Bellabeat_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "75cb7355",
   "metadata": {},
   "source": [
    "# FitBit Case Study\n",
    "Data Analysis method: Ask -> Prepare -> Process -> Analyze -> Share -> Act"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d479ec",
   "metadata": {},
   "source": [
    "## ASK\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fc8f99",
   "metadata": {},
   "source": [
    "#### The Business Task\n",
    "1) Analyze the data to understand the trends in smart device usage.  \n",
    "2) Make recommendations on how to modify device for better usage. \n",
    "#### Stakeholders\n",
    "* Primary: FitBit’s cofounder and Chief Creative Officer.  \n",
    "* Secondary: FitBit marketing analytics team and executive team.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786bd872",
   "metadata": {},
   "source": [
    "## Prepare   \n",
    "* FitBit Fitness Tracker Data: 30 eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring.  \n",
    "* Data Source: Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6851d4a3",
   "metadata": {},
   "source": [
    "## Process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e916e9ec",
   "metadata": {},
   "source": [
    "**Load R packages and CSV files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73ce4587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "package 'lubridate' successfully unpacked and MD5 sums checked\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\tC:\\Users\\asus\\AppData\\Local\\Temp\\Rtmpq4YJJk\\downloaded_packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n",
      "Registered S3 method overwritten by 'rvest':\n",
      "  method            from\n",
      "  read_xml.response xml2\n",
      "-- Attaching packages --------------------------------------- tidyverse 1.2.1 --\n",
      "v ggplot2 3.1.1       v purrr   0.3.2  \n",
      "v tibble  2.1.1       v dplyr   0.8.0.1\n",
      "v tidyr   0.8.3       v stringr 1.4.0  \n",
      "v readr   1.3.1       v forcats 0.4.0  \n",
      "-- Conflicts ------------------------------------------ tidyverse_conflicts() --\n",
      "x dplyr::filter() masks stats::filter()\n",
      "x dplyr::lag()    masks stats::lag()\n",
      "Warning message:\n",
      "\"package 'lubridate' was built under R version 3.6.3\"\n",
      "Attaching package: 'lubridate'\n",
      "\n",
      "The following objects are masked from 'package:dplyr':\n",
      "\n",
      "    intersect, setdiff, union\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    date, intersect, setdiff, union\n",
      "\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  Id = col_double(),\n",
      "  ActivityDate = col_character(),\n",
      "  TotalSteps = col_double(),\n",
      "  TotalDistance = col_double(),\n",
      "  TrackerDistance = col_double(),\n",
      "  LoggedActivitiesDistance = col_double(),\n",
      "  VeryActiveDistance = col_double(),\n",
      "  ModeratelyActiveDistance = col_double(),\n",
      "  LightActiveDistance = col_double(),\n",
      "  SedentaryActiveDistance = col_double(),\n",
      "  VeryActiveMinutes = col_double(),\n",
      "  FairlyActiveMinutes = col_double(),\n",
      "  LightlyActiveMinutes = col_double(),\n",
      "  SedentaryMinutes = col_double(),\n",
      "  Calories = col_double()\n",
      ")\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  Id = col_double(),\n",
      "  ActivityDay = col_character(),\n",
      "  Calories = col_double()\n",
      ")\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  Id = col_double(),\n",
      "  ActivityDay = col_character(),\n",
      "  SedentaryMinutes = col_double(),\n",
      "  LightlyActiveMinutes = col_double(),\n",
      "  FairlyActiveMinutes = col_double(),\n",
      "  VeryActiveMinutes = col_double(),\n",
      "  SedentaryActiveDistance = col_double(),\n",
      "  LightActiveDistance = col_double(),\n",
      "  ModeratelyActiveDistance = col_double(),\n",
      "  VeryActiveDistance = col_double()\n",
      ")\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  Id = col_double(),\n",
      "  ActivityDay = col_character(),\n",
      "  StepTotal = col_double()\n",
      ")\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  Id = col_double(),\n",
      "  SleepDay = col_character(),\n",
      "  TotalSleepRecords = col_double(),\n",
      "  TotalMinutesAsleep = col_double(),\n",
      "  TotalTimeInBed = col_double()\n",
      ")\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  Id = col_double(),\n",
      "  Date = col_character(),\n",
      "  WeightKg = col_double(),\n",
      "  WeightPounds = col_double(),\n",
      "  Fat = col_double(),\n",
      "  BMI = col_double(),\n",
      "  IsManualReport = col_logical(),\n",
      "  LogId = col_double()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "install.packages(\"lubridate\")\n",
    "\n",
    "library(tidyverse)\n",
    "library(lubridate)\n",
    "\n",
    "activity <- read_csv(\"Dataset/dailyActivity_merged.csv\")\n",
    "calories <- read_csv(\"Dataset/dailyCalories_merged.csv\")\n",
    "intensities <- read_csv(\"Dataset/dailyIntensities_merged.csv\")\n",
    "steps <- read_csv(\"Dataset/dailySteps_merged.csv\")\n",
    "sleep <- read_csv(\"Dataset/sleepDay_merged.csv\")\n",
    "weight <- read_csv(\"Dataset/weightLogInfo_merged.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4fec581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 940\n",
      "Variables: 15\n",
      "$ Id                       <dbl> 1503960366, 1503960366, 1503960366, 150396...\n",
      "$ ActivityDate             <chr> \"4/12/2016\", \"4/13/2016\", \"4/14/2016\", \"4/...\n",
      "$ TotalSteps               <dbl> 13162, 10735, 10460, 9762, 12669, 9705, 13...\n",
      "$ TotalDistance            <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, ...\n",
      "$ TrackerDistance          <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, ...\n",
      "$ LoggedActivitiesDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
      "$ VeryActiveDistance       <dbl> 1.88, 1.57, 2.44, 2.14, 2.71, 3.19, 3.25, ...\n",
      "$ ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, ...\n",
      "$ LightActiveDistance      <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, ...\n",
      "$ SedentaryActiveDistance  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
      "$ VeryActiveMinutes        <dbl> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66...\n",
      "$ FairlyActiveMinutes      <dbl> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27,...\n",
      "$ LightlyActiveMinutes     <dbl> 328, 217, 181, 209, 221, 164, 233, 264, 20...\n",
      "$ SedentaryMinutes         <dbl> 728, 776, 1218, 726, 773, 539, 1149, 775, ...\n",
      "$ Calories                 <dbl> 1985, 1797, 1776, 1745, 1863, 1728, 1921, ...\n"
     ]
    }
   ],
   "source": [
    "glimpse(activity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77a3e050",
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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    }
   ],
   "source": [
    "n_distinct(activity$Id)\n",
    "n_distinct(calories$Id)\n",
    "n_distinct(sleep$Id)\n",
    "n_distinct(weight$Id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397a9cc3",
   "metadata": {},
   "source": [
    "* Not Enough participant tracked their weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbbdfa45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><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></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": [
       "\\begin{tabular}{r|lllllllllllllll}\n",
       " Id & ActivityDate & TotalSteps & TotalDistance & TrackerDistance & LoggedActivitiesDistance & VeryActiveDistance & ModeratelyActiveDistance & LightActiveDistance & SedentaryActiveDistance & VeryActiveMinutes & FairlyActiveMinutes & LightlyActiveMinutes & SedentaryMinutes & Calories\\\\\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",
       "| Id | ActivityDate | TotalSteps | TotalDistance | TrackerDistance | LoggedActivitiesDistance | VeryActiveDistance | ModeratelyActiveDistance | LightActiveDistance | SedentaryActiveDistance | VeryActiveMinutes | FairlyActiveMinutes | LightlyActiveMinutes | SedentaryMinutes | Calories |\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    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(activity)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d164088",
   "metadata": {},
   "source": [
    "## Analyze"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccdc9e9a",
   "metadata": {},
   "source": [
    "**Clean and Manipulate the Data**\n",
    "* Merge the data.\n",
    "* Change formate of Date column from character to Date type.\n",
    "* Create a new column with Weekdays.\n",
    "* Create a new column TotalActiveMinutes summing up all active minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb0f7b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "activity_w_calories<- merge(activity, calories, by = c(\"Id\",\"Calories\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88dd8178",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_merged <- merge(activity_w_calories, sleep, by = \"Id\", all = TRUE) %>% \n",
    "  drop_na() %>% \n",
    "  select(-SleepDay, -ActivityDay, -TrackerDistance) %>% \n",
    "  rename(Date = ActivityDate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9686def",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_merged$Date <- as.Date(daily_merged$Date, \"%m/%d/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c63d6455",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_merged$Weekdays<- wday(daily_merged$Date, label = TRUE, abbr = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "289b4e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_merged <- daily_merged  %>% \n",
    "    mutate(TotalActiveMinutes = VeryActiveMinutes + FairlyActiveMinutes + LightlyActiveMinutes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6801535b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Id</th><th scope=col>Calories</th><th scope=col>Date</th><th scope=col>TotalSteps</th><th scope=col>TotalDistance</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>TotalSleepRecords</th><th scope=col>TotalMinutesAsleep</th><th scope=col>TotalTimeInBed</th><th scope=col>Weekdays</th><th scope=col>TotalActiveMinutes</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1503960366</td><td>1783      </td><td>2016-05-11</td><td>12770     </td><td>8.13      </td><td>0         </td><td>2.56      </td><td>1.01      </td><td>4.55      </td><td>0         </td><td>36        </td><td>23        </td><td>251       </td><td>669       </td><td>1         </td><td>327       </td><td>346       </td><td>Wednesday </td><td>310       </td></tr>\n",
       "\t<tr><td>1503960366</td><td>1783      </td><td>2016-05-11</td><td>12770     </td><td>8.13      </td><td>0         </td><td>2.56      </td><td>1.01      </td><td>4.55      </td><td>0         </td><td>36        </td><td>23        </td><td>251       </td><td>669       </td><td>2         </td><td>384       </td><td>407       </td><td>Wednesday </td><td>310       </td></tr>\n",
       "\t<tr><td>1503960366</td><td>1783      </td><td>2016-05-11</td><td>12770     </td><td>8.13      </td><td>0         </td><td>2.56      </td><td>1.01      </td><td>4.55      </td><td>0         </td><td>36        </td><td>23        </td><td>251       </td><td>669       </td><td>1         </td><td>412       </td><td>442       </td><td>Wednesday </td><td>310       </td></tr>\n",
       "\t<tr><td>1503960366</td><td>1783      </td><td>2016-05-11</td><td>12770     </td><td>8.13      </td><td>0         </td><td>2.56      </td><td>1.01      </td><td>4.55      </td><td>0         </td><td>36        </td><td>23        </td><td>251       </td><td>669       </td><td>2         </td><td>340       </td><td>367       </td><td>Wednesday </td><td>310       </td></tr>\n",
       "\t<tr><td>1503960366</td><td>1783      </td><td>2016-05-11</td><td>12770     </td><td>8.13      </td><td>0         </td><td>2.56      </td><td>1.01      </td><td>4.55      </td><td>0         </td><td>36        </td><td>23        </td><td>251       </td><td>669       </td><td>1         </td><td>700       </td><td>712       </td><td>Wednesday </td><td>310       </td></tr>\n",
       "\t<tr><td>1503960366</td><td>1783      </td><td>2016-05-11</td><td>12770     </td><td>8.13      </td><td>0         </td><td>2.56      </td><td>1.01      </td><td>4.55      </td><td>0         </td><td>36        </td><td>23        </td><td>251       </td><td>669       </td><td>1         </td><td>304       </td><td>320       </td><td>Wednesday </td><td>310       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       " Id & Calories & Date & TotalSteps & TotalDistance & LoggedActivitiesDistance & VeryActiveDistance & ModeratelyActiveDistance & LightActiveDistance & SedentaryActiveDistance & VeryActiveMinutes & FairlyActiveMinutes & LightlyActiveMinutes & SedentaryMinutes & TotalSleepRecords & TotalMinutesAsleep & TotalTimeInBed & Weekdays & TotalActiveMinutes\\\\\n",
       "\\hline\n",
       "\t 1503960366 & 1783       & 2016-05-11 & 12770      & 8.13       & 0          & 2.56       & 1.01       & 4.55       & 0          & 36         & 23         & 251        & 669        & 1          & 327        & 346        & Wednesday  & 310       \\\\\n",
       "\t 1503960366 & 1783       & 2016-05-11 & 12770      & 8.13       & 0          & 2.56       & 1.01       & 4.55       & 0          & 36         & 23         & 251        & 669        & 2          & 384        & 407        & Wednesday  & 310       \\\\\n",
       "\t 1503960366 & 1783       & 2016-05-11 & 12770      & 8.13       & 0          & 2.56       & 1.01       & 4.55       & 0          & 36         & 23         & 251        & 669        & 1          & 412        & 442        & Wednesday  & 310       \\\\\n",
       "\t 1503960366 & 1783       & 2016-05-11 & 12770      & 8.13       & 0          & 2.56       & 1.01       & 4.55       & 0          & 36         & 23         & 251        & 669        & 2          & 340        & 367        & Wednesday  & 310       \\\\\n",
       "\t 1503960366 & 1783       & 2016-05-11 & 12770      & 8.13       & 0          & 2.56       & 1.01       & 4.55       & 0          & 36         & 23         & 251        & 669        & 1          & 700        & 712        & Wednesday  & 310       \\\\\n",
       "\t 1503960366 & 1783       & 2016-05-11 & 12770      & 8.13       & 0          & 2.56       & 1.01       & 4.55       & 0          & 36         & 23         & 251        & 669        & 1          & 304        & 320        & Wednesday  & 310       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Id | Calories | Date | TotalSteps | TotalDistance | LoggedActivitiesDistance | VeryActiveDistance | ModeratelyActiveDistance | LightActiveDistance | SedentaryActiveDistance | VeryActiveMinutes | FairlyActiveMinutes | LightlyActiveMinutes | SedentaryMinutes | TotalSleepRecords | TotalMinutesAsleep | TotalTimeInBed | Weekdays | TotalActiveMinutes |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1503960366 | 1783       | 2016-05-11 | 12770      | 8.13       | 0          | 2.56       | 1.01       | 4.55       | 0          | 36         | 23         | 251        | 669        | 1          | 327        | 346        | Wednesday  | 310        |\n",
       "| 1503960366 | 1783       | 2016-05-11 | 12770      | 8.13       | 0          | 2.56       | 1.01       | 4.55       | 0          | 36         | 23         | 251        | 669        | 2          | 384        | 407        | Wednesday  | 310        |\n",
       "| 1503960366 | 1783       | 2016-05-11 | 12770      | 8.13       | 0          | 2.56       | 1.01       | 4.55       | 0          | 36         | 23         | 251        | 669        | 1          | 412        | 442        | Wednesday  | 310        |\n",
       "| 1503960366 | 1783       | 2016-05-11 | 12770      | 8.13       | 0          | 2.56       | 1.01       | 4.55       | 0          | 36         | 23         | 251        | 669        | 2          | 340        | 367        | Wednesday  | 310        |\n",
       "| 1503960366 | 1783       | 2016-05-11 | 12770      | 8.13       | 0          | 2.56       | 1.01       | 4.55       | 0          | 36         | 23         | 251        | 669        | 1          | 700        | 712        | Wednesday  | 310        |\n",
       "| 1503960366 | 1783       | 2016-05-11 | 12770      | 8.13       | 0          | 2.56       | 1.01       | 4.55       | 0          | 36         | 23         | 251        | 669        | 1          | 304        | 320        | Wednesday  | 310        |\n",
       "\n"
      ],
      "text/plain": [
       "  Id         Calories Date       TotalSteps TotalDistance\n",
       "1 1503960366 1783     2016-05-11 12770      8.13         \n",
       "2 1503960366 1783     2016-05-11 12770      8.13         \n",
       "3 1503960366 1783     2016-05-11 12770      8.13         \n",
       "4 1503960366 1783     2016-05-11 12770      8.13         \n",
       "5 1503960366 1783     2016-05-11 12770      8.13         \n",
       "6 1503960366 1783     2016-05-11 12770      8.13         \n",
       "  LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance\n",
       "1 0                        2.56               1.01                    \n",
       "2 0                        2.56               1.01                    \n",
       "3 0                        2.56               1.01                    \n",
       "4 0                        2.56               1.01                    \n",
       "5 0                        2.56               1.01                    \n",
       "6 0                        2.56               1.01                    \n",
       "  LightActiveDistance SedentaryActiveDistance VeryActiveMinutes\n",
       "1 4.55                0                       36               \n",
       "2 4.55                0                       36               \n",
       "3 4.55                0                       36               \n",
       "4 4.55                0                       36               \n",
       "5 4.55                0                       36               \n",
       "6 4.55                0                       36               \n",
       "  FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes TotalSleepRecords\n",
       "1 23                  251                  669              1                \n",
       "2 23                  251                  669              2                \n",
       "3 23                  251                  669              1                \n",
       "4 23                  251                  669              2                \n",
       "5 23                  251                  669              1                \n",
       "6 23                  251                  669              1                \n",
       "  TotalMinutesAsleep TotalTimeInBed Weekdays  TotalActiveMinutes\n",
       "1 327                346            Wednesday 310               \n",
       "2 384                407            Wednesday 310               \n",
       "3 412                442            Wednesday 310               \n",
       "4 340                367            Wednesday 310               \n",
       "5 700                712            Wednesday 310               \n",
       "6 304                320            Wednesday 310               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(daily_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f3fc4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Id'</li>\n",
       "\t<li>'Calories'</li>\n",
       "\t<li>'Date'</li>\n",
       "\t<li>'TotalSteps'</li>\n",
       "\t<li>'TotalDistance'</li>\n",
       "\t<li>'LoggedActivitiesDistance'</li>\n",
       "\t<li>'VeryActiveDistance'</li>\n",
       "\t<li>'ModeratelyActiveDistance'</li>\n",
       "\t<li>'LightActiveDistance'</li>\n",
       "\t<li>'SedentaryActiveDistance'</li>\n",
       "\t<li>'VeryActiveMinutes'</li>\n",
       "\t<li>'FairlyActiveMinutes'</li>\n",
       "\t<li>'LightlyActiveMinutes'</li>\n",
       "\t<li>'SedentaryMinutes'</li>\n",
       "\t<li>'TotalSleepRecords'</li>\n",
       "\t<li>'TotalMinutesAsleep'</li>\n",
       "\t<li>'TotalTimeInBed'</li>\n",
       "\t<li>'Weekdays'</li>\n",
       "\t<li>'TotalActiveMinutes'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Id'\n",
       "\\item 'Calories'\n",
       "\\item 'Date'\n",
       "\\item 'TotalSteps'\n",
       "\\item 'TotalDistance'\n",
       "\\item 'LoggedActivitiesDistance'\n",
       "\\item 'VeryActiveDistance'\n",
       "\\item 'ModeratelyActiveDistance'\n",
       "\\item 'LightActiveDistance'\n",
       "\\item 'SedentaryActiveDistance'\n",
       "\\item 'VeryActiveMinutes'\n",
       "\\item 'FairlyActiveMinutes'\n",
       "\\item 'LightlyActiveMinutes'\n",
       "\\item 'SedentaryMinutes'\n",
       "\\item 'TotalSleepRecords'\n",
       "\\item 'TotalMinutesAsleep'\n",
       "\\item 'TotalTimeInBed'\n",
       "\\item 'Weekdays'\n",
       "\\item 'TotalActiveMinutes'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Id'\n",
       "2. 'Calories'\n",
       "3. 'Date'\n",
       "4. 'TotalSteps'\n",
       "5. 'TotalDistance'\n",
       "6. 'LoggedActivitiesDistance'\n",
       "7. 'VeryActiveDistance'\n",
       "8. 'ModeratelyActiveDistance'\n",
       "9. 'LightActiveDistance'\n",
       "10. 'SedentaryActiveDistance'\n",
       "11. 'VeryActiveMinutes'\n",
       "12. 'FairlyActiveMinutes'\n",
       "13. 'LightlyActiveMinutes'\n",
       "14. 'SedentaryMinutes'\n",
       "15. 'TotalSleepRecords'\n",
       "16. 'TotalMinutesAsleep'\n",
       "17. 'TotalTimeInBed'\n",
       "18. 'Weekdays'\n",
       "19. 'TotalActiveMinutes'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Id\"                       \"Calories\"                \n",
       " [3] \"Date\"                     \"TotalSteps\"              \n",
       " [5] \"TotalDistance\"            \"LoggedActivitiesDistance\"\n",
       " [7] \"VeryActiveDistance\"       \"ModeratelyActiveDistance\"\n",
       " [9] \"LightActiveDistance\"      \"SedentaryActiveDistance\" \n",
       "[11] \"VeryActiveMinutes\"        \"FairlyActiveMinutes\"     \n",
       "[13] \"LightlyActiveMinutes\"     \"SedentaryMinutes\"        \n",
       "[15] \"TotalSleepRecords\"        \"TotalMinutesAsleep\"      \n",
       "[17] \"TotalTimeInBed\"           \"Weekdays\"                \n",
       "[19] \"TotalActiveMinutes\"      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(daily_merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce59e54e",
   "metadata": {},
   "source": [
    "**Summary of daily_merged**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bfd3ad76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   TotalSteps      TotalDistance       Calories   \n",
       " Min.   :    0.0   Min.   : 0.000   Min.   :   0  \n",
       " 1st Qu.:   12.5   1st Qu.: 0.010   1st Qu.:1698  \n",
       " Median : 6530.0   Median : 4.530   Median :2018  \n",
       " Mean   : 6385.3   Mean   : 4.506   Mean   :2218  \n",
       " 3rd Qu.:10524.0   3rd Qu.: 7.380   3rd Qu.:2640  \n",
       " Max.   :22988.0   Max.   :17.950   Max.   :4900  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_merged  %>% \n",
    "    select(TotalSteps, TotalDistance, Calories)  %>% \n",
    "    summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "457fe67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes\n",
       " Min.   :  0.00    Min.   :  0.00      Min.   :  0.0        Min.   :   0.0  \n",
       " 1st Qu.:  0.00    1st Qu.:  0.00      1st Qu.:  0.0        1st Qu.: 687.0  \n",
       " Median :  0.00    Median :  4.00      Median :174.0        Median : 782.0  \n",
       " Mean   : 18.69    Mean   : 13.87      Mean   :157.2        Mean   : 936.9  \n",
       " 3rd Qu.: 28.00    3rd Qu.: 20.00      3rd Qu.:240.0        3rd Qu.:1440.0  \n",
       " Max.   :210.00    Max.   :143.00      Max.   :518.0        Max.   :1440.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 0.8283708\n"
     ]
    }
   ],
   "source": [
    "daily_merged %>% \n",
    "    select(VeryActiveMinutes,FairlyActiveMinutes,LightlyActiveMinutes,SedentaryMinutes) %>% \n",
    "    summary()\n",
    "\n",
    "print(mean(daily_merged$LightlyActiveMinutes)/mean(daily_merged$TotalActiveMinutes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "55b27566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " TotalMinutesAsleep TotalTimeInBed  TotalActiveMinutes\n",
       " Min.   : 58.0      Min.   : 61.0   Min.   :  0.0     \n",
       " 1st Qu.:359.0      1st Qu.:402.0   1st Qu.:  0.0     \n",
       " Median :427.0      Median :459.0   Median :227.0     \n",
       " Mean   :416.8      Mean   :455.7   Mean   :189.7     \n",
       " 3rd Qu.:490.0      3rd Qu.:527.0   3rd Qu.:296.0     \n",
       " Max.   :796.0      Max.   :961.0   Max.   :540.0     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_merged %>% \n",
    "    select(TotalMinutesAsleep,TotalTimeInBed,TotalActiveMinutes) %>% \n",
    "    summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2bf6bc",
   "metadata": {},
   "source": [
    "**Key Findings**\n",
    "* Average steps taken were 6385.\n",
    "* Average distance were 4.5 miles.\n",
    "* Lightly Active Minutes took up 82.8% of all.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec01d345",
   "metadata": {},
   "source": [
    "## Share\n",
    "### Creating Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88bc5574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oqIMrJqEVGwqjIjs8YvnFCwqsu0DUS0YVwXMqoEMHRoBWHPRNR1xib3HDptaT+u\n+q/QH9/U2vCAjfl+5WcXqqo0OaHdrLPs0C7Z84PvVH9D8vrdl7txfpCyoIQjjUEJR0oh7FmJ\ngavI9hyuxxxhz0qso1c8G9ZA6xXPSo9sz4yiyMpDItLXY940oVuNl0j2zGRkZrI9M0HJq+RL\ns0ZHgWzPjL6SNIhI+3HLuRk+JeJ/fbYz3Bnkv3Nke3aqBnr92I7cHDmbgrBnJQYgIqFgyI0W\n8bq+mhj2hzve+XchXYFApw66McOh9WqNcPMIE1idqRuzdYcON4OQahqz4QxCZa9sOifN7ceV\ntB9XUnnquP4S2ZgVe7YI1zfrl7bo0HGYNZhuyIqsOLSu1MKh9fqNjfl+bqLHszMIZXRjhkMD\nG4SCrjQrV5bgHp/PJ3cKVw7XD2IkHiUcOTk5Ii4tLXW8HwATtk3va3EVDrvVma7y26eWbBin\n1lQITLxZJoqVN9qPKxFx5anjwarK2hddIh8QnTcrsECHQiG5qEOvBDCs3/hg4aj4zB1MB25+\nbL7S037c8q3TIixWY179nBTeDIAjoAY6nXFdoHNychQJ5odO9QMQkYir1wmi8+bNk3spNRv1\nLvtJFOdR6DJt/Yl//52Ids19zMrxFqcbCkXOqneR8pSedc7IzKo4fpQdWi+SVtg4Pkep4tg4\nPof0apBz+RWfz6cURssObVL9LOehlZoN1EC7TXRzBwFITRIh0JxRjv91gQJKOFIHfdYg5hHG\nDXnWYL3LflJx/GiXaRssliUoswaJ6KIrq/dQuPCKnxFRq2EvRzyJsOdQKNhhwsoOE4yX4VMS\nzFaGR0QVx49GtGeGjVmJa7y25reT7NB0bh4hGVUCmCMbczrb811j3+LGD3sVH+AWyzn1VHRG\nrTodJ62J5ZxeQ581iHmEwDqr77lcbtGVPOsniXhd1DR7Adcz0MgTx5O9S8ZjFQ43uHPw80TV\nf/G/99Iww2NE1rni+FHR2WXaBqGGJuyeN1jUPQt7lmk17GWRh9YzzbI9i5d0mLByy5QaCXXZ\nnpnKU8dFHrqq4pTJCNnIlRMaIju0YN/c/uXl5fosQ8bKW2RCOnszI7yZ4/pXXy8e9io+sCrv\n5nAvPPDyUKWKQ5HmjFp1gmfPiNiZ4XqM1+++HKtwgOi4e/nhhJxEzkAjIZ0oUmQZuyZNmsgP\n9+/f7+rlsrOzXT1/LPxl/fN2X+Ll27GLG/dyfZ/xwp6J6M7Bc794uyDcwbI9M37V6MAAACAA\nSURBVF2mbfjouQcjXuUPyyb89qklJgfwrTUZcj4b3XHi6v0vnq/u0PMWHSas/OSlQebXlb3Z\nl5kZqqoxvS9Ycz0yKycMR4fxb2fWrmv4VNJ9Ah0fcNNhr3Gwb+5Ddl/7m0fVf/LHvv5Ccehd\n+WG3Q5cd+sDLQ/UDdG/28s8rurFteNSLd+Tl99kuqXQvNUiEuUKXPUKcBFrM/3MpIa0Ys6sb\n7WZnZ3tzI9/ocs+evZ0ocONe5Nyz4Po++eHy0IaEG5UyZ3HDuK5dptUoF+YaaHESkWkWNBny\nsnnFs903RHZoYc/ypty3DF5gJQ+t0GH820RUVXFacehQKLRpQrfk+gQ6/jGTp042Hfaa9fIV\n6xgOuO3TSzjY8Wz/cIdtntRDqdnYPKmHZ39e+G3mTTxyL25IPLbyTmfiVANdeg55JQ3gIFgB\nOuG88+Ttto433DlcXnlDtmfzeYQWN1vZOi1X6amqOKVXQm+dlrt1Wq6hPUcH2/O5K54WMduz\nyau4xXh1j2O4fYyVF9771lfcoruusGeOH1p39KF16pcnzOZJPQxjAEBCtvJG3bNHSJESjjTH\ncAVo1ECb0OrJYg52PZ/n4GnfefL2u5//QKniMCzwNdk5nB06Ozv7hvunVQ/SwiocvOm3kg7R\nU8Vbp+WKSmjZp8WkQ/GS7TPvl/u10VZ77ZapfSKOTYEd+uv31lzTpm+nKesMHVr25g7j347i\nKm4gcrEJ90jZm6+4ofm/P98jPyvXbxCRqIFuM+IVDnyZ6p4433ywtuHt3R9ad/S1bpeQRsLv\nFwBvkvAMNAqgE0i8l7EDIOEIe+b47KkfODbcpOO9l57UqzhM6jc4Dy3W39Dtmf1V39VPR/dm\ntmSlR4l1FVbQ89AmB2+Z0ltx6C1Tejtrt/rO53rW2QsOLVcydJy0JoFOqWedZYducMPtRHTm\nh++++7NYguNmkuyZiEJVVbpDn/zunxdc9pNwDg0AMCBBNdCGSWilX4h1uH4QI8hAg/RCtmci\nEvZMRLcPnG3FoXV71p1V9ub2+Ss42Fpwj7wORt3LruTg9Hc2pmDLDr15cs87B82tHtWCYcoY\nnEJ2aMWemQ7j3+Z6j23T7zU+w9Q+yqu+fq9GWa3u0F5DX7vNKYe2uH1MRLbNuE/E9731/yR7\npjYjFhOp/7s1dGgAgC0SJaPhrmu3H8RCPJL/8d+JMA0nESpVHNbrN7x5O9Fhfi/NB8zioPaF\n9TmQ7Vlgd7NoPUErPxT2XM25f271sn8qd5/+7rCyh4uVn4uwZ0Y4tKsYFiWLgulwDp2dnX3L\noOp1+r5+b83Vd1arpy+jehqGLNCGl0hsBtpw8WMHk9BWto8RGNY9L7vvGhG3GbHY6HWqQ8sC\nnX39b4nogst+QkSvdbuk98KD3L9yYOOI4/EO6fPbLLnwyL24MYkwZ0mUkxDMKe1/TeSDQKKJ\nRwY6nOw61Q8IK0BHQtgzEVWcOCYcOkb0EmF59WXVnonI56NQSLFnkrLR1lHsmXvi49Am3PXM\nMpM89C9a5RKRsGciCgWDwqHlIxWHTnj9htvYyjovu+8axaFle46CcPZMRL0XHkwuhwYgriCz\nm8aghCN1gDeHQ7ZnxkGHtkjl6RMcZNW5IJ7XdRzdbpW1ok34064SPbscCgZ1P5avErs9K8sF\nRoHham4xjsoWLYe+wMH1PYdRTYeOzp7Lih4honte+4IfGtozA4cGIBwJn0QIEggEGqQvter9\nSKnisFu/YRFhz0RUeeak4THtxizdPrOfG1d3HNluFXvOrFWHS7QtrqxnfpUYz8AoywVG7dDy\nhnzCnuUC9xjGGAFhz0T0xeq51/cclrcry8Sbz546UavehXJPWdEAkuYRsj0T0Y5Rd3rhu3UA\nkhVkoNOYOK0DDYDX2PV83q7n82RjjsKe9Rl7cg9LlWzPzA9//4vSc+Lw10TUbsxS65fWqzXi\nWb+xZWofbnJnprRlnb7nS0IwXC4wivO0H7dcxMKk5RIdg3Idy+Qu+ZKb4bOyPTNfrJ5LRHm7\njNMfdw5+nojOnjr/qRPxZdfexE1/Vb8Vf+dmf/gApC8hd0j0bQFL2BPosrIyMZPv0KFDPp8v\nJycnELC07D8AUXBHXhG3WE6yZ/Fok54PFo7iFt3JZWM2WHc5TGJy3VMtRMz2HAWyMSeq+llU\nPGdqGz4bOrRhtYYbA3MQ2Z5Fj27M0Tm07M3hHNo6Q/ZcJOKzp05w44e9FnwinpJjIpK9uW59\ndaIV6jcACEso6EoDyYCNEo6ysrK2bdtyfOTIkZEjRxJRIBAIBAKlpaV+v/Pbz4I0R/bmO/KK\n3i8eEfWp9iweLSqhdZ+OEd2bWw59kYPd84cYvuT2gYX17E8c1En4rEE659DWU87O1je7xF3P\nVEvztul93buKbsy5S74s6X+dldd+e+iT3oeIJMGV7VlBMWbuWTXoFqppz0zd+tmnj1XXdcCe\nATAhFES2OH2xIdArV64koi+//JKIJk6cGAgEduzYce2111599dWLFi2CQANnuSOvKFh5luOM\nrFrkhEM7M7JICHvmePf8IW1GviofcOY/3xHRqe8OKw7tYA10q2HntlqcG9NWi3cOeZ6D9158\nMtYxScTNm7dM7a3UbFipgRb2TETiZ5dVp56zY7PI7vmPK1Uc1/cc9u2h80LMk/zYnj8veTYj\nq5b4h8O899KTukCbA28GwBLIFqcxNko4iouLiahRo0YHDx4sLi7Oy8tr06ZNw4YNiQhVHMBx\nZAlQhCDOtBn5apuRr/pnbeVmfrBsz6KnrPBh8ZDtmTklbaHihj0rsV2EPSuxIfqsQYvzCNs+\n/To3u8OziGzMdu25quKUiCvPnI+3TuurF+e4N49w9/zHRazYM8NLZ3xe8iw/5D84mfdecvIv\nHwCADGqg0xkbAs055iNHjuzbt4+Ixo4dS0SHDh0STwHgFM0HzFR62KH1xY/dhhOQF17xc9ET\n0aENKSt8mJvSf+q7w6e+O+ySPYfrsYJuzLYc2ro9G8ZR03r4Ym5y55apvbnZOpVsz0zlmVPc\n+KFszNHZs16tEa5+Y/f8x7kVtzJeNFDYM5ORVSsjq5awZ67WkBE9S+/5mfKU3gMAMCYUcqWB\nZMBGCcejjz4aCASuvPJKIvL7/Zx7vu6664jo3nuNt04AwEF8vkyys13IHXlzOHi/eHh0V9Tt\nmfHP2hoY3T66c6Y24by58UOFHPgyMrbPeoCI7hr7FhFlZGbJC+G1ffr1Hc8+GPXVZW9uPXzx\nzjkDoj6VOW1Gvsp/C8WedS7pf52ohBb2fM+rf+RgxcO/jPH8glWDbhGFHIpPL73nZ6ISGvYM\ngHWQLU5nbAi03+9fvnx53759/X7/1KlTRee9996bm5vrzvBAcvPbByZz8NEbE+N8abbni392\nLRF1nlq6cXxOpFc4w+75Q5QqDmUe4QcLR94+sFDpicfI4oioG2bLbDtqiXgqFAy2G/2GL+P8\nJtKKQ0eNknWmaB162/S+chVHOIRDC8Q0yrqXXbn2yTusX1HJOgt75lh36JUDGyvbnawc2Ljl\nEx9EvJCehxbAmwGIAmykks7YW8YuNzc3FAqVlpY2blw9xaS0tBT2DAwR9qzEVtizeIzSk1mr\nrvWXy/bMdJ7q7j7wLYbM40Y1jdlwFQ7ZmB23Z33WYHTzCN978cl6l17JTfToh3GBuDxLUoll\ne2Zke2YyMr21oxOvvJFZ28asQXkRktPfHe7+/PvRXVq253A9VHOSH8e75w1WjtF7AAAOg2Xs\n0hhspAJcQTfmWBxatmeL9RuyPTNRODSnGE/8+/+UfqV+g71ZjnfPH8It3Jk/WDiSm90hWUE2\n5qhX4WifXyLiepdeGc6elVhZb8QusdRvOMu26X23Te+r16wboi/hF4tDW2TlwMbcRI9szLBn\nAOIAJhGmM7YFuqysrKioyOfz+Xw+Iho/fvw333zjwsBAWnNH3pw78uZkZNXes3iMbNLxX/ZY\nd2gTew7XE392zc3jFt3LZXsO16O7cmLtWa/WcKQG+oLLf8pN6bfo1nFm97zB3BI9EADSg0RP\nImQTkx8KrPSDWLAh0MeOHRs0aFDbtm15CxWmoKDg6quv5rU4AHAEMflPxO8tGMbN4hminjVo\nSFnhwxU/fP/9X3///V9/j7mDdtFrBEPBKqVn24z7orPnVk8Wc+OHsjE7PoNQdmj37FmveHZw\nHiEAwFlCoaAbzeLVdUuW09ji2XD9IEZsCPTbb79dXFy8fPly+fuFDz/8kIiWLl3q/NCAN2j2\n4BRutl6lzxq0OI9QtudwPVbQZw1GPY/wjseKDGOzl0Q15hRAN8uywod3zO4vHvIqHNtm3Cd6\n5NgWwpvleOecAdyiO6c54bLR+tojducRysjGDHsGwNMkLgPNWuz2/QETbPwA+K8WPj5c7BHK\ny8vdO3l2drar548z5rejePPe1yfYOnkUq3AYqqfFjLJ+L6Lu2RF7Pj+el2tsiKjUbIg1JWJJ\nhCf8Y6bUbGwtMJgrrNRsCHtWVuEgF25HtmfBrudj2nYxHF1nbJYf7i263/Beol6FI4Ek/GPm\nLKl0O8l4L33f+AsHyx+oMQXFI/eSnZ3t+DnvmrPX8XMS0bbhzcwPEPYsa7Si1IbH6A9B1ECg\nbeOR3wVOYXI7hllnuw5tF2cF2oHxWBDo7s+//92hAxxf1ujm8i/2nT8yWof2wsdMOLShPTO6\nKxti8XbuHPQcB+8teMrksBaPz8/IrKX3X/o/N77zRHPx8LGy6m/YXm4T66x24dDrx3b0wo/G\nKVLpXii1bifp7kXYMyM7tEfuxQ2BVriraE8Ur9o2orn88K6iPUqPDgTaC9hYPWrhwoUDBw4s\nKSmR160rKSnhp5wfGkh+mj1UwEHtC+sT0bsvDI34kveLhysObcVBmz1Ynere+3q8F5zmxRYu\na3Sz6Mm+vqns0MmLiTcLdG8esPkMB4s71rF1OWHPHIdz6BaPzzc5yd3z9rBDC3vmOEaHXj+2\nYxSv6jZnNwfrhreM5eoAeBzFnrlHyUOnJO0KI6+/7vhJYMAewUYNdJ8+ffx+f9++feXKdN5X\npVu3bu4MDyQxwp6JqOLEMYqkPgLZmG3ZsxI7gpJsNuwBAmHPShwR2Z7D9ZD0EQpWnVWeuvR/\nbhSxbM/hetxG2LMSAyvcX/I3bokeCABmxH8SIezZO9j4n0r9+vVLS0tLS0vz8qoLDfPy8pYv\nX7506dIGDRq4MzyQSIKVqqPoPeGQ7Zmx69DcIl9IM2ZXHdqWPTu7GIhMuzFvcnPp/NGhG7Mt\nh7aL7NA/axpNhtg9dGOGQ1tH9mY4NPA0iZhEqKxJh1U1EoXtDcD8fr/f71+wYIEbowFeI1h5\nNiOrlogTO5gEYuLNa5+8Q9kyg+s3XLVnOd4+836XLuR92KGvus1/8tt/cI9cAw2SEd2Y7y/5\n25u5VyVkMMAiyx+41qQGOoWJfzJYuSIS0gkEOxGCsOx7ayoRBSvPchM9QEFecmHtk3dEzJ03\nvS+fWxTX0rPOXstDR4de8WxYA62X0Rvas17xHPs8QgCACbIxp4k9E2Er77Qm8t8uYpEN868J\nPPU3EFbhYG7uXb3lzYGVheGOiXg7Te8bz4Fde1aqOKzPIzSh5RMvcSDvtabUbMR/HqEtFG/e\n95Za68KE+7kY6nKck9D9137HwZLulylPKTUbYh6h46twcPC/nR9Rnnql8wUidnAVDhmL96LU\nbHhzHqEHf5sZ1mxYzEB78Hai5oHVRzh4o2fSV0h65OfixiocraeXOX5OItr5TBuLR+orbIjY\nSj+IBQi0bTzyuyAiwp6ZcA7t6u0oq3BUnjqx55Uxpq8wQ9gzY+jQyWXPjKFDe1aghT1Xc+7X\nwpJul3JguAqHGx+zRzae1DtlgXYJ6/fi/VU4vPnbTHFo6/Ub3rydKBD2zCS7Q3vk5+KKQE/b\n7vg5iWjnuHZunBY4S+QaaGHGnlJkYI5iz9xjkod2iYyM6g9Y5akTHDR/ZGZ0Dq3YM/cIh2Zv\nzs7O/kXP6pO//7L6DqQG22ferzi0F+yZiPqv+54d2u7qde7Rdea5xZvHJGaKoWe92eO8mXuV\ncOg0rH5W7Jl7kt2hUxV4UTpjowZ6/PjxixYtcm8oAMSIsGciuuOxeP+1EDdkY07kDELtK6n+\n676P+mR9Xvmcm+hp9WQxt3Av0ZPNco+wZyUGScGbuVdxS/RAADAlcVt5g4RjQ6APHjw4cOBA\n94YCQCzoxuxBh9arNcLVQJuzfeb93JwYVOKRvZlj2ZstOnQ4ew7X403uefWP3BI9EABAZELu\nkOjbApawIdAvvPBCfn5+SUnJkSPqF0zAa+jVGvGv3yAivVoj6hpoueI5XI+g8sxpbrYu0fqp\nhdyiGZ9lZGOOzp4TiD5rMHZke2Z0YzZ3aG6ODyz+yN4Mh05b9GoN1G94F6zCkcbYWAf66quv\nNnkWfzN5jQMrC62swhGR285NBPzwteoJcGImXDj/a3LPaA72r5i155UxzR+ZefbU8agHINg9\nb3DLJ1469e0/zQ+TvblZ/4K9SyytFid7c+unFu58zsUvW8T71mP+Xg7WDG3m3uWio3NBgION\n+X65f0n3y6oroUMhpYpDzCMEUaAb8z2v/nHFw79MyGBAYnmjZ4NUWoUjhTHfNRCkNjaW4MYq\nHIxHJhQ7hfnt3FZzKbrKM6eVT4FwwVvvHcdBqKpSPmD/ilm33ltDYT9eFn3aVaypd+7qNVbW\nu+OxQsOsc0SHNsw6W3ToZg9Oqb7K6xOsHC8Q9swoDp3Yj5mwZ0ZxaBlR92xuzya3o2egj/zu\nff2wXc/nmZxfR6nZcHAeoUs/Glmg6132Ew6WdHf3b5K0+m2WXOBe3BiG4+dsMXG94+ckoncn\nd3XjtMBZbJRwoGQn3bhN2447q05d5UfN2Whhz0Tky6zxtYZiz4Y9FlHsWe/50+qZ0Z05aoQ9\nK3FEFHsmonvf/Cs3Z0YWA4o9G/YIlnS7lFvUl3v7kRuUHt2V7doz1TTmRK3CER3Cnomo/9ro\n52UCAFwHkwjTGNtbeScFbvyhGc/zxxm7t6N8dS/bM+PLzFLy0LaueHWbhzn4uuzViIOxMvjo\nfl4RX3VtlyeUnmYPTvnLhnlRXKvupee/pb33zb9uHdbUygDiiTKYxg9VFwUdfM3qcoEmt1M2\nulWbWbtETESfv5V/w33Vf799/laUf3HtLXRrkqUbP5odT7do++y7sj0z/dd+v37A/zp+OYGn\nPmaxk0q3g3vxPijhSGfsCfSxY8c2bdq0e/fu4uJiIsrLy2vZsmWnTp3q16/vzvCiBCUc1knI\n7Zhc8ZY+T4v46jYPf/L2s9ZPlZ2dvXdJfrP+NRLne5fkR7zBnc8NVKo4dj43MLq3JYpXyfbM\ntJ+7b+uwpp76mMmDaTtqiYgbP1R4yTW/Wj34VvOXR/yYiTy0OExknT31PpCb/2RWPPxLw5Sz\ne+8Afpt5FtyLG8Nw/qTIFqcxNko4jhw50q9fv759+7I9E1FxcXHfvn379euHdTmSjqb3T+Bm\ncoyYNSjQK4wjriOhVzyb1EDL9qz36HuJG+4uLlc8W5xBSDUrnh2cQZgzewc3pd+DswYFesWz\n3CPbM3P0qz/0fOljt0cFAABeA8vYpTM2BHrdunWBQCA/P//w4cP8Mz58+HB+fn4gEFi3bp17\nQwSOI3vzf3cMuxgc1XRoYc+ifoPt+eNl05RXifqN/StmUU1jluPWwxdxsz5y2ZgN7ZnZuySf\nm/UzE9HO5wZys3i8PmtQ6ZG92a5D3zJ4QYcJKy2OxHFkYzaZQSgDh3YEfdag2/MIAQDRg2Xs\n0hjbq3Dox4frTyAo4TDBMOu8780pRHRzz6f44YHVzykHiJl/Sv745l4jOMisXffcAapPG6J4\n8845j5JRBpqIIlZxCBLyowm3CoduzERUOqotB80HzOJgz+LRObN3XPSTa+TDvj30iYi3TOlt\nazx9Xz/EwfIHG9l6oUX0DDQRXXLNr4jIpJAj2f/VyMThXkQhB1bhsEUq3Q7uxY1hOH7O259Z\n5fg5ieiD6b3cOC1wltScRAiiQNgzx4pDG9ZdCHsmoqqK0wdWFVm8lp51bj180c45j37y9rOK\nQ1u350Rhd/U6kuyZ4+zrmhz/51fCoWV7JqIOE1Zad2hhzxzrDt1iyHwO3n1xqN1hMztm91cc\nmu0ZOAiyzgAkBaEgssXpi40SjoULFxLR+PHjRcXzkSNHxo8fL54CnqJJ7lhut/QZdUufUeYH\ny/Ycrkc9QLLncD1RIBuz9+05CmR7Zsq/3E9Ex//51fF/fqXYs0W6ztzCTemXfZoke1Ziu+yY\n3V/Ewp4jziMEAIBUI0HL2PkkYukHsWAjA92tW7dAIFBQUFBQUCMZ6ff7u3Xr5vTAQEw0yR0r\nYl9GVihYeUufUZ+8PZuI9r05Rani2PfmlIi6HE88682dC6rXzN+YH3mV+9JRbZUqDlG/4Syy\nN//wz69+VLMaRKAbc4sh82PJQ5NU9+y2PfdZ9HsO3n70165eCAAArJOQ4lWfr0bxrXhotx/E\niI0MdIMGDZYuXbp8+fK8vOrlpfLy8pYvX7506dIGDbDXaLy5pfdIbvpTsj0zvowsIhJ5aK54\nZv66+SXXxhiWE4e/jtjjNYQ9K7EJsjGb2/Pxw99wUOdHlylPmddv6FnnH/75lX5YiyGRV6fO\nXfIlt4hHClYPvpWb9ZdEgbBnJQbxoffLn3JL9EAA8CBBdxpIAuzVQNevXz83Nzc3N3fBggUu\nDQhYQfbmm3sO5+DA6jnWzyA7NBEdWP2ckoTW5xEqHFhVpNRs6DXQJmM7cfjrC6+8WsTWRp0w\ndGPuXLDeJA8ttmXWvXnP4tFKFUfdSxoQ0fHD31x0ZUM+Xqy/YXcGoYL1eYSyN+cu+bKk/3Wx\nXDccPV/az8HqwU1Ep3ivVjz8S/0lujH3WfR75KHjhuzNvV/+dOVjv0ngYADwGqFgAlK5yB97\nBBsZaOARZHuWZzAIW40O2Zgj2nP1YZIxm9izPjZe/+7E4a+5kYX1pJMIYYQibjtqCTfu3LN4\ntDiA7ZkReegtU3p/8tIgp+1Z/Z0r6jf0rLOtPLRFhD3Lsf5eAe+gZ51jyUP3Kj7ALbZBAeAh\n9hbeJ7foKp4NTgKSgcgZaIsl5/iTKP7o839v7jn8wOo5+0tmKFUcoWAlEXEN9PmDz4n4gZXV\n2zJb9GaZcCtv6DbPYxMP971V0PS+fBHbva5n0S1QXrOi7aglXD28Z/HoNiNfi/1y68d0UKo4\n1o/poB/27otPtBgyjyhE5DvXY6P6Wfh01JnpllM2Kz09X9qfVfcCpfOeV/9omIcGyY7szb2K\nD6zKu9n6a+NWag+AXZqNWJqokwg3g30lCmSgU5OqilMiNrdnJY4n+94q4JaQq9tCr9awMo+Q\niMq/2Kf0GK6jbELPBfu5hTtANmZDe2beffEJIiIKEYWis2dyKDMdqqrkZuXgyjMnlR7UbyQd\netbZeh5a3qAHm/UArxEKBd1o1i5dDRbWSBSRBRo7T3qNT84ljMPxmx7DiKiq4hS3YOXZYOVZ\n+YDmj8yse3F23YvPryqfKIdOImRjPln+91bDXm417OVYTlhW+FDEHtmbzR2am/kV333xCW5K\nv55XFj2OV3dY9GamxwsfUU2H1n0auIde8RznGmjdmOHQwFskaBk74AWQgU5K2KF9GeqPz8o8\nwuaPzBSx7NDOoo/E1hxHp+gwYRU3R862Mb/rxvyuJ8v/Lnp0hzapQLj4qkbcRI9szOb2HK6H\nueuZZdxMh2+G7NBuzCDcPaEjafZ89sR/lMPCvXuVZ05yc3xgwBzZmCvPnE7gSADwIO4koCMI\nNFLOHsGeQJeUlPjC4NL4QDg+WVn4ycpC2Urt2jMTH4dOlD0bxrGgG7O5Q4s9R2RvvnveHhGX\nFT7ELeohyd4co0Nzs3j83fP2dHvuXW5WjpdX3hDIDn3m2Lfdn3uPiO559QtuFkdihe7PvcvN\nwXOmCZVnTnMjorvnfRjFGfSKZ1s10AB4GCxjl77YWE+7pKSkb9++4Z71VBVHeXm5eyfPzs52\n9fyOwFUcgk/XzOVAF2giOv2fch/5iOiTlbP1Z5OFu8a+ycG2GfdTGGPeMqVXdCdv8fgLHGRk\n1dKf3TX3MfOXy8YseOeJ5vJDsesKr2SXnZ3dcqo68Y6IVg+qoaGGxrxt+r3m47GFUrPBhn33\nvD3BmunkdU+1MDkJ/6vpMX+v/pRSX1Snfo2/6MQf5xUnjhHRmsd/a2Po51C8ea3pUCOSFL8B\nLBLxXgyN+Z0nboviWqLuWdhz97nvcbB22J3hXqXUbJjPI0yrH00S4ZF7yc52Plt06xOLHT8n\nEX08b4D5AXLWUtkkxVY/iAUbGehly5YR0ddff52fn09Ehw8fPnr0KMdffun8olfAnJu6D+Vm\n+KwwZiU2xEdJ/wWCsGc5FlXgMZ5c2LN7yHsWilhxZcMet+n54r6zJ46Jh2dPHOv54j7dnomo\n23Pv5hSWyT05s3dwEz1rhjZTXmXRnomo9oX1HbFnwx4QB1bl3cyNHwp7VmIF2ZixCgfwHAmq\ngQ43A81uP4gFGxupBAIBImrYsGHTpk2J6J///Gfjxo1HjhxZUFCwdOnSqVOnujXGlObGLtXJ\ny99tiDAj7dddBnLw+w0LZW++qfvQz9aquzSTkTfztS76cY2tns/851ubQ048jXOGcHCw9EWq\nac/MXWPflH9NVFWcyqxdTzmm3eg3ONg+6wGTayn2HKw8qyShI6afw+F/dhsHPp/6d2zO7B17\nZt1DRKsHNRF1z/G35/NaIzm0OTmFZaUj25D2JwHfDhGtGdpM5KHXDG3GNRuG6IVhua/9qeSh\nX1gcSe+FBy0eCeKPbszd574XLg8NbwaexeKKGSAliWYSYcOGDYno22+/JaL69esTUUFBEqxE\nllhu7JrHrUZnl8cMYx1hz0SkZ53D5aENr3X8X+e3epbt2bP1G21HvcaNMLJCwAAAIABJREFU\nHwp7VmJzqipOyfUbwp6V2Apy0tSiPSvVGkRUefqExcutHtSEm+GzerVG/YZWFVOnW9FOblRT\ncarOnrF+kpzCMtmemeajV4h4zdBm3Iho7VNhv7uPBdizU+jVGtHVbwCQmmAVjjTGhkAXFhYS\n0d69e3/yk58Q0cqVK4no0KFDLo0sqfl1pwHc+KHszSLWjVnp+VX7/txke44O5czH//XV8X99\nteeVMaLHy/Ysx7oxW3dogW7Mdh1619zHuFl/iezQ1u3ZCrIxcxzdZm/szYyeIBQOvXpI03ee\naJ6RqX55ZWt9OhnZoVc8fL2Vl+QU7eRm+Kxiz7UurK9dMaYa6HRDNmbYMwAyFtf5tUuibwtY\nwkYJh9/vHzly5G233RYKhfLz8wsKCoqLi/mphQsXujO8pER4M8c+TTVu7Jr3u/XF5if5Vfv+\njg9M55OVs+1O77jt4WkcfPjquKivKzYTEYtUhDnM0l5922bcr1RxOPgL6N0XHleqON594fHo\nTiUcWhRvMKFQUKni4HmE1okl68x0CyOjCquHNOWgVr2LODhz/CjFYM+M4tC8/kZI2yCA6zdk\nb84p2lk6onXE89e6sL6o5HbKnkXxifUkuly74sgY4obj3rx22J3KH2km8wgB8C4o4UhjbAh0\no0aNPvvsM5bmqVOnNmzYcODAgUS0fPny3NxctwaYbMj2zISqKnWHNsfcnqvOnsmsVUfuMayB\ndgNhzxxH4dDKPnxia2sdW1lh2aF5FY52Y2psjrp9Zj87w6yB7NBR27M5skPbsmf/s9vqXHy5\nxYNzX/sTB0olsX/WtoysyB9RYc99Fv1OdNa56JLTx2r8AabXQBPRnln3WP87Tc5DK2PWs84W\nHVr35i7TNnCwYVwXiwMTyKXb3Z97L0TVf7CZLEUir0DSY/5eWw6dvOZtguzQsGeQpCBbnM7Y\nWMYuiUjgMna6QBORLtCcgVYqK8Q8Ql2gM2rVlh/K8wgt2nO4a1nPQMv2LLDl0OF2sTZ06Haj\n3wgFq/T+8i9rbCbC8wgZ5V6EQ+v2rNi5+TxCN1CS0IGn71IOiPhz8c/aWh35fIpDGy6yK0yU\nkR3aUKCVD61QHNmeBcKh2Z4ZeWE+p9axMizbUAS625zdtS+6RO6pOH503fCWco+wZ8aWQxtO\nfBQOXeuCH3GwStqCxHD9Pos2rLzW8FX+Z7dzEHi6nZVzCjyyvphT2L0dsR6LB6t6UulH45F7\ncWMZu988Ns/xcxLRpy+r+8UCD4KdCJ3khg6WtsMQ9Rvyyhvmq3AEz1aI+PcbFhLRZ2vnc7M4\nNuvXcoPWwxeFs2edlk+81PKJl4jIl5GpPBUKhS5vdIt4KNuzzvaZ/bgZPCUZc/ztmWoas27P\nETlvz0QUCsmTQa3Ys94TrFRrMOSkYMQEYenINtxqdI5qy838tW5QcfyoYcwo9mzYEx3Cnomo\n18ufxn5C3bz1HmHPSgzMkVczxMqGIEowiTCNsVpaUFZWNnfu3NLSUtFz6NCh3bt3d+jQgRfl\nAGzPoWCVon2/37SYpLmDSvWzocv6MjKV5Osfti6JfYTx92am9fBF1g9mdSais6eO16p3kfxW\niG9L2KHLCh+OZVQJ8WaZKLyZqWHPzDmHDjx9V9P78rlv31tW18YJjL7LP2tbsLJS5KHXjWjd\n86WPM2vXpXgtIiZuKjC6vclhpSNaK0nocPUbuje7jWzPTK+XP5Xz0G6gG7P/2e1289BpiOHq\n4B7MQwOvA9lNYyxloMePH9+2bdtAIPDNN9+IzkaNGgUCgauvvrqkpMS14SUNcu5Zdl+2ZyL6\n3fpibpFP1fERIsqsU48bOWTPsaNXa1ip34hoz3L9hrBn5uyp40Tky8j0ZWTumP2QbMwR7bnZ\ng1O4RRxhEuGftdXAniWEPStxRAKj7yKiYGVlsLKS7Vk8pWwF9/ajNyqvffvRG7vN2c3N+hVl\n5JuSY8PTysZsaM9KtYZhTyy8P+1upUfUb4QjWFkRsQcAkHRgFY50JrJA7927t6CgwO/3f/bZ\nZ0qyubCw0O/39+3bd+9egwq/9OGX7dQigVCwKhSsEvZsHbZnueKZHdojyMb84avjWg59kZuV\n1warzuqd5qtwENHZU8e3z3pAZIvLCh/mZv4q2ZtTxqHN1ZmIDh9U/VU4tL7/iN4TGH1X7R9d\nWvtHl/Z8ab/ylIlDsz2Lh7YcOpx2852anLZ0RGtu4c4sG7Mc+2dt88/alpFVWznevAa6y7QN\n3ESPvPJGRHsmorVPtZCNOVhZYTHZqVc8p9I8QgCSnlDQlQaSgcgCvWHDBiKaOnVq48aNlaca\nNWrEGxDyMenG9W3vv77t/bo9x4gyX5CIbvQPcvYStmjx+Avc+OGHr47jJntzFA4dCgUj2nN0\nyMZcp352nfrZSmI7GYlgzz5fxIIQ2ZgN9/Or6c0Rdnc/Wf6Pk+X/qDxzStdfiw4tH5ZRc1UZ\nw5PYTW+vG96Sm+jxzzo/cbOGQ2tbHsrI3qw4NDex8sbZkz8or5XrN9ihudkqFZCNWbdnvVoD\n9RtW0H8EqN8AUYAMdDoTWaB5l0HdnhnuT8OdCK9vez9Juw0HtXVwP99iaQ1jjyMvgSzHujGH\nc+iqihqb2AWrzp49dXzH7P6GWeTd8wZH7LFOnfrn51wn1qFzZu/gFt3Lf9HKbJnIupc0qFv/\nCivnKXnoF9z0p/Sss4lDd52xmYhq/+hSKxdV6D73PW5Kv+7QRFR15hS3KC6kINtz9RWzapPP\nx/bcZfpGw1dZnG5Ydfokt9PfHRadevXz2qdacLM7eHnvRh3ZmGHP1pF/ELBnECWYRJjG2Fuf\nOBx+v9+R8yQLbM8KwapKsT1b1Pb8+eZXbOWbb+37DAcfL58e8eDf9pvIwUdLJ5O0PTgv66Gj\nbCDCPYYLIfPX03cOeu69BU/pz1ZVnMmsXUfE5oPcPW+wkF3Fnls9uYCDXc9Hfotke2ZaPvFS\nLDoeNbI358ze4eCqFPUu/ynvYLLysZuI6OKrrvvP376UD7j4quscuZA8j1C258pTx22tcS57\nsy8jIxRUv6nMqnshB8GqStmbq86ccruWqcv0jRue6Sw2lFlnYW1pQ2SHjhvw5uiAN4NYgeym\nMZEz0Pn5+UR05MgRw2e5P30E+hetcrNvaBdu37VgVWWwqjLG3PPvAgsi9jDCnpVY5oZOA7gJ\neyai3/abKG8PHuNW4XJx552DnlOeZeGuqjjDjSzsRbJ73mBucqewZyXW2fv6BGsDjwd61jm6\nPPSfN72q9NS7/KdE5MvMOnP8e7GvtWzMbtizjv5vIdyMPT3r7Muo8ftH2DMR6VlnR/LQ5sjb\nMVrcmhEAkM4kqoTDJxFLP4iFyALdunVrIlq3bp3hs9zftGlTZ4flTeRv0tkb9A/6H7cvpZiR\njdmKPYfrueHcri4XZv9MeeqCy37CQd36V9Stf8Wt96rraegqLLN7/hAOlMUEzvzwfdP7JzS9\nv4bC+jIyuFG0O/npxiz3tBv9BjfR4ymHjp0/7Sqhmg7N9swo9RsXX3UdN1uXWD24id6zevCt\nVtawkx066j29M2zu1mkXXmZERpnVmqVluLsV7azUrF3vIaL1YztG7AEApB6hUNCNZn5Rn88n\n27ZwYrv9IEYiC3SbNm38fv/AgQOLiooOHTok+g8dOlRUVDRw4MC8vLxwFdKphF6Hqju0I/bM\n/C6wgFvUZ7jBaE9EBdm9dIcOaetmyPorHFpw5ofvRSwcWi4+VjKOjiB7802PzDk/PEfLqW3R\n/fn3uTl4TuHQ9S7/qWzPMhdc8XOlx9YG5rJD6z4tYDWs+OF7IsqqdxF3hqoquYUrULk5r7pE\nvqriNDd+FU85P/nv/7M4yA7jV3IzP0xfN4NqOrTu0+GQjdnQnhnZmFPPnu+et4dbogcCgMfA\nKhxpjKWtvI8cOTJgwIBAIKA/5ff7Fy9e3KBBAxfGFj1u7BpqOJGLC0C/2PGm45eLiGHNhlwJ\nLQu0noEmo4mPRPTxsur9ukUG2pdZi4NQ1Vm9xFkcJtuzoN5lP9Y7oxBZw5qNXc8PUnbkZrbP\nekBs4i0WRoinPcsP1z55h1KzYbEGOtzmt30Wf653nj72bxELGbVlz3ZRKqG5M9yttc9fwUGd\niy+T++Wss760nLKoReXpE0Qkl01vmdrb8HL6Nt3hdmkRcwfl6meZ08e+1Ts/eSnPC/sSO4KV\nPZYVb37nieZujigmPLJltCPgXtwYhuPnvOF+V1ZQ+PxNG6v4c4JZDiz2gxix8T7u3bv3gw8+\nGDlyJD8sLCy8/fbbmzWLvChpTk6OiOW9DJ3q14mTQPt8RPSnncsdv5ZFFIcOBiuJ6JMVz/JD\nJQOtOPTJ7/5puHSDEGjSqjgMJwiKw1wVaNIcmucRGgq0siqZqx6pYJh1lh3aUDFFcbC8Y7bJ\n/3IUh5btmcLvzJdAWKAVe2bYoUVmt/aF9cVTp777l6iKZnummgJNRg5tuEpGRlYtEZvsdKg4\nNM8j7DBhVY0rTunlERuwhdh4T5k2Z3Iv/DcSEdW68GLlKc86dDL+aMKBe3FjGG5f4ob7pkbx\nqs/fGq+cROkxBwKdKFx/H3NychQJ5odO9RsStwy0dYf+1bmtCv/g6PJ2wqHZnuncAyL6ZGVh\nOIf+aOnkpvdPUHYLZ2SBJsmhw9mzOMxtgSajVTisCDTF0aHDCbRwEf3L/e5z3xNS6MvIEA5t\n/r8c4dBes2eRbN5acI/cmXVuW02FjMwspS6CHfqdJ2/vOHG1fnzsAk3WHFpehUM49JYpvcgz\nNmAdZdtq2aH5XsQa22ICqPjEMopDQ6DjAO7FjWE4fs5f3TvZ8XMS0R+WTYx8EBHVtGEIdJxx\nd+JOmvCL1n3NHfpX0kbfv+rwkIMOXcOba3JL75Gfb1osHPpzaVtELlD2ZWQqDq3YM0XyZuUw\nZe7gvjenkLYAc3T2LOqzlRFun/WA6tA2p0e0HVX9s9gx+yHzI6NGdpGuMzbLDi3bMxGFgsHu\nc9+T89DheHvADSLOOed83rFnjoVD6/PzBHpVccWJYxue6UxEmyf3VBxaX/bOcQxXr2NvjoVe\nxQc4WJV3c4ynsotiz9wjO7Sy4+O64S0Veyaisyf+o+ehAQBYxi6dcX5Sl4J5rUU6INtzuB6F\n3/R8ipvxs3cP4xbx0rf0HnlRg4afb1r8eZhNxX0ZmSKW3fTmXiO4RbyEDBuzEsvGHKM9k9FM\nR7HLNxF99spwW2cW9qzEUbP2yTuUnsqTx5Ue2U50IwwFgzmzy2xdVNnXuuOkNdxsnSR2ZHs2\n7NHXoYu48sbmyT0NY8awBvrU9+qCm3pPnBH2rMQmdH/uXW6uDaqa259R0/wRd3z0bPoZgPjj\n9Pp11Vi8OnLJiSVFMtBNmtRYNGD/fn1bNXex+92QyfFXtTxfb/Cbnk/9bXeNlT2uuvP8Hi6/\nuXvY39578+q2Nbf005ys2YNT/rJhXrjLCYcWQ2rY6kHx7M29Rnyz6/Vwr9X562aDDf/+sCz6\nReWuaf+Y0nPrveO+2vqy3CN782eLn7ppQI3S7aw69TpOXLX/xTzlPI0fmq30tB312sHXRkU9\nVOb9qd3vGL9WxM1GGEwwjfhpyZldtmdWnyi+cGzy+Pk9cTpOWrP/hZhW+I4d5Rbk/VDOHCs3\nrPBRXrX/xfMfgE8WDLpl0AIRh7voqe+P1Lu0gYiVZz+cfa/V0YfH+o+mVYG6B3uv4gO78sPW\nkBDRHePO//HT/bl335/WQ362+ei3Odgzq4/cf/vY6pVJPphhPLdSJurvst+bkhP5oIQShzrX\nuIF7SQIS56+w54QTV4E2L1yOBcWY41xu9aedy+1eMdzxetb5qpb9Pl1dbYR61vmqO+//9J25\nt9zzdPXjMN9xX9vlCXlp5H1vTtHLLXhIeta5YasHD6wqCn8rCUB599o+Xa34B18dUV5evn1m\nP7EKhygeaDKkePPkyF/EO/LJEXno8vJy4Yty/tXKVZqPfrt0VBu9v92YaiPfPlPdDlPPOjd5\nfOHmST0ocfCdbhyf03lq9T98fh94UuCp7/5FRLWkiYNEtOGZzibvj8g6m7+H4bLOgdHtY/8R\nx17QafJyPet8x7g1ouJC/nZC/oTI0x9vH7tSLkSpPH2Sg6y6F3Cw9qkW5uNfP7ajUsUh6o68\nUMlqgkdqbR0B9+LGMFw4a2IUFvbsBVwv4Uh5Is4g1CuenZ1HSESfrHiWm9Jf64LzZYu33vsM\nN35oWG6RjAh7JqLGD1eL/vaZ/bLq1FNKbztOrLGcQhyQvw0XJi3XQOub9oWqDGZ2CoQ9K7EX\nkGcN6j37nj//PYlYUoOItk7ryxXPjBxHR1mhWh9VVvhQYHR7bhZP8vD649xiHIyz6LU93KMv\nwCd6ZA9mk1ZW4fhguloYw5/J1F7WGgCnCAWDbjTzi/JOKPrmgrxJikAYdrh+ECPxE2j30s/x\ngXeyqInP4hp2shVFYc+/6RG53Jn5ZGWhiGV7lpEdet+bUwyLLryDPq9R7pHtOVxPOPRZg87O\nI9RrSTPr1FNcpMu0DWIRZbJqz6FzzZJDW39DYqF9/gql4ln36Y3jc7idP2ZaXw42PNOZm76p\nZBTIDq37dERkb+a434q/c7N1ns4F6zsXrD9Z/g+lP27zCPW5gCIbLSP/FSfH68d25ObS8ABI\nBUIhV1qEa4atmQ5XSB1FgTWISJxKOJLdnpk/7SoRi9kZ+bQxv2z3AEVyI8Gnq59TqzhCISL6\nTY9hn66Z++k7c5Uqjk/fmauc4ZOVhU3vG6+s2xWsrLGn4K33PvPxsulkxIFVRUoVx4FVRc0f\nmcnxnlfGWLkLZ/l42bRwq3CEQ9m7JBw7Zj9kaxWOzgXrOdiY35WDrjM2cbB+bCdlsbOIiAXX\n2KEza9fIl++Z1cfoS89QzbjGkiObJ/VQqjiUvUhcIqI6KwhvVpC9ud3oN+TpoXaJwpsZPess\n9/Rb8fel9xjsSaQjPipEdLL8H1UV1QU8W6ZEKFBe+1QLk1XnXEL/JgQAYAX4aDoTjwx0atgz\n86ddJeWfb7drz+Y9Mjd2eaxKzhJp/zhlY9btmdn31lTZmIOVZ0MhS/rOyBXPsj0TkRzHh5ZD\nX2w59MULLv/px8umCXtu8fgL3AxfwvZc+6JLlX7DGugdsx/iFnEkshJx3HXGpjM/fM9N3m6j\nw4RVnSavNT+bvlyxcCwiMqx+Niq2U3tkY06IPRv2WEHPOseYh3YJK3lo+aPCiL+OOkyIsAk5\n1TRmOdY/FdyjL71nuBgfAMAFgu40kAS4noHm7QP1TQRLS0sNdxa0259K3Njl3IIDpn/UhvNm\nmX1vTW163/hzJ7Nhz4xwaN2Ymz8yU89DN+tfvf3S3iU29k8iopZDX+Rg9/whJs9yzMfI3nz2\n5A+1LviR/JILr/i5iGtfdGnF8ertXTZP7mU3USqjK1HngvVnT/5HPAxWViibUVf88D3vdC2I\nmOdjHxI1DFGkYHc8+6BSs7Hj2QfDHQwSQocJK63koQ37S0e1EZXQsk+vG9Fa3wLGZC4gAMAR\nQkFkoNMX1wXaxHTDPWW3PzU4b881V/tiPl0TWZoV9r11fk9RUffMhKvfiA5hzxxbd2hDPzZ8\nVvTo35fJDn3w1RHNR9ewZM5Dl45qa5goNXfoNiOrqzsM6wFke2bMHbrCaKdGndjLGGSHTnZ7\n7vv6ISJa/mCjuF2x/It92dc3lXvOnjhmcrzdup3YCfPthHHWWXZo2DMAzoMSjjQGq3C4yx+3\nq19D6z2GyKueRWHPCrIxu2fP4XoMMfTj6MaQkZnF7TcDjZe7DldU0D5/hVx6IegyfVOX6ZvE\nKsXCpM+e/A+3iEOqOH60uv3wPbdNE7vLB2wY10V5yYZxXczLGPQNycNtUb7j2Qe5RRxn7Jgv\nvmEd/U+FK37ZjAPW6LhR/sU+w5iRa6CVuh0RixJ5gVyfE2cwFxAA93BnCiGkPDmAQNvjF61y\ns29oJ6YSWkE2Zov2zFSdOfXpmrmx2zPz8bLp3Gy9Sq/WSMg8QquEQqe+P6z0nT4aYSM6xaG7\nTN8kYuHQp4/+W/Zmc4eWF9aoOH6UiBR7ZmSH1n3aENmYw9lz/JGNOTp7ZmSHFvbMmDt0x4mr\nuUV9aYXyL/ZxI6Jj33wp+sPZs94jO7RizxHrNwAASUMo6EoDyUCK7EQYH2Rv/kWrXOtTCa14\n8+82vCxXcXCPreG5xJ5XxiRkFY7d84coOWm9BpqI6tSsM6ZQqHRUW7EKR0R7ZjpMWJVZuw4R\n+TLUfxH1Lvsx7/cREa7fkO2ZYYc2xKI3y3jHm2Vi8WYZdmhbKWfZmztOXK3v+G0F8R3F1oJ7\nlOWWK8+c/PbPn+pJZZmzJw3KPOSXiLmDsGcAUgmswpHOpOaS2m5semSYdbbu0BYRDh03e45l\nj6jfPjCJpM3AmehqoCnSPEKxj9qO2Q8Jh1bt+Ry+DO2rFZ/Bly1i5GzPZCTQdG7DPHGMYNv0\ne+96ZpmIOWg7aol+hh2z+xuOU0ep4ohlKTdPYf1j5n92+0VXNtT7DSuhO4yv3tda/hDadWil\nwier7oVZdao/bJVnqpfEkW2Y70XkmxV7Lit8xNbVE4tHtohzilS6HdyLG8Nw/JzXdrW6S4Mt\n/rLemW+egasgA+0wv+pQPeEsuu0GPZJ1tkUoWCX0JRS0seKHnGM2tGfRL1Zr5vjdFx7n+K6x\nb6mDCYVIWR753MgUh646czKr3o/IyIx1wh0jvNkpts96QDh0ytizdfzPbiei44e/URz62z9/\nSqQKtLBnqvkhtIVeHy9vlMgYpp+3TOnVYcIqPffcZuQryeXQAIAYQLlF+gKBdhJhzxw7vmW3\nZwnnzTd0qN7A+fMtrxoeEM6bZWR7Fj07Zj/UZsQr/DCzdt3qYZh+nVJ1psY2bGzPCqFgpZKE\nPvXdvzIys0JVVb7MGn4WTp13zO6vJKGtp58Z9maP5Gyc4tYnqn9YmyZ0s3K87NDf/vlT0lZN\nke2ZidqhdeRlcEyKN7ZM6dVm5CuOXBEAkIyk5Hf4wCKYROgYsj2H60kxPnpjkkmPsGclVug4\naQ03w2dbDXuZiMiXUd3OIeyZiKoqTlPNX2RBbd/HUDAoW77JzimhYKWIzxwrz8is9mnZmM0T\nz7Ix27Xn1EDZkbvTlHXiKTmWaTPiFflnSkTHD3/DTfRY2aIluhponY35Xbk5cjYAQGqSiK28\ngUdABhrExEdvTOJKaApvz6JHzkPfOfh5IrqwwVWip+OkNZsn9ZBfct6eBb4MCgVDQfVbs6qK\n0xm1alRZBKuqMjJFYUnwXFBFRGVFA1oPX8Q9O+c8SkSdCwLya88cq5H6DVZWEFGbEa+UFVn9\naj49vZlRlrKudWF95YBOU9YpeWihzicOf33hlVfLT535z7fWLx2FPW8tuEfxcp9RuXw4Lrzi\nqhP//pvSY3cM6cnd8/Zw8M4TzRM7EgCiB7KbxiADbRV9vqDjMwiTlI/emMTN+kt0e2b0PLQo\nzziPFb8JBSkUDFaeDQVV25btmYg43pjvFz0b8/3bZ51f6YLtmVFSpEBHX8rafCMS0t7VE4e/\nFrGJPW+Z2idij0XkyhC25y1TbayVIRsz7Nkiwp6VGIDkIhQKutESfVvAEhBoG8jGrNuzXvGc\nPjXQtmB7dpzKUz9UR9JvH1G2sW3GfWVFA0KhkGzPjHBobtzJDi3bM+Nxh+6z+HNuCbl6uDoc\nIqo6c4qblfOcOPz16e8Pn9aW9FYWy5ONOWp7Fmf2+TKisGfmwiuu4hbLGNIH3Zjh0CBZQQlH\nGoMSDnv8aVeJmN11fZv7qnt91as+fLHjzRhX4UgZPt/yqqjiuPy6JhzckTeHtGXvTNg19zGx\nK7VMWdEjbUa8It52CoV4veeK40cvyP6ZcnAoWMXl0bo607ktUe7IK3q/eITy1PZZ/Tyuywqy\nN/dZ/PnbA26I59XN7VmOt06ztA/Rthn3Uc0VmvVjYvTmmqeKZoXmwOj2/llblR6HRgQA8DqJ\nnUTo86krEft859egkp8K1w9iARnoKNHtmYiub3v/H7a8xi0xw/ISXPEs7Jm0eowTR/6mvERP\nOur7UVf3SG+7iGtdePHZUz+cFalo6SUm9szckVcU5j6SAz3rnJA8dFa9i5Qe3l9Gpv24Gt/e\n6JXlmbXrsj0T0daCe7g5OkwnkY05UfZ897wPuSXk6gCkL4nLQMtOLHpCEuKAcP0gRiDQ0WBo\nz9VPtb0/3qPxBi2GzOcmd8r2zGTWrhsKVvkyMrjJxlxx/GhGrTptRqoL3skOzbF+TN1Lr6x1\n4cXioe7QOvp23LpD625nfR6hIT1f3MfN4vE9XviIWywXlek2Zzc3p07IyKl62aEtLmUtv6tl\nRY8Ie04WAqPbc0vI1WVvTgqH1mcNYh4hSFISVQOt555B/IFA2+MXrXKzb2hna7uQdED2ZsWh\nubRUXtkgI6uWfABXu8r7XRs6NLdwA6iqUItrhUPHuB2J4naxnEr2ZisOLXuzIw4te7PjDl3x\nw/cizqp3UVa9i2y982VFj3BzdlQpj27MSefQsGeQxCQoAw179gKogbaBvJu3g7s2JDuKMXPP\nuy8OpZorgvl8Gcof1ly4TEQX/fga5QxtRr5aVhh26WjrCIfbOedRwyoOKzhidbox93xx3+oh\nTbvN2cUP1w1vJT/bYtIG5fgeL3y05vHfhjv/2wNuUGo2lBpo3Zi7zdm9bnjLiCO3TsUP39c+\nt7m68Omt03KVmg2LNdDAKcTa2xZ3sYkP8GaQAvy/HTXmyfxXm2j+t/X/ymrkjKI7CYg/EGir\nyPbMVDt0zSqOL3a8GcdBeZfmA2bV1hYA9vkyglVnORb2TETH//VVrQuqCzDqXHx5xJOXFT78\n/7d3/9FRVncex+8EBel2F7tmYd1t3XN6eji0xx/YQ63oFrDgj6ITf7S24SitdIsnqYA/qtbW\nBKugUAn+AoQSTrHEbtLtKpKp4A8CAatgZSUR16L/kfZ0F0x3SXVquap/AAAauklEQVT5FZLZ\nP25yubl3ZvLcmeeZ55ln3q8zf9x58uSZ+2RI+OTme++1R6ltxgionqHl8s9GzYY9j9BJHuva\nqvQs20aGdqVn6CLPIByY1qnlZv23Dj1Dk54L5/TFNHaxiVSGBkrdP33Vh+3SfLkIio8AXbB0\nWl+FI9y+lAo9PQshVHoWQhz/y59dM/TAHt1Df5PZ8VSGTcJlblZ+u+YHKkO7pudrH98pG5vu\nnCKsdW29ZOiTQ7cWFz5l6EI+vRAqQ4tMY/Zv/2xe9Hcmz/0r0DVLX5aN3Q15lmir/Xr0dcdd\n6cP5V9zf8srD1UbNxsYFk1Xb3veRDA34iTWby1g869CD+K/aHoEWJbWXysRr58lGx6aV+nG1\nKp8Xk+cslo1d6+u0gw/rCVgMRkN7BFoI0f5kzZR5K/QAbXyu9Praezx2aep87XYGM3TG9Owj\nlZ4ltd23zg5hRhWHHaCFVshRWVlpVHHkqN/wyKji8Ld+Izenf2ahMJYiNt4+lZ6l39x3pcfL\nJh99VTbkhvOKzNBXLvx3+TQ9+JeZ0/9qzIs/npntakYxjKRnaD09iywbp+sBOvrvi5M43Q73\nEkQ3fL/mOdMKmmOTTVe7uSNVRsZUwmxPc5+GvDGJ0KsMWbl01oJR6dloO1Hp2Wj3Hv34yJ//\npJ6qXHjUWpOu/ckaIcTOlfPz60BGO1acup3//NcHdjx1W5HTsxCiv++kl0987raLMraz0RNz\n4elZDE3MxUzP0Zdja4+vPfTC1x56wZilauTpbFR6FtYajjMXp+z0LITo/b+eqx/Z7LnjAzYu\nmCwfrp8IoCBspFLGKOHwKsMIdDo94bJZ+7c3h9EdB3ZinnjtPGMcelh6YlZHdq2vm1T9I/lU\nZuiRnzxTPpXjbUf/9+DoT42TR2R6ln737KKLbq536kAOeob20TVLX5KN39x3lV/X1HPzprum\n6TXQwppHKHzKzUNfgtzs4GsPvaDafSeOjhg52vvn6ulZGjHyDGMcWgxNz5LM0DnGob3bsvBa\nYxCa+g3AT4TdMkaAjqJs5RZRo9KzcuLjwyM/eaaeEuxxaEll6N4jfzGqOLzXbwRk5uKUvtbe\nNUtf6u89IYTYXF/l8Qoe5xHqGbrA6mcM64r7B37XfeXhWV7O19Oz5JqhfZfHkiZ6hiY9A/6K\nVC2EsUmK6lu24ygQATpyjHKLiGdoW3/vcY9n/u7ZRap96a3LZCNq6VmqOH1kf++JmYtaN9dX\nbbpzilHFYcwjdFqfi9wctKsfHiglHzVmoALyivubjQy9ccEldg20HaAV7zXQ2fSd8PptYshj\nSRNyMxCQcMOo/erZ+kNoDkI8a8kDmq9gVnEkEr7Xb2QsUC4kQ190U50QQt+mxLigPr1j8pyH\nZWPX+vvtSxlVHHr9hu60UaMz/qN6c8MDbl13V/hUFblOgh2ghRByEFoMjkMbq3D4LiLTbvwS\n6O1MmTewErldXq/Sc8XpQ3YUP97TbY9DG78CZQzQI0aO3t1wk8d7Mao41F9mss0glHLPI/QX\n/8wii3sJohu+X/PTl3r6c5arP74e9dJQCEagnexvbzmVoQNIz/764tfvEkKcdsYnhBAjP3mm\nytCqTNmg0rNs2xl61/o6YxWOPS1LjAx92qjRQog3Nzzw5W8/qB8vQnr27oq6X8nGK4u/lfdF\nAsrNcKXSs2zLDH3VT54XQow4fZQ8bqRnoY1G64w/HWxZeJ2RobcsvM6pb6l7L1cZOnXv5cZH\nX37oGzJDJ0ac7nEVDgCREsshSHhEgHYwZAQ62t82Mj3rsuVmIcSFN9xxxpi/Mw5my9DGET1D\ny/S8+xcLxdAMHc30LNsZM3T/yV5jEFoNP6M4LrtrnWxsf+x7OU7T07M68onKf/SrG3qGdk3P\nkp2bdS8/9I18ugUgIqKdBBAolrHzyl6FI+PK0AWyqzXyqN/Q0/PJY+Ziw7/75ZBKjM9Mudn1\n+oY9LUtOGzVaT8/SmxsekI8Cr+8jPT1nPKJ2uOg/eeqv6np69j6PEN5d9ZPn5UM+VenZaHvh\nY3qWtiy8Tj78vSyAGEin+4N4hH1b8IQR6Mjp2LTS31U4Th47Igs5hJWeL7zhjsKvL4bm5rDM\n+OHAyvPGDt6uNtclZSV0/8nevuNHhbaCL+k5CCo3y/bxv/yPccJld63LPQ6dTV/vcVnF0d97\nwqjiMGYFAEA+GIEuYwToKHLNzZO+9UPZ2POrn2Y84eSxI28/91iOKxzr+dCo4sg4jzAKpt/7\nC9loe/Q76uCMH27ofv+twbYPGVo2rqz/NyFE34ljLy/6ZiEXRDYyPR8bXOvwjE+NG/U3f2tn\n6Gx2rpxvV3HoMmZo0jMAX6QFAbp8UcLhlb0TYUT28VbpWbXtrJw7PUvHej5U7eKn5+n3rJeP\nYU4bTM96W0/PQoju999So9EGu+I59zzClxd9Uz5y9wrDumBOw/R7npl+zzP2h45pK4Ufy7Jq\neA76yhs7V85/6Sc3GCe8eP/VL95/9cnjR058fFg+hBAvP3Sj6wsBgImdCMsYy9h5lbHiucgZ\n+sLrb5eNvRufFEJceMMdap0BnRyHVpXQudOzUcWx9/knfOmqEyM3ty2bk/k0LT2fOvnR70y8\nLsPe4B0vZB2Y9GUVjqAVf+GnjEP7/lx5aG5uW3aLak+7fbV9fmLEkBmcedRvqLIQPU9fufDX\nslFgeo7Imly+iNO9iHjdDvcSRDd8v+bZX7re92sKIf7rrY1BXBb+imeADkLluTPsg93vbvX9\nhT4zdbZs/GFH05DjX7lJCJGoGCGfynkGGQN0ur//wPZhhnKHXHlwHuEfdj7r3t9CXTBnmX2w\nc/09X6gemH34XsvAah4XfHd5hjN//oNP/3OGlTj/+NtILzIYNcbXNlFRIYToWHenD1ee02Af\n7Fx/t2yce9Mi+6Pv/rL+vG8vle19G+4rvA8AEIS/nxTILkX/vWdTEJeFv+JZA120X3Z9f6EL\nv34qsnxm6uy9zz0+cPz624WWnoUQiURFjrm63jtWWVmpRp19vB17b4sptz01cGTVgmE/XaVn\n2bY3yFCy9TnQfwOX/+iXsvHqkpsCeolijtnYQ/vp/v5ERcXE7z2+densIF4x9611d3erUeco\nDFwZIjKc5os43YuI1+1wL0F0w/+LMgRZxqiB9ipDtUYiMeEyP3ch0tOzfURPz4OvX9Fn7Zud\n7i/qCjhT56+SD/2gsbeF0NKz0c6o9+jHxhF5Ebu0QB6xqzVy1G8UTqVnox2QGfc9Kx8BXX/K\nvFwzVmfc15Tjo4Vrf7J22CMAEE3pYIR9W/CEAO0ikcjcDpWeoWV6/o9fZyiKCIKem1U7w94W\nVmLWj2SreM5Iz9B6W0/MRUvP2Y74SM/NQWTo3OnZF3rFc8YjemImPQMoKelgHigB8SzhCJB/\nufm8mXNlY9/mxmFP3rvxSXtzQaWv93jF4LyrUNKzOrJjxW15XKpt2Rw1j7Bt2ZzcC5Nlm+LW\n8cKKiPyh0IvL7lwrG9sfvzXbOXZinnHfs1uXFrrxja33yEenf+Kv9SOyBtovbctuUfMI7Twt\nyM0AShObnpQzArSD/dubjZqN/ds9zVQ7b+ZASFLfbAktiJ83c67M0H3HjowY3PFE6tP2EXz7\nuceMDK2uFtzSGZfOfVQ2Xm+81+P5I0aNzuOF9HFoe3HfHDXQRaCCrF/hVaVn2c6RoYtGz9B6\nevarBrpz/d2l8rsNAHgVUrmFHiEo+QgLJRyOtH+prulZCJFIVIih//QHzxkYjdYTc5+1C7e+\nIN3bzz229/kn5MNTz92p9Gy0c5O79+nsWYPDziM0Fvf1+NJBMIoo7FmDrvMI9fSc7UjR7Fw5\nT7V7j3zUe+QjfYA/oBmEABATYawDnUgk9IJpO1GgOBiBdjCwFPTQf9yf/+pAfvr9tgy1sHp6\nlhKJitwVTnZu1nnZEsWjS/5liWzI3193/fzH+kftxHzp3Ef1cej+vt6Koev19vf1ykbf8aNq\nHFrG352rFjitwiHCzs1StiIKv1bhOHnC/GVDt3XpzUYHgqjf2LlynqqElnma3AwAXjD6W84Y\ngfbK3khlwrRqlZ6FlqTz9s6LPxv2iC8uvmWRSs9icER88ncfcbrIa0/foRKzEKK/r7e/94R6\n2nf8aN/xo0MGklctkI/8+x0Zry65ST4KuYienr9y25MZz9ETcxDpWdq5ct5Z47941vgvXv/U\nGwG9BADEEZMIyxcB2k8eM7T9O6uaR6gn5uDSc8UI8y8PhWTogYeWnpGDrHi2x55zZGj5CK5L\nem4mQwOARx++84r+SPen83jYFwn7tuAJJRzB2rd5rVHFIWf+6XVLxiocAeVm6eJbMmz8ltHr\njfcaVRwZ5xG+9vSpncC9nF9aAiqi2P74rdnicvHZifn6p97YuOCSUDoDACUk4xbFoVwExccI\ndOD2bT41RUytm/HulnX7NjfKR0j9Gp6egL2kYdfzS0JxiigAAEAJYQTaTxnnEYqhGToK+vtO\nGlUcGecRCvccHJvcrAsiN7+26nZjEPq1Vbf7/ioAACAIBOiCpPv71aK52dJzpOx+pl5WcegZ\nOlt6RqD0DB1iet644BKjioP6DQDwovvdrWF3AaEhQHu1v73FWogjsb/d01LQeTvv6oH66X0v\n+jaGrWfo3c/Ul9DuffETkVFnPUOTngEAGBYB2sHQDF289Czb/mZovy6FeCA3A0BJMDZPYS3q\nsBCg3exvbynOkK2entURHzM0AAAoRYTmKCBAY4iJ1w7s7dyxaWW4PZGmzl8lGztW3BZuTwAA\nACSWscMpKj0b7bCo9Gy0AQAAQkSALqrzk7XycUHy+xckv5/jTLtaI+j6DTsxh5uh7cRMhgYA\nAFFAgC6e85O1qp0WaSGE9wxd6tXPE6+dJx9hdwQAAKBQBOgi0dOz5DFDy0eAPQte1CpDAAAA\nCkGAxgB71qAv8wjzrgyxZw0yjxAAAEQBARqn6Ik5Cqtw6ImZ9AwAACKCZeyK5J3UaqOKIyES\nQojO1NMh9SizKORmHbkZAABEDSPQPvjCjNnykfu0d1KrVTua6TkIAVWGAAAAhIUR6ELpufkL\nM2a/t7VpyEcv/7ZsvPfqBjE0Q0fNl2c/IBtvNj3o75U7Nq2M2v4sAAAAeWMEuiD2qPOQPD2Y\nno12BKn0bLT90rFppXz4fmUAAIAiI0AHxU7Mkc3QdmIOIkMDAADEAwEaAAAAcECALohR8Zzx\nCAAAAOKEAF0oPTEPab+6wTzTOhIR9qxB3+cRAgAAxAYB2gfvbW2SD/O4lpgjm54lPTGTngEA\nAHJgGbtgFT83n3/NwHYt7/zGbck8cjMAAIAXjEDHikrPRhsAAAB+Kd4IdFVVVWtrq3FEtfUP\nuR6HZCfm86+pdR2HBgAAQG5FCtB69lVHjHAsn7oeBwAAAIqpGCUchF0AAADERjECNOm5OOxq\nDeo3AAAAfBeTVTgmTZqkP92zZ0+gL1dZWRno9fP2p92//oeLb1Rtj58V2dvJA/cSWXG6He4l\nsuJ0O9wLEGUxCdBGYu7u7g7utSorKwO9foHUqLPHTkb8dpxwL5EVp9vhXiIrTrfDvQTRjbC7\ngFiJSYBGpHxp1o9l463mR8LtCQAAgO9YBxo+U+nZaAMAAMQDARp+shMzGRoAAMQMARoAAABw\nEFoNdGtra8adBV2PAwAAAMVUvABtR95sIdj1OKLjreZHjJoN5hECAICYoYQDPtMTM+kZAADE\nD8vYwX/kZgAAEGOMQAMAAAAOCNAAAACAAwI0AAAA4IAADQAAADggQAMAAAAOCNAAAACAAwI0\nAAAA4IB1oOHmgqrvy0Zn69Ph9gQAACAUjEDDgUrPRhsAAKB8EKDhlZ2YydAAAKAMEaABAAAA\nBwRoAAAAwAEBGl7ZswaZRwgAAMoQAdrNhGnVlefOmDCtOuyOhENPzKRnAABQnljGzoGemydM\nq97f3hJiZ8JCbgYAAGWOEWiv7FHnsh2HBgAAKGcEaAAAAMABARoAAABwQID2yq54Ls8aaAAA\ngDJHgHagJ2bSMwAAQHliFQ43+9tbKisru7u7w+4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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 8, repr.plot.height =4)\n",
    "ggplot(data = daily_merged, mapping = aes(x = TotalSteps, y =Calories, color = TotalActiveMinutes)) +\n",
    "  geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a479fc22",
   "metadata": {},
   "source": [
    "* Positive relationshp between total steps and calories burned.\n",
    "* The data shows that even if there is Less total steps and less total active minutes then also we can see high calories burn, which can be due to other activites other then Walking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b5091b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_very_active_minutes <- daily_merged %>% \n",
    "  group_by(Weekdays) %>% \n",
    "  summarise_at(vars(TotalMinutesAsleep),\n",
    "               list(TotalMinutesAsleep = mean))\n",
    "\n",
    "ggplot(average_very_active_minutes, aes(x=Weekdays,y=TotalMinutesAsleep, group=1))+\n",
    "  geom_line(color = \"red\")+\n",
    "  expand_limits(y = 350)+\n",
    "  geom_text(aes(label=round(TotalMinutesAsleep, digits=0), hjust=-0.5, vjust=0.5),size=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c059bbfb",
   "metadata": {},
   "source": [
    "* The data shows that the total sleep time is equally distributed among all weekdays. The average sleep time is 416 minutes, which means on average, participants have adequate sleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "964fd312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ey+/usE/tc9fpqD9V8hsJIwcNryz7x3X4/m3Ry7x/dB4+cgsJIwcNrybfbtsCM9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\nW9+whIwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_very_active_minutes <- daily_merged %>% \n",
    "  group_by(Weekdays) %>% \n",
    "  summarise_at(vars(TotalActiveMinutes),\n",
    "               list(TotalActiveMinutes = mean))\n",
    "\n",
    "ggplot(average_very_active_minutes, aes(x=Weekdays,y=TotalActiveMinutes, group=1))+\n",
    "  geom_line(color = \"red\")+\n",
    "  geom_text(aes(label=round(TotalActiveMinutes, digits=0), hjust=-0.5, vjust=0.5),size=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2ac338",
   "metadata": {},
   "source": [
    "* The data shows that on average, people are MOST active on Saturdays with 214 total active minutes and the LEAST active on Sundays with 152 total active minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a505e7f8",
   "metadata": {},
   "source": [
    "## Act"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4396bdc",
   "metadata": {},
   "source": [
    " #### Insights/Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9771a8d2",
   "metadata": {},
   "source": [
    "1) Majority of the participants didn't track their weight this can be due to manually inputing the data everyday. It may be appealing to users if FitBit offered **auto tracking for their weight** to make the process easier.  \n",
    "  \n",
    "2) Average daily steps are 6385 which is less for having healthy benefit, around 8000 steps per day is considered healthy. FitBit may **encourage/promote people to walk and exercise** for their health.  \n",
    "  \n",
    "3) **Adding more catagoies to track the calories burn** would help people to understand their lifestyle.  \n",
    "  \n",
    "4) The average sedentary minutes are 936.9 minutes (15.6 hrs). **Adding reminder to stand and move** by notification or vibration."
   ]
  }
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