | 4/13/2016 | 10735 | 6.97 | 6.97 | 0 | 1.57 | 0.69 | 4.71 | 0 | 21 | 19 | 217 |  776 | 1797 |\n",
       "| 1503960366 | 4/14/2016 | 10460 | 6.74 | 6.74 | 0 | 2.44 | 0.40 | 3.91 | 0 | 30 | 11 | 181 | 1218 | 1776 |\n",
       "| 1503960366 | 4/15/2016 |  9762 | 6.28 | 6.28 | 0 | 2.14 | 1.26 | 2.83 | 0 | 29 | 34 | 209 |  726 | 1745 |\n",
       "| 1503960366 | 4/16/2016 | 12669 | 8.16 | 8.16 | 0 | 2.71 | 0.41 | 5.04 | 0 | 36 | 10 | 221 |  773 | 1863 |\n",
       "| 1503960366 | 4/17/2016 |  9705 | 6.48 | 6.48 | 0 | 3.19 | 0.78 | 2.51 | 0 | 38 | 20 | 164 |  539 | 1728 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         ActivityDate TotalSteps TotalDistance TrackerDistance\n",
       "1 1503960366 4/12/2016    13162      8.50          8.50           \n",
       "2 1503960366 4/13/2016    10735      6.97          6.97           \n",
       "3 1503960366 4/14/2016    10460      6.74          6.74           \n",
       "4 1503960366 4/15/2016     9762      6.28          6.28           \n",
       "5 1503960366 4/16/2016    12669      8.16          8.16           \n",
       "6 1503960366 4/17/2016     9705      6.48          6.48           \n",
       "  LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance\n",
       "1 0                        1.88               0.55                    \n",
       "2 0                        1.57               0.69                    \n",
       "3 0                        2.44               0.40                    \n",
       "4 0                        2.14               1.26                    \n",
       "5 0                        2.71               0.41                    \n",
       "6 0                        3.19               0.78                    \n",
       "  LightActiveDistance SedentaryActiveDistance VeryActiveMinutes\n",
       "1 6.06                0                       25               \n",
       "2 4.71                0                       21               \n",
       "3 3.91                0                       30               \n",
       "4 2.83                0                       29               \n",
       "5 5.04                0                       36               \n",
       "6 2.51                0                       38               \n",
       "  FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories\n",
       "1 13                  328                   728             1985    \n",
       "2 19                  217                   776             1797    \n",
       "3 11                  181                  1218             1776    \n",
       "4 34                  209                   726             1745    \n",
       "5 10                  221                   773             1863    \n",
       "6 20                  164                   539             1728    "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>Time</th><th scope=col>Value</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2022484408</td><td>4/12/2016 7:21:00 AM</td><td> 97</td></tr>\n",
       "\t<tr><td>2022484408</td><td>4/12/2016 7:21:05 AM</td><td>102</td></tr>\n",
       "\t<tr><td>2022484408</td><td>4/12/2016 7:21:10 AM</td><td>105</td></tr>\n",
       "\t<tr><td>2022484408</td><td>4/12/2016 7:21:20 AM</td><td>103</td></tr>\n",
       "\t<tr><td>2022484408</td><td>4/12/2016 7:21:25 AM</td><td>101</td></tr>\n",
       "\t<tr><td>2022484408</td><td>4/12/2016 7:22:05 AM</td><td> 95</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Id & Time & Value\\\\\n",
       " <dbl> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 2022484408 & 4/12/2016 7:21:00 AM &  97\\\\\n",
       "\t 2022484408 & 4/12/2016 7:21:05 AM & 102\\\\\n",
       "\t 2022484408 & 4/12/2016 7:21:10 AM & 105\\\\\n",
       "\t 2022484408 & 4/12/2016 7:21:20 AM & 103\\\\\n",
       "\t 2022484408 & 4/12/2016 7:21:25 AM & 101\\\\\n",
       "\t 2022484408 & 4/12/2016 7:22:05 AM &  95\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| Id &lt;dbl&gt; | Time &lt;chr&gt; | Value &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 2022484408 | 4/12/2016 7:21:00 AM |  97 |\n",
       "| 2022484408 | 4/12/2016 7:21:05 AM | 102 |\n",
       "| 2022484408 | 4/12/2016 7:21:10 AM | 105 |\n",
       "| 2022484408 | 4/12/2016 7:21:20 AM | 103 |\n",
       "| 2022484408 | 4/12/2016 7:21:25 AM | 101 |\n",
       "| 2022484408 | 4/12/2016 7:22:05 AM |  95 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Time                 Value\n",
       "1 2022484408 4/12/2016 7:21:00 AM  97  \n",
       "2 2022484408 4/12/2016 7:21:05 AM 102  \n",
       "3 2022484408 4/12/2016 7:21:10 AM 105  \n",
       "4 2022484408 4/12/2016 7:21:20 AM 103  \n",
       "5 2022484408 4/12/2016 7:21:25 AM 101  \n",
       "6 2022484408 4/12/2016 7:22:05 AM  95  "
      ]
     },
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    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>SleepDay</th><th scope=col>TotalSleepRecords</th><th scope=col>TotalMinutesAsleep</th><th scope=col>TotalTimeInBed</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>4/12/2016 12:00:00 AM</td><td>1</td><td>327</td><td>346</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/13/2016 12:00:00 AM</td><td>2</td><td>384</td><td>407</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/15/2016 12:00:00 AM</td><td>1</td><td>412</td><td>442</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/16/2016 12:00:00 AM</td><td>2</td><td>340</td><td>367</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/17/2016 12:00:00 AM</td><td>1</td><td>700</td><td>712</td></tr>\n",
       "\t<tr><td>1503960366</td><td>4/19/2016 12:00:00 AM</td><td>1</td><td>304</td><td>320</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " Id & SleepDay & TotalSleepRecords & TotalMinutesAsleep & TotalTimeInBed\\\\\n",
       " <dbl> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 4/12/2016 12:00:00 AM & 1 & 327 & 346\\\\\n",
       "\t 1503960366 & 4/13/2016 12:00:00 AM & 2 & 384 & 407\\\\\n",
       "\t 1503960366 & 4/15/2016 12:00:00 AM & 1 & 412 & 442\\\\\n",
       "\t 1503960366 & 4/16/2016 12:00:00 AM & 2 & 340 & 367\\\\\n",
       "\t 1503960366 & 4/17/2016 12:00:00 AM & 1 & 700 & 712\\\\\n",
       "\t 1503960366 & 4/19/2016 12:00:00 AM & 1 & 304 & 320\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 5\n",
       "\n",
       "| Id &lt;dbl&gt; | SleepDay &lt;chr&gt; | TotalSleepRecords &lt;dbl&gt; | TotalMinutesAsleep &lt;dbl&gt; | TotalTimeInBed &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1503960366 | 4/12/2016 12:00:00 AM | 1 | 327 | 346 |\n",
       "| 1503960366 | 4/13/2016 12:00:00 AM | 2 | 384 | 407 |\n",
       "| 1503960366 | 4/15/2016 12:00:00 AM | 1 | 412 | 442 |\n",
       "| 1503960366 | 4/16/2016 12:00:00 AM | 2 | 340 | 367 |\n",
       "| 1503960366 | 4/17/2016 12:00:00 AM | 1 | 700 | 712 |\n",
       "| 1503960366 | 4/19/2016 12:00:00 AM | 1 | 304 | 320 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         SleepDay              TotalSleepRecords TotalMinutesAsleep\n",
       "1 1503960366 4/12/2016 12:00:00 AM 1                 327               \n",
       "2 1503960366 4/13/2016 12:00:00 AM 2                 384               \n",
       "3 1503960366 4/15/2016 12:00:00 AM 1                 412               \n",
       "4 1503960366 4/16/2016 12:00:00 AM 2                 340               \n",
       "5 1503960366 4/17/2016 12:00:00 AM 1                 700               \n",
       "6 1503960366 4/19/2016 12:00:00 AM 1                 304               \n",
       "  TotalTimeInBed\n",
       "1 346           \n",
       "2 407           \n",
       "3 442           \n",
       "4 367           \n",
       "5 712           \n",
       "6 320           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>Date</th><th scope=col>WeightKg</th><th scope=col>WeightPounds</th><th scope=col>Fat</th><th scope=col>BMI</th><th scope=col>IsManualReport</th><th scope=col>LogId</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>5/2/2016 11:59:59 PM </td><td> 52.6</td><td>115.9631</td><td>22</td><td>22.65</td><td> TRUE</td><td>1.462234e+12</td></tr>\n",
       "\t<tr><td>1503960366</td><td>5/3/2016 11:59:59 PM </td><td> 52.6</td><td>115.9631</td><td>NA</td><td>22.65</td><td> TRUE</td><td>1.462320e+12</td></tr>\n",
       "\t<tr><td>1927972279</td><td>4/13/2016 1:08:52 AM </td><td>133.5</td><td>294.3171</td><td>NA</td><td>47.54</td><td>FALSE</td><td>1.460510e+12</td></tr>\n",
       "\t<tr><td>2873212765</td><td>4/21/2016 11:59:59 PM</td><td> 56.7</td><td>125.0021</td><td>NA</td><td>21.45</td><td> TRUE</td><td>1.461283e+12</td></tr>\n",
       "\t<tr><td>2873212765</td><td>5/12/2016 11:59:59 PM</td><td> 57.3</td><td>126.3249</td><td>NA</td><td>21.69</td><td> TRUE</td><td>1.463098e+12</td></tr>\n",
       "\t<tr><td>4319703577</td><td>4/17/2016 11:59:59 PM</td><td> 72.4</td><td>159.6147</td><td>25</td><td>27.45</td><td> TRUE</td><td>1.460938e+12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " Id & Date & WeightKg & WeightPounds & Fat & BMI & IsManualReport & LogId\\\\\n",
       " <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <lgl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 5/2/2016 11:59:59 PM  &  52.6 & 115.9631 & 22 & 22.65 &  TRUE & 1.462234e+12\\\\\n",
       "\t 1503960366 & 5/3/2016 11:59:59 PM  &  52.6 & 115.9631 & NA & 22.65 &  TRUE & 1.462320e+12\\\\\n",
       "\t 1927972279 & 4/13/2016 1:08:52 AM  & 133.5 & 294.3171 & NA & 47.54 & FALSE & 1.460510e+12\\\\\n",
       "\t 2873212765 & 4/21/2016 11:59:59 PM &  56.7 & 125.0021 & NA & 21.45 &  TRUE & 1.461283e+12\\\\\n",
       "\t 2873212765 & 5/12/2016 11:59:59 PM &  57.3 & 126.3249 & NA & 21.69 &  TRUE & 1.463098e+12\\\\\n",
       "\t 4319703577 & 4/17/2016 11:59:59 PM &  72.4 & 159.6147 & 25 & 27.45 &  TRUE & 1.460938e+12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 8\n",
       "\n",
       "| Id &lt;dbl&gt; | Date &lt;chr&gt; | WeightKg &lt;dbl&gt; | WeightPounds &lt;dbl&gt; | Fat &lt;dbl&gt; | BMI &lt;dbl&gt; | IsManualReport &lt;lgl&gt; | LogId &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1503960366 | 5/2/2016 11:59:59 PM  |  52.6 | 115.9631 | 22 | 22.65 |  TRUE | 1.462234e+12 |\n",
       "| 1503960366 | 5/3/2016 11:59:59 PM  |  52.6 | 115.9631 | NA | 22.65 |  TRUE | 1.462320e+12 |\n",
       "| 1927972279 | 4/13/2016 1:08:52 AM  | 133.5 | 294.3171 | NA | 47.54 | FALSE | 1.460510e+12 |\n",
       "| 2873212765 | 4/21/2016 11:59:59 PM |  56.7 | 125.0021 | NA | 21.45 |  TRUE | 1.461283e+12 |\n",
       "| 2873212765 | 5/12/2016 11:59:59 PM |  57.3 | 126.3249 | NA | 21.69 |  TRUE | 1.463098e+12 |\n",
       "| 4319703577 | 4/17/2016 11:59:59 PM |  72.4 | 159.6147 | 25 | 27.45 |  TRUE | 1.460938e+12 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Date                  WeightKg WeightPounds Fat BMI  \n",
       "1 1503960366 5/2/2016 11:59:59 PM   52.6    115.9631     22  22.65\n",
       "2 1503960366 5/3/2016 11:59:59 PM   52.6    115.9631     NA  22.65\n",
       "3 1927972279 4/13/2016 1:08:52 AM  133.5    294.3171     NA  47.54\n",
       "4 2873212765 4/21/2016 11:59:59 PM  56.7    125.0021     NA  21.45\n",
       "5 2873212765 5/12/2016 11:59:59 PM  57.3    126.3249     NA  21.69\n",
       "6 4319703577 4/17/2016 11:59:59 PM  72.4    159.6147     25  27.45\n",
       "  IsManualReport LogId       \n",
       "1  TRUE          1.462234e+12\n",
       "2  TRUE          1.462320e+12\n",
       "3 FALSE          1.460510e+12\n",
       "4  TRUE          1.461283e+12\n",
       "5  TRUE          1.463098e+12\n",
       "6  TRUE          1.460938e+12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(daily_activity)\n",
    "head(heartrate_seconds)\n",
    "head(sleepDay)\n",
    "head(weightLog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e449e5b",
   "metadata": {
    "papermill": {
     "duration": 0.014339,
     "end_time": "2023-11-29T09:28:37.062798",
     "exception": false,
     "start_time": "2023-11-29T09:28:37.048459",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "I noticed some problems with the data. So, first I am going to correct them, before going to the analysis process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3174c42c",
   "metadata": {
    "papermill": {
     "duration": 0.014096,
     "end_time": "2023-11-29T09:28:37.091123",
     "exception": false,
     "start_time": "2023-11-29T09:28:37.077027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "988610f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:37.124229Z",
     "iopub.status.busy": "2023-11-29T09:28:37.122072Z",
     "iopub.status.idle": "2023-11-29T09:28:44.926390Z",
     "shell.execute_reply": "2023-11-29T09:28:44.924511Z"
    },
    "papermill": {
     "duration": 7.823957,
     "end_time": "2023-11-29T09:28:44.929327",
     "exception": false,
     "start_time": "2023-11-29T09:28:37.105370",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# daily_activity\n",
    "daily_activity$ActivityDate <- as.POSIXct(daily_activity$ActivityDate, format = \"%m/%d/%Y\", tz=Sys.timezone())\n",
    "daily_activity$date <- format(daily_activity$ActivityDate, format = \"%m/%d/%y\")\n",
    "#heartrate_seconds\n",
    "heartrate_seconds$Time <- as.POSIXct(heartrate_seconds$Time, format = \"%m/%d/%Y %H:%M:%S %p\",tz= Sys.timezone())\n",
    "heartrate_seconds$date <- format(heartrate_seconds$Time , format = \"%m/%d/%y\")\n",
    "heartrate_seconds$time <- format(heartrate_seconds$Time , format = \"%H:%M:%S\")\n",
    "#sleepDay\n",
    "sleepDay$SleepDay <- as.POSIXct(sleepDay$SleepDay, format = \"%m/%d/%Y %H:%M:%S %p\",tz= Sys.timezone())\n",
    "sleepDay$date <- format(sleepDay$SleepDay , format = \"%m/%d/%y\")\n",
    "sleepDay$time <- format(sleepDay$SleepDay , format = \"%H:%M:%S\")\n",
    "#weightLog\n",
    "weightLog$Date <- as.POSIXct(weightLog$Date, format = \"%m/%d/%Y %H:%M:%S %p\",tz= Sys.timezone())\n",
    "weightLog$date <- format(weightLog$Date , format = \"%m/%d/%y\")\n",
    "weightLog$time <- format(weightLog$Date , format = \"%H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03a2deef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:44.962182Z",
     "iopub.status.busy": "2023-11-29T09:28:44.960484Z",
     "iopub.status.idle": "2023-11-29T09:28:45.078989Z",
     "shell.execute_reply": "2023-11-29T09:28:45.077099Z"
    },
    "papermill": {
     "duration": 0.137973,
     "end_time": "2023-11-29T09:28:45.081592",
     "exception": false,
     "start_time": "2023-11-29T09:28:44.943619",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 16</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>ActivityDate</th><th scope=col>TotalSteps</th><th scope=col>TotalDistance</th><th scope=col>TrackerDistance</th><th scope=col>LoggedActivitiesDistance</th><th scope=col>VeryActiveDistance</th><th scope=col>ModeratelyActiveDistance</th><th scope=col>LightActiveDistance</th><th scope=col>SedentaryActiveDistance</th><th scope=col>VeryActiveMinutes</th><th scope=col>FairlyActiveMinutes</th><th scope=col>LightlyActiveMinutes</th><th scope=col>SedentaryMinutes</th><th scope=col>Calories</th><th scope=col>date</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>2016-04-12</td><td>13162</td><td>8.50</td><td>8.50</td><td>0</td><td>1.88</td><td>0.55</td><td>6.06</td><td>0</td><td>25</td><td>13</td><td>328</td><td> 728</td><td>1985</td><td>04/12/16</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-13</td><td>10735</td><td>6.97</td><td>6.97</td><td>0</td><td>1.57</td><td>0.69</td><td>4.71</td><td>0</td><td>21</td><td>19</td><td>217</td><td> 776</td><td>1797</td><td>04/13/16</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-14</td><td>10460</td><td>6.74</td><td>6.74</td><td>0</td><td>2.44</td><td>0.40</td><td>3.91</td><td>0</td><td>30</td><td>11</td><td>181</td><td>1218</td><td>1776</td><td>04/14/16</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-15</td><td> 9762</td><td>6.28</td><td>6.28</td><td>0</td><td>2.14</td><td>1.26</td><td>2.83</td><td>0</td><td>29</td><td>34</td><td>209</td><td> 726</td><td>1745</td><td>04/15/16</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-16</td><td>12669</td><td>8.16</td><td>8.16</td><td>0</td><td>2.71</td><td>0.41</td><td>5.04</td><td>0</td><td>36</td><td>10</td><td>221</td><td> 773</td><td>1863</td><td>04/16/16</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-17</td><td> 9705</td><td>6.48</td><td>6.48</td><td>0</td><td>3.19</td><td>0.78</td><td>2.51</td><td>0</td><td>38</td><td>20</td><td>164</td><td> 539</td><td>1728</td><td>04/17/16</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 16\n",
       "\\begin{tabular}{llllllllllllllll}\n",
       " Id & ActivityDate & TotalSteps & TotalDistance & TrackerDistance & LoggedActivitiesDistance & VeryActiveDistance & ModeratelyActiveDistance & LightActiveDistance & SedentaryActiveDistance & VeryActiveMinutes & FairlyActiveMinutes & LightlyActiveMinutes & SedentaryMinutes & Calories & date\\\\\n",
       " <dbl> & <dttm> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 2016-04-12 & 13162 & 8.50 & 8.50 & 0 & 1.88 & 0.55 & 6.06 & 0 & 25 & 13 & 328 &  728 & 1985 & 04/12/16\\\\\n",
       "\t 1503960366 & 2016-04-13 & 10735 & 6.97 & 6.97 & 0 & 1.57 & 0.69 & 4.71 & 0 & 21 & 19 & 217 &  776 & 1797 & 04/13/16\\\\\n",
       "\t 1503960366 & 2016-04-14 & 10460 & 6.74 & 6.74 & 0 & 2.44 & 0.40 & 3.91 & 0 & 30 & 11 & 181 & 1218 & 1776 & 04/14/16\\\\\n",
       "\t 1503960366 & 2016-04-15 &  9762 & 6.28 & 6.28 & 0 & 2.14 & 1.26 & 2.83 & 0 & 29 & 34 & 209 &  726 & 1745 & 04/15/16\\\\\n",
       "\t 1503960366 & 2016-04-16 & 12669 & 8.16 & 8.16 & 0 & 2.71 & 0.41 & 5.04 & 0 & 36 & 10 & 221 &  773 & 1863 & 04/16/16\\\\\n",
       "\t 1503960366 & 2016-04-17 &  9705 & 6.48 & 6.48 & 0 & 3.19 & 0.78 & 2.51 & 0 & 38 & 20 & 164 &  539 & 1728 & 04/17/16\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 16\n",
       "\n",
       "| Id &lt;dbl&gt; | ActivityDate &lt;dttm&gt; | TotalSteps &lt;dbl&gt; | TotalDistance &lt;dbl&gt; | TrackerDistance &lt;dbl&gt; | LoggedActivitiesDistance &lt;dbl&gt; | VeryActiveDistance &lt;dbl&gt; | ModeratelyActiveDistance &lt;dbl&gt; | LightActiveDistance &lt;dbl&gt; | SedentaryActiveDistance &lt;dbl&gt; | VeryActiveMinutes &lt;dbl&gt; | FairlyActiveMinutes &lt;dbl&gt; | LightlyActiveMinutes &lt;dbl&gt; | SedentaryMinutes &lt;dbl&gt; | Calories &lt;dbl&gt; | date &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1503960366 | 2016-04-12 | 13162 | 8.50 | 8.50 | 0 | 1.88 | 0.55 | 6.06 | 0 | 25 | 13 | 328 |  728 | 1985 | 04/12/16 |\n",
       "| 1503960366 | 2016-04-13 | 10735 | 6.97 | 6.97 | 0 | 1.57 | 0.69 | 4.71 | 0 | 21 | 19 | 217 |  776 | 1797 | 04/13/16 |\n",
       "| 1503960366 | 2016-04-14 | 10460 | 6.74 | 6.74 | 0 | 2.44 | 0.40 | 3.91 | 0 | 30 | 11 | 181 | 1218 | 1776 | 04/14/16 |\n",
       "| 1503960366 | 2016-04-15 |  9762 | 6.28 | 6.28 | 0 | 2.14 | 1.26 | 2.83 | 0 | 29 | 34 | 209 |  726 | 1745 | 04/15/16 |\n",
       "| 1503960366 | 2016-04-16 | 12669 | 8.16 | 8.16 | 0 | 2.71 | 0.41 | 5.04 | 0 | 36 | 10 | 221 |  773 | 1863 | 04/16/16 |\n",
       "| 1503960366 | 2016-04-17 |  9705 | 6.48 | 6.48 | 0 | 3.19 | 0.78 | 2.51 | 0 | 38 | 20 | 164 |  539 | 1728 | 04/17/16 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         ActivityDate TotalSteps TotalDistance TrackerDistance\n",
       "1 1503960366 2016-04-12   13162      8.50          8.50           \n",
       "2 1503960366 2016-04-13   10735      6.97          6.97           \n",
       "3 1503960366 2016-04-14   10460      6.74          6.74           \n",
       "4 1503960366 2016-04-15    9762      6.28          6.28           \n",
       "5 1503960366 2016-04-16   12669      8.16          8.16           \n",
       "6 1503960366 2016-04-17    9705      6.48          6.48           \n",
       "  LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance\n",
       "1 0                        1.88               0.55                    \n",
       "2 0                        1.57               0.69                    \n",
       "3 0                        2.44               0.40                    \n",
       "4 0                        2.14               1.26                    \n",
       "5 0                        2.71               0.41                    \n",
       "6 0                        3.19               0.78                    \n",
       "  LightActiveDistance SedentaryActiveDistance VeryActiveMinutes\n",
       "1 6.06                0                       25               \n",
       "2 4.71                0                       21               \n",
       "3 3.91                0                       30               \n",
       "4 2.83                0                       29               \n",
       "5 5.04                0                       36               \n",
       "6 2.51                0                       38               \n",
       "  FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories date    \n",
       "1 13                  328                   728             1985     04/12/16\n",
       "2 19                  217                   776             1797     04/13/16\n",
       "3 11                  181                  1218             1776     04/14/16\n",
       "4 34                  209                   726             1745     04/15/16\n",
       "5 10                  221                   773             1863     04/16/16\n",
       "6 20                  164                   539             1728     04/17/16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>Time</th><th scope=col>Value</th><th scope=col>date</th><th scope=col>time</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2022484408</td><td>2016-04-12 07:21:00</td><td> 97</td><td>04/12/16</td><td>07:21:00</td></tr>\n",
       "\t<tr><td>2022484408</td><td>2016-04-12 07:21:05</td><td>102</td><td>04/12/16</td><td>07:21:05</td></tr>\n",
       "\t<tr><td>2022484408</td><td>2016-04-12 07:21:10</td><td>105</td><td>04/12/16</td><td>07:21:10</td></tr>\n",
       "\t<tr><td>2022484408</td><td>2016-04-12 07:21:20</td><td>103</td><td>04/12/16</td><td>07:21:20</td></tr>\n",
       "\t<tr><td>2022484408</td><td>2016-04-12 07:21:25</td><td>101</td><td>04/12/16</td><td>07:21:25</td></tr>\n",
       "\t<tr><td>2022484408</td><td>2016-04-12 07:22:05</td><td> 95</td><td>04/12/16</td><td>07:22:05</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " Id & Time & Value & date & time\\\\\n",
       " <dbl> & <dttm> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 2022484408 & 2016-04-12 07:21:00 &  97 & 04/12/16 & 07:21:00\\\\\n",
       "\t 2022484408 & 2016-04-12 07:21:05 & 102 & 04/12/16 & 07:21:05\\\\\n",
       "\t 2022484408 & 2016-04-12 07:21:10 & 105 & 04/12/16 & 07:21:10\\\\\n",
       "\t 2022484408 & 2016-04-12 07:21:20 & 103 & 04/12/16 & 07:21:20\\\\\n",
       "\t 2022484408 & 2016-04-12 07:21:25 & 101 & 04/12/16 & 07:21:25\\\\\n",
       "\t 2022484408 & 2016-04-12 07:22:05 &  95 & 04/12/16 & 07:22:05\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 5\n",
       "\n",
       "| Id &lt;dbl&gt; | Time &lt;dttm&gt; | Value &lt;dbl&gt; | date &lt;chr&gt; | time &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 2022484408 | 2016-04-12 07:21:00 |  97 | 04/12/16 | 07:21:00 |\n",
       "| 2022484408 | 2016-04-12 07:21:05 | 102 | 04/12/16 | 07:21:05 |\n",
       "| 2022484408 | 2016-04-12 07:21:10 | 105 | 04/12/16 | 07:21:10 |\n",
       "| 2022484408 | 2016-04-12 07:21:20 | 103 | 04/12/16 | 07:21:20 |\n",
       "| 2022484408 | 2016-04-12 07:21:25 | 101 | 04/12/16 | 07:21:25 |\n",
       "| 2022484408 | 2016-04-12 07:22:05 |  95 | 04/12/16 | 07:22:05 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Time                Value date     time    \n",
       "1 2022484408 2016-04-12 07:21:00  97   04/12/16 07:21:00\n",
       "2 2022484408 2016-04-12 07:21:05 102   04/12/16 07:21:05\n",
       "3 2022484408 2016-04-12 07:21:10 105   04/12/16 07:21:10\n",
       "4 2022484408 2016-04-12 07:21:20 103   04/12/16 07:21:20\n",
       "5 2022484408 2016-04-12 07:21:25 101   04/12/16 07:21:25\n",
       "6 2022484408 2016-04-12 07:22:05  95   04/12/16 07:22:05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>SleepDay</th><th scope=col>TotalSleepRecords</th><th scope=col>TotalMinutesAsleep</th><th scope=col>TotalTimeInBed</th><th scope=col>date</th><th scope=col>time</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>2016-04-12 12:00:00</td><td>1</td><td>327</td><td>346</td><td>04/12/16</td><td>12:00:00</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-13 12:00:00</td><td>2</td><td>384</td><td>407</td><td>04/13/16</td><td>12:00:00</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-15 12:00:00</td><td>1</td><td>412</td><td>442</td><td>04/15/16</td><td>12:00:00</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-16 12:00:00</td><td>2</td><td>340</td><td>367</td><td>04/16/16</td><td>12:00:00</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-17 12:00:00</td><td>1</td><td>700</td><td>712</td><td>04/17/16</td><td>12:00:00</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-04-19 12:00:00</td><td>1</td><td>304</td><td>320</td><td>04/19/16</td><td>12:00:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " Id & SleepDay & TotalSleepRecords & TotalMinutesAsleep & TotalTimeInBed & date & time\\\\\n",
       " <dbl> & <dttm> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 2016-04-12 12:00:00 & 1 & 327 & 346 & 04/12/16 & 12:00:00\\\\\n",
       "\t 1503960366 & 2016-04-13 12:00:00 & 2 & 384 & 407 & 04/13/16 & 12:00:00\\\\\n",
       "\t 1503960366 & 2016-04-15 12:00:00 & 1 & 412 & 442 & 04/15/16 & 12:00:00\\\\\n",
       "\t 1503960366 & 2016-04-16 12:00:00 & 2 & 340 & 367 & 04/16/16 & 12:00:00\\\\\n",
       "\t 1503960366 & 2016-04-17 12:00:00 & 1 & 700 & 712 & 04/17/16 & 12:00:00\\\\\n",
       "\t 1503960366 & 2016-04-19 12:00:00 & 1 & 304 & 320 & 04/19/16 & 12:00:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 7\n",
       "\n",
       "| Id &lt;dbl&gt; | SleepDay &lt;dttm&gt; | TotalSleepRecords &lt;dbl&gt; | TotalMinutesAsleep &lt;dbl&gt; | TotalTimeInBed &lt;dbl&gt; | date &lt;chr&gt; | time &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1503960366 | 2016-04-12 12:00:00 | 1 | 327 | 346 | 04/12/16 | 12:00:00 |\n",
       "| 1503960366 | 2016-04-13 12:00:00 | 2 | 384 | 407 | 04/13/16 | 12:00:00 |\n",
       "| 1503960366 | 2016-04-15 12:00:00 | 1 | 412 | 442 | 04/15/16 | 12:00:00 |\n",
       "| 1503960366 | 2016-04-16 12:00:00 | 2 | 340 | 367 | 04/16/16 | 12:00:00 |\n",
       "| 1503960366 | 2016-04-17 12:00:00 | 1 | 700 | 712 | 04/17/16 | 12:00:00 |\n",
       "| 1503960366 | 2016-04-19 12:00:00 | 1 | 304 | 320 | 04/19/16 | 12:00:00 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         SleepDay            TotalSleepRecords TotalMinutesAsleep\n",
       "1 1503960366 2016-04-12 12:00:00 1                 327               \n",
       "2 1503960366 2016-04-13 12:00:00 2                 384               \n",
       "3 1503960366 2016-04-15 12:00:00 1                 412               \n",
       "4 1503960366 2016-04-16 12:00:00 2                 340               \n",
       "5 1503960366 2016-04-17 12:00:00 1                 700               \n",
       "6 1503960366 2016-04-19 12:00:00 1                 304               \n",
       "  TotalTimeInBed date     time    \n",
       "1 346            04/12/16 12:00:00\n",
       "2 407            04/13/16 12:00:00\n",
       "3 442            04/15/16 12:00:00\n",
       "4 367            04/16/16 12:00:00\n",
       "5 712            04/17/16 12:00:00\n",
       "6 320            04/19/16 12:00:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>Date</th><th scope=col>WeightKg</th><th scope=col>WeightPounds</th><th scope=col>Fat</th><th scope=col>BMI</th><th scope=col>IsManualReport</th><th scope=col>LogId</th><th scope=col>date</th><th scope=col>time</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>2016-05-02 11:59:59</td><td> 52.6</td><td>115.9631</td><td>22</td><td>22.65</td><td> TRUE</td><td>1.462234e+12</td><td>05/02/16</td><td>11:59:59</td></tr>\n",
       "\t<tr><td>1503960366</td><td>2016-05-03 11:59:59</td><td> 52.6</td><td>115.9631</td><td>NA</td><td>22.65</td><td> TRUE</td><td>1.462320e+12</td><td>05/03/16</td><td>11:59:59</td></tr>\n",
       "\t<tr><td>1927972279</td><td>2016-04-13 01:08:52</td><td>133.5</td><td>294.3171</td><td>NA</td><td>47.54</td><td>FALSE</td><td>1.460510e+12</td><td>04/13/16</td><td>01:08:52</td></tr>\n",
       "\t<tr><td>2873212765</td><td>2016-04-21 11:59:59</td><td> 56.7</td><td>125.0021</td><td>NA</td><td>21.45</td><td> TRUE</td><td>1.461283e+12</td><td>04/21/16</td><td>11:59:59</td></tr>\n",
       "\t<tr><td>2873212765</td><td>2016-05-12 11:59:59</td><td> 57.3</td><td>126.3249</td><td>NA</td><td>21.69</td><td> TRUE</td><td>1.463098e+12</td><td>05/12/16</td><td>11:59:59</td></tr>\n",
       "\t<tr><td>4319703577</td><td>2016-04-17 11:59:59</td><td> 72.4</td><td>159.6147</td><td>25</td><td>27.45</td><td> TRUE</td><td>1.460938e+12</td><td>04/17/16</td><td>11:59:59</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " Id & Date & WeightKg & WeightPounds & Fat & BMI & IsManualReport & LogId & date & time\\\\\n",
       " <dbl> & <dttm> & <dbl> & <dbl> & <dbl> & <dbl> & <lgl> & <dbl> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 2016-05-02 11:59:59 &  52.6 & 115.9631 & 22 & 22.65 &  TRUE & 1.462234e+12 & 05/02/16 & 11:59:59\\\\\n",
       "\t 1503960366 & 2016-05-03 11:59:59 &  52.6 & 115.9631 & NA & 22.65 &  TRUE & 1.462320e+12 & 05/03/16 & 11:59:59\\\\\n",
       "\t 1927972279 & 2016-04-13 01:08:52 & 133.5 & 294.3171 & NA & 47.54 & FALSE & 1.460510e+12 & 04/13/16 & 01:08:52\\\\\n",
       "\t 2873212765 & 2016-04-21 11:59:59 &  56.7 & 125.0021 & NA & 21.45 &  TRUE & 1.461283e+12 & 04/21/16 & 11:59:59\\\\\n",
       "\t 2873212765 & 2016-05-12 11:59:59 &  57.3 & 126.3249 & NA & 21.69 &  TRUE & 1.463098e+12 & 05/12/16 & 11:59:59\\\\\n",
       "\t 4319703577 & 2016-04-17 11:59:59 &  72.4 & 159.6147 & 25 & 27.45 &  TRUE & 1.460938e+12 & 04/17/16 & 11:59:59\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 10\n",
       "\n",
       "| Id &lt;dbl&gt; | Date &lt;dttm&gt; | WeightKg &lt;dbl&gt; | WeightPounds &lt;dbl&gt; | Fat &lt;dbl&gt; | BMI &lt;dbl&gt; | IsManualReport &lt;lgl&gt; | LogId &lt;dbl&gt; | date &lt;chr&gt; | time &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1503960366 | 2016-05-02 11:59:59 |  52.6 | 115.9631 | 22 | 22.65 |  TRUE | 1.462234e+12 | 05/02/16 | 11:59:59 |\n",
       "| 1503960366 | 2016-05-03 11:59:59 |  52.6 | 115.9631 | NA | 22.65 |  TRUE | 1.462320e+12 | 05/03/16 | 11:59:59 |\n",
       "| 1927972279 | 2016-04-13 01:08:52 | 133.5 | 294.3171 | NA | 47.54 | FALSE | 1.460510e+12 | 04/13/16 | 01:08:52 |\n",
       "| 2873212765 | 2016-04-21 11:59:59 |  56.7 | 125.0021 | NA | 21.45 |  TRUE | 1.461283e+12 | 04/21/16 | 11:59:59 |\n",
       "| 2873212765 | 2016-05-12 11:59:59 |  57.3 | 126.3249 | NA | 21.69 |  TRUE | 1.463098e+12 | 05/12/16 | 11:59:59 |\n",
       "| 4319703577 | 2016-04-17 11:59:59 |  72.4 | 159.6147 | 25 | 27.45 |  TRUE | 1.460938e+12 | 04/17/16 | 11:59:59 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Date                WeightKg WeightPounds Fat BMI   IsManualReport\n",
       "1 1503960366 2016-05-02 11:59:59  52.6    115.9631     22  22.65  TRUE         \n",
       "2 1503960366 2016-05-03 11:59:59  52.6    115.9631     NA  22.65  TRUE         \n",
       "3 1927972279 2016-04-13 01:08:52 133.5    294.3171     NA  47.54 FALSE         \n",
       "4 2873212765 2016-04-21 11:59:59  56.7    125.0021     NA  21.45  TRUE         \n",
       "5 2873212765 2016-05-12 11:59:59  57.3    126.3249     NA  21.69  TRUE         \n",
       "6 4319703577 2016-04-17 11:59:59  72.4    159.6147     25  27.45  TRUE         \n",
       "  LogId        date     time    \n",
       "1 1.462234e+12 05/02/16 11:59:59\n",
       "2 1.462320e+12 05/03/16 11:59:59\n",
       "3 1.460510e+12 04/13/16 01:08:52\n",
       "4 1.461283e+12 04/21/16 11:59:59\n",
       "5 1.463098e+12 05/12/16 11:59:59\n",
       "6 1.460938e+12 04/17/16 11:59:59"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(daily_activity)\n",
    "head(heartrate_seconds)\n",
    "head(sleepDay)\n",
    "head(weightLog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a7b0560",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:45.116729Z",
     "iopub.status.busy": "2023-11-29T09:28:45.115038Z",
     "iopub.status.idle": "2023-11-29T09:28:45.230606Z",
     "shell.execute_reply": "2023-11-29T09:28:45.228752Z"
    },
    "papermill": {
     "duration": 0.136097,
     "end_time": "2023-11-29T09:28:45.233451",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.097354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "33"
      ],
      "text/latex": [
       "33"
      ],
      "text/markdown": [
       "33"
      ],
      "text/plain": [
       "[1] 33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "14"
      ],
      "text/latex": [
       "14"
      ],
      "text/markdown": [
       "14"
      ],
      "text/plain": [
       "[1] 14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "24"
      ],
      "text/latex": [
       "24"
      ],
      "text/markdown": [
       "24"
      ],
      "text/plain": [
       "[1] 24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "8"
      ],
      "text/latex": [
       "8"
      ],
      "text/markdown": [
       "8"
      ],
      "text/plain": [
       "[1] 8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_distinct(daily_activity$Id)\n",
    "n_distinct(heartrate_seconds$Id)\n",
    "n_distinct(sleepDay$Id)\n",
    "n_distinct(weightLog$Id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a6db3d5",
   "metadata": {
    "papermill": {
     "duration": 0.016241,
     "end_time": "2023-11-29T09:28:45.266191",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.249950",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From above results, it is clear the data of weightLog and hearrate_seconds is too less.I am not using them for any analysis and insights. Beacause insights from insufficient data may cause unreliable and misleading conclusions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13d399a",
   "metadata": {
    "papermill": {
     "duration": 0.016154,
     "end_time": "2023-11-29T09:28:45.298440",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.282286",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Summary of each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5c8d1f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:45.334675Z",
     "iopub.status.busy": "2023-11-29T09:28:45.333016Z",
     "iopub.status.idle": "2023-11-29T09:28:45.413508Z",
     "shell.execute_reply": "2023-11-29T09:28:45.411573Z"
    },
    "papermill": {
     "duration": 0.101293,
     "end_time": "2023-11-29T09:28:45.415977",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.314684",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   TotalSteps    TotalDistance    VeryActiveMinutes LightlyActiveMinutes\n",
       " Min.   :    0   Min.   : 0.000   Min.   :  0.00    Min.   :  0.0       \n",
       " 1st Qu.: 3790   1st Qu.: 2.620   1st Qu.:  0.00    1st Qu.:127.0       \n",
       " Median : 7406   Median : 5.245   Median :  4.00    Median :199.0       \n",
       " Mean   : 7638   Mean   : 5.490   Mean   : 21.16    Mean   :192.8       \n",
       " 3rd Qu.:10727   3rd Qu.: 7.713   3rd Qu.: 32.00    3rd Qu.:264.0       \n",
       " Max.   :36019   Max.   :28.030   Max.   :210.00    Max.   :518.0       \n",
       " SedentaryMinutes    Calories   \n",
       " Min.   :   0.0   Min.   :   0  \n",
       " 1st Qu.: 729.8   1st Qu.:1828  \n",
       " Median :1057.5   Median :2134  \n",
       " Mean   : 991.2   Mean   :2304  \n",
       " 3rd Qu.:1229.5   3rd Qu.:2793  \n",
       " Max.   :1440.0   Max.   :4900  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       " TotalSleepRecords TotalMinutesAsleep TotalTimeInBed \n",
       " Min.   :1.000     Min.   : 58.0      Min.   : 61.0  \n",
       " 1st Qu.:1.000     1st Qu.:361.0      1st Qu.:403.0  \n",
       " Median :1.000     Median :433.0      Median :463.0  \n",
       " Mean   :1.119     Mean   :419.5      Mean   :458.6  \n",
       " 3rd Qu.:1.000     3rd Qu.:490.0      3rd Qu.:526.0  \n",
       " Max.   :3.000     Max.   :796.0      Max.   :961.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# daily_activity\n",
    "daily_activity %>%\n",
    "    select(TotalSteps,TotalDistance,VeryActiveMinutes,LightlyActiveMinutes,SedentaryMinutes,Calories) %>%\n",
    "    summary()\n",
    "#sleepDay\n",
    "sleepDay %>%\n",
    "    select(TotalSleepRecords,TotalMinutesAsleep,TotalTimeInBed) %>%\n",
    "    summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a453282",
   "metadata": {
    "papermill": {
     "duration": 0.016581,
     "end_time": "2023-11-29T09:28:45.449518",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.432937",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Basic insights from the summary**\n",
    "\n",
    "* Most the time participants are sedentary\n",
    "* Almost every participant having sleep once in a day\n",
    "* The average calories burnt by participants is 2304"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a039f9",
   "metadata": {
    "papermill": {
     "duration": 0.016612,
     "end_time": "2023-11-29T09:28:45.482904",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.466292",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Merging data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e2dcf9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:45.519897Z",
     "iopub.status.busy": "2023-11-29T09:28:45.518066Z",
     "iopub.status.idle": "2023-11-29T09:28:45.569775Z",
     "shell.execute_reply": "2023-11-29T09:28:45.567755Z"
    },
    "papermill": {
     "duration": 0.07293,
     "end_time": "2023-11-29T09:28:45.572330",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.499400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 21</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Id</th><th scope=col>date</th><th scope=col>ActivityDate</th><th scope=col>TotalSteps</th><th scope=col>TotalDistance</th><th scope=col>TrackerDistance</th><th scope=col>LoggedActivitiesDistance</th><th scope=col>VeryActiveDistance</th><th scope=col>ModeratelyActiveDistance</th><th scope=col>LightActiveDistance</th><th scope=col>⋯</th><th scope=col>VeryActiveMinutes</th><th scope=col>FairlyActiveMinutes</th><th scope=col>LightlyActiveMinutes</th><th scope=col>SedentaryMinutes</th><th scope=col>Calories</th><th scope=col>SleepDay</th><th scope=col>TotalSleepRecords</th><th scope=col>TotalMinutesAsleep</th><th scope=col>TotalTimeInBed</th><th scope=col>time</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dttm&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1503960366</td><td>04/12/16</td><td>2016-04-12</td><td>13162</td><td>8.50</td><td>8.50</td><td>0</td><td>1.88</td><td>0.55</td><td>6.06</td><td>⋯</td><td>25</td><td>13</td><td>328</td><td>728</td><td>1985</td><td>2016-04-12 12:00:00</td><td>1</td><td>327</td><td>346</td><td>12:00:00</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1503960366</td><td>04/13/16</td><td>2016-04-13</td><td>10735</td><td>6.97</td><td>6.97</td><td>0</td><td>1.57</td><td>0.69</td><td>4.71</td><td>⋯</td><td>21</td><td>19</td><td>217</td><td>776</td><td>1797</td><td>2016-04-13 12:00:00</td><td>2</td><td>384</td><td>407</td><td>12:00:00</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1503960366</td><td>04/15/16</td><td>2016-04-15</td><td> 9762</td><td>6.28</td><td>6.28</td><td>0</td><td>2.14</td><td>1.26</td><td>2.83</td><td>⋯</td><td>29</td><td>34</td><td>209</td><td>726</td><td>1745</td><td>2016-04-15 12:00:00</td><td>1</td><td>412</td><td>442</td><td>12:00:00</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1503960366</td><td>04/16/16</td><td>2016-04-16</td><td>12669</td><td>8.16</td><td>8.16</td><td>0</td><td>2.71</td><td>0.41</td><td>5.04</td><td>⋯</td><td>36</td><td>10</td><td>221</td><td>773</td><td>1863</td><td>2016-04-16 12:00:00</td><td>2</td><td>340</td><td>367</td><td>12:00:00</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1503960366</td><td>04/17/16</td><td>2016-04-17</td><td> 9705</td><td>6.48</td><td>6.48</td><td>0</td><td>3.19</td><td>0.78</td><td>2.51</td><td>⋯</td><td>38</td><td>20</td><td>164</td><td>539</td><td>1728</td><td>2016-04-17 12:00:00</td><td>1</td><td>700</td><td>712</td><td>12:00:00</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1503960366</td><td>04/19/16</td><td>2016-04-19</td><td>15506</td><td>9.88</td><td>9.88</td><td>0</td><td>3.53</td><td>1.32</td><td>5.03</td><td>⋯</td><td>50</td><td>31</td><td>264</td><td>775</td><td>2035</td><td>2016-04-19 12:00:00</td><td>1</td><td>304</td><td>320</td><td>12:00:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 21\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Id & date & ActivityDate & TotalSteps & TotalDistance & TrackerDistance & LoggedActivitiesDistance & VeryActiveDistance & ModeratelyActiveDistance & LightActiveDistance & ⋯ & VeryActiveMinutes & FairlyActiveMinutes & LightlyActiveMinutes & SedentaryMinutes & Calories & SleepDay & TotalSleepRecords & TotalMinutesAsleep & TotalTimeInBed & time\\\\\n",
       "  & <dbl> & <chr> & <dttm> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dttm> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 1503960366 & 04/12/16 & 2016-04-12 & 13162 & 8.50 & 8.50 & 0 & 1.88 & 0.55 & 6.06 & ⋯ & 25 & 13 & 328 & 728 & 1985 & 2016-04-12 12:00:00 & 1 & 327 & 346 & 12:00:00\\\\\n",
       "\t2 & 1503960366 & 04/13/16 & 2016-04-13 & 10735 & 6.97 & 6.97 & 0 & 1.57 & 0.69 & 4.71 & ⋯ & 21 & 19 & 217 & 776 & 1797 & 2016-04-13 12:00:00 & 2 & 384 & 407 & 12:00:00\\\\\n",
       "\t3 & 1503960366 & 04/15/16 & 2016-04-15 &  9762 & 6.28 & 6.28 & 0 & 2.14 & 1.26 & 2.83 & ⋯ & 29 & 34 & 209 & 726 & 1745 & 2016-04-15 12:00:00 & 1 & 412 & 442 & 12:00:00\\\\\n",
       "\t4 & 1503960366 & 04/16/16 & 2016-04-16 & 12669 & 8.16 & 8.16 & 0 & 2.71 & 0.41 & 5.04 & ⋯ & 36 & 10 & 221 & 773 & 1863 & 2016-04-16 12:00:00 & 2 & 340 & 367 & 12:00:00\\\\\n",
       "\t5 & 1503960366 & 04/17/16 & 2016-04-17 &  9705 & 6.48 & 6.48 & 0 & 3.19 & 0.78 & 2.51 & ⋯ & 38 & 20 & 164 & 539 & 1728 & 2016-04-17 12:00:00 & 1 & 700 & 712 & 12:00:00\\\\\n",
       "\t6 & 1503960366 & 04/19/16 & 2016-04-19 & 15506 & 9.88 & 9.88 & 0 & 3.53 & 1.32 & 5.03 & ⋯ & 50 & 31 & 264 & 775 & 2035 & 2016-04-19 12:00:00 & 1 & 304 & 320 & 12:00:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 21\n",
       "\n",
       "| <!--/--> | Id &lt;dbl&gt; | date &lt;chr&gt; | ActivityDate &lt;dttm&gt; | TotalSteps &lt;dbl&gt; | TotalDistance &lt;dbl&gt; | TrackerDistance &lt;dbl&gt; | LoggedActivitiesDistance &lt;dbl&gt; | VeryActiveDistance &lt;dbl&gt; | ModeratelyActiveDistance &lt;dbl&gt; | LightActiveDistance &lt;dbl&gt; | ⋯ ⋯ | VeryActiveMinutes &lt;dbl&gt; | FairlyActiveMinutes &lt;dbl&gt; | LightlyActiveMinutes &lt;dbl&gt; | SedentaryMinutes &lt;dbl&gt; | Calories &lt;dbl&gt; | SleepDay &lt;dttm&gt; | TotalSleepRecords &lt;dbl&gt; | TotalMinutesAsleep &lt;dbl&gt; | TotalTimeInBed &lt;dbl&gt; | time &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 1503960366 | 04/12/16 | 2016-04-12 | 13162 | 8.50 | 8.50 | 0 | 1.88 | 0.55 | 6.06 | ⋯ | 25 | 13 | 328 | 728 | 1985 | 2016-04-12 12:00:00 | 1 | 327 | 346 | 12:00:00 |\n",
       "| 2 | 1503960366 | 04/13/16 | 2016-04-13 | 10735 | 6.97 | 6.97 | 0 | 1.57 | 0.69 | 4.71 | ⋯ | 21 | 19 | 217 | 776 | 1797 | 2016-04-13 12:00:00 | 2 | 384 | 407 | 12:00:00 |\n",
       "| 3 | 1503960366 | 04/15/16 | 2016-04-15 |  9762 | 6.28 | 6.28 | 0 | 2.14 | 1.26 | 2.83 | ⋯ | 29 | 34 | 209 | 726 | 1745 | 2016-04-15 12:00:00 | 1 | 412 | 442 | 12:00:00 |\n",
       "| 4 | 1503960366 | 04/16/16 | 2016-04-16 | 12669 | 8.16 | 8.16 | 0 | 2.71 | 0.41 | 5.04 | ⋯ | 36 | 10 | 221 | 773 | 1863 | 2016-04-16 12:00:00 | 2 | 340 | 367 | 12:00:00 |\n",
       "| 5 | 1503960366 | 04/17/16 | 2016-04-17 |  9705 | 6.48 | 6.48 | 0 | 3.19 | 0.78 | 2.51 | ⋯ | 38 | 20 | 164 | 539 | 1728 | 2016-04-17 12:00:00 | 1 | 700 | 712 | 12:00:00 |\n",
       "| 6 | 1503960366 | 04/19/16 | 2016-04-19 | 15506 | 9.88 | 9.88 | 0 | 3.53 | 1.32 | 5.03 | ⋯ | 50 | 31 | 264 | 775 | 2035 | 2016-04-19 12:00:00 | 1 | 304 | 320 | 12:00:00 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         date     ActivityDate TotalSteps TotalDistance TrackerDistance\n",
       "1 1503960366 04/12/16 2016-04-12   13162      8.50          8.50           \n",
       "2 1503960366 04/13/16 2016-04-13   10735      6.97          6.97           \n",
       "3 1503960366 04/15/16 2016-04-15    9762      6.28          6.28           \n",
       "4 1503960366 04/16/16 2016-04-16   12669      8.16          8.16           \n",
       "5 1503960366 04/17/16 2016-04-17    9705      6.48          6.48           \n",
       "6 1503960366 04/19/16 2016-04-19   15506      9.88          9.88           \n",
       "  LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance\n",
       "1 0                        1.88               0.55                    \n",
       "2 0                        1.57               0.69                    \n",
       "3 0                        2.14               1.26                    \n",
       "4 0                        2.71               0.41                    \n",
       "5 0                        3.19               0.78                    \n",
       "6 0                        3.53               1.32                    \n",
       "  LightActiveDistance ⋯ VeryActiveMinutes FairlyActiveMinutes\n",
       "1 6.06                ⋯ 25                13                 \n",
       "2 4.71                ⋯ 21                19                 \n",
       "3 2.83                ⋯ 29                34                 \n",
       "4 5.04                ⋯ 36                10                 \n",
       "5 2.51                ⋯ 38                20                 \n",
       "6 5.03                ⋯ 50                31                 \n",
       "  LightlyActiveMinutes SedentaryMinutes Calories SleepDay           \n",
       "1 328                  728              1985     2016-04-12 12:00:00\n",
       "2 217                  776              1797     2016-04-13 12:00:00\n",
       "3 209                  726              1745     2016-04-15 12:00:00\n",
       "4 221                  773              1863     2016-04-16 12:00:00\n",
       "5 164                  539              1728     2016-04-17 12:00:00\n",
       "6 264                  775              2035     2016-04-19 12:00:00\n",
       "  TotalSleepRecords TotalMinutesAsleep TotalTimeInBed time    \n",
       "1 1                 327                346            12:00:00\n",
       "2 2                 384                407            12:00:00\n",
       "3 1                 412                442            12:00:00\n",
       "4 2                 340                367            12:00:00\n",
       "5 1                 700                712            12:00:00\n",
       "6 1                 304                320            12:00:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data <- merge(daily_activity,sleepDay, by=c(\"Id\",\"date\"))\n",
    "head(merged_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8066ba9",
   "metadata": {
    "papermill": {
     "duration": 0.017011,
     "end_time": "2023-11-29T09:28:45.606339",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.589328",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938f6a03",
   "metadata": {
    "papermill": {
     "duration": 0.016954,
     "end_time": "2023-11-29T09:28:45.640201",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.623247",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **1.Daily_Activity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99488765",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:45.679032Z",
     "iopub.status.busy": "2023-11-29T09:28:45.677171Z",
     "iopub.status.idle": "2023-11-29T09:28:45.737737Z",
     "shell.execute_reply": "2023-11-29T09:28:45.735661Z"
    },
    "papermill": {
     "duration": 0.0827,
     "end_time": "2023-11-29T09:28:45.740235",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.657535",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>Avg_steps</th><th scope=col>Avg_very_active</th><th scope=col>Avg_light_active</th><th scope=col>Avg_sedentary_min</th><th scope=col>Avg_calories</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>12116.742</td><td>38.7096774</td><td>219.93548</td><td> 848.1613</td><td>1816.419</td></tr>\n",
       "\t<tr><td>1624580081</td><td> 5743.903</td><td> 8.6774194</td><td>153.48387</td><td>1257.7419</td><td>1483.355</td></tr>\n",
       "\t<tr><td>1644430081</td><td> 7282.967</td><td> 9.5666667</td><td>178.46667</td><td>1161.8667</td><td>2811.300</td></tr>\n",
       "\t<tr><td>1844505072</td><td> 2580.065</td><td> 0.1290323</td><td>115.45161</td><td>1206.6129</td><td>1573.484</td></tr>\n",
       "\t<tr><td>1927972279</td><td>  916.129</td><td> 1.3225806</td><td> 38.58065</td><td>1317.4194</td><td>2172.806</td></tr>\n",
       "\t<tr><td>2022484408</td><td>11370.645</td><td>36.2903226</td><td>257.45161</td><td>1112.5806</td><td>2509.968</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " Id & Avg\\_steps & Avg\\_very\\_active & Avg\\_light\\_active & Avg\\_sedentary\\_min & Avg\\_calories\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 12116.742 & 38.7096774 & 219.93548 &  848.1613 & 1816.419\\\\\n",
       "\t 1624580081 &  5743.903 &  8.6774194 & 153.48387 & 1257.7419 & 1483.355\\\\\n",
       "\t 1644430081 &  7282.967 &  9.5666667 & 178.46667 & 1161.8667 & 2811.300\\\\\n",
       "\t 1844505072 &  2580.065 &  0.1290323 & 115.45161 & 1206.6129 & 1573.484\\\\\n",
       "\t 1927972279 &   916.129 &  1.3225806 &  38.58065 & 1317.4194 & 2172.806\\\\\n",
       "\t 2022484408 & 11370.645 & 36.2903226 & 257.45161 & 1112.5806 & 2509.968\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 6\n",
       "\n",
       "| Id &lt;dbl&gt; | Avg_steps &lt;dbl&gt; | Avg_very_active &lt;dbl&gt; | Avg_light_active &lt;dbl&gt; | Avg_sedentary_min &lt;dbl&gt; | Avg_calories &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1503960366 | 12116.742 | 38.7096774 | 219.93548 |  848.1613 | 1816.419 |\n",
       "| 1624580081 |  5743.903 |  8.6774194 | 153.48387 | 1257.7419 | 1483.355 |\n",
       "| 1644430081 |  7282.967 |  9.5666667 | 178.46667 | 1161.8667 | 2811.300 |\n",
       "| 1844505072 |  2580.065 |  0.1290323 | 115.45161 | 1206.6129 | 1573.484 |\n",
       "| 1927972279 |   916.129 |  1.3225806 |  38.58065 | 1317.4194 | 2172.806 |\n",
       "| 2022484408 | 11370.645 | 36.2903226 | 257.45161 | 1112.5806 | 2509.968 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Avg_steps Avg_very_active Avg_light_active Avg_sedentary_min\n",
       "1 1503960366 12116.742 38.7096774      219.93548         848.1613        \n",
       "2 1624580081  5743.903  8.6774194      153.48387        1257.7419        \n",
       "3 1644430081  7282.967  9.5666667      178.46667        1161.8667        \n",
       "4 1844505072  2580.065  0.1290323      115.45161        1206.6129        \n",
       "5 1927972279   916.129  1.3225806       38.58065        1317.4194        \n",
       "6 2022484408 11370.645 36.2903226      257.45161        1112.5806        \n",
       "  Avg_calories\n",
       "1 1816.419    \n",
       "2 1483.355    \n",
       "3 2811.300    \n",
       "4 1573.484    \n",
       "5 2172.806    \n",
       "6 2509.968    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#daily_acitvity\n",
    "activity <- daily_activity %>%\n",
    "    group_by(Id) %>%\n",
    "    summarise(Avg_steps = mean(TotalSteps),Avg_very_active = mean(VeryActiveMinutes),Avg_light_active = mean(LightlyActiveMinutes),Avg_sedentary_min =mean(SedentaryMinutes),Avg_calories=mean(Calories))\n",
    "head(activity)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2d27308",
   "metadata": {
    "papermill": {
     "duration": 0.01765,
     "end_time": "2023-11-29T09:28:45.775677",
     "exception": false,
     "start_time": "2023-11-29T09:28:45.758027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **2.Sleepday**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4dc6292e",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>Avg_steps</th><th scope=col>Avg_very_active</th><th scope=col>Avg_sedentary_min</th><th scope=col>Avg_time_asleep</th><th scope=col>Avg_time_sleep</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>12405.680</td><td>37.9200000</td><td> 759.2800</td><td>360.2800</td><td>383.2000</td></tr>\n",
       "\t<tr><td>1644430081</td><td> 7967.750</td><td> 2.5000000</td><td> 920.5000</td><td>294.0000</td><td>346.0000</td></tr>\n",
       "\t<tr><td>1844505072</td><td> 3477.000</td><td> 0.0000000</td><td> 443.3333</td><td>652.0000</td><td>961.0000</td></tr>\n",
       "\t<tr><td>1927972279</td><td> 1490.000</td><td> 0.0000000</td><td> 977.2000</td><td>417.0000</td><td>437.8000</td></tr>\n",
       "\t<tr><td>2026352035</td><td> 5618.679</td><td> 0.1071429</td><td> 653.9643</td><td>506.1786</td><td>537.6429</td></tr>\n",
       "\t<tr><td>2320127002</td><td> 5079.000</td><td> 0.0000000</td><td>1129.0000</td><td> 61.0000</td><td> 69.0000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " Id & Avg\\_steps & Avg\\_very\\_active & Avg\\_sedentary\\_min & Avg\\_time\\_asleep & Avg\\_time\\_sleep\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 12405.680 & 37.9200000 &  759.2800 & 360.2800 & 383.2000\\\\\n",
       "\t 1644430081 &  7967.750 &  2.5000000 &  920.5000 & 294.0000 & 346.0000\\\\\n",
       "\t 1844505072 &  3477.000 &  0.0000000 &  443.3333 & 652.0000 & 961.0000\\\\\n",
       "\t 1927972279 &  1490.000 &  0.0000000 &  977.2000 & 417.0000 & 437.8000\\\\\n",
       "\t 2026352035 &  5618.679 &  0.1071429 &  653.9643 & 506.1786 & 537.6429\\\\\n",
       "\t 2320127002 &  5079.000 &  0.0000000 & 1129.0000 &  61.0000 &  69.0000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 6\n",
       "\n",
       "| Id &lt;dbl&gt; | Avg_steps &lt;dbl&gt; | Avg_very_active &lt;dbl&gt; | Avg_sedentary_min &lt;dbl&gt; | Avg_time_asleep &lt;dbl&gt; | Avg_time_sleep &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1503960366 | 12405.680 | 37.9200000 |  759.2800 | 360.2800 | 383.2000 |\n",
       "| 1644430081 |  7967.750 |  2.5000000 |  920.5000 | 294.0000 | 346.0000 |\n",
       "| 1844505072 |  3477.000 |  0.0000000 |  443.3333 | 652.0000 | 961.0000 |\n",
       "| 1927972279 |  1490.000 |  0.0000000 |  977.2000 | 417.0000 | 437.8000 |\n",
       "| 2026352035 |  5618.679 |  0.1071429 |  653.9643 | 506.1786 | 537.6429 |\n",
       "| 2320127002 |  5079.000 |  0.0000000 | 1129.0000 |  61.0000 |  69.0000 |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Avg_steps Avg_very_active Avg_sedentary_min Avg_time_asleep\n",
       "1 1503960366 12405.680 37.9200000       759.2800         360.2800       \n",
       "2 1644430081  7967.750  2.5000000       920.5000         294.0000       \n",
       "3 1844505072  3477.000  0.0000000       443.3333         652.0000       \n",
       "4 1927972279  1490.000  0.0000000       977.2000         417.0000       \n",
       "5 2026352035  5618.679  0.1071429       653.9643         506.1786       \n",
       "6 2320127002  5079.000  0.0000000      1129.0000          61.0000       \n",
       "  Avg_time_sleep\n",
       "1 383.2000      \n",
       "2 346.0000      \n",
       "3 961.0000      \n",
       "4 437.8000      \n",
       "5 537.6429      \n",
       "6  69.0000      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sleepday\n",
    "sleep <- merged_data %>%\n",
    "    select(Id,date,TotalSteps,VeryActiveMinutes,SedentaryMinutes,TotalMinutesAsleep,TotalTimeInBed) %>%\n",
    "    group_by(Id) %>%\n",
    "    summarise(Avg_steps = mean(TotalSteps),Avg_very_active = mean(VeryActiveMinutes),Avg_sedentary_min =mean(SedentaryMinutes),Avg_time_asleep=mean(TotalMinutesAsleep),Avg_time_sleep=mean(TotalTimeInBed))\n",
    "head(sleep)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81052b97",
   "metadata": {
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     "start_time": "2023-11-29T09:28:45.886562",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **3.Type of users per activity level**\n",
    "\n",
    "Let's try to categorize people based on activity level\n",
    "\n",
    "We can categorize users as follows:\n",
    "\n",
    "* Sedentary - Less than 5000 steps a day.\n",
    "* Lightly active - Between 5000 and 7499 steps a day.\n",
    "* Fairly active - Between 7500 and 9999 steps a day.\n",
    "* Very active - More than 10000 steps a day.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60f687b",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "First we will catogerise the people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a81ee4a5",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Id</th><th scope=col>Avg_steps</th><th scope=col>Avg_very_active</th><th scope=col>Avg_light_active</th><th scope=col>Avg_sedentary_min</th><th scope=col>Avg_calories</th><th scope=col>user_type</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>12116.742</td><td>38.7096774</td><td>219.93548</td><td> 848.1613</td><td>1816.419</td><td>very active   </td></tr>\n",
       "\t<tr><td>1624580081</td><td> 5743.903</td><td> 8.6774194</td><td>153.48387</td><td>1257.7419</td><td>1483.355</td><td>lightly active</td></tr>\n",
       "\t<tr><td>1644430081</td><td> 7282.967</td><td> 9.5666667</td><td>178.46667</td><td>1161.8667</td><td>2811.300</td><td>lightly active</td></tr>\n",
       "\t<tr><td>1844505072</td><td> 2580.065</td><td> 0.1290323</td><td>115.45161</td><td>1206.6129</td><td>1573.484</td><td>sedentary     </td></tr>\n",
       "\t<tr><td>1927972279</td><td>  916.129</td><td> 1.3225806</td><td> 38.58065</td><td>1317.4194</td><td>2172.806</td><td>sedentary     </td></tr>\n",
       "\t<tr><td>2022484408</td><td>11370.645</td><td>36.2903226</td><td>257.45161</td><td>1112.5806</td><td>2509.968</td><td>very active   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " Id & Avg\\_steps & Avg\\_very\\_active & Avg\\_light\\_active & Avg\\_sedentary\\_min & Avg\\_calories & user\\_type\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 12116.742 & 38.7096774 & 219.93548 &  848.1613 & 1816.419 & very active   \\\\\n",
       "\t 1624580081 &  5743.903 &  8.6774194 & 153.48387 & 1257.7419 & 1483.355 & lightly active\\\\\n",
       "\t 1644430081 &  7282.967 &  9.5666667 & 178.46667 & 1161.8667 & 2811.300 & lightly active\\\\\n",
       "\t 1844505072 &  2580.065 &  0.1290323 & 115.45161 & 1206.6129 & 1573.484 & sedentary     \\\\\n",
       "\t 1927972279 &   916.129 &  1.3225806 &  38.58065 & 1317.4194 & 2172.806 & sedentary     \\\\\n",
       "\t 2022484408 & 11370.645 & 36.2903226 & 257.45161 & 1112.5806 & 2509.968 & very active   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 7\n",
       "\n",
       "| Id &lt;dbl&gt; | Avg_steps &lt;dbl&gt; | Avg_very_active &lt;dbl&gt; | Avg_light_active &lt;dbl&gt; | Avg_sedentary_min &lt;dbl&gt; | Avg_calories &lt;dbl&gt; | user_type &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1503960366 | 12116.742 | 38.7096774 | 219.93548 |  848.1613 | 1816.419 | very active    |\n",
       "| 1624580081 |  5743.903 |  8.6774194 | 153.48387 | 1257.7419 | 1483.355 | lightly active |\n",
       "| 1644430081 |  7282.967 |  9.5666667 | 178.46667 | 1161.8667 | 2811.300 | lightly active |\n",
       "| 1844505072 |  2580.065 |  0.1290323 | 115.45161 | 1206.6129 | 1573.484 | sedentary      |\n",
       "| 1927972279 |   916.129 |  1.3225806 |  38.58065 | 1317.4194 | 2172.806 | sedentary      |\n",
       "| 2022484408 | 11370.645 | 36.2903226 | 257.45161 | 1112.5806 | 2509.968 | very active    |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Avg_steps Avg_very_active Avg_light_active Avg_sedentary_min\n",
       "1 1503960366 12116.742 38.7096774      219.93548         848.1613        \n",
       "2 1624580081  5743.903  8.6774194      153.48387        1257.7419        \n",
       "3 1644430081  7282.967  9.5666667      178.46667        1161.8667        \n",
       "4 1844505072  2580.065  0.1290323      115.45161        1206.6129        \n",
       "5 1927972279   916.129  1.3225806       38.58065        1317.4194        \n",
       "6 2022484408 11370.645 36.2903226      257.45161        1112.5806        \n",
       "  Avg_calories user_type     \n",
       "1 1816.419     very active   \n",
       "2 1483.355     lightly active\n",
       "3 2811.300     lightly active\n",
       "4 1573.484     sedentary     \n",
       "5 2172.806     sedentary     \n",
       "6 2509.968     very active   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_type <- activity %>%\n",
    "  mutate(user_type = case_when(\n",
    "    Avg_steps < 5000 ~ \"sedentary\",\n",
    "    Avg_steps >= 5000 & Avg_steps < 7499 ~ \"lightly active\", \n",
    "    Avg_steps >= 7500 & Avg_steps < 9999 ~ \"fairly active\", \n",
    "    Avg_steps >= 10000 ~ \"very active\"\n",
    "  ))\n",
    "user_type1 <- user_type\n",
    "head(user_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32876761",
   "metadata": {
    "papermill": {
     "duration": 0.018009,
     "end_time": "2023-11-29T09:28:46.094559",
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     "start_time": "2023-11-29T09:28:46.076550",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now let's find out the percentage of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49f624eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:46.134089Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>user_type</th><th scope=col>percentage</th><th scope=col>labels</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>fairly active </td><td>0.2727273</td><td>27.3%</td></tr>\n",
       "\t<tr><td>lightly active</td><td>0.2727273</td><td>27.3%</td></tr>\n",
       "\t<tr><td>sedentary     </td><td>0.2424242</td><td>24.2%</td></tr>\n",
       "\t<tr><td>very active   </td><td>0.2121212</td><td>21.2%</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 3\n",
       "\\begin{tabular}{lll}\n",
       " user\\_type & percentage & labels\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t fairly active  & 0.2727273 & 27.3\\%\\\\\n",
       "\t lightly active & 0.2727273 & 27.3\\%\\\\\n",
       "\t sedentary      & 0.2424242 & 24.2\\%\\\\\n",
       "\t very active    & 0.2121212 & 21.2\\%\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 3\n",
       "\n",
       "| user_type &lt;chr&gt; | percentage &lt;dbl&gt; | labels &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| fairly active  | 0.2727273 | 27.3% |\n",
       "| lightly active | 0.2727273 | 27.3% |\n",
       "| sedentary      | 0.2424242 | 24.2% |\n",
       "| very active    | 0.2121212 | 21.2% |\n",
       "\n"
      ],
      "text/plain": [
       "  user_type      percentage labels\n",
       "1 fairly active  0.2727273  27.3% \n",
       "2 lightly active 0.2727273  27.3% \n",
       "3 sedentary      0.2424242  24.2% \n",
       "4 very active    0.2121212  21.2% "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_type_percentage1 <- user_type1 %>%\n",
    "    group_by(user_type) %>%\n",
    "    summarise(total = n()) %>%\n",
    "    mutate(totals = sum(total)) %>%\n",
    "    group_by(user_type) %>%\n",
    "    summarise(percentage = total/totals) %>%\n",
    "    mutate(labels = scales::percent(percentage))\n",
    "\n",
    "\n",
    "user_type_percentage1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a3344be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:46.257256Z",
     "iopub.status.busy": "2023-11-29T09:28:46.254884Z",
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     "shell.execute_reply": "2023-11-29T09:28:46.301264Z"
    },
    "papermill": {
     "duration": 0.071446,
     "end_time": "2023-11-29T09:28:46.305815",
     "exception": false,
     "start_time": "2023-11-29T09:28:46.234369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>user_type</th><th scope=col>percentage</th><th scope=col>labels</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>fairly active </td><td>0.2727273</td><td>27.3%</td></tr>\n",
       "\t<tr><td>lightly active</td><td>0.2727273</td><td>27.3%</td></tr>\n",
       "\t<tr><td>sedentary     </td><td>0.2424242</td><td>24.2%</td></tr>\n",
       "\t<tr><td>very active   </td><td>0.2121212</td><td>21.2%</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 3\n",
       "\\begin{tabular}{lll}\n",
       " user\\_type & percentage & labels\\\\\n",
       " <fct> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t fairly active  & 0.2727273 & 27.3\\%\\\\\n",
       "\t lightly active & 0.2727273 & 27.3\\%\\\\\n",
       "\t sedentary      & 0.2424242 & 24.2\\%\\\\\n",
       "\t very active    & 0.2121212 & 21.2\\%\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 3\n",
       "\n",
       "| user_type &lt;fct&gt; | percentage &lt;dbl&gt; | labels &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| fairly active  | 0.2727273 | 27.3% |\n",
       "| lightly active | 0.2727273 | 27.3% |\n",
       "| sedentary      | 0.2424242 | 24.2% |\n",
       "| very active    | 0.2121212 | 21.2% |\n",
       "\n"
      ],
      "text/plain": [
       "  user_type      percentage labels\n",
       "1 fairly active  0.2727273  27.3% \n",
       "2 lightly active 0.2727273  27.3% \n",
       "3 sedentary      0.2424242  24.2% \n",
       "4 very active    0.2121212  21.2% "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_type_percentage <- user_type1 %>%\n",
    "    group_by(user_type) %>%\n",
    "    summarise(total = n()) %>%\n",
    "    mutate(totals = sum(total)) %>%\n",
    "    group_by(user_type) %>%\n",
    "    summarise(percentage = total/totals) %>%\n",
    "    mutate(labels = scales::percent(percentage))\n",
    "\n",
    "user_type_percentage$user_type <- factor(user_type_percentage$user_type,levels = c(\"very active\", \"fairly active\", \"lightly active\", \"sedentary\"))\n",
    "\n",
    "user_type_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172ebcc8",
   "metadata": {
    "papermill": {
     "duration": 0.018656,
     "end_time": "2023-11-29T09:28:46.343319",
     "exception": false,
     "start_time": "2023-11-29T09:28:46.324663",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ce8333",
   "metadata": {
    "papermill": {
     "duration": 0.018621,
     "end_time": "2023-11-29T09:28:46.380564",
     "exception": false,
     "start_time": "2023-11-29T09:28:46.361943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* I assume or it is usual that as the number of steps increases the calories burnt also increase. Let's find out through visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c64e8f1",
   "metadata": {
    "papermill": {
     "duration": 0.018606,
     "end_time": "2023-11-29T09:28:46.417778",
     "exception": false,
     "start_time": "2023-11-29T09:28:46.399172",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **TotalSteps vs Avg_calories**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4954c22e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:46.458846Z",
     "iopub.status.busy": "2023-11-29T09:28:46.457127Z",
     "iopub.status.idle": "2023-11-29T09:28:48.413965Z",
     "shell.execute_reply": "2023-11-29T09:28:48.412145Z"
    },
    "papermill": {
     "duration": 1.980737,
     "end_time": "2023-11-29T09:28:48.417111",
     "exception": false,
     "start_time": "2023-11-29T09:28:46.436374",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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zisINgBAAAT0zQtFAoZXUXfIdgBAADT8nq9RpfQpwh2AADAtLLqAjtBsAMAAGYV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yY+n/zts/UC66hjV/fK+Gz0huzdrEZy7v\nEC+i85Ge7iqY09kKhcLhcMRisaGhIeyRgpmgKMrlcrEsK3YgMD0kdrOAIokvrmvzvzmnrV83\nMniw1WLRxW9e0SViYJBppMrr1C8j/4qiqHSiNpLSwbkKRpW/3lofT46mywqa/7urWzTyaR1G\n07Tdbler1WIHMvtUKlVJSUkwGPR6vViZhSmkD8DKaO9BkZPrgoLUKGn+/o3NWedh3z7q3nHC\nKVZIcH4EQeB5nuO4ZDLJ8zzP88jqzg/Hk4/tqB0OjVbWJUnic2vOVNllswKoVqvLysoKMqtL\nI0nSYDCUlZXpdLrpXw1FSa1Wu91uZHUygsRu1mjZ1Nc3nzZpx6xrvLC/4nA7Gq1AMXpmb2Vz\n75h69JuW9K6sGxIrnnNCkqTZbHY6nfLdTjdz6VlJt9ud1d8CQK/XF8mnoJDgtzWbTNrE/Rub\nM8/D8gLxh501Tb1otwLFZdsx197T9syRJZW+65fLY2eCUqksKSkxmUyFsaNuhtIrs+gNBSOs\nVqvNZiuqT0FhQGI3y8qtkS9f3UJTo4t3SY761db6rDZKAAXsxFnTSwfLMkdKLZG71rZScrhB\naLXakpKS4py7IknSZrNZrVbcy4scRVFOp9NgMEz/UpAeJHazb15p4AtXtmVeGONJ+uEtczo9\nmsm/CaBA9PnUv3+3RhBGPwBaNvXlq1tUSqlvz6coymq1OhyOIl94MhgMLpcLe6qKlkKhcLvd\nI52IQXaK+vqVOytqh7POw0YT9CNvN/T70SwZClk4rvjl2/XRxGjJQAUtfGVDs90g9ZZWDMNU\nV1djiiJNpVJVVFQU57RlkWNZtrS0FL96WUNilysbF/duuqg3cyQYVf78zTlDIdQBgsLE8eT/\nba/zBMe8w++47EydKyRWSDOkVCorKytxM8vEMExZWRmmbYqKRqNxuVxFPmNdAPD7y6GbLu66\nelFf5ogvzPzizTm+sHKybwGQrzcOlWSdE7pqYd9lczxixTNDNE2XlJRg5XG89EYrs9mMLXfF\nwGQy4QBsYcCvMLduXXn2soYxN7bBAPvQW3NCMdxFoKB0eLRvH3Vnjiwo99+2SurHYNO5i1KJ\nZ61JmUwmu92O3K6AkSTpcDjMZlTmKhBI7HKLJInPrjmzvGY4c7DXp354y5zMfUgAspbiqMd3\nVvMZByacxtg961pJUtK1ndElaYa0Wq3b7cZcTkGiadrtdqMDbCHBBzXnSFL44tq2RRW+zMFO\nj+bhLQ2xJH7+UAhe/ai01zfanoEihS+ubVMzku6cm56lQFY3QyzLIrcrPCzLlpSU4FNQYPAp\nzQeaEu69qnVOSSBzsH1A9+i2+iSHXwHIW1u/bvvxMa3zNl3UWyntvmEkSRZqB9jcYRjG5XLR\nNJYaCkT6qAR2lxYeZBV5km4mW+cKZg429hgefgvzdiBjiRT1+K7qzKp1ZZbI5ot6RAxpJqxW\nK9aezgPLsk6nE7ldATCZTCjZWKjwS80fRsHft6G5zBrJHGzu0/9qa0McuR3I08sHywYyqjMq\naOGute0KWtJb66xWKxpnnbd0boeEQL5w2Lng4cOZVxqWe3Bzk8sUzRxs7tU/8nZDPImHYJCZ\npl7Dex+PWYS9bmlPqSUy2eulwGKxoArxBUJuJ1/oKlEM8MnMN70q+c3rT5daxuR2LX36h7Y0\nxJDbgXzEk9STu6v4jLm5Cltkw+Leyb9DfDqdzmg0ih1FIVCpVMjtZEelUqGrRDHAx1IEelXy\n65sbsyY22vp1P3tjTiSOfawgD3/ZXzEYGD1Mp6SFu65soynpLsIyDGOz2cSOonAgt5OXdAtg\n/L6KAX7H4tCrU9+8/nSlbczJwU6P9hdvzQkjtwPJO9Vt2Hvanjly04outzk62etFR1GUw+HA\npqLZpVKpsAFf+kiStNlsVqsV7/8igQ+kaDRM6sFrm7KqQnR6NL94E7kdSFo0QT+xq1rImJur\ncYbWL+gXL6Lp2Ww2tJfIBbVajb4UUpauP4zTQkUFiZ2YNEzqwc1NWfN2Z4c0P38TPcdAup57\nv8IbHt2mwyr5u65sk3KTCYPBgOImuaPRaKxWq9hRwATSm+pQf7jYILETmYZJff3a09WOUOZg\n15Dmf1+fm3nvBJCIU93G/S1jdqrdsuKs3RAXK55psSyLJpi5ptfrTSaT2FHAGEajEQWlixMS\nO/GpGe6BTU01Y3O7Pp/6f1+f159RIQxAdEmOemZvZebI3JLAFfMGxIpnWhRF2e12bALLA7PZ\nrNPpxI4CCOKvO0otFguWyIsTrneSoGa4BzY31TrH5HZDQeZ/X5/X6cESEkjFm4fdmSdhVUr+\ns2vapXzvsNvt2FqXNzabDV3aRKdUKt1uN/YeFDMkdlKhUnJf23S6wT2mn2wwqvjpG3OaelFP\nFcTX61NvP+7OHLl+WZdVnxArnmkZDAYUYs0nkiQdDgfKpIlIq9WiUh0gsZMQlZL/2qamZdXe\nzMF4kn54S8OhdotYUQEQBCEIxLN7K1Lc6OxcqSWybqF0F2FVKpXFgk9NvlEUhb7yoiBJ0mq1\noqYPEEjspEZJC19a33r53MHMwRRH/nZHzZ5G+2TfBZBr+5rtmTPHFEl85vIOSqonYdNb63CH\nEwVN0yhcnGcKhcLlcqFXHqThsyc5JCncufrM9ct6MgcFgXx6b9Ubh0rFigqKWTiueOlAWebI\nFfP6s45yS0d6QRCTRiJiGMbpdCKxzo/08qtKhZN28AkkdlJEksR1y7pvWXk288IoCMTrh0pe\nPFAuSHSWBArWn/eVZxZWNGmTN63oFjGeqRkMBmzhF51KpcKkaa5RFJVefsX8KGTCu0G6Ni7u\n+8IVbVmrXduOuX67oy7J4RcHedLcqz/QOqZw3d9c0qFScmLFMzVUrZMOrVaL30XupE+/YvkV\nxkN+IGmr6of+7upWBc1nDh5qN/8CrSkgL5Ic+eSeqsxJ4oXlvqzzPdKBhrBSYzQakXnkgk6n\nw+lXmAwSO6lbUul9cFOTmhkzQdLar/vxK/P7fNhUAbm19WjJQEaVbEbB/+1lnSLGMzW73Y6t\ndVJjsViwMj6LaJq22+1Y5oYpILGTgXp38BvXNho1ycxBT5D9n9fmNfehtTPkSr9f9fbRrMJ1\nPTa9RLuHGY1GVK2TIBS3m0UqlaqkpAQdPmBqSOzkocIW+aebPy63RjIHI3HFQ2/NyWrcCTBb\nnt1bmcwoXFdijq5f2CdiPFNgGAbbuSSLoiin04nJ1AtBkqTZbHa73fgxwrSQ2MmGSZP45vWN\n88v8mYMpjnz8verXD5XgqCzMrv0ttsaesYXr1pyhKSm+z1C1TvoUCgUKoJw3lmVLS0tNJpPY\ngYA8ILGTE5WS+9o1zWvnjyn3LwjEG4dKf/dubQpHZWGWhOOKFz4YU7hu9ZyBGqkWrrNarVjp\nkz6GYRwOh9hRyEx6oq6kpAQtj2HmkArIDEkKf3tZx60rz1JjH30/arP84q2GYAwffpgFL+wv\nz3wvGdTJm1d0iRjPFPR6PbYcyYVGo0Gft5ljGMbtdmOiDs4VEjtZ2rC470tXtTCKMWVQWvr0\n//nS/A6PVqyooDA09Ro+aB6zcfP2Szo1rBQL1zEMg0RBXlAAZSZIkjSZTCUlJSzLih0LyA8S\nO7laWuX9++sa9eoxR2W9YeYnr83NuisDzFyKo57aU5m5ZXN+mX9F7bB4EU0qvbUONfdlx2q1\n4vzyFFiWdbvdZrMZWxLh/OCaKGNV9vB3bjrlNkczB1Mc9fh71U/tKuF4XBTgnL11xJ1VuO6O\n1R0ixjMFm82GrXUy5XA4MBc1HkVRNpsNE3VwgZDYyZtVF//OjaeWVmV3Ath+zPqTV+sCUWy5\ng3PQ71dtOyaPwnUmk0mrxa4DuSJJ0uVyIS/PpNPpysvL9XqUJoULhcRO9lgld+9VLbes7Mo6\nTtHcq/3Pl+a3D+DmBzMiCMTTe8YUriu1SLRwHZqQFgCKotxuNw57EgTBsmxJSQn2FcBswduo\nEJAksXFx730bm7J2uPsizM/emPd+k12swEBG9p62N/WOKVx35+VSLFzHsqzdjrd0IUjndsVc\ncZeiKKvVirVXmF1I7ArHwnL/t2/82GWKZQ4mOfKJXVVP76lElTuYQjCqfOnAmMJ1V8yTYuE6\nhmFcLhc2lRcMmqZdLhdN02IHIgKdTldWVoYzwjDrcLMvKE5j7J9u+nj8lrvdjY4fvTS/14tW\n3DCxP39QEUmMTpwYNckblkuucB1FUQ6HA8tVBUapVLpcrqL6tarV6vTaa3FmtJBrRfRZKhLp\nLXc3rezP2nLX61P/+NV5B1qsIsUF0vVxl/Fg65hqcH97WYfUCtelt9tjS1ZBSk/EFkNuly5l\n4nK5sPYKuVP4H6QiRJLEzSsHHri2LeveHE/Sf9hZ88f3auJJ/N7hE0mOevb9ysyRheX+8ZO+\norPb7bgXFjCWZR0ORwEvsqc7qpWUlKhUqulfDXAB8rRrVUh5X3rs0bfePzoUo9zl9Td+7ivX\nLHURBEEQ/M5nf/XarkNng/S8havuevCL1eqRqenJvjTFt8CoRRWBf7nl5O921GYdjN3fbO0Y\n1HxpfWupJTrZ90LxeO2jksHAaMLEKvlPS69wndlsRnGTgqdWqx0Ox8DAgCBI7sjOhVAoFOi3\nAfmUp5mbrT/6hyd39l1/14M//uF31tfGf/WDr75yNkQQRNsL//qz5/Zdeuu9//aNz2tat3/3\nm4+NfKAn+9IU3wJZrLr4N69rvHL+QNZ4n0/9P6/N39eEBhXFrmtYs+OEK3PkhuVdVp20Ctfp\n9Xq0yywSGo2mkLZRpk9w44QE5Fk+Pj9c/OxvPvJc8f1/u+mqS+vnLr7tqz/aYKJf+U0jISR+\n+typus/9++1XX7pg+Zpv/Piroe43n+kNEwQx6Zem+ACGL1QAACAASURBVBaYiILmP31Zx71X\ntaiZrGVZ6k+7qv+4szqexHxnkUpy5B93Vmd2KKm0hdcvyH4MEBfLslYrNoYWEY1G43a75X6q\nQKPRuFyukpISnU5XwOvLIE15SexiZyqrq6+tGSmoTS41sgl/KO7f1RnjNq8vSY+y5jVLdMzB\n9/oJgpjsS1N8C0xhWbX3X289Mb56xf4W2w9fWNDci1rnxeiVA+7u4dGWnSQp3Hn5GZKU0Aw4\nSZI2mw33xWLDMExpaakct1SSJKnT6UpLS51Op1qNKgQgjnzssWOMa37+8zUjf0yGGn/fE6q6\nty4Rfp4giPma0RgWaBRbT/gJgkiEj034pcSaicdH/rhr167HHnts5I8/+MEPKivHbAzPkWhU\nWvvVSJKkaTrzCKFGQ3z3bzqf3+vcetSauYNlKMT+7M051y0fumXVgIKW0E09F3CpHXG6R7v1\nqCNzZPOyobkVBEFIqDu7zWbLz3Rdeu2PJEms+Y6gKEqj0Yj4kbFYLP39/X6/f/qX5l766YIk\nSY1m4g8IRVFms9lkMhVPvWWKorRa7WQ/kGKTvoZQFJWfawjP81N8Nd9vwTMH33j4oT+kaq79\nlw2lyTNhgiBsytEpd5uSTgaSBEHw8Ym/NNn4yB99Pt+pU6dG/phMJvPwMaNpWpqbQrKmOhiK\n+OzagQUV0Ue3usOx0Z+hIJCvf2g70q6/b1NPhT027v+mcEjz15R/0QT12NZSPiONL7XGb7/U\nI6mfjyjHJIvnrjwToq+HlpWVabXa/v5+6RynyPqMkCSp1Wr1er3BYJDUxyc/RH+HSA1Jkvm5\nhnDcVOWo8ncVi3sbf/+Lh7ccHb7y9vv+4871KpIMshqCIIaTvIv55PPgSXIKs4IgCGqSL002\nPvK31NXVfeELXxj5o06ny8NcWjweTyaT078uj2iaFgRhwqR+YfnwDz4V+r9t5c29Y44Zdg2x\nP3iu6tZVfRuWDFKFtfY18mGT2q9JLI/vKB8MjM7mKmnhyxs6CCEhnR9PumpdLJanxwyFQpGe\n3pba1LuIVCpVMpmc+v6RnzBsNltfX9/UUxS5RlFU+qKaSqXSIyzLGo1GnU6XTm7icWkdOcoD\nlUqVSCTE/b1Ih1KpVCgUgiDk56rF8/wUhQLylNgF27d/6x9/SS/e/N+PfX6O7ZMqPkrNIoJ4\nrzGadDGf7KVoiqaMC4xTfGmKb0mbP3/+/PnzR/7o8/nC4ZwfrYhEIlL7VKtUKo7jJstjdEz8\nG9d+vPOk8+WD5ZlN35Mp8rm97oMt+ruubLMbpPUvuhAKhSKd2CUSCek8+ovlaId5b6M5c+TG\ni7vsOr+k3sJGo5HjuDx8eNNUKpVSqRQEIW9/o/QxDBOPxyVyZUsvy44kVfmnVCrTCRzP8zqd\nTqfTpS8peXv2kCCWZePxeCKREDsQSdBqtQqFguf5vF1Dpkjs8jF1LPCR//jnX7NXPfCr7//d\nSFZHEITKtK6Uod98fzD9x2To0IfBxLJ1rim+NMW3wDmhSGL9wv5v3/hxiTl7iqKtX/efLy/Y\n12Qr+hSoAAWiyid3V2WO1LuD6xf2iRTOxBiGwV43yMQwjIgnTNPbDR0OR1VVVVlZWVFtpAM5\non/wgx/k+u+I9P7+Ny813n7bVaGBvp6/GvRqXQ7NXP7os0+96aiby0Z7nvmvn/Rqrvh/d1xO\nEgRB0hN/abLxSf7qWCyWh4nieDwutRWc9JzwtP92gyZ52ZxBjifbBnQEMfpTTHHU0Q5zU6+h\n2hHWqUR7Sp4tFEVhKZYgCEEgfrejtivjJKya4R7YdForpe5hJEk6nc48tw5TKBQMwxBYis2g\nVqulsBQ7Ir1VX6/XUxSVn6l3pVKp0+mMRmP6EI/RaKRpGu+QEWq1OpFISOcdIi6GYdKz/nmb\nxJ3i2AqZh49Hz7v//JWfncwaNNZ874mfryAEbtuffv7ctgNDMbJ2yZVf+da9dSMnXif70hTf\nMo7P58vD7H0gEBgaGsr133JOpl6KHa+pV/+nXTVDQSZrXEHzm5b0blzSq5TzgVmFQpFu4xMO\nh4t5KXZ3o+PpPWMOid+1tn1VnUeseCZkMBjyX7hOpVLpdDpBEKT2QRaR2WyW4CaTNJ7nQ6GQ\n3++f3cs7RVEMw7Asy7KsWq3OPAyhVqu1Wi3P88PDw7P4N8qaxWIJhUJYik3TarVqtZrjOK83\nT/0YbbZJWwzkI7ETERK7mX9LLEm/uL98d6N9/Jccxvgdl52ZWxqYvQDzCokdQRCDAfZHLy2M\nZbQJXlbjv3d9k4ghjadQKEpLS/N/uhCJ3XhSTuzSBEGIRCJ+v//8giRJUqlUpidaRv5jshcj\nsRsPiV0mSSV22CgAn1ApuTsvP7Og3P/0nspAdMwFbsDPPrRlziX1nttWndWysl+ZLUK8QP5h\nZ21mVmfSpr6w9qyIIU3IZrMVYc0IOD/pUiNarXbkkEd6/0l6CwrP84IgCIJAUVT6TGt6S0b6\nvyVbowrgwiGxgzGWVHrrXYHXD5W+97Ezs86ZIBD7mmxHz5huXtl1+ZxB9AKQl7cOl7QPjB6h\nIkni7qu6tWxKUnsO9Xo9KkjDeUgvnoodBYBU4JEFsmlY7lOXdn7z+lPjD8xGEoqn91T99I25\nnR5UG5eNk13GNw+7M0eumDewpCq7v5y4aJq2WCxiRwEAIHtI7GBitc7Qv9xy8qaLu5R09tHa\nlj79j19Z8MSuqmA0r0cX4TycHdI89k4tL4xOsTqMsVtXYhEWAKAw4UoKk6IpYdNFvd+77cT8\nsux2jbxAvN9k//7zi7Yec6U4rMtK1HCI+dXWhnhytO0PTQlfXNvGKKRVLF6v16PjJADArEBi\nB9OwG+IPbGq6e12rQZ29ISuWpF86UP7DFxcd60A5WcmJxOlH3m7whcfMqv7tZR1Vdmk1V1Ao\nFGazefrXAQDADCCxgxlZUTv8b7cfv2pRP01llwsZ8LO/3lb/0Ftzer3Y+S4VKY58dHtd1m/k\nmiW9a+YOihXShEiStNvtaCUOADBbkNjBTGlY7vZVnd+77cTC8uyVWYIgTnUb/v3FBU/tqfJF\nsqscQ54JAvHE7uqmXkPm4Ira4Zsu7hIrpMkYjcZ0iUEAAJgVKHcC58ZpjH31mqbGbsOfP6jo\nGTshxAvknkb7B822S+s9Nyzv0qtR8U4cr35UdqBlTPOGelfwc1e0Sa1IDXrCAgDMOszYwfmY\nWxr4l1tO3n5Jp4bJzt5SHLm70f6DPy/adsyd5PAGy7fdjfYtR8YUN3Gbol/Z2CK1pnAURdnt\ndlF6ugMAFDDcd+E80ZRw1cL+H/7t8XUL+ikyO2mIJBQvHij7/nOLdjfaM2ttQE6dPGt8du+Y\nbrBGTfKr1zSNz79FZzabGQar9gAAswyJHVwQDZv61KWd37315KIK3/iv+iLM03uqfvjCwkPt\nZl5aE0YFqH1A99sddZlptErJ37+xyaqXXDNHtVptMBimfx0AAJwj7LGDWVBijt6/sbl9QPfS\nwbLmXn3WV/t8qsfeqXObo9cs6VtROzR+eg8ukCAQ7550vnywPJlRU5AkhS+uba2wRUQMbELp\nRVixowAAKExI7GDWVDtC37yu8ViH6eUPy8aXPun1qv+4s/rNw+7NF/WurEN6N2sCUeUfd1af\n6jZmjd+5umNx5QTTqKKz2WyobwIAkCNI7GCWLa70LazwHTljeelAmSeY3Zl7wK96/L3q1w+V\nrl/Qd8W8QcW4fmVwTk51Gx5/r8Yfye7ttuminsslVrIuTa/Xa7VasaMAAChYSOxg9lEksax6\neHGF771Tji1H3KFY9ttsKMj8+YOKd064NizuXT3HM74dLUwryZEvHyx/96RTGDv1SZLCtUt7\nrlvaI1JcU1EqlRaLRewoAAAKGRI7yBUFzV+1sO/yOQN7T9u3HXdn9bYiCGI4xDz3fuWrH5Zd\nUu/ZsLjPrJXcHn/J6vOpfv9u7dmh7P6qZm3ii+va6l1BUaKaWnprHUXhwBYAQA4hsYPcYpX8\n+oX9l88d3NNo33bMNb4vRTRBv3vSubvRsax6eP3C/kqbtDqZSo0gkLsb7S/sL0+ksjOkZdXe\nz6w5I8HKJmk2m41ls5fmAQBgdiGxg3xgFPz6hf1XzBv4qN365uGSAX/2DT7FkQdarAdarBW2\n8PqFAzg8Ox4vEEfOWF77qLTPl92DS0nzN6/oWr+wX5TAZsJqtWJrHQBAHiCxg/xR0MKqOs/F\nNUP7W2xbjrgHAxPM33R6tH/cWf3aR6VXzhu4pN6jVyfzH6fU8AJxuN3yxuGS8WeNCYIos0bu\nWdfqMsXyH9gMmUwmVK0DAMgPJHaQbzQlXNYweEm951CbecdJV/vABBM5Q0HmxQNlr3xYuqDc\nf1mDZ2G5j6aKcQJPEIgjHeY3DpV2D0+Q0pEksX5h/00Xn5Vau7BMGo3GbDaLHQUAQLFAYgfi\noEjh4trhi2uH2wd0O046D7ebOT678xjHk8c6TMc6THpVcmX98GUNgyXmqCjR5p8gEMc6Ta8f\nKu0ad0IizaxNfGbNmQVl/jwHdk5YlnU4HGJHAQBQRJDYgciqHaF7HCHfSmbnx47djfZIfIL3\nZDCmfOe4853jzkpb+JIGz8raYQ0r0SMCFy4YU37QZN172t7vz95Ll6ZXJ69Z3HvF/EGJl4lR\nKpUul4sk0SkYACB/kNiBJJi0iZtXdF23rPtQu3XrUVfPRJvJCILo8Gg7PNrn91XUOMLLqoeX\nVXtNhVIkhReIph7D7kb7sU5zips4GdKpUmvnD1y9qI9VcnkO71xRFOV0OlHcBAAgz5DYgYQo\naWFVnWdlraelX7+vyXa43RJLTpAZCALZ2q9r7df9ZX9FpS18UZV3SaVXyqcHpuYLM3tP2/Y1\n24eC2bVgRmjZ1IbFfVfO71cpJT1Ll0ZRlMvlUiqzKxcCAECuIbEDySFJot4VrHcFP3Vpx6F2\ny74mW2u/XpjoeIAgEGcGtWcGtS8fLHOZohdV+eaV+mscYVl0KhsKsie7jMc6TB93GwRh0vVK\nDZvasKh/3YJ+6c/SpZEkabfbUbIOAEAUSOxAulRK/rIGz2UNnoGAal+T9YNmmy886ZxWn0+9\n5Yh6yxG3khYq7aEGd7DeHaxxhBiFhJK8FEc19+lPdhlPnjWOL0eXxWGMrZ4zuGbuoJqRR0qX\nZjQaNZqJD3wAAECuIbEDGXAYYjdd3H3D8u7TPcZD7eajZ0zB2KTLfEmObOnTt/TpicOEghYq\nbeG5peF5FYlyW5wVYx8/x5N9PlVzn/7kWWNTr2F8x4gsSppfVu1dPWewzhWU3cEDhmFMJpPY\nUQAAFC8kdiAbFEnMK/XPK/XfsZroGtIe7zQdbLVMdnQ0LcV9shvvjUMEQRAqJecwxtzmWIk5\n6jJFq+xhQw4KIEcSip5hVadH2+dT93jVnR5NkpvRGQKXKXppw9BlDYM6lSzP/FIU5XA4cAwW\nAEBESOxAfiiSqLCFK2zh65Z1dw1pjnaYD58xdQ9Pv/wXS9KdHm2nZ7QkskGdtBnielXKqEkY\n1Cm9OmnUJPSqlFGTVLMcQRAMzSnGlf9NclQwqghElaGYMhhVBGOKYFQZjCoCUaZ7WB2Intuh\nAS2bSk/RVdrl3SfXYrHgwAQAgLiQ2IG8lVkjZdbIdcu6h0NMY7ehqVff0m+Y4nhplkBUea55\n2KwgSaLcGllQ5p9f5qtxhgugMa5Go9Hr9WJHAQBQ7JDYQYGw6BKXzfFcNsdDEMRQkGnpNzT1\n6Jv79BN2pBWLhk3NLw0sKPcvKPMXUhtcmqbtdrvYUQAAABI7KERWfcKq96yq8xAE4Qsz7YPG\nvoC+y8N2DCqHQ+yElVNyhKYElylWaomUWaK1rmC1PUzKf3IuC0mSDocDtYgBAKQAiR0UOJM2\nscLoVamiBEGEw+FoguzzqbuHNb1edbdX3e9TBWPKyTo9nAe9KllqjZZZImXWaKk54jJFx2/R\nKzBGo1GlmqZ0CwAA5AcSOyguKiVfZQ9XjT2mEEqffogpfWFlMKbwR5SBiDLJUZHExB8QmhR0\n6pROldKrknp1Uq9K6VQpgyapY5OsHDpDzCLUNwEAkBQkdgCETpXSqVJuIip2IDKTbjKB+iYA\nANKBbTEAcJ6sVivDzPQAMgAA5AESOwA4H6hvAgAgQUjsAOCcURSF+iYAABKExA4AzpnFYkF9\nEwAACcKlGQDODcMwOp1O7CgAAGACSOwA4NzYbDachAUAkCYkdgBwDvR6PctKqEsbAABkQmIH\nADNF07TZbBY7CgAAmBQSOwCYKZPJRNO02FEAAMCkkNgBwIwwDIPCdQAAEofEDgBmxGq14swE\nAIDEIbEDgOlptVqVSiV2FAAAMA0kdgAwDYqiLBaL2FEAAMD0kNgBwDSMRqNCoRA7CgAAmB4S\nOwCYilKpNBqNYkcBAAAzgsQOAKZisVhwZgIAQC6Q2AHApDQajUajETsKAACYKSR2ADApnJkA\nAJAXJHYAMDG9Xq9UKsWOAgAAzgESOwCYAEmSJpNJ7CgAAODcILEDgAno9XqUOAEAkB0kdgCQ\njaIos9ksdhQAAHDOkNgBQDaDwUBRuDgAAMgPrt0AMAZFUahIDAAgU0jsAGAMs9mM6ToAAJnC\n5RsARtE0rdPpxI4CAADOExI7ABiF6ToAAFnDFRwAPqFQKDBdBwAga0jsAOATZrOZJEmxowAA\ngPOHxA4ACIIgGIbRarViRwEAABcEiR0AEASm6wAACgISOwAgWJbVaDRiRwEAABcKiR0AEBaL\nRewQAABgFiCxAyh2arVapVKJHQUAAMwCJHYAxc5sNosdAgAAzA4kdgBFTavVsiwrdhQAADA7\nkNgBFDVM1wEAFBIkdgDFS6/XK5VKsaMAAIBZg8QOoEiRJGkymcSOAgAAZhMSO4AiZTQaFQqF\n2FEAAMBsQmIHUIxIksTuOgCAwoPEDqAYWSwWmqbFjgIAAGYZEjuAokNRFKbrAAAKEhI7gKJj\nMpkwXQcAUJCQ2AEUF5qmDQaD2FEAAEBOILEDKC4mk4kkSbGjAACAnEBiB1BEFAqFXq8XOwoA\nAMgVJHYARQTTdQAAhQ2JHUCxYBhGp9OJHQUAAOQQEjuAYmE2mzFdBwBQ2JDYARQFlUql0WjE\njgIAAHILiR1A4SNJ0mq1ih0FAADkHFqAFyCaptVqNU3TgiAoFIr06pvwVzzPp/+DIIh4PB6P\nxzmOEztkyC2j0cgwjNhRAABAziGxKwQ0TWv+f3v3HR9Fnf9x/Dsz20uy2XQB6SKCgv4swOmh\n2EsookhRQT0RxYJYUEFBTrELp6h4eKCiZ0Q90EM4T1FEQc6CCgioHEYUCCUhu9ne5vfHkBhQ\nuQSSLbOv5x88Mt+ZzXwXPkze+c53vmuzmUwmo9FoNBoNBkNOTk40Gg0Gg415eTweDzeQSCRa\nusNIJqPR6HK5Ut0LAEAyEOwymCzLNpvN4XBYLJZDmRSv5cL6CVjRaDQUCgUCgWAwqA3sIaPl\n5+fzzAQAZAmCXeaRJMlsNtvtdofDIcvNP0tSG/ZzOp2JRMLv95PwMprD4bBaranuBQAgSQh2\nmcRkMjmdTrvdnpxPcJdl2el0Op3OeDzu8/kCgUAoFErCedFcZFnmmQkAyCoEu8xgMBjcbrfd\nbk/J2RVFyc3Nzc3NjcVitbW1Xq+XeXgZwe12t8SYLgAgbRHs0p0kSS6XKycnJx1+QhsMhry8\nPJfL5fP5vF5vJBJJdY/wu8xmMx8LCwDZhmCXviRJstvtbrc7OTdeG0+SJO0WbSgU8ng8gUAg\n1T3C/iRJKigoSHUvAADJRrBLU1ar1e12p/naYxaLxWKxRKPR2tra2tpa7s+mDxauA4DsRLBL\nOyaTye12Z9CTjEaj0e125+bmejwer9fL87Mpx8J1AJC1CHbpxeFw5Ofnp8N0uqZSFMXtdrtc\nLq/X6/F4GL1LIRauA4CsRbBLF7IsFxQUpOq51+Yiy7L2qAfxLlVYuA4AshnBLi2YzeaioiKD\nQSf/HPXxzuPx1NbW8lm0SWMymVi4DgCymU6SROaSJCk3N9flcunv3pksy3l5edrcO+JdEpjN\n5pKSkky8jw8AaC4Eu1RSFKWgoKD+Q1p1qT7ecXO2RZnN5uLiYlIdAGQ5gl3KWK3WwsLCdFuj\nroU0nHvn9XoZvWteFoulqKgoS2oJAHAABLvUyM3Ndbvdqe5Fsmnxzul01tTU+Hw+Ru+aBWN1\nAIB6BLtkkyRJuzWZ6o6kjKIo+fn5LpeLde8OncViKSkp0d8ETQDAwSHYJZUkSYWFhZm+pkmz\n0Na90+beeb1eRu8OgsViKS4uJtUBAOoR7JJHluXi4mKLxZLqjqQRRVF4tOLgaKmOO7AAgIYI\ndkliMBiKi4v5+M7f1HDdO0bvGsNmsxUWFpLqAAD7Idglg8lkKi4u1s36wy2EhVEaQ1EULQSn\nuiMAgHRE1Ghx3DJrkvrRO5/P5/F4YrFYqnuURmw2W0FBAcuaAAB+D8GuZdnt9sLCQqa3N5Us\nyzk5OTk5OX6/3+PxhMPhVPcoxWRZdrvdTqcz1R0BAKQ1gl0LItUdOrvdbrfbI5GIx+Px+/3Z\nuTaKzWbLz8/nVj4A4H/iR0VL0aa3k+qahclkKiwszMvLq62tzaqnK7R5h8yoAwA0ks6DndFo\nTMI4RzgcNhqNDVtsNltpaWkKU502DUt/41sOh6OoqMjn89XW1oZCoUa+qn6CYwYNekmS5HQ6\n8/PzW2hGnSRJ+xVtNtP+KiRJYjWielqF8Kuphgr5Na1CmD6u0S7USauQA/9wl/T3s7+hSCSS\nhLKrqanZsWNH/abNZmvdunVqL4iKoqiqqu+RrUgk4vV69+zZ05i3qZVBRvyFSJKkfeJciwav\nbKiQxpNlWasQHtappyhKIpHQ9w+IxqNCfs1gMMTjcSpEo1WIqqrJ+ST0RCJxgNXTdB7sampq\nkvD/0Ov1VlVVaV9brdaioqKU/xKTk5MTjUaDwWBqu5EEqqoGAgGfzxcIBH7vGIPBoP0Wleaz\n9LRRutzc3CSMLLpcrlAo1PhRT32zWCwOh0NV1fr/yMjLywsEAjy3pLFarXa7PZFIVFdXp7ov\n6cLtdvt8vkgkkuqOpAW73W61WuPx+J49e5JzxoKCgt/blTF3pjKCxWJJh1SXVSRJ0h6wiMVi\nPp/P7/dn4oVGlmUt0rGUCQDgUBDsmo3ZbGa9uhQyGAwul8vlcsVisUAg4Pf7w+FwOo/PaRRF\n0SIdlQMAOHQEu+ZhMplKSkr42ZwODAaDtgZeIpHw+/2BQCAajaa6U/uTZdlWh/npAIDmQrBr\nBkajsbS0lFSXbrT7m06n02g0Kori9/tjsVhqh/FkWXY4HDabzWKxkOcAAM2OYNcMrFZrqruA\nA9HilMPhMBgMiUQiHA4Hg8FwOBwKhZIQ8iRJMplMZrPZbrezVgIAoEUR7JBdtHWGtIClqmok\nEolEIrFYTPszFosd+gog9UnOVIfBOQBAchDskL0kSTKbzWazuWFjLBaLRqNayNNWetNW86r/\nQhvk01YtUhSl/k/tC6PRyMK/AIBUIdgB+zAYDBn0ARUAADTEfH8AAACdINgBAADoBMEOAABA\nJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKATBDsAAACdINgBAADoBMEOAABAJwh2\nAAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKATBDsAAACdINgBAADoBMEOAABAJwh2AAAA\nOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKATBDsAAACdINgBAADoBMEOAABAJwh2AAAAOkGw\nAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKAThlR3AC0iHo8nEolU9yJdVFVVrV27VgjRqVMnWeaX\nGSGEiEajVEi9HTt2rFmzRpKkzp07p7ov6YIKaWjbtm27du0yGAwdOnRIdV/SBRXS0JYtW6qq\nqkwmU7t27VLdF70HO5fLleoupJLT6Ux1F9LC6tWr77rrLiHEypUrTSZTqruTLqxWa6q7kC6W\nL18+bdo0s9m8YsWKVPcljVgsllR3IV0sWrRo5syZhYWFS5YsSXVf0ojZbE51F9JFeXn5888/\n36FDh/nz56e6L9yKBQAA0AuCHQAAgE4Q7AAAAHRCUlU11X0AWtaOHTu0hyf69evHwxP4ta1b\nt27YsEGW5X79+qW6L0hHFRUVmzZtMpvNp5xySqr7gnS0adOmiooKu93eu3fvVPeFYAcAAKAX\njF4AAADoBMEOehD2eoIJxp4BANlO5+vYIRuE9vzn6isfOPmZl68psde1JZaVP/3P5at/qlW6\ndj9p1I1XtLcqB9WODKbG9vxz7qwln3yzK6i07dj94muu69WGCsFez1870jJ11tDCvas5qrE9\nC2Y/u2Tl11UhubRN5/6XjTn72JK6Yw+iKiiYjLdfhQghfljx+suLV67/dmtu666D/3TTGd3z\n6vak1/WEETtkNjURmjVhuie+zwLom9+YNP3VT3pfePXkcZfb/vvexPGz1YNqR0ZbOu3WF5ZW\nDbj61mkTb+wir3/oljt2RvfWCRWS3dRNH89ZsK0m1mCK+b+n3frSssoLRt340J8n9OsYfnrK\n2Dd/8mm7DqIqKJgM9xsVsvuLOeMe/nv+CedNuv+es7sGZk4evz4Q03al3fVEBTLZl3PGX3rL\nM2VlZbO2+/Y2JcJjLx44/o3N2laoenlZWdnL23xNbkcmSyRCFw3oP+mTHdpmLPhdWVnZw5s9\nqkqFZLXty6dfdelFZWVlZWVl83b4tcZYaMvA/v2nr6uuOyrx5OUXX3HXZ6ra9Go5wEuQCX6z\nQlRVvXf44Otnr6vbik+fPOm5tdWqmo7XE0bskMG8mxbctyR49+TBDRvDnuVbQvFz+x2mbZrz\nTunhMH324Y6mtifzjaAFqAlVKOa6S5xslyQpoaqCCslu7mMuumPyA48+NKFhYzxU0bZ9+/M6\n1H8Go3Rsrjni8YmmV8sBXpKMt4dD9psVEqn9IWSEOgAAC6lJREFU5PPayPmDO9Y1yOOm/Pmq\n7nkiLa8nBDtkqkSkctrdL50zYWpn2z5TRSP+NUKIoxo0drMZPOs8TW1v6f6jRUmS5abT2nzz\n+JOfrN9c+dOm15+YasrpflUbp6BCspspt3WnTp06dmy7b+MpM2bMOMK695846ts4Z5uvXVkn\n0fRqOcBLWu5NoRn9ZoVEvJ8JIYq/eXvC2CsuGjx07PiJS76q3Lsr/a4nPDyBTPWvRyZVHzf2\nT/9XoMb3NGxPhP1CiALjL3NRC4xK1BttantL9x8trfdV495aNeGBO8YJISRJHnz3lAKjLKgQ\nHFDFZ28/+cTcWIfz7jqzlWh6tRzgJcl6B2h+8bBXCPHw0x8NGX3tlcXmDR/OnzX52vDMeQPb\nONLwesKIHTLSzlVPzVlfMm3cqb/eJZttQojq6C+PU+yOxg0OQ1PbW6zvSIZ4ZPvEMXeE+4x4\nZu7Lb7w6b+oNF751//WvbKwRVAh+R3jPxmemjL1p2kutzrn22Yf+ZJMl0fRqOcBLkvU+0Pxk\ngyKEOPWeyYNOPbFL1x4Dx9x/tsu48Ol1Ii2vJwQ7ZKRdH62J1K65cvDA/v37Dxg0Ugjx9uhh\nFw27WwhhtB0thNgY/OUXoO+CsdxuuU1tT9ZbQYuoXvvMt3552thBrfKdRmtujzMuv+5w66In\nPxVUCH5L7Q/v3XD1nWvkHg/Pnjt+xOkWSdLaD6IqKBj9Mdg6CyH6HO6ob+lVagvv3ibS8npC\nsENG6nj5XY/XeezRKUKIP0y8/+Fp1wohLK7TWpmUxSt3aUdGfas/r40cd1pJU9tT8K7QfBSz\nWajRhuvgVIdiitksqBD8ipoI3H/nM+bTb3j6ntFdCiwNdx1EVVAw+mPJOzvPIL//nXfvthpf\ntjXg7NhRpOX1RJkyZUozfjsgOQwOl7uey1L+6sJjLrv6zDb5QgghKUcmvi5/eXFRpyPNwW2v\nPPjodtsfpw47WWpqe6rfIw6F2X3U14sXv/l5ZUlhTrimcsVbs5//YuuQe8YclWemQqDGva/O\nf7tb/4uOsRuFEIHtc2Yt2HjR4NN9Oyu31dm1x15SZGlytYjfvwSl+l2j8farEEk2dwl/Medv\n71pKigyh3UtffmzRd4Gx913VxmJIw+uJpKqsm4jMpsb3DBg08vy/vvLLJ0+o8XdfnPHqu59W\nhaSOPfqOueXqTtojSE1tRyaL1Gyc++xLqzf+UBVUWrftdOYlo88/ru7XYioku8UjPw+66Loh\nz5VfWmQTQmz74M4x07/Z75jcDnfPm3GCEAdVFRRMhtuvQoQQQo29M++JN979tCpiatux6wUj\nr+vXpe7+aZpdTwh2AAAAOsEcOwAAAJ0g2AEAAOgEwQ4AAEAnCHYAAAA6QbADAADQCYIdAACA\nThDsAAAAdIJgB0Bv1LinvdUoSVKbM19PdV8AIKkIdgD0ZucXt1SEYkKI7R+Nq46lxRrsO/8z\nqaysbKU3kuqOANA5gh0AvXln/GJJku4f3SUe3nrL5ztT3R0hhAhUfrJo0aLKaDzVHQGgcwQ7\nALoSj/x886c7HK1uvGnKzUKId255J9U9OnhqPBJPiwFHABmDYAdAV7Z9cFN1NNHj7jH20mtO\nd1l2fjZ+WySh7XrtzDayrHzo2ed+6PWtc0z2rrVxVQix/eMXLjnvj61dtsLW3a57dHHFm6dL\nklQZTTTmvIno7qfuuPKYjiUWozEnv83pl9y4andI2zWtvav9wPeFEIMLbDltbtcafT8uHzf0\n7MMLXWa7+8hj+9377OL609gUuc+sr2fedEGB3WZUTIVtul1++1O7G3TjAOcCkO1UANCRx7vn\nS5KywhNWVfXjq48UQgx572dt166vrhdCnPK3b+sPDla/LUnSESOXqqpa9fWMHINsb3Xy2AlT\nxo8eUWhU2vbME0Jsj8Qbc97HzmglSUq/oddOnTbt1jEXOhTZXjogklBVVd384dIX7ukphJg0\n/633ln2rqqpv64KOVqPR1m7U2Fvvmzzh4r4dhBA9L5+rfSurLLm6lUqS4awhV06aOL7/KYcL\nIUpOvj3WiHMByHIEOwD6EfWvtcpSboeJ2qan4n4hRGHPmdpmIu7rYjPmtptYf/yah08QQjy7\nzaeq6hWtHOackzb6o9quXZ/PlCSpkcEuGvhWlqTDz32jvmXlbX0KCgrKdwa0zR8W9hNCvLF7\n7+aUbvlGW9eVu4P1xy8Y31MIcd9/a1RVtcqSEOLG1zbs3ZeIzhnTXQgxatnWxpwLQDYj2AHQ\nj01/P0MI0e+l7/duJ6LHOUyy4vg+uHe0a/Gg9pJk/Lw2om1eXmy3l4xSVTVY9ZYQ4th7Vjf8\nbne3zWlksIuFtlhkKafdiM+2eH/zgIbBLupfp0jS0bd82vCAcM2HQoijb/1UVVWrLNmLL2u4\nNxr83qbIpb1fa8y5AGQz5tgB0I85Ez8TQrT57+uPaR7/SzeXORH3jfv3z9oBvR8crqrROxZt\nEUIEd7364g7/8X++QwgR3P0PIUTHIYc3/G6nn1TYyPMq5jbvPHCZ+tMrJ7Z1tT+mz4jR458t\nf+f3VloJVS+Jq+rax06UGjC7+gohPGs92jGuLsMbvsRg6XS+21L74wdNPReAbGNIdQcAoHmE\nPR8+VOEVQrww+c79dn0yoVz0nyCEcHWefLzzkS8mzxdD79zwxIOy4nhqeEchhJoI//obSgap\n8Wf/4+0v7Bx158KFi5Yt/3jFu8//ffb08Tf3WrjugzPzLfsfKpuEEEffPueRfoftt8ec27Pu\n3Puf2ij90skmnAtAtkn1kCEANI91f+kthPjjXzfu05qI9s4xS7L5K9/e26/LRh0hyZZv/NGB\nBdaSXrO1Rn/lc0KI46Z+2fCl0zq6RONuxUZqN65ater7YLS+Zf3iqUKIrmNWapv73IoNfq9I\nUtfRKxp+h2hgQ3l5+bLtflW7FVtyRcO9sVCFU5GLjn2xMecCkM24FQtAJx5/YI0km2cM67BP\nq2R4ZHgHNREev/BHreG4e0eridDYl6Ys3B28cOYArdFWNHJAgfWbR8duDsa0luq1s+/e7Gnk\nqf07nunVq9eQB7+sb2l3/AlCiJg/1vAwVRVCCIOl05Sj3N/PG7m0MlC/65WxA4YNG7al7pLs\nr5x725ub6nYmym8fWBtPnHpf38afC0B2klSVmRkAMl5w92u2wiEFxzy+6+ub99vl3z7Lcdi1\nue3vqtl8vxBCCPWMPNv7tWGjtUeN50trXZbavXrGkb1uDZeecvXIs8w13784u/zEHo6F/9nl\niSVylP9xT1aNe84qLV1apZ4z4ope3TokaioWPjdnncf24uaK4a0dQoit75/T+vR3zrrniZFd\nTxw+9CTflvndjhixXWkzaGj//+vsXvf+q/PeXX/0qHlr5l4qhLApslx6XKRy7VnDrjyxU+7X\ny+b/Y9kPRSfe9NOqGSbpf58LQFZL9ZAhADSDz+/qIYQYuvTn39w7qMAqScpHnrC2uWpcdyHE\nUdd+vN9h1WtfG3jaCQU2a+kRfR5cuPHdcw+XFGsjOxCoXHHDJWccXpBjkBVnfuu+A69a8OXu\n+r0R31cXHNfOohhKj7lXa6n59l/XDOxb4nKYbO4je548efaSaN1CdFZZaj/w/e//+VCfrq0s\nBqP7sC7Dx09veEf4wOcCkM0YsQMAIYT6xRerTblHHN3JWd/0XJf8G3f3DlQtSnJXbIpc0n/p\n5gWnJfm8AHSAOXYAIISQxvb7w8n9ptRvxwLrJ//oKf7DdanrEgA0GcudAIAQQsya1PfY2x8/\neZT1mnOPlWq3lD8+tTLuLP/rqUKIigUXHHvligO81pzbt7JiYZI6CgC/j1uxALDX20/cdt9z\nb27YVBEzu3v2OfumKY9efEJj1yhuRiMuGeI6/p6nbuue/FMDyHQEOwAAAJ1gjh0AAIBOEOwA\nAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBO/D+mqydy\n2LFuuAAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DailyActivity\n",
    "activity %>%\n",
    "    ggplot(aes(x=Avg_steps,y=Avg_calories)) + \n",
    "    geom_smooth() +\n",
    "    labs(title = \"TotalSteps vs Avg_calories\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c55155b",
   "metadata": {
    "papermill": {
     "duration": 0.019582,
     "end_time": "2023-11-29T09:28:48.456420",
     "exception": false,
     "start_time": "2023-11-29T09:28:48.436838",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As a contrast to my assumption the graph is not always increasing with increase in steps.\n",
    "* From we can conclude that above 11000 steps, as the participants walk more means they burn more calories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17158526",
   "metadata": {
    "papermill": {
     "duration": 0.019474,
     "end_time": "2023-11-29T09:28:48.495272",
     "exception": false,
     "start_time": "2023-11-29T09:28:48.475798",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **TotalSteps vs Avg_very_active**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9759679a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:48.538343Z",
     "iopub.status.busy": "2023-11-29T09:28:48.536624Z",
     "iopub.status.idle": "2023-11-29T09:28:48.959481Z",
     "shell.execute_reply": "2023-11-29T09:28:48.957666Z"
    },
    "papermill": {
     "duration": 0.447837,
     "end_time": "2023-11-29T09:28:48.962541",
     "exception": false,
     "start_time": "2023-11-29T09:28:48.514704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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ByqqjY0NGhdhRH0zoidqqjCbN99LlO6JEmKqgadn2wPyMcc3j/abM+dMT7D9s3K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uGHvQ7wOdXVR3R9/LDeKix9tWdrYRgmFAq5XC6KNSDY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ldY7ZbLZiZzKGYbBXWfXF0tJPnlicKw6e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\nAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DailyActivity\n",
    "activity   %>%\n",
    "    ggplot(aes(x=Avg_steps,y=Avg_very_active)) + \n",
    "    geom_smooth() +\n",
    "    labs(title = \"TotalSteps vs Avg_very_active\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8db9e4",
   "metadata": {
    "papermill": {
     "duration": 0.021137,
     "end_time": "2023-11-29T09:28:49.004642",
     "exception": false,
     "start_time": "2023-11-29T09:28:48.983505",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From above it is clear that a linear relationship exists between the amount of time the particpants are very active and the steps they walk.\n",
    "By this we can conclude that:\n",
    "* People who walk more, are very active for a long time.\n",
    "* People who are very active for a long time, walk/work more."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0faf098b",
   "metadata": {
    "papermill": {
     "duration": 0.021211,
     "end_time": "2023-11-29T09:28:49.046817",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.025606",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **TotalSteps vs Avg_sedentary_min**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9777d25a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:49.093663Z",
     "iopub.status.busy": "2023-11-29T09:28:49.092003Z",
     "iopub.status.idle": "2023-11-29T09:28:49.385297Z",
     "shell.execute_reply": "2023-11-29T09:28:49.383417Z"
    },
    "papermill": {
     "duration": 0.320069,
     "end_time": "2023-11-29T09:28:49.388380",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.068311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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SKRSHb1wxKCXbbALhQAAAAZlo3dZQh2WQNTKAAAADIpG7vLEOyyBqZQAAAAZEw2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+/0zeIP7CV9UD8+gIguA86u6lTXIRvMFy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\nQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DailyActivity\n",
    "activity %>%\n",
    "    ggplot(aes(x=Avg_steps,y=Avg_sedentary_min)) + \n",
    "    geom_smooth() +\n",
    "    labs(title = \"TotalSteps vs Avg_sedentary_min\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abff562f",
   "metadata": {
    "papermill": {
     "duration": 0.022228,
     "end_time": "2023-11-29T09:28:49.432796",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.410568",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "1. The person who are walking less than 8000 steps and above 120000 daily are less active and sedentary.\n",
    "2. It is recomended to walk between 8000 to 12000 steps daily."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b43893",
   "metadata": {
    "papermill": {
     "duration": 0.022186,
     "end_time": "2023-11-29T09:28:49.477145",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.454959",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **Sleep time of people**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "60b768ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:49.525546Z",
     "iopub.status.busy": "2023-11-29T09:28:49.523920Z",
     "iopub.status.idle": "2023-11-29T09:28:49.810121Z",
     "shell.execute_reply": "2023-11-29T09:28:49.808261Z"
    },
    "papermill": {
     "duration": 0.313123,
     "end_time": "2023-11-29T09:28:49.812486",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.499363",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
     "data": {
      "image/png": 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OxZWlR7e3sIob+/v9mDtKhJkyZlMpmB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\nsMJJkuRyuQgeyATJZDLZbDZN08ZcpddE1aSL9RK9+n80crlckiT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DPHfn8X8ZGAkhPP2bj84dadBboz/3+1P333//W3uHqp9mcrlsznUP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\nAaxR4YbdRurxAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sleep %>%\n",
    "    ggplot(aes(x=Avg_time_sleep))+\n",
    "    geom_histogram(fill='darkblue') + \n",
    "    labs(title=\"Sleep time of people\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b36acc0",
   "metadata": {
    "papermill": {
     "duration": 0.023864,
     "end_time": "2023-11-29T09:28:49.859476",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.835612",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "I observe most participants are sleeping in the range of 350 to 525 minutes a day, which is not a good thing.\n",
    "\n",
    "Let's analyse the reason."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e7594f",
   "metadata": {
    "papermill": {
     "duration": 0.022885,
     "end_time": "2023-11-29T09:28:49.905037",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.882152",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **VeryActiveMinutes Vs TotalTimeInBed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3fc97ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:49.955240Z",
     "iopub.status.busy": "2023-11-29T09:28:49.952945Z",
     "iopub.status.idle": "2023-11-29T09:28:50.285075Z",
     "shell.execute_reply": "2023-11-29T09:28:50.283275Z"
    },
    "papermill": {
     "duration": 0.361002,
     "end_time": "2023-11-29T09:28:50.288623",
     "exception": false,
     "start_time": "2023-11-29T09:28:49.927621",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using method = 'loess' and formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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9dnbVFVdcmSJQ8//HDRfiGHw+Hun5bMzEz+1QHg/yh2AApmxYoV2v2Rc8jMzJw8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\nufLrr79mZmYKBIKePXuOGjXK7Q0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MMm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\nAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data %>%\n",
    "    ggplot(aes(x=TotalTimeInBed,y=VeryActiveMinutes))+\n",
    "    geom_point() +\n",
    "    geom_smooth() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2511ea5e",
   "metadata": {
    "papermill": {
     "duration": 0.024514,
     "end_time": "2023-11-29T09:28:50.338197",
     "exception": false,
     "start_time": "2023-11-29T09:28:50.313683",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "People who are very active and working for a long time are having TotalTimeInBed of range 375 to 525 minutes.\n",
    "\n",
    "From this I conlude that who are very active and working for a long time are neglecting sleep, which may cause health issues."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566c2893",
   "metadata": {
    "papermill": {
     "duration": 0.024272,
     "end_time": "2023-11-29T09:28:50.386604",
     "exception": false,
     "start_time": "2023-11-29T09:28:50.362332",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **Share of each type of people**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "82be4cfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T09:28:50.439126Z",
     "iopub.status.busy": "2023-11-29T09:28:50.437450Z",
     "iopub.status.idle": "2023-11-29T09:28:50.914691Z",
     "shell.execute_reply": "2023-11-29T09:28:50.912815Z"
    },
    "papermill": {
     "duration": 0.52561,
     "end_time": "2023-11-29T09:28:50.936444",
     "exception": false,
     "start_time": "2023-11-29T09:28:50.410834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2skb1WdA0CAcWXKlnfFW6o6B4DDo9gFGXe5993XjNS9qnMACEjuHEl6T7wlqnMA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     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_type_percentage %>%\n",
    "    ggplot(aes(x=\"\",y=percentage,fill = user_type)) +\n",
    "    geom_bar(stat = \"identity\",width =1) +\n",
    "    coord_polar(\"y\",start = 0) +\n",
    "    theme_minimal() +\n",
    "    theme(axis.title.x= element_blank(),\n",
    "          axis.title.y = element_blank(),\n",
    "          axis.ticks = element_blank(),\n",
    "          panel.grid = element_blank(), \n",
    "          plot.title = element_text(hjust = 0.5, size=14, face = \"bold\"),\n",
    "          axis.text.x = element_blank())+\n",
    "    geom_text(aes(label = labels),position = position_stack(vjust = 0.5))+\n",
    "    labs(title=\"User type distribution\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dbf6623",
   "metadata": {
    "papermill": {
     "duration": 0.025419,
     "end_time": "2023-11-29T09:28:50.987891",
     "exception": false,
     "start_time": "2023-11-29T09:28:50.962472",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Summarizing the insights and recommendations for the company\n",
    "\n",
    "\n",
    "![](https://imagedelivery.net/6fPUSNcQhoEe4ndYlFOD6w/326ac096-0bff-4150-b4f4-1f9ceb602e00/email600)\n",
    "\n",
    "* After 11000 steps there is a linear correlation between steps and calories burnt. So, the company should include a target steps of 11000 or above and minimal rewards or vouchers inorder to motivate the participants to burn more calories.\n",
    "\n",
    "* Participants who very active for a long time are working a lot and neglecting sleep, which have consequences of health issues. So, there should monitering of sufficient sleep for a person by gadget. And recommending to sleep when ever necessary.\n",
    "\n",
    "* By the visualizations I assume that most of the participants are working professionals and negelecting their health due to heavy work. \n",
    "\n",
    "* It is also clear that the participants are busy at the day time and free only in the evenigns.\n",
    "\n",
    "* So, I recommend the company to promote about the healt care the women should take and about the advantages and features of thier products in the evenings.\n",
    "\n",
    "* We can also observe that most people are either fairly active or lightly active. So, we should focus more on them and convert them into very active persons.\n",
    "\n",
    "Thankyou for visiting my Case study. This is my first project on Data analysis.\n",
    "\n",
    "I am happy to receive any suggestions and recommendations from you."
   ]
  }
 ],
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