RepData_PeerAssessment1/PA1_template.html at master · hoogenm/RepData_PeerAssessment1 · GitHub

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<h2>Loading and preprocessing the data</h2>

<p>In the repository, the data is available as a .csv-file compressed into a .zip-file. If the zip-file has not been unpacked already, first unzip it. We then read the .csv file into a variable, which we shall call <code>activity.data</code>.</p>

<pre><code class="r">if (!file.exists(&#39;activity.csv&#39;))
    unzip(&quot;activity.zip&quot;)
activity.data &lt;- read.csv(&quot;activity.csv&quot;, as.is=T)[, 1:3]
</code></pre>

<h2>What is mean total number of steps taken per day?</h2>

<h3>1. Total number of steps per day</h3>

<pre><code class="r"># Setup reporting options
options(digits = 2, scipen = 6) # Prevent scientific notation,

# Calculated value to be reported: first calculate total for each day, then calculate the mean
total_nr_of_steps_per_day &lt;- aggregate(activity.data$steps, by=list(activity.data$date), FUN=sum)
mean_total_nr_steps_per_day &lt;- mean(total_nr_of_steps_per_day$x, na.rm=TRUE)
</code></pre>

<p>The <strong>mean</strong> total number of steps taken per day is <em>10766.19</em>.</p>

<p>Also report the <strong>median</strong>:</p>

<pre><code class="r">median(total_nr_of_steps_per_day$x, na.rm=TRUE)
</code></pre>

<pre><code>## [1] 10765
</code></pre>

<h3>2. Histogram of the total number of steps taken each day</h3>

<pre><code class="r">hist(total_nr_of_steps_per_day$x,
     main=&quot;Histogram of total number of steps per day&quot;,
     xlab=&quot;Total number of steps per day&quot;)
</code></pre>

<p><img 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" alt="plot of chunk histogram_1"/> </p>

<h2>What is the average daily activity pattern?</h2>

<pre><code class="r">## Calculations
avg_steps_for_each_interval &lt;- aggregate(steps ~ interval, data=activity.data, FUN=mean)
interval_with_max_steps &lt;-
    avg_steps_for_each_interval[avg_steps_for_each_interval$steps ==
                                max(avg_steps_for_each_interval$steps), ]

## Plot daily activity pattern
plot(x=avg_steps_for_each_interval$interval,
     y=avg_steps_for_each_interval$steps, type=&quot;l&quot;,
     xlab=&quot;Intervals from 0000(-0005) to 2355(-2400)&quot;,
     ylab=&quot;Nr of steps (average across all days)&quot;,
     main=&quot;Average daily activity pattern\nTime Series plot&quot;)
## Visually depict the interval that has max nr of steps (on avg across all days)
# First draw a vertical line
segments(x0=interval_with_max_steps$interval, y0=0,
         x1=interval_with_max_steps$interval, y1=interval_with_max_steps$steps,col=&#39;red&#39;)
# Then add a label
text(x=interval_with_max_steps$interval, y=interval_with_max_steps$steps, col=&#39;red&#39;,
     labels=paste(&quot;Max at interval&quot;, interval_with_max_steps$interval), pos=4, offset=0.2)
</code></pre>

<p><img 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vQttKqVZ+RVyuLD4wQAAF7Nurw6qEe8DwBA2Jx/RFXrMb5FVPWLsZKbH7cEAACvuC5dqLeCZh9cEZEG96VCyDDs6FGDx4qacfDKxpldwopSd6dclieM/OTwxTX9fav9bBPurZelXt707sCWIezC+7duP9OEJQ5auOXqyaWteYRJ72DycZk+/dec3jyvf3N25plDB04+0DTuPW/78S9e4Rl4i0avf7u0p1vek3KP6B7vppz7uiNf5wmuCUvP3Ngwq1sTdvqZMw8ZsV1nrL36zydteADgEjxk2fSWfCg7t+9UroG/FhDSwmWKEbIB2c1FTZO+zAqeeyX9+zZGtv5GyFqwo0cIIQeHQY8QQg4Oh24QQsjBYUePEEIODoMeIYQcHAY9Qgg5OAx6hBBycBj0CCHk4DDoEULIwWHQI4SQg8OgRwghB4dBjxBCDg6DHiGEHFwD22GqpKTkzz//xGUbEEIOhsPhjBs3jsVi1cebN7COPjU19dSpU7auAiGErGzdunXZ2dn19OYNrKMHgI4dO06fPt3WVSCEkDVdvny5/t68gXX0CCGE6gqDHiGEHBwGPUIIOTgMeoQQcnAY9Agh5OAw6BFCyMFh0CObevoDEASMOAL/3QMnhIkCIJrBQ4V1DlFxCHy6QmmVRy7PhpYrwNjb6z/fQtfehWZLQF7lEbISfhgLfkwgOND+TbgnAQBQ5cH7vYFPAMGG1pPgDvVgJiQRQLz8X/tNoLFeYchpYNAjWyMATm2Cipefiq7DsTIg6vMnM2oirBxsy3tI7nwFSx/D1juQcw06XYDxa0AJcOId2OoP5wsh/xq0PwNTtoIaQJEP5c3gxEN48gSePIF9I/GURWbAnxpka95tIfwyXCx/8enNbeA/EnwZAACaCvhpPPizgXCBhAlwXwqggvW9oN1XoACQ3oD4EPiz8L+30n++4j4MmAClZ6DlEHj+shnO2AKL94MK4PZSaPsRfDwQvBjgnwxHn1d/vgJSP4YmXkBwoNu7kK0AALi5CNp/Cou6QONRkOgJa54BAIASVsZB/z9ArV+wIWw+cLnA9wA+H9zY4MkHgoTYWfDHt9DSB7ge4OEKPl5AAEhzQBQMrWIhKgqioqCRe718C5Cjw6BHtuYSABPDYdMlAAAQw7ZTML7/i3Y7Yz0sewI7HkDBbehwDj6+AMCEiauB8R1sfwJr3obwlTA44L+30n8+uxn8tQ0EXeDffeBv6Kf91mpgLIZnOfCWFBZsBaLK81W7YfRW+PQCFN2EJgdg5C+gBACAG99C3mj442uY3gR2nAYNgDILdhXC9O7wVL9gQ5rMhndE0CkYPCPga1/YNAmYBER1h1cC4M9e4B0BP4XAqiHAACi8D4ocGNwMAqNg2IfwzErDWcjJYNAjm2NCn0lwaRNUAEhuQ5ovDAh58S8hE+HfI9AzCng8EHCgWAwAwG4Cv7wJ83vCF1z4cQy4VHkng8+vmUcnmNMJ+EEwtD/IC0Ct/Qc1HFwFiSthZDPwbQYrvoDHO6FIAwDg3gl+eAsSwqDfeLizHcpIyDkCBcnQVWBqAdc+ga9ZcDIXhE9hZj6M++m/CwbD0kCYAdOewYhvQAFQWQbB0fDeNji+HpjrYfB3Ri8tIGQcBj2ygbPTplX7PLAPhF2ASxVwZwd4jYXQlwv4ERJYPRmaNYHeM+Fq+X/PbzkVorIhcRZEVF/qz9jza+ATDRwAAGAwgKj6D0rILIbUyeAjAIEAYqeAXAlCFQCAbyy4EQAAoQMg+AZcKYVjv0PydPAmTCtABrt2Qc/PoHsQuEfAko/h3m/wTA73/oFsCQCAeyS8+w48OQilGujwHdw7BINbQ3wP+PFLeLQNspUm/XchVAW9QU+qJJUiBU4bcHpuu3ZV+5zZCCaGwaZTsOMYjB348jKpGraMhWNt4MwduHAEZjV7+WwS0j6D4o5wfSlcrdoyG3t+jQjCyD8wIVgAI/dCaSmUlsLzTDi7HWLYAPDfhWJWOIzyg+1/w/ZcmNYRCJMLcGGAWvu3gwZIAhhK2PA6fHL9xWMyITC4wNTA+e1w5uVFCJYrMF3AxVjBCBlFR9CTogd/fDSmbZAbwWDxPPkcF4LbqM2opbsfSnBZeSelVqtVKlWVB1yg7+twcjGkuMOrYS8fJEEmA54P8FwgPxW+vAByEZAAwgvwVips2AsrQ2DWT1WmLRp5PgBopCBRg+k0UpAQ8Np0OLUQ9j2AogewrC9MPag3tZENQ4bDwYXwuCMke9VUQDVcGD0WUt+Hw+nw/C4sWwrNJ0OIOwzuDn8shbPPoOAmLP8OukwHbxco/ROGTYETD6HwHny8HFpMgZCGt+Issrn6D3p1QcqUrtOOB83ccim7qFKmkFYWP7u2Y27MpbndJ6cU1OXsQw5Do9GIxdXHr4P6QcR98BwN4drRGCaM+wYYn4CHFwzcCP9bBHdmwZZH8H/TIf5L6OoLE1aB5hvYklnj83OAGwNtsqFZ//9m3dRM+3zXibChL8xrA/4t4VQb2PU26O8JETMUBHnQ/U3wNF6AvoQPYE0XmJkAAR0gfRjseQtYAJ2+hA8CYHwTCO4O4lmwYSS4EDBwI3zoCa8nQKNX4NEQ2D3LQA0I1Yao992aKo+8GrX8tRsXZ4RWvWoGZPGenvFbFjw82N+jDm+2e/fu4uLit956y8pFInpd4HLDnjwJDg62dSEI2YupU6cuXrw4Ojq6Pt68/jt6dmBz76wDR9Kl1X6hyDOP703nxfqz6/34yA6RJCkSiWxdBULOov7H+7iJS7bOHjY4TvBpQnJCuK8HW1VZlHX7wnVh20/++iWJW+/HR3YIgx4hOtFwYYfw7LAsNWfW7bS0a0+e5RXL2N4BI+d836t7vC+ONjorkiSFQqGtq0DIWdB1BZ/lG99nZDypkghlTHd3Nk7fd27Y0SNEJ5xeiWwAgx4hOtV/R09Nr8wd//WWS30SI/w9WYrK4uzbadtXzO0+mXF9x4hAF8Ovy87OvnTpks6DZ86c8fX1rfeaUT3DoEeITvUf9OIbv50M++LGV2++nF7J8Qlp0e31FXHci/Gbb4hHGJteWVBQkJGRofPgrVu3PD0967VeVN9IksSgR4hO9R/0L6dXTpzW1PW/m7drn17Zrl27du3a6TxYUFCQn59fT5UieqhUKoIgMOgRog1Or0R0UyqVLi4uGPQI0QanVyK6qVQqFouFQY8QbWibXukd2apbRHdfPuvl8I1aWlai4Ak82bgYn5NRqVRMJhODHiHa0DC9khT/+9OISBbfz9+D13zipgfUWgiKx98nhw05Uln/x0d2RqlUYtAjRKf6D3rFw58mLkkffSxXInx6ePTj9159/1wlzp93ZtjRI0Sz+g966ZO0Z83ffadHkKt7RO9lf65N+OPt727L6v2wyG6pVCoXFxe5XF77UxFC1kDD6pX+TTwyD5/OUwEAuAQO+W5FyK9jl54tx32mnJVSqSSM7uuEELK++g9618T3PutyfkxUSKs3dueqgBn2+sYNPf7u37T/T49wm2OnRM2jt3UVCDkRGmbdsMLG73zS7cqJcwVh7gwAcAkYsOrqgzEpO/+64xaF69E7H5VKxcGgR4hG9EyvJLjB7V4dXfXzkI7j53ek5djI3iiVSi4GPUI0wvWCEd1w6AYhmmHQI7ph0CNEMwx6RDelUslg4A8eQvTB8w3RTaVSAQBBECSJd84hRAcMekQ3lUrFYDDYbLZCgRNsEaIDBj2iG3XDFJfLlcnwDmmE6IBBj+hGXYzFoEeINhj0iG5U0HM4HFzuBiF6YNAjuuHQDUI0w6BHdMOOHiGaYdAjuuEYPUI0w6BHdMOhG4RohkGP6IZDNwjRDIMe0U0b9NjRI0QPDHpENxy6QYhmGPSIbtqLsZcuXbp586aty0HI8WHQI7pRa91wOJx9+/ZduXLF1uUg5Pgw6BHdtB19ZmYmXo9FiAYY9Ihu2qBXqVQ4TI8QDTDoEd2oi7EcDgcAMOgRogEGPaKbtqNnMpk4dIMQDTDoEd20QR8VFYUdPUI0wKBHdKOGbiIjI6dNm4YdPUI0wKBHdKM6ej8/v0GDBmFHjxANMOgR3aigBwAul4sdPUI0wKBHdKOGbgAAl7tBiB4Y9IhuSqWSwWAAAC53gxA9MOgR3RQKBQ7dIEQnDHpEN+3QDZvNxqBHiAYY9IhuCoWCGrqh4h4hVN8w6BHdtEM3AECSpG2LQcgZYNAjumk0GuzlEaITBj1CCDk4DHqEEHJwGPQIIeTgmLYuADkRjUaD8ykRoh8GPaJPamoqbhKLEP1w6AbRRyQSSSSSqo+wWCylUmmrehByEhj0iD5SqVRncRtc7gYhGmDQI/oYDHoctUeovmHQI/roBz2uVIwQDTDoEX0kEgl29AjRD4Me0UcqlUokEmpFMwp29AjRAIMe0UcmkwmFQjabrX0kICAgPz/fhiUh5Aww6BF9JBJJRUVF1aBPSkq6ceOGDUtCyBlg0CP6SKVSoVDIYrG0jyQmJt66dcuGJSHkDDDoEX2ooK/a0YeGhj579syGJSHkDDDoEX2kUqlYLK7a0QMAh8PBiTcI1SsMekQfqVTq4uJStaMHgBYtWty9e9dWJSHkDDDoEX2kUqmXl5dO0CclJV2/ft1WJSHkDDDoEX1IknR1ddUJerwei1B9qyHoSbVMKJSqcfNmZEVcLlcn6Js1a3b//n1b1YOQM9ANeo3o8fFfFo7rERfAZjBdPTzcmAyWf4se4xb+cvyxSGOTEpEj4XK5OhdjqZWKSRI7CoTqS7WgP//DhDYtR/ycET5s8fqT6cUSNUmqpSVP0jZ+MCzi6S+jEtpOXPWPrQpFjkE/6AEgOjo6IyPDJvUg5Ayq7TBVEvr24fvtG3GIKo9xBWEt2oe1aN9rxKxP8y8fxu2BkEX0h24AICEh4ebNm9HR0TYpCSGHV62jHzSsQyMOAaSiJCO9UE6S4of7f/h05boT2TIAAILTqP2wQbYpEzkKY0GP12MRqj96F2NJ4cXFrQOaTjlUWH52Xo8J6y5dWD2iw4xjZTiCiixDkiRBEAaDHifeIFSv9IJefHXVZs2yq8emCG6tP+K3fO/eP/d9GpS6+ZbYFtUhByKXyzkcjsExei8vr/LycptUhZAz0J9eqVETbr6eLMntPed4A/qEsYAkNUqZEjt6ZBmpVOrq6srhcPQ7egDw8vIqKyujvyqEnIFe0POS3uiVMy+5XcfBm9wnjI/K3Tpr2OL0xKEt3GxRHXIgVEevf8MUBdcrRqj+6AU9Iei3/vqpb6fPXHXyxKIWHJIZNW7j+T3jglxsUR1yICqVislkGhy6Abwei1B9Yuo+ILn6xdIjjV4dPbZLrCcTIHrs4gXWOxqpkghlTHd3Nq684HyUSiWLxTI2dJOYmLh//376q0LIGeglLtMnyu3BhmlJ3t6xPd/4cMOx+6VKS49Bih788dGYtkFuBIPF8+RzXAhuozajlu5+KMGBfydCdfQhISF+fn76/xoREZGZmUl7UQg5Bb2gZ0eO+mz7mScVZdfXvhFbfOD97kGC6K6vL/n1r9tFCrOOoC5ImdJ12vGgmVsuZRdVyhTSyuJn13bMjbk0t/vklAK1xf8FqIGgOvrJkyd36tRJ/18JgmCxWLgwPUL1wdgYCpPnGxQaHhkdGxMgz7h05u8tC7sHCZqNW/egzkcQ3/jtZNgXO796s1d8qC+fw+LyfUJadHt9xY5VTc9uvoGzNp0G1dHX8ITmzZvj6mYI1Qe9oFdmH/jircFtg90EiZN/vky2+d+2e+WirGv/3CnMS+l8ZtGSOh+BHdjcO+vAkXRptXEaeebxvem8WH8Do7XIMVEdfQ1PoBZCoK0ehJyHXoelyL/+xLXr/F3f9X8lyqPqVBsXn+R31myoqPMRuIlLts4eNjhO8GlCckK4rwdbVVmUdfvCdWHbT/76JYlrSfGoIam1o09MTNy6dStt9SDkPPROPF77j9a1r/aIMuf4H/ebj+gdzG8+cKgZhyA8OyxLzZl1Oy3t2pNnecUytnfAyDnf9+oe71tTe4ccTa1Bj3sKIlRP9LRdBiUAACAASURBVE88Unxvx+dfpdwpeXEzrEb0MPVm4qHXegdbksss3/g+I+MBADTS4txSZoAPpryTUSqVNQc9h8ORyWTUkji0VYWQM9Abo1c8XDV6+h55UteIgqtFcYNH9I6SE0M3/tjd3dwjiC8sHDJtV44KAJTPUt5uzXfzCwvx5vh0mn8g1+KZm6jhUKlUNY/RA0BERER2djY99SDkPPSCXpZxriDxw++XzFsyN87Fp+vY2d9un52zcs0D8+ZWAoDq+ZXj5zLEGtAUpMyYmhL9050KlUaataX14alTduTg9EqnUWtHD3g9FqH6oXfiufADOIU3ngjHJUTHCnfeLH83xitCUHgsSwbNLZwhI8s8fb/RtIPjWni4AIT1n/9OeNLu2+KJIR6Gn75nz561a9fqPPjo0aOYmBjL6kC2YUpHn5iYeO7cucGDB9NTEkJOQv9ibLv3FwUnde4WdD91fM/0gV36bHS7cS700+8sWdSMlBXlFYsjQjrEatLyJGQchwCQFzwqcw30MN7hjRw5cuTIkToPzps3Lz8/34JSkM2Y0tEnJiauXr2annoQch76Jx4ndvaJ/CHZUj+vgJWp+1vtPlcy/fNJwyJrOUNrOIJ/6zacnUPDv6tQMRmgevrD/Nsfhlx4t0vf1R5Lr7TCRTGdhikdvY+PT3FxMT31IOQ8DOY30yM0ygMAILjzhHmdLTwC75Wvzzz4GoBUVORnZjwtdg1lkcBv9/7ROYO7RLpa+Oao4TClowcALy+viooKT09PGkpCyElUuxj7qhehj+HCcQ8ZebTS0iMRbM+g2KSOyU09GQzP1mPHdYvk4RqWzsSUjh5wvWKE6kG1rN2ZWVpamnv+s64Rnd7dfPpuVm7WvTNb3u0c1eODT7oYuWSKkIlM7Ohx4g1CVlftxHP38gbRzR9XV8w5+38To5kAAEGvf7GhuG3njy9M3NnDvKn0GlH2o2yRxtA/MdzDYsPcsa93DqZ39EeOHKGhHoSch4EOi1AV3csSkdFeBAAAKX6WXqpmmp/G8vQtc15bdjwXgO3O51S75dGj7557e/ri3wrOwcSOPjo6OiMjg4Z6EHIeeieee4fF80PbDWhfNGti9xh30eO0Lb8cC/nscnuz74x1TVp69F7X/8WPEm99vLkLz7JyUcNV61o3FAaDwWAwal3qEiFkOv1O3TV+QdqjI/NaSO+dPnr2obzlguPpJ9+Ls2h6DOHRauK4bjF8HKRxZiYO3QBA06ZNHzyo+84HCCEjDHVYBDe0+8wV3Wda8TC8Dit3dLDi+6GGx8ShG3h5PTY+Pr6+S0LISWCTjWhiekffrFmzR48e1Xc9CDkPDHpEExPH6AGAx+NJJJL6rgch54FBj2hi+vVVNputUJi9XCpCSFe1DuuNuPCTQkPP4vfYdGFTDz49JSHHZHpHz+Fw5HJ5fdeDkPOoduIt2fbn2wYXiHfxjMHVx5BlsKNHyFaqBX1MYmtb1YEcHnb0CNkKDt0gmmBHj5Ct4NANogl29AjZiglDN8qc43/cF7WI8XShqSbkkEwPeuzoEbIu/ROPFN/b8flXKXdKlCQAAGhED1NvJh56rXcwLj2CLGD60A2LxVIqlfVdD0LOQ28eveLhqtHT98iTukYUXC2KGzyid5ScGLrxx+5mL2qGEADUpaNHCFmX3oknyzhXkPhh6pJxZOTfwwu6jp09qQujx+g1DwYsa862RYHIUeCClAjZil5H78IP4BTeeCIEfnSs8NTNcpLpFSEovJols0V1yIFgR4+QreideLx27y8KTurcLeh+6vie6QO79NnoduNc6Kff4awbZBns6BGyFf0OixM7+0T+kGypn1fAytT9rXafK5n++aRhkdiKIctgR4+QrRg88ZgeoVEeAADBnSfM60xvQchRqdVqFxecoouQDeDqlQgh5OAw6BFCyMEZCnpSUZKRXignSfHD/T98unLdiWyccoMQQg2WXtCTwouLWwc0nXKosPzsvB4T1l26sHpEhxnHykhbVIecFYPB0Gg0tq4CIQehF/Tiq6s2a5ZdPTZFcGv9Eb/le/f+ue/ToNTNt8S2qA45K1zuBiEr0h+60agJN19PluT2nnO8AX3CWECSGqVMiR09ohEGPUJWpH/DVNIbvXIGJbdbp3zgPudKVO7WWcMXpyeuaYE3TCEa4UrFCFmRXtATgn7rr5/au/9fMnHY8Bac7H+jxm08P2NkEE6ARjTCjh4hKzJ0wxTLp0nbntHBMX6KhwcOPQavV7zYOAsT0Qo7eoSsCGfdIHuEHT1CVoSzbpA9wo4eISvCWTfIHmFHj5AV4awbZI+wo0fIinDWDbJHLBYLO3qErMXQrBt2o+TRM5PVkpKiYmX42MULaC8KOT0Oh4NBj5C1GJg3qSo4/sngWC6T59vIz53FiRn0yfECFf2VIWfGZrNx6AYha9ELetWzra+P2cCbf6ZApiE1soIz893WjZ647RlGPaIRdvQIWZFe0Etu77keveTLN9sFcAggOAHtp321NPr6ntsSW1SHnBWXy5XJcHVshKxDL+iZ3mFuuef/LX+5RKym/PY/eW5hAtzsE9GIy+VKpVJbV4GQg9DLb7e2S7/s2HZI8+QhwzpF8cQZ5/fuy+yy+UobnF6JaMTlcouLi21dBUIOQv9iLDNk7M7H97ZOa+sHMvBrO3Xz3fSdY0OwoUeWIMm63XHn6uqKQzcIWYtegIvSpiQvi9x2YtmCPraoBzkmjUbj4lKHezFw6AYhK9Lr6N1aTuir3v1NSroUFz1AVqPRaBiMOqyByuVycXolQtaiP+vm/oHLwpItE2LdWJ4BQcGUphNThbaoDjmKuga9q6srdvQIWYv+xdiEuat/n6KzLTODH8mjqyLkiMzo6HGMHiFr0Tv3GPyIaH7BhYMnK0LiWjbhXvltw9/P+NERfNx6BFkAgx4hG9I79zRFh2d0Gv7jXYY7iwCmd+Pg0u1jOrx5qEhj6NUImQaHbhCyIb1zT3Rt7ZFGX/61dU4SnwAXvy7v/Xbwy7Dj666JbFEdchTY0SNkQ/p3xvL9OSV3s8Qv59yQ4sw7RWxfd1ymGFnAjKDHjh4ha9G/GNtu2eeJ7fq1Sh87LDmSL8u+kLL1YtPvr7TDi7HIAnUNelzUDCEr0j/3WOETU9KvfDswSJGTnikU9Pj0XPqh6dFsG9SGHEddgx4hZEUGlzZgeDQfNOeTQS8+U+Yc33G8+YjewSwa60KOxYygr+uqCQghY/SDnhTf2/H5Vyl3Sl7sB64RPUy9mXjoNQx6ZD7s6BGyIb1zT/Fw1ejpe+RJXSMKrhbFDR7RO0pODN34Y3d3W1SHHIUZQU8QRD0Vg5Cz0evoZRnnChI/TF0yjoz8e3hB17GzJ3Vh9Bi95sGAZc1xnB6ZC4duELIhvXPPhR/AKbzxRAj86FjhqZvlJNMrQlB4NQvnNCML4NANQjak19Hz2r2/KDipc7eg+6nje6YP7NJno9uNc6GffocbjyAL4NANQjakfzGWEzv7RP6QbKmfV8DK1P2tdp8rmf75pGGRuPMIsoAZQc9kMlUqFZOJP3kIWcrgWcT0CI3yAAAI7jxhXmd6C0IOyYygp26O5fP59VQSQs4Dh00RHcwIetxNECFrwaBHdDC7o6+nehByKsbPPbW0rLBYiqsTI2swI+jd3NwqKirqqR6EnIqBc0+R/efcZD8G000Q+9aJ04uSk6dvS8e/oJFFzAj6sWPH/vLLL/VUD0JORe/cU2VtGj/lr/hf0guOjQlycW05Z+krl+ZO2vBUZYvqkKMwI+i7du168+ZNjQb/qETIUvqbg9879KDJouXDot0YAECwg/svXNDsyd8PJTYoDjkMtVrt4lLnPQ0iIiIKCwvrox6EnIpe0LMDmnhkH79UpH7xuab4ytFMXrQvrmiGLGDenbEhISE5OTn1UQ9CTkVvHj038YO1Y7oOapLQIb4sMyNnSKtHF8WDd59PcrXG0UiVRChjuruzcbKPkzE76J89e9a2bdv6KAkh56F/7jEEPb+7npW2YuzQmfOnDxq5ZF/6nbUD/S3ZSZAUPfjjozFtg9wIBovnyee4ENxGbUYt3f1QgqtWOQvzgj40NBQ7eoQsZ/j+cpZv0uCpSYOtcgR1QcqUrtNyx3+95VKfxAh/T5aisjj7dtr2FXO7T2Zc3zEiEHejdQJmd/QXLlyoj3oQcip6QV95bEzLMccqqzxCMDjuPn6BEW0Gz1o0Z3AT9zouNSW+8dvJsC9ufPVm6ItE5/iEtOj2+oo47sX4zTfEI/p7WFA+aiDM7uifPXtWH/Ug5FT0zj1+x2Xvdw+IHb58XcqR40dS1i0fEduo0zvf/bB8lF/qjJ6zDtf5COzA5t5ZB46kS6uN08gzj+9N58X64xr3zkGj0ZixGqW/v//z58/rox6EnIpeRy+++sVnT2ec2fVOFBMAoHX7zknQodc+zp21X3TwudtmE8CrdTsCN3HJ1tnDBscJPk1ITgj39WCrKouybl+4Lmz7yV+/JHGt85+B7Jx5HT2DwVCr1bU/DyFUI72gJwDU5ZkFUjKKTwAAkNKCpyVikVwDamFeKZixlCDh2WFZas6s22lp1548yyuWsb0DRs75vlf3eJyy6Txw4xGEbEh/45EOy5Y37tC73dMpoztF8sSZ5/dsTAtfcaH5tXc7DFmrmXPGzOOwfOP7jIzH6ZXOCoOeNpcuXUpLS3v//fdtXQiyI/rnHid21uH0cx935ObfunQjl9n2/dTHx+c25wf1WnLg3vmPWplxDJxeicwOeg6HI5fLrV6PA7t161Zpaamtq0D2xfDGIz5JoxYmjXrxmTLn+I7jzUcMGNrYrCPg9EpkQdB7eXmVl5cHBARYvSRH9fjxY/zViHToBz0pvrfj869S7pQoqX5bI3qYejPx0Gu9g80bUjd3euXly5fT0tJ0Hrx69aqHB87HbHgw6Gnz+PFjf39/W1eB7Iveuad4uGr09D3ypK4RBVeL4gaP6B0lJ4Zu/LG7u7lHMHd6ZUBAQGs9/v7+LBZew214NBqNGYuaAYCnp2d5ebnV63FgT548sauOXiwW//3337auwtnpdfSyjHMFiR+mLhlHRv49vKDr2NmTujB6jF7zYMCy5ubNeTd3emV4eHh4eLjOg4cPH87PzzerDmRL5s2jh5cdvdXrcVQajUYikdhV0D979mz79u39+vWzdSFOTS/oXfgBnMIbT4TjEqJjhTtvlr8b4xUhKDyWJQMzgx6nVyILOnoM+jqprKwMDAxUKBS2LuQ/UqlUIsFVzm1Mf3plu/cXBSd17hZ0P3V8z/SBXfpsdLtxLvTT79wsOw41vdKy90ANl9kdvbe3d1lZmdXrcVQymczLy8uuNlXHoLcH+hdjObGzT+QPyZb6eQWsTN3fave5kumfTxoWaXj1M4RMYsnF2KysLKvX46hkMpmnp6dd7daCQW8P9PJblDYleVnkthPLQgmA4M4T5nW28AgaUfajbJHB/eAY7mGxYe54G40TsCTocYtw08lkMj6fn5uba+tC/iOVSqVSqa2rcHZ6Qe/WckJf9dxvUsb8Oq6xqzl/a+uSp2+Z89qy47kAbHc+p9o7evTdc29PX5wt6QQsmXWDQW86mUzG4XBsXUU1MpkMO3qb0wt6yf0Dl4UlZybEbpnk4e/vziQAAPg9t1zZ0tOMdW4AwDVp6dF7Xf8XP0q89fHmLjwL60UNk1qtNvvOWLu6tGjnZDKZq6tVdoOzGolEgkFvc/odfcLc1b9P0RlpYfAjLUlowqPVxHHdjvNxkMZpkSRpXtAzmUyVSmX1esyzcOHChQsX+vr62roQo+yzo8ehG5vTC3oGPzKuJQCAWlpWLOb6+bpaI555HVbu6GCF90ENlNkdPYvFUiqVVq/HPFeuXDlz5sywYcNsXYhRMpmMy7Wvtb/xYqw9MHDuKbL/nJvsx2C6CWLfOnF6UXLy9G3pdjRbCzVEZl+MtauOvqys7Pz587auoiYY9MggvXNPlbVp/JS/4n9JLzg2JsjFteWcpa9cmjtpw1N7OddQg+QYQzeurq7Xr1+3dRU1kcvldhj0BEHYz59lzknv3JPcO/SgyaLlw6LdGABAsIP7L1zQ7MnfD/FXMrKAY3T0HA6HJO16bW377OgFAgEO09uW3rnHDmjikX38UtHLDdw0xVeOZvKicbkCZAmzx+jtJ+gVCgWLxTLv/l7aSKVSOwx6Hx8fHL2xLb2LsdzED9aO6TqoSUKH+LLMjJwhrR5dFA/efT7JvqZsoQbG7I7efi7GlpWVCQQCu7rpVJ+2oydJ0k5+J2HQ2wP9lQ0Ygp7fXc+a+Ne+tH+Thdzg5p22DnolyL4mbKEGxwGGbsrKyry9vRtE0LPZbKVSyWabuQyhdclkMoFAgEFvW/pLIJx/f+YuvyETxk6YN5hrFx0BcgBmBz1BEHYyLF5aWioQCGxdRS2ooKf2X7SToKc6ehyjty29c48T1jFOfvKTAaH84A6jF64+dLvYLv5uRg2bA2wOTnX0djIeYoxcLudwOHa10S4O3dgDvXOPFfra+2sO/1skyjzwfmfiny+HxPhFd5u25oG9/NigBskBgr60tNTb29vWVdSiakdv61pewKEbe2Ds3CO4gqCwiKiY2NgQ4unFc/+W2MUwKWqoHCDoqYuxLi4uarW69mfbiB0GvUajcXNzw6C3Lf0bpvJTf/lgUq/mArfg3ov354eP+v6fYuH91R1xNTJkAQcI+oqKCk9PTyaTaSezgAyyw6AHADc3Nxyjty29i7HSh3sPPfUftOLY5n5tgq2yTjFCjhD01PA3i8Wyk1lABtlt0ItEIltX4dT0gp7f7afD3ao9osw5/sf95iN6B+M9U8hcDhD0SqWSxWLZz3RPg7TTK+1qbefQ0NCUlBRbV+HU9OfRk+J7Oz7/KuVOiZKa1KYRPUy9mXjoNQx6ZD4MenrYZ0ffuXPn5cuX79q1q2XLlo0bN2YycWNSuumde4qHq0ZP3yNP6hpRcLUobvCI3lFyYujGH7u726I65CjM3mHKflC3INn5GL09TK9UKBRTp07VeXDNmjVPnjz57LPPOnfunJycPH78+C+++OLQoUPFxcU2KdLZ6P1qlWWcK0j8MHXJODLy7+EFXcfOntSF0WP0mgcDljW3i9svUIOEHT09qK+zbYO+srLy6dOnVesBgNjY2A8++ED7YGZm5r179/7555+DBw+uWbPGVqU6D71zz4UfwCm88UQI/OhY4amb5STTK0JQeDULV6RHFnCAoKcWNbPzoKdwuVwbznIRiUTaP3okEombm5vOExgMRlRU1Kuvvjpv3rySkhLaC3RGeucer937i4J/6dztu9wm43umT+/SZ0DPSSdD+zXV/WYhVAf2s8aW2aihGzufdUN9kZs1a3bnzh1b1VA16MViMY9ndGq2QCAoKyujqy6npn9VhBM7+0T+kGypn1fAytT9rXafK5n++aRhkXj5BFlArVY7wBg9k8m0846eWhcoKSlp1apVtqpBLBZrv0Q1B72d333mSKrl98mTD1p3berpwvQIjfIAAAjuPGFeZ+0/qysenL7WtEcPmktEDsCSoRs7WdRM29Hb88VYikAgKC0ttdXRdTp6d3ecyGF71c495dkFyc27Tln5e+q1R/mVChIAgFRU5j+6lvr7yildW3RccNbef8SRfbIk6O1kzKcBjdEDQHBwcG5urk0OXTXoRSJRDR09ok21c6/vhwdvnP22H5n29dTuEZ4cBtfLi8vgeEZ0n/p1Gtnv2zPXD37Y11aFogbNkqBnMBgajca69ZhBO73SnoNe+0uxdevW165ds0kNpo/RA4C93dvlqHSH3tn+rUd9sG7UBwBqWUVJiQjcfXw8uQ17cBXZniVBT2WrzVdX106vtP+hGwBo06bNqVOnBg0aRP+hxWKx6UHv7e1dWloaGBhIS2nOy/i558L19A8O9seUR1ZgedBbtx4zUNve2vOsm6ob87Zq1er69es2KaNOQzc48YYeDXtqM2ooLAx6+2miXVxc7DboqdtiqY89PT3Ly8ut+OZ37twx8ap41Vk3EonElI7eCvWhGmHQIzo4QEdPseeOvmrQg7Wvx77zzjsmtt46HX3Ns26wo6eHoXOPVJRkpBfKSVL8cP8Pn65cdyIbb4tFlrEk6O0kW6nrnHb1W0eHTtC3bt366tWr1nrz4uJioVBoyjPFYjF1zwT1klqHbrCjp4HeuUcKLy5uHdB0yqHC8rPzekxYd+nC6hEdZhwrs4upzKihcpiO3q6K0aET9G3atLHixJvi4mIT15TXdvFLly49depUzUHP4/HEYrF1SkTG6Z174qurNmuWXT02RXBr/RG/5Xv3/rnv06DUzbfwe4Es4ABBT41Q29UFAx06QW/d67F1Cnoq3CsrK2/evFlz0HM4HJxeSQP9c0+jJtx8PVmS23vO8Qb0CWMBSWqUMiV29MgCFg7d2EO2NoihGy6Xq/3UitdjKysr5XK5iUM3IpHIw8NDo9EIhcKysrKax+jZbLZdLZ3vqPSWsOElvdErZ1Byu3XKB+5zrkTlbp01fHF64poWuKgZsoAlQW9XE13s5IKBQTodPQCEhYU9e/YsNDTUwnem1pg0saOXy+U8Hk+lUlHPxxum7IHeuUcI+q2/furb6TNXnTyxqAWHZEaN23h+z7ggnE2PLOAAF2O1Qzf2UIxB+kFvreuxxcXFXl5eQqHw5s2b69atq/X51B9hGo3G1dUVg94eGDr32I2SR04a2or98OyZf5Xd5r43uqUHzsJEFnGAMXrt0I09jCMZZDDoda7H/vjjjydOnKjrOxcXF0dGRopEovz8/MzMzJqfTJIk9buZIIiYmBj99eirwjF6ehg49yT3fh3TzCswvu/rMycPTAzyajrml7sS+itDjsQBgp5iV8XokMlkOkGvfz22qKjIjN37iouLIyIiRCKRTCYzJZepjp4giN27d9f8fccxenrofQ/kd78aOf/2wP05wqLMRxmFlbkHXr29YPQ3d/F7gcy1YMGCixcvOsadsXYyjmSQfkfv4eFRWVlZ9RGRSGTGdEaqoxcKhVKp1JRcpoKeJMmmTZvW/EwcuqGHftBnXSpsMe+9PsHUElLsoN7vzWuefyELgx6Z6+jRo1lZWQ26o9doNA1i1o1O0ANAeHh4VlaW9lPzgr6kpCQ8PJzq6E0JeiaTqVAoTFlfGodu6KF37rm1HNepdFfKfQk1n5KUPEjZUfzKqDicdYPMUlRU9OTJE6VS2aCDnlq60k6KMcZg0Ldp06bq9Vjzgl4kEjVq1KhOQzeVlZU1j85TcOiGHnrnnrK0WBB4c26cf0y77r27t4vxi5t7ihD+8fawQYMGDRo05hNbFIkasNTUVGqx3IYe9NQ6yXY1jqRDZx49pW3btleuXNF+al7Qi8XigIAAoVBYa0dPbQ7MYrFKS0v5fH6t74wdPT305tEzBS16TPmy85SXn0+cVfVfWSF0FIUcSFpa2tixY3fu3Nmgb5jSdvR2Pkbv6+ur82BiYuJnn32m/dTsoA8MDDRljF6hUHA4HNODHsfo6aEX9KyQ3hPeAABQS8uKxVw/X1ecWoks8ODBg6+//hoafkffQIdu3N3dRSIR1WiDBUHv5+cnkUjkcnnNQU/N/GGxWLXeE0vBoRt6GDj3FNl/zk32YzDdBLFvnTi9KDl5+rZ0XL4S1SI1NTU7O1vnwby8vMDAQD6fz+PxGnTQUxvG2kkxxhgMegCIjIzUTn4Xi8USSZ1nS0skEnd3d5Ikax2jp2pgMpmmBz129DTQO/dUWZvGT/kr/pf0gmNjglxcW85Z+sqluZM2PLXTn21kL9LS0u7du6f/YPfu3QEgMDCQWrrWDPaQrVU7epuPIxljLOir3h/LZDLN6Oi1//m1jtHLZDIul0t19KYM3djzUJgj0Qt6yb1DD5osWj4s2o0BAAQ7uP/CBc2e/P0Qb5lCNZLJZPoJUjXoG/QSCFUvxtq8GGOMBX3V9YotXBa41qCnajB96AZeri2B6pXeuccOaOKRffxSkfrF55riK0czedG+LJoLQw2MXC7XHxN4+PBhkyZNAOD77793dXU1753tIVsbxMVY/TtjKYmJiTdu3KA+tnCopNahG21HX1paamLQmzLdHllI72IsN/GDtWO6DmqS0CG+LDMjZ0irRxfFg3efTzLzJEXOQi6X67SKWVlZ4eHh1Mdt2rQx+52pu28sKs5iNVyMPXnyZHFx8ahRo2xU2n+MdfTu7u5isZgkServErO/mAwGQywW1zxypR2jLy0t9fDwMO9AyOr0gh5It1e+uJo18e99af8mC7nBzTttHZjkhWtXolroB/2JEyd69uxp+TszmUwzrh9al0KhMDZ0k5ube+HCBTsJev159JTGjRs/fvxYIBDw+XxqzeE6oZpuPp9fVFRk8HeJlrajLyoq8vLyquuBUD2pNnSjVqlUJfsHtXrnKjt+4OQ5iz9cMm/q0FbqPX2iRx6vNPYOCAEAyGQynTg+ffp0165dLX9nexgtqeFirFKpvHv3ro3qqkYul1O/jfQlJSVdv35dLBbXvGhwzaigN/a7RFsDNUZfUlKCHb39qNbR9+GyTqoBAI57/lr1cb8+P0TW9M1FyEBHn5GRERUVZfk728MMvBrG6BUKxe3bt21UVzXGhm4AoHXr1vv374+Pjzdx3Lwq7dA/n88vLS0NDAysuQaqo8egtyvVOvpUFUlWpk579dNbUrKq50fnNDHcKCD0gk7Qp6enN27c2CrvzGKx7Cfo9YduFAqFTCbLycmxUWn/qSHoExMTb968qd22u060fwd4eHhIpdKan6y9YUosFnt6etb1WKie6M264fdYe2BhcF56oZwkxQ/3//DpynUnsvF+KVQbnVk32omVlrOrjl4/6JVKZUJCgj009TWM0fN4PIlEUlFR4e7uzuVyZbI6nNLaoOfz+TUP0GtrYDKZAIBBbz/0gp4Ux723uAAAIABJREFUXlzcOqDplEOF5Wfn9Ziw7tKF1SM6zDhWhlNdHd2JEyfUanXtzzNCp6N3pKBXqVTr169PTEwEQ2P0CoWidevW9hD0SqWSSliDYmJi9uzZExcXV9ep9BKJRBv0NQ/QQ5WO3tXV1dgFA0Q/vaAXX121WbPs6rEpglvrj/gt37v3z32fBqVuvmX+PRaoYfjyyy/Ly8vNfrlSqdR29CRJWmVPaoptg16j0bzxxht9+/bt3bs3GFptUalU2knQ16xNmzbbt2/v3Lmzu7u7UCg0/YVVh26obr2GGZbaMXrTB+gJgsB7puqb/s2KGjXh5uvJktzec443oE8YC0hSo5Qp8Rvh6Ki1Cc1+OZfL1faJd+7ciYuLs1Jdtgx6KuU7dOgwY8YM6hH9cQ+FQtG4ceOqm3vYp7Zt20ZHR/v5+fn6+tZpN0GdoK95YWFtR2/6uI3N/2JzBnp/6PGS3uiVMyi53TrlA/c5V6Jyt84avjg9cU0L3HjE0YlEIkuC3sXFRTvyY62JlRQbBsGSJUtCQ0P/97//1fAc6i4kFxcX7Ti+fUpMTPy///s/APDx8anTVPqqY/RU0MvlcmPTNKmOXq1W1ynoa7iMjKxCr6MnBP3WXz/17fSZq06eWNSCQzKjxm08v2dcEN4y5eiEQqEl9yVV/ev71KlT3bp1s0JNAGC7oP/iiy9EIlHVxdwNoha2bNKkyaNHj0x8Z5vcFsDhcPr37w8AZnT01F5RVYPe2JOpjp7JZJo+dIN7j9DA0KUbdqPk0TOTqY+jxy5eQGtByFYsHLrRjrRqNJrCwsJGjRpZqzCbBP3PP//84MGDTZs21fpM6qbZuLi427dvt2jRotbnS6XS+Pj4c+fO1Twh3QwmLhrj6+v77Nkz099WLBZTkzKpoK/520H15iRJ4tCNXcFdRdALFga9tqP/999/W7ZsaaWiAGwRBNu2bTt58uT69etNiU5q6KZly5YmXo+lGuSxY8eKRCKLK63GxEuavr6+JSUlpl891s6+147R19zRUxdjMejtSrWgLy8uV2jkZcWVdrreNqo3MplMqVRa2NFTH1hxYiWF5iA4fPjwjh07tm/fXsNUxaqooZv4+HgTo1MqlSYkJCxatGjChAnWHcMxvaO/d+/e22+/beLbaoPe399/2LBhNQe9dgmEuo7Rm/hkZJ5qQT++xcDNN/4Ykfze2eei6qTmz69GDQE1387sMXqql6RGb06dOmXFK7FAe9Bv2bJl7dq1pl8bpDp6b2/vsrIyU54vlUpdXV379es3YMCA2bNnW1CpLtM7+tOnT5uerdqg53A4b7zxRs3fDqqjT0hIeOedd0x8fxyjp0H1oO+eN6fthJPp63sG8KsJGfl3ha0qRHQQCoUMBsPsjp7aEpqaYVlWVubn52fF2mgOeolEUnM36uLiUrUT1+4y6OHhUVlZ++J/VNADwPTp0z09PamZMJZTqVQmzvkRCAQlJSWm3xyrs3CCKR29q6urdoXqWuHQDQ2qBf24nU+llafnjP7yjqzaWjdk+aGBeDOzQxMKhQKBwOygp+ZaNGrUaN++fa1atbJubWw2m87d+7RBbIybm1vVL5R286m4uLg7d+6Y8v7UJBYAWLly5c2bN3fu3GlZyQDGdx3Rx2Qyvb29zejoKaaM0Zv4zhRvb+/S0tI6vQTVld7FWPcuP+xc0IIDoJaUFBSL7XQvHWRlQqHQ39/f7KCn+rjevXsvX77cugP0AEAQhEajse571kCtVte8va2rq2vVMS7tUvXUxJta318ikWh/kRAEsWnTpvXr1589e9ayqmta0UxfcHCwj4+PiU8WiURVZ83XPKRuxoz4oKCg3NzcOr0E1ZWBWTeqguOfDI7lMnm+jfzcWZyYQZ8cL8C8d3Aikcjf39/sMXrq9O7Tp09OTk6XLl2sWxvNah3p1unotUM3NUy8WbFihfZjnb8YuFzuzp07FyxY8ODBA0vKrlPC/vXXXyZeak5JSdHv6GudXmliGZTg4GAM+vqmF/SqZ1tfH7OBN/9MgUxDamQFZ+a7rRs9cdszjHqHJhQKAwICzOvojx49St0P6ePjc/jwYW9vb6uXR6da5664urpW/UKp1WoqNJs2bXr//n2DL9m1a5f246pDNxRfX9+tW7dOnDjRkqHqGpau1BcSEmLK00iSXL58uXZRM0rNQ+ragSzTBQcH5+Xl1eklqK70gl5ye8/16CVfvtkugEMAwQloP+2rpdHX99y28VZuqH5VVlaaF/RKpXL+/PnaAWJq5S+ro3P/aFM6+qp/+mifTw1e679cqVRWnZBTdehGq3HjxomJiRkZGWaXXR+rCIjF4ry8PI1Gw2D8FxQ1bw9gxvJkwcHB9rCav2PTC3qmd5hb7vl/y1+OiWrKb/+T5xYmMOnvPNRQiUQiPz+/OgX9nTt3Hj58WFBQIBKJ6nutEjpXNzSlo79x40Z2drb+P4WGhuqPQojF4qpfWGMXeyMiIjIzM+tc7kumX4ylmLJmpFAorKio0Il1q18b9/LysmTZVGQKvfx2a7v0y45thzRPHjKsUxRPnHF+777MLpuvtLHKomakSiKUMd3d2XhDrp2hLsbWqaM8dOgQj8dr166dWCx2pEWpao0/V1fXbdu28Xi8sLAwqP6LgboeqzMwonPLsVQqDQgI0H/byMjIp0+fml12Xb8F1N8fNY/2CIVCkiR1psSw2ew6LWeP7IF+4jJDxu58fG/rtLZ+IAO/tlM3303fOTbEkoaeFD3446MxbYPcCAaL58nnuBDcRm1GLd39UIJrH9uLkpKS0NDQOnX0WVlZubm5ubm5VNDXdVKdfSJJsuowhUFubm4PHz402NUavD9WJBLJZDLq98ecOXNyc3N1xugpkZGRlnT0df0WmLLPFHUbnc7vj/qY9s5kMi3Z9AbVymCAM3gxfd5c0Mc6R1AXpEzpOi13/NdbLvVJjPD3ZCkqi7Nvp21fMbf7ZMb1HSMCcV1MO5Cbm9u4ceO6Br2Pj09eXp5UKpVKpfbQ0d+6dSswMNBgv2wiU6aBu7q6FhQUGAy7li1b7tmzR+dBkUik0WioIM7IyKDuJ9J/Lc0dvYlBz+FwdLaZrY8tfKnLHnw+37pvi7TqfwxFfOO3k2Ff7PzqzV7xob58DovL9wlp0e31FTtWNT27+Qb+CWgfiouLqY4+Ozs7NTXVlJcIhcL8/Pzc3FzqTkt76Oj37t175coVS97B4JVSHVQ/bjDswsPD9XcgoRYvo36JSqXSZ8+eGTyEv79/YWGheWVDvQV9VFSUztLz9XH/GpfLxeVu6lX9Bz07sLl31oEj6dJq4zTyzON703mx/rippH0gSZKaH3316lUTg57aJjsvL69x48b5+fn20I5ZuHcKGJr7qI+KaYNhRxAEtQOJTlVQJeizs7MNBj1BEJZMLqrrxVhTslUkEkVFRel09PUxdFPz3bbIcnpBL0qb0rLTp//WYZP4WnATl2ydLVsWJwhr3fPVYaPHjRn+as824d7R/8udsW15ku27QARV58/l5+ebcttUcXGxr68vAGRnZ8fExOTl5Xl5edVfhQwGw5SJN1TQW7J9q4kdfQ1hp78DCbWOEPVVlUqlz58/r/V3iRnqOkbP4XBM6ehjYmLoCXrT195BZtCfddNyQl/13G9Sxvw6rrGrVSYvE54dlqXmzLqdlnbtybO8YhnbO2DknO97dY/3td9t15xLYWGhdly7oKDAlKDPzs4ODQ2lFtLy9vbOzc2t16CnxoVr7Vip+S2TJ0++du2aeQeqdaEbAHB1dY2IiDA2fKG/A4lIJPL29tZ29CRJGjsEtVyaifes6qinoZt27drpbJBSwxh9ratH1FAMdvT1Su9HSnL/wGVhyZkJsVsmefj7uzMJAAB+zy1XtvS05E9zlm98n5HxAAAaaXFuKTPAp7aU37t3788//6zz4KNHj2JjYy2oAxmQk5NDzQjUaDT5+fmmnHKFhYX+/v5BQUE9evTYs2dPfQc91UXWGmRUR19QUGDG9q2DBw/ev3+/RCKptd1OSEh46623jC1KHB8ff/z4cZ2qtPcoUP9vLOg9PT0rKysFAkGdKqfI5XKd1rtmJgZ969atdXaFrGGM3uxZtjh0U9/0O/qEuat/n6KzhBSDH2l4K2ATiC8sHL+x9U+/jA5hKp+lzBsycfV1CQB4d3zvt10rBwUbPRuHDh06dOhQnQfnzZuXn59vbinIsLy8PGrnPz6f//DhQ39/f+pxqVR6//59g6tRFhcX+/n5vfnmmwBw+PDhvLw80zeaMIOJwwVU0ItEopKSkrpu1EetXmBKRy8QCNq1a3fw4EGD/xofH//NN9/oVKUNeqpbryHoKyoqzAt6M8boTQl6/UsvNXwvzJ5li0M39U1vjJ7Bj4xrqSsukm/2VVvV8yvHz2WINaApSJkxNSX6pzsVKo00a0vrw1On7MjBubN2QCwWUzMrWrRocePGDe3Qzf3793v06HHv3j39lxQVFVFj9ADA4/Hy8/Np6OhrfRoV9BKJpKSkpK6HoGa8mNLRQ5XhC/0/HQQCgU6zTy0YRwW9r68vk8k0thqMp6fnqVOn1q5dW9fiod7m0dcp6Ov6y0YLO/r6Vq2jf6dbm3MGt7F07/T9oe871eHvQkNkmafvN5p2cFwLDxeAsP7z3wlP2n1bPDHE1M3iUX3R/sUdFxcXGBioPf/FYvGQIUOmTZu2b98+nb1EiouL27dvT33M4/GUSqWddPTl5eUqlaquQU+SJDW+b0pHD1WGL7RLV1ZF7UDi4fHiJ1soFGo7ejc3txrWB/b09Lx8+bIZX0mhUHjixIl+/fqZ/hJThsUNBn0NY/RmD93gGH19qxb0o+cv7VZtlUpV6ZWNyz8/kh/Xg2PJbU2krCivWBwR0iFWk5YnIeM4BIC84FGZa6AHLqFjB+RyOdWPt2jRIigoSDu/RSKRNG3adNasWWPHjj106FDVhpEauqE+5vF4PB7PvEuIJjI96KmIr2vQU4uRlZeXm9jRa+sxuFgjtQNJcnKytqrIyEipVErlYEREhLG39fDwOHv2bFxcXJ2KB4BNmzaNGDEiMTHR9JdY0tHjGH2DU21E5pVXh2gN6hEtPPHVvK/utfn8WPa1L9vW3uUY8f/tnXdAU9cXx+/LDiuQQNggQxERcVZEcaJUK1WpuKrVurq0v2qtHa62dmirtf5qtbWto9U66p4/rQOt4qhbwYHsDSEhhOyQ9/vjaYxZJjFhvJzPX+G+9+499z3yfSfn3nsujd+tO3PnqHAPVuj0E/c3z1qdrcTrMuck9vnBa+bsro6fZgbYjO77GRcXN2XKFF05EdLp2bPn9OnTZ8yYoT/B0SB049S4DbJa6HEcFwgEVCrVDqFHCInFYis9ejqdrvPoTQq9/hRPXa5/ovIjR46Yq5bD4dy7d8+OpQAnT55MT0+36RJrwuIikchgtRR6VozebqGHGL1TMRV6x6UPds0fENXto4epW+/f2/vx4ODnWdbk3mvF2XuldWqtsq7o7rVzm8aF0nHM84WPjt3/Z0GC3a8PwIHovp9MJnPq1Km6cqlUSri348aNi4qKWrp0qe5QXV2dLu98yxF6NpstEAgCAwNtFXpCZezz6I1DN/Hx8fp7CuoGYwmhtzDWyuFwrFzHoI9Kpaqrq9MNoVuJzqNXKpXG+9ZWVFSkp6enpqYar+FyxmAshG6cjYHQ44rCw58Oi4l/+3LPdXceHP18eATLUYnAMQYnqF2X3kntORQKp9v4Cf0j3CGHZcvAnCOmv+PEkiVL7t69q9vdVKvV6iTA3d3dqQF6ZJ3Qq1Qqb2/v2trasLAw+zz6urq6ixcvWhM50dlj0qOPjY3V34FEpVJ5enpaMwBA3EZbPfr8/Pz27dvbdAnSE/qlS5euWLFCf7vzrVu3jh8/fuHChYsXLza+0BkxeoPQjVarXbBggR31AOZ4Smszv3mlQ/uJx9ouu/rw1Dej27k33WYPQHNi7vupm42DEMIwbMOGDT/99NPFixcNTmshHj3hOBNJe+zw6DEMKysrq6qqioyMfOb5utCNUqk0FnrjiDOxKZU1Qm+wq4k1CIVCO2ZkEkKfk5Nz9erVjIwMYiPDqqqqjIyMnJyc48ePm9vk3UKM/nlm3eiHbpRKZWZmph31AOZ4Sug/+3BvgbLu4g+T4jlUTB/WkIPi5rIQcBhbtmy5cOGCcbmB0GMYplQq79y5owvdELDZ7O3bt8+ePfvq1av68QoPDw9nbx9opdDzeDyFQmGfR8/lcvfu3WvlxBWdPWKx2OSvmZCQEGLXJGKlKyH0z8yvwOFwwsPDbQ1iiEQiu4V+3bp1S5Ys6dq167Vr13bs2DFmzJj58+d/+eWXFrYDJHIcmTzkqFk3KpUKUt47lqdmSuyuKFeaTCiCMbkwCbL1c+nSpczMzF69ehmUG3w/6XT6tGnTTp48OXnyZIOxuICAgF9++eXFF1/ctm2brjAqKurbb791quXWCL1EIvHw8GCxWIGBgbbmsFQoFP7+/v/888+cOXOsOV8XvhCJRCZfcrodSKqrq/l8vvUefWBgoEFm9tu3b+/YsWPx4sXmxFcoFNrxoiVMqqqqioiIYDAYL7300muvvXbs2LHnyULqqNCNSqWye596wCRPCT03ILC57ACagMrKyuzsbOPxQ4Pvp5ub24MHDxISEqqrq40nXXTu3LmyslK/BMMwW5eh2oqVHr2Hhwebzfbw8LB160GlUunv75+Tk2PlDEVd+MKc0BM7kNBotCtXrgQEBLi5uRFCb3mkl8/nL168eMmSJfqFGzduvH79+unTp1NTU01eJRKJrNzsWx9fX9+ampra2loej+fr63vq1KnY2FhbKzHAUStjlUolCL1jgfFQF6KmpiY9Pf3EiRMG5cZCX19fz+fzS0pKnJFn0Q5sEno7bFYqlQEBAeHh4RZWM+mj23DVgtDfuXPn3LlzGzZsCAgIYLFY1nj0VCq1X79+xrZNmzZtz5495q6yz6P39/evqqoiIktUKvX5VR6BR9+CAaF3IbRa7bhx43bu3GlQbvD9JJxiLpdbUlJi7NE3C9YIPTFHyM3NzQ6bFQpFUFBQ9+7dbb3QnNC3adOmqKgoNze3oKCAEHqlUmnlJH2CrKws4oNSqezVq9fNmze1Wq3JM+2L0Tsj27CjUiCA0DscEHoXAsOwjh07PnjwwGC4z9ijDw4O5vF4paWlLUToiU1RLJ9D7AJIePQeHh7Edh9WolQqw8PDd+3aZath5oQewzAWi/Xw4cPExER/f38iNGGT0M+cOZP4QKhnUlLS+fPnTZ5pn0ePEMJx/Hm2OjHGgYOxSqXS3JAvYAcg9K6CWCwmsq+kpqYa5NE1KfRcLtdg1k0z8kz3s6CgQCf07u7uPB5PKBRaX781W8WaxJzQI4Q6d+6sUCheffXVmJgYwmO1Xui1Wm1eXh4RHSKezqhRo/bu3WvOBvsSXtJoNMfOixUKhQ6J0RPPGpx6BwJC7ypUVlYSuYjHjBljEL0xFvqgoCAul8tisezbR8LhWBZ6mUw2YsQIfY+e2MbW+vrtdkUtCH23bt3atm371ltvEdNaiHCENUJPo9HKysoUCgXh5BK29e7dOysry+QgMzE4YYfxAQEBujwWz0ljY+Nnn312586d3r1723G5LnRTXFwsFouJzyD0DgSE3lWoqKgghL59+/aE/6s7ZLCrEZvNJkI3LSRug54l9FeuXBGLxfoePZfLbRqPXiwWm3OK+/fvP2nSJOIzMXirUCisEXpi1hN6nJiBEHoKhdK5c+dr167ZYaQ5/P39HSL0ZWVlw4YN43K5u3bt0uXstAld6OaPP/7IzMwEj97hgNC7CvX19TpJGjp06P/+9z9zZ6ampvbv35/L5baQuA1CiMFgKJXKgoICk0cvXLggl8sJsSYGY20Vers9ev3tdg3g8/kjR47UL5HL5da8TthsNiH0RC4EXZYFc9EbW+eS6vD39zfIPm0Hhw8fHjNmzLJly2bPnm13xF8XupHL5RKJhBB6WDPlQEDoXQX9rCxjxoz566+/zJ2ZkJAQERHB5XJbmkf/yiuvmDx64cIFf39/QkZXrVoVFBTUZDF6m7DSoyeEXrfNLEKIENABAwacPn3a4GS5XG7fKwoh1KlTpw4dOth3LUJIpVLNnTt3586dx44d69Kli931IL3QjUwmI4SeRqOBR+9AQOhdBX2hj46OLisrs5w8qwUKfWFhobGXh+N4bW2tr68vIdbBwcEYhjWZR28TVnr0ROimbdu2Bpl7GQxGZGQkkZRGR15eXnR0tH32DBs2bOjQoXZciGFYbm5uampqQkLC5s2b7Rsh0EeXVkEmkzU0NBD56UDoHQgIvatgkGdx2LBhFhKjI4Q4HE6fPn2cb5dV6AYzCwsLDQ49fPgwOjqawWDU19frZNTf39+mvYXtmwBu6/RE6z36a9euxcfHG7+J09PTDVZO5ebmtm3b1nobHAKDwZgwYcJPP/00efJkx9ZMePRKpdLHxweE3oGA0LsKBkKfkZFhIXqDEMIw7Pvvv3e+XVbBYDCIWSj5+fkGhy5cuJCUlMRms0UikU5GY2JiDDxfy9i3dt/K5PU6rAwQubm58fn8wMBAY6FPTU09duyYfkmzCD2dTudyuTExMQ6vWRe6AY/esYDQuwoGQh8REVFdXd1axrsYDIZYLKZQKMbjsVlZWb169WKz2fqTuG3dyMIOj55KpYrFYpuiW1bOo2ez2X369DG51Z+bm5ufn19RURHx55o1a3bv3t0sHn1CQoIzatYX+tbyz9kqAKF3FYy3yBg+fPjhw4ebyx6bYDKZYrE4PDzcWOhzcnI6dOjg5uYmEon0/eXg4OCysjIr67fDo2cwGHV1dTYJvZUePZ/PHzhwIJFd0vjoyJEjdXNvsrKyxGKxNQn0HQuDwejUqZMzatbF6CF041hA6F0FlUplkLTymdGblgPh0bdr166kpES/vL6+3sPDg0KhEKEbfRmNj4+/deuWlfXb4dHT6XSTW6qaA8MwhUJhIc+7junTp7/yyivmhF7/9SwQCG7evGm8l6GzYTKZNm1Ebj1KpRJCN84AhN5VMPboQ0ND6+rq6urqWsjyVwsQQu/n52fw5b906VLPnj0RQsZCHxYWZvBWsIB9Hr1NQk/MILR+8JZIeGlc7u3tzWAwqqurUVNNFjJm1apVcXFxzqiZyWTKZDIYjHU4IPSugsndTdPS0nbv3t0sYmEThNB7e3sbhK2zsrKSkpIQQmw2u66uTr8jvr6+1mdBsMOjt0/ora+fzWYrFAqTE3tGjBixf/9+66tyOD4+Po7NhqaDWEJMePQW0tJJJBJbd9Z1cUDoXQW1Wm0s9KNHj96yZUtrEXp3d3eDVaD6Hj2FQtFfpMrj8QQCgZX125G+jQjdWD+F3A6hl8vlJvek1Qm9k9S2WaBSqbo8zCqVqkuXLtevXzd38ubNmy0k6AeMAaF3FUx69EFBQQihViH0+tPkCbRabUNDA5Fcxc3NzeCor6+v9UKP47i5TAYWTLLVoze3p7ZJiNCNyeCMv7+/QqEoKSnx9PS0vsIWjv4sI5VKRaRaqqqqMnmyTCYzeLgnTpwwl68fQAZbCQIkxqTQI4QyMjIuXbrU9PbYBDGKYDA3kZhvQ3xms9nGQk+EbhITExkMRtRjIiMjo6Kinj+Zl4+PT0lJifWrUplMpjUjsTqI0I25KPzw4cM3bdr0/JlqWg4GQs9gMF5++eUDBw7MmDHD+GS5XG4Q2Fm2bFm3bt2cvUl96wU8elfBgtAPGzas6e2xFQaDYSD0xAx64rOx0BN7j6hUKg6Hc/To0Xnz5nXu3FkgEOzcufONN96Ij48nEofZjZ+fX2FhoU0evfW7jiC90I3JUeJRo0b99ttvjkoy3BJgsVj19fV0Op1KpcpkMgaDkZaWduDAAZMny+VygwEYqVQKg7cWAI/eVTAn9H5+fmPHjm16e2zFWCjPnz+/ePFi4jObzTYpo7W1tUTSnri4OP2JInv37t2/f/8HH3yAHu9BaKs9fD6/oKDAJqG3aWKPTuhNevTh4eEOyT3ZcmCxWEKhkBgpqa2tZTKZfn5+arXaZMZ/uVxuELppaGiABVYWAI/eVTAn9K0FY48+Ly9Pt1bI2KNHCOE4bm73pUGDBp08eZL4bGHzEAv4+vratKeurUJPhDIsTKBMT08nNhggBywWSyQSubm5cTgcgUBA/K8OHTrUIOUDgVwuN0haJ5VKYR6OBUDoXQXSCD0x8UYgEPB4PN20E5NCT6fTq6qqTAq9l5eXTheEQqEdW/ER/mYThG7MCf2HH344ceJE6yts4eg8+qCgoMLCQuJ/VX8ZsD4mQzfg0VsAhN5VMF4Z27oghF6XxObChQu6AD0yNesGIcTlcnNzc82JOIvFIja4sFvoEULNFbohH4TQs9nsoKCgsrIyQujDw8OrqqqMc/7I5XKDHccgRm8ZEHpXgRwevS4xgIHQm/To+Xz+3bt3zYk4h8MRi8W7d+9euXKlHaEbW4We2ObQ+votTK8kJTqPPjg4WKvV6v5XU1JSTpw4YXCyQcJnrVYrk8lA6C0AQu8qmFww1YrQefSE0P/77789evTQHTUp9NHR0ZcvX7Ys9BcvXjx69KgdHr37Y6w83+ExepKhL/RIb23HqFGj9u3bZ3CyXC4nduAiwjUymQzHcQjdWACE3lWwdZeMloa+R69Wq1Uqlf5aVnd3d+OlrTExMdeuXePxeCYrJIQ+Ozs7ODjYDqFHCPn5+TlP6CkUCo7jLiX01dXVXl5eQUFBNBpNt34tLi7u3r17jY2N+idrtVo+n19TUzNkyBCEkFQqxTAMPHoLgNC7CnZvId1CYDAYRPRDLpffvHnTIB96UFDQmjVrDC5p166dQqEwJ+JeXl5isVgikYwdO5ZYIWwrffv2tT4fnK2DsQQuJfRlZWVcLjcwMNDgRvXu3fvcuXMG5/v6+uavwAIAAAAdq0lEQVTm5paWliKEGhoaOBwOCL0FQOiB1oG+R0/sKqV/FMMw4zh7mzZtmEymhdBNUVGRt7f38uXL7dvbevPmzdafbKtHT2DlXiUkgMVilZeX83g8Go0WEhKif8g4eoPjOJ/Pz8nJqampQQhJpVI+nw9CbwEQelehVcdt0GOPmBB6/TWxFqBSqdHR0eYGWjkczpUrV5yxH55J7PPo6+vriWQ+pIfw6Ik429dff61/6IUXXrh8+bLBT1I+n5+dnU3kQiCEHmL0FgChdxVae+imT58+Xl5ehNAXFRWFh4dbc9WuXbvMTSrlcDi3b98OCwtzqJlmSUlJmT59uq1XuZTQ19fXEz+/RowYoX+IQqEkJCQYJLMkPHqEUE1NTUNDg/FeBYA+IPRA62DhwoWEU/zw4UNiYoY1tG/f3twhDodz586d0NBQBxn4DNzc3MwNC5uDwWAIBAIypai0ABHXMneLjKM3hEcfEBBQU1MDoZtnAkLvKrT20A0Bm80+deqUNXGbZ8LhcEQiUZN59Hbg4eFRXl7uOh49hUIxF2fr379/ZmamfgmfzxcKhbGxsdZ49F988YVjrW11gNC7Cq09dEPAZrMzMzMdJfQIoSbz6O3A1YSew+GYm8VEp9PDw8Nzc3PR46V/fD4fIdShQwedR28uRq/Van/88UfnWd4qAKF3Fcjh0bu5uSkUiu7duz9/Vd7e3mw2uyUngPT09CwrKyNeSKSHxWJZDm2NHj169+7d6PFMJA8PD3d39w4dOlRXVzc0NFgI3YhEosrKShcfqgWhdxXI4dGzWKzOnTs7JGmPl5dXSEhIS37/ubu7V1dX25FCuTXyTKFPSUn5+++/kd6UUz8/v/bt2wsEAqlU6uvray57JZHQmJhx77KA0LsEdmyV1zLx8vJKTk52SFUMBmPRokUOqcpJeHh4GGyES2JYLJbl9cnu7u4cDqeiokKX6Oall17q2LHjgwcPCgoKQkJCzLkytbW1dDq9uLjYKXa3EmDjEZegtaeu1NG3b9++ffs6qrZJkyY5qipn4OHh4SIBeoSQr6/v1KlTLZ9D7Irer18/QuiJtdC1tbWVlZUW1kMIBIK4uDgXF3qXcBaA1p660jVxKaFns9mjR4+2fM7w4cMPHDhgsFo4MTGR2DssNDR048aNxlcJBIKuXbuWlJQ41uDWBXj0LgEIfWvEpYTeGng8XlhY2KeffqofvpsyZQoRnd+wYcPkyZOlUumsWbP0rxIIBF26dLl27VpTm9uSAI/eJQChb42A0BuzevXqGTNmzJs3T1cSFxdHzMKi0Wi///77tWvXli9frn+JQCBo3749kRXHZQGhdwmqqqpsXZYJNDsg9MYwmcy0tDRzc6WoVOpvv/1WXFysP8wuEAgiIiIkEklT2dgSAaF3CS5fvuyQuedAUwJCbwcYhq1Zs0Ymk82dO1e3vbCvr29z29XMgNC7BJcvX37hhRea2wrANiIjI9PS0prbitYHhmErV650d3d/8803FQqFQCBwkUVnFgChdwnu3btnIb0X0DLh8XgjR45sbitaK0uXLg0PD09MTCRelhiGEQ7+nDlzmtu0ZgBm3bgE+nuzAYCL8Mknn4SFhaWkpKDHG4oJhcL169evWrWquU1rakDoyU9r3y0WAOxm4sSJxAcejycUCg8dOqRSqVxwEhp4eeRHKpW6SL4UADAHl8sVCoX//PNPUlKSSCRqbnOaGhB68iORSFxk8woAMAePx6utra2trW3Xrl1dXV1zm9PUgNCTn4aGBhB6wMXhcrm1tbVarZbH44FHD5AQiUTi4eHR3FYAQHNChG4QQhwOBzx6gIQ0NDSA0AMuTkBAQHV1NULIx8cHhB4gIRCjB4DQ0NCcnBwPDw9vb28I3QAkBDx6AAgODr59+zaPxwOPHiAnMBgLAHQ6XSKRcLlcb29vEHqAhMBgLAAghIKCgng8nk1Cv3379v379zvVqqYBhJ78gEcPAAih0NBQQuitjNFrtdpPP/30wYMHzjasCQChJz/g0QMAQigkJITL5fJ4vNLS0vr6erVabeHkDz744OTJk76+vuSI84DQkx/w6AEAIZScnBwTE0Oj0caNG9emTZtt27ZZOHnjxo0fffTR66+/Tg6hh6Rm5Adm3QAAQmjMmDHEh3feecfPz8/CduFisdjHx6e0tHTIkCEnT55sKgOdCHj05AdCNwCgD5VKbdu2bVVVlbkT8vPzX3rppUOHDvF4PPDogdYBLJgCAAP8/f0tC31kZGSPHj0QQjKZrAntchbg0ZMflUpFp9Ob2woAaEH4+/sTGRFMkpeXFxkZ2ZT2OBsQepJDbJ8GAIA+DAZDqVSaO/rw4UNjoZfJZLm5uc+sWSwWZ2dnP699jgaEvpVx/Phxm86vrq729/d3kjEAQD5kMllOTk5sbKxB+d69e8eOHZuYmPjee+8dP37c3Hti//79c+fOdb6ZtgFC38p4/fXXRSKRlS6DVCotLCwMDw93tlUA0OqgUqlarda4fMeOHWPHjtXtvslms+VyOULo7t2733///fnz5zMyMs6cOTNo0KCRI0euX7++vLxc//Lz588XFhZaGABoFkDoWxMikai8vHzTpk0rV6789ttvi4uLzZ25fv367du3x8fHHzx4sE2bNk1oIwC0Dng8nkAgMC7PzMx88cUXdX/6+PgQK2kJN59Kpfbu3fvLL788d+7cjz/+SKFQxo4de/bsWd35OTk5hMvfBF2wHhcSeo1G09wm2ElRUdGCBQsQQgUFBVQqdd++fTdv3ty+ffuJEydMnl9VVbVw4cJZs2bt2rVr9erV4NEDgDFJSUnp6emffPLJ6dOnVSqVrjwvLy86Olr3py43Tk1NjZ+fn34NwcHB06dP37Nnz7fffkuU5OfnBwQE9OjR48aNG03SCWtpWqHHNbL6BpWJX0vOQvdebWxs7NixY2NjI7JF8ZcvX27yx90z0Wq1xHY2llGr1aWlpc88bd++fQcPHkQIFRQUdOvW7fz580qlsq6uTt+P0GfHjh3ffvvt9u3bu3btunbt2q5du9pqPwCQnjlz5pw8eTIlJeXYsWMpKSlpaWmrV6/Oysri8Xi6uA1CKCIi4sGDByqVislkmqzHz89PLBYTn5ctW/buu+/GxcXduXOnKfpgNU0h9HjDvV2fjusR5IZR6O4cTyYVYwV2H7Nw532Z0yeELF++nJgGe+PGjYKCgvPnzyOEMjIyLl269MxrN2/e/M0331y9evWZZx4/ftzgfbBv377k5ORnxul+/vnnkSNHmpsYI5VKjx49+vnnn2/fvp3BYCgUioKCgsGDB/N4vOTk5PT09JycnKysLOP31pEjR0aOHJmSkoIQmjRpUlBQ0DO7AAAuCJPJHDhw4LJly86ePbthwwY+n//LL79MmjRJ/5wBAwacPHny8uXL3bp1M1ePp6enRCI5cuSISCRKTk5ms9kKhQIhdOHCBaf3wTqcv2CqsXL31H4zyl5d8fulIZ3b8Dl0Vb2g+PbprV/+Z8AUyrVtowOoTmx8+PDh+/fvHz9+/NmzZ99+++1x48b16dPn3r1769at69mzp8HJEomkoqJi1apVX331FUJo7dq1GzZsOHz4cExMjEQiCQ4ONtmESCQaPXr01q1b09LSdIX79u2bO3fuzJkzhUJhREREYmJiYmJip06daLQnN7y6unrr1q29e/f+6KOP5s2bR/wq1Gq1N27cOH78+KlTpzQaTb9+/bp27crj8XJzc8+dO3flypV33nnn6tWrw4YN8/Lyevnll3ft2vXhhx96eXn169dvwIABXbt2FYvFVCqVw+E4/m4CAHnx8/MbP378+PHjDcq7dOny3nvvUanUCRMmmLs2Li4uNTU1IiJi/fr1REloaOjixYtVKlWvXr2caLTVYE6fZ11/dHjk4rTrF98IfUrRccFfg+J//+D+waFepq87duzYnj17DAovXLgQGBh47NgxKxuvqamZMmWKUqm8f//+v//+KxQKFy1aNHjw4Nu3b2dnZ9NoNAx7dAcwDPP09HRzc0tOTv7ll1+kUuno0aMXLlzYt29fHx8fd3d3hUIREhJi3ER+fv6gQYP+/PPPuLg4qVRaWVkZFhZWUVFB/HogTrh48eKlS5du3bpFoVCioqIwDFOr1Xfv3v3hhx/i4uKOHj26bNmyhIQEsVhcUlLSpUuXwYMHDxw4UF+sN2/evGTJkrS0tBUrVhj/hBQKhWfOnDl9+vTVq1eVSuW0adPeeustK29R8zBoECJFChHAFfj888/37dt39epV/ZCOPn/88ceBAwf++usvXUlpaemHH364du1a612u6dOnf/zxx1FRUQ6w2AjnC73i+vz41OwPzu6a0Z795C4pC7a93ndR4L47K7uxTF9XX19vPCa+f/9+tVo9f/58u82RSqV0Op3BYFg+TSaTUSgUFuuJcbW1tbpInAHh4eGVlZUYhtHpdA8Pj9raWiaTaTBuQ6BQKHSTscLCwnQOvlKpLCsrY7PZgYGBJptQqVRqtdrd3f2ZHaytrfX09HxmB5sZoRBxuc1tBAA4BqVSqdForPl6WsCpQu/80A2r84I/ZqeP6MhdmpCUEO7rxdDU1xTdvnBN0uPzI+u6mFF5hJCXl5eXl6G3HxwcbHJGlPVY+TDc3NwMSng8Ho/HM3e+fmDHpONPwGKxTC6tZjKZlpdcMxgMK7XbgpEtCFB5gEQwmUxzQ7UthCZIaoZxEhedLH3r9unTV/NKygUKho9/xrvfpwyI94X8KwAAAM6nqbJX0n3jh2TEN1FjAAAAwBNcaMEUAACAawJCDwAAQHJA6AEAAEgOCD0AAADJAaEHAAAgOSD0AAAAJAeEHgAAgOSA0AMAAJAcEHoAAACS4/ykZg7l+PHjs2bNMs6BY47s7GwKxbVeZo2NjRiGuVqv1Wo1ne5aKTVc80EjhIy37SYH9fX1mZmZTto9opUJva0MGDDg9OnTzW1Fk/Ldd99FR0e//PLLzW1Ik+KCD/rXX39lsVgTJ05sbkOaFBd80A7B5dwBAAAAVwOEHgAAgOSA0AMAAJAcEHoAAACSA0IPAABAckgu9K425Q4hRKVSqVTqs88jF/CgXQQXfNAOgeTTK5VKZQvfy9HhqNVqKpXqatOrXfBBazQaDMNcTetd8EE7BJILPQAAAOBafh8AAIALAkIPAABAckDoAQAASA4IPQAAAMkBoQcAACA5IPQAAAAkB4QeAACA5JBV6LXCrG9GxXpTabxO49felJBrrYCmcE0X7Ak9N1ZokZkuk+I+NJZtHNxj0W3l47+t72lr7r5Br8n/0JV522YNaMelYnR+18k/32rAEXKVZ90E4GREKzw63o87bM2l4oLML5K8wt+9KG1ukxyJNOvNNrHzT9zPy8vLy8srKJdozHS51d8HrazwzJ8r33iBjSIX3FI8KrO6p621+6Z6TfqHrs5f09M9bPIfNytr847MT3CPmndF5gLPuqkgp9CLj7/Ci5x/XY7jOC7JfC0g9J2Lsua2yXFoBTv7+6bsFT5VaLLLrf4+aKr+/uLNqa8NjWBF6yTP+p621u6b6jXpH3rtrv5eXdcVaR79McAz4fs8NfmfdVNBytCNRpCdq2rTK5yFEELsyKRg0c1CKXl+yqmr7lapSteOiA0KiOyevuRwicpMl9Wt/j5Q+SkL1v22bmEKX5fRxfqettrum+g1+R86Z8iftw+/FkJFCOHi7LMFzLYxXET+Z91UkFLotYp6FdPbnYYQQojqxnVDcrFC28xGOQ5NvYgdHNXn/S2n/v51Cu3X0SNW3VOZ7LKGjPfB+p6Sqvukf+hUz8CwADeKtuHezjmDhv3Z8etlA71d9Fk7A1pzG+AMKExPurJO1ogQQqhRJpQhlieTPK80t8RVV3MefW73wzc7opYdKn53lIku08h4H0w+XJM9JVX3XeGha2rOfDtl4jdFyYuPZM9O5tOQxjWftTMg5b2g+XVoyyi+XKxECCFF4eUKn4RID6y5rXIUjTXnt245W6VBCCFEobNZNCqdSjfVZZOFrf0+mHy4pO8++R86Lrm0KGX0nvbr7lz/c04yn4aQqz5r59DcgwROQVt7eKyf/5gtufWiW2uGcMNnZTU0t0mOQys4kM7lDVvx973Kyuzd78Zxk1bnqkx3mST3QXp+RljMk2FJq3vaurv/VK9J/9Abq7YP4cZ/evZB3iMKyurVLvOsnQ85hR7HG0VZXw9vx6HRfePHrbvdoG1uexxKY92//x3fJYCJMK+2Ly44XKoiSk11mRT34Wmht6Wnrbn7Br0m+UOXXZ4d9LQL2mVtkcZVnrXzgY1HAAAASA4pY/QAAADAE0DoAQAASA4IPQAAAMkBoQcAACA5IPQAAAAkB4QeAACA5IDQAwAAkBwQegAAAJIDQg8AAEByQOgBAABIDgg9AAAAyQGhBwAAIDkg9AAAACQHhB4AAIDkgNADAACQHBB6AAAAkgNCDwAAQHJA6AEAAEgOCD0AAADJAaEnD1+29xh0QGz6mPjQEF6/XUKHtSW/Ordt7IJbSosnaUp3TOno7dXn5yKNg5rVCrO+GRXrTaXxOo1fe1OCO6IQIaS49dXI9zLrcQe2pSlc0wV7Qs+NFVptzYHpGT/mqgz6ZO2jUeZtmzWgHZeK0fldJ/98qwFHCGnKD380ONITwzCGf7fJP9+R4aabNlNotu+m2nqM/NYXnXmph8Q23SUzfQeajObenRxwGF/EuA/cX2f6WN3Bwdy+f9U6rC3ZlTnR7T+5qbB4kuRERkDX7/NUjmpUKzw63o87bM2l4oLML5K8wt+9KH3uQhzHNSUbhw9c+UDlyLZwadabbWLnn7ifl5eXl5dXUC7R4Dguv76gz7h9NY1PtWTdo1Hnr+npHjb5j5uVtXlH5ie4R827IsNFRzN8gyZsullVV3Hrr7fasHr8VKwx07TJQjN9N9nWI+S3v+yMEHXwwTrb7pLpvgNNBQg9eSCEXnFrQXz3JVs+HdaWg1H9er3/vyqNMuebJA5CiBk84q8qZeWJT4e142CIEdxvzp4iJY7j8uvz4174fPP85ODojDcSvF74qViD4ziuuvdVnM+Lf1Vr6q78MCHBj44QxafTqz/nyHB9oZff3zStO5+GEDN4wMd/V2seW6PO/2kQFyHECEhde+D9R/XHzPlXKsvd/k5SMAthnHZDlxyvUOM4rri1IL7L+yveSAxk0njd39lxcu24OG8MeXZ6c1+55kkHxcdf4UXOvy7HcRyXZL4WEPrORdnzFuK48t6yxOT/5qufvpnPWa1WsLO/b8peocEjUt75tMfAn4uetKX/aCobjO+Mjtpd/b26rivSPPpjgGfC93kqad6pw1mValwjFxWe+DCW9+L2ykbTTZuzx2TfTbVFnKLIXt4ralBauOcjobfpPhv2HWhCIHRDQuQ3f1xP+fifktITb8t//uCPAiz2gyNbBnP7brm1b5Rm5+SxfwQvvVBdc+O7mAOvZazLUyOEkPz6d1+Xj924a8V7M2MebDtTo0VIXXRoR1XSzAGcgl+nLcobve1eTeXtXxLPLfzsQsOTpvDaw7P/c6bPb9lVpZnva3+ave7+41/ntIg39u7MCEpaf/3I66HUR/XvmNe2eM2YmWd6rrstqDy7wOfXcdN3V2oRQkh2fdP5nhvvlpx9T/Pj2IxTQ/cWVpx9T/v7Z3tKGx+3pRFk56ra9ApnIYQQOzIpWHSzUKp+vkIcacqO7ZX3HxBEe+oWPmdbuLrqbpWqdO2I2KCAyO7pSw6XELeFEZHavXz7GYH2cTuMJ4/m5bq1Ju8MAWfIn7cPvxZCRQjh4uyzBcy2MVy6W+SAYb38JXtS3H3apKwJ+eK/I/0pyGTTpu0x3XeTbdEQQsp7a2b+Gr1i+RAexda7hJvoO9CEgNCTEMyrz0fv9vH3DOo1amiIslKik0rUWH7wv5c7f/1VRqyvb2zGl8t6Ptx+pkaLEEIefZavfntwQlj0i69G3NmaJcI1pUe3VSbN7Melh7y2/9bR+YMifdzdOVymXCBt1GtLiyNcUVMmUHFfmH3owdn/RNNN2/Soft+K3ZtKBy1fNDyax+/06leftDm79rQARwjRwtLnjW3P8ev8Yv+IsDHvj27L8e8+vH+guvZJa1pFvYrp7U6oEtWN64bkYoXm+Qq1qOHOsfLwxFDm0+Y+Z1taTb2IHRzV5/0tp/7+dQrt19EjVt1TIYQQOyqZ//DoXZnxDVLl7zJ9ZwionoFhAW4UbcO9nXMGDfuz49fLBno/OuSTflolyT80o2Te6JV3Vchk06btMd13M22pHqyb+VPYNyuG+VFtv0tai30HnA4IPQmh8aL8iS8vhULB9I+ohYUC0ckpbXlcLpfLazf1nFJdK9EghGi+7YLdMIQQLXTY+ODrf/4rLDv+Z0XSzGQfDMNk13+c0ic2psvgN3+6Uve0Q4b5jdh8aAFvz2sxHt6dJq0+U6k2Y9Kj+jWiUqlPDNEUonKjw5iisnoNQojK5rApCCGMQqWxvdkUhBCiUJ8ynsL0pCvrZITwN8qEMsTyZNKer5DSKC4WMIK4dISQ+uGqnu4YhmGe/TaU056vWrfEVVdzDi0e0a19/MC3f/im+4Mth4rVCCGM5efXWFohe3rsEyELd+bJGTVnvk6L7fV59atHsvdOj2KgxrqcrH+LZThCVI+IF+e+F5N38F+h1mTTJgvN9L200URb6ryfZ67x/+q7ND71iUXWPxGKxb4DTgeEnoRgGIaZPkLjBHMDM/YWCYVCoVBYXXjjn60zohkIIQyjPPpXoIenjfG7tPV/+7eWJc3o7Y01lvw+/vXj3b8/e+fWhaO/vBXLeqpCrTivgJn61f8eSupvrow9OG3CrwUmZ9g8rp/mHewuul8mRwgh1CjKK1ZyAj1ppq4wYbxfh7aM4svFSoQQUhRervBJiPSgP1+h/n2iR8+5JMVxHJecmdom4Lmq1dac37rlbBVxKyh0NotGpT95a+GmpO4ZdwaXXFqUMnpP+3V3rv85J5lPQwghdd5vk9I/v0a4yFqFREVhsWkmm7Zsj0HfQygm2pLl7D+ft2tCGJvBinj7mvjvtIB2715U2X6fTfYdcDog9C6DVl4nw0LSZrbLnL9k372amnuHFqX2nn6w2jBmyogY+YrnwfmfPew9I8kbQ7hGoUDuPK47VVVxctU3F8TKBtWTL2tj1YE3UkavOFvWoNBgGI6oNMv/UYyo0ZOCTn70xZF8Uc2tPz76oqDPWwN9zbyVjPDsMS1F/NuXux5K6m5v+mIffcTEjuznLaRywnxV5SKjHyLPVy2FItzzn/Spq0/cr6rK2fPZ4mtxU18KoSGEcIVAQA0JdHu6y1p5nYxq8c5oa44sXo/P+u7NDsqS/Pz8/PzCcomG1W7EgMZdC9f8UyKsvLFl8aryvjP7+FBNNc0waY+Zvptsy2vwtoLi4vy8vLwH55bGefT+7WrmZ13Z1t8ls30HmobmHQsGHIhu1k2HmHnX5DiO408+K+5+l+JP8Ri8vUpefGD+wDbuCNEDEt/8I1eO47j8+vyYdo8uwXEcV9z4OAJ5T/hbjOM4jjcKTn3c159O9QzuMmHF74v6eHMGbSqRPJ510yg6/+VLUe4IIbp/96m/5kj1DKo/kRGUtKmi8an6tbIHW9/qFcxCyDMqddGxx7NuHtssv/p+25iPbshxHJf9+5/ojp9lK/VqbBRlfT28HYdG940ft+52g9YBher81T0TFt02nij6fNU21v373/FdApgI82r74oLDpY8mMEovvt1+wOZy/UmGukdT2WB8Zx4juzw76Okvbpe1RRq8sfaf5WN6hLIRxbt92tITlWqzTZssNN13M209RlO0tivn0awbW+6Sqb4DTQWGw28pwKVR3V/Wb4bHn6dmRVgZQnqetnKWJs/i7zz+Rrjz27IGV+67awGhG8DFYURPfIOzZ2+BmWFkR6K4u/3vyNnpoS1G6Vy5764FCD3g6lBDxn4x6MyP5+ud/NtWK/h7zd0xn7/k14K+dK7cd5cCQjcAAAAkB16wAAAAJAeEHgAAgOSA0AMAAJAcEHoAAACSA0IPAABAckDoAQAASA4IPQAAAMkBoQcAACA5IPQAAAAkB4QeAACA5IDQAwAAkBwQegAAAJIDQg8AAEByQOgBAABIDgg9AAAAyfk/rts3H56G8zUAAAAASUVORK5CYII=" alt="plot of chunk time_series_plot"/> </p>

<p>The interval (starting at) <strong>835</strong> contains
the maximum (206.17) number of steps on average across all the
days in the dataset.</p>

<h2>Imputing missing values</h2>

<h3>Step 1: Calculate and report total number of missing values</h3>

<p>Calculate <em>the number of rows with missing values</em> as the number of incomplete cases (which is the same number):</p>

<pre><code class="r">number_of_missing_values &lt;- sum(!complete.cases(activity.data))
number_of_missing_values # Report it
</code></pre>

<pre><code>## [1] 2304
</code></pre>

<h3>Step 2 - 3: Fill in the missing values and show a histogram</h3>

<p>The <strong>strategy</strong> for filling in the missing values, does not have to be sophisticated: I will therefore use
the mean for each five minute interval.</p>

<ul>
<li><p>The means for each five minute interval will firstly be merged as an &#39;estimate&#39; into a new, temporary
dataframe called <code>activity.data.plus.estimate</code>.</p></li>
<li><p>Then we will <code>replace</code> each missing value (NA) with the correct estimate (merged into the same row) and
use it to recreate the original <code>activity.data</code> data frame, now with the NA&#39;s replaced by an estimate.
Also, we will round the estimate (as a number of steps can in reality only be a discrete value).</p></li>
</ul>

<pre><code class="r">names(avg_steps_for_each_interval) &lt;- c(&#39;interval&#39;, &#39;estimate&#39;)
activity.data.plus.estimate &lt;- merge(activity.data, avg_steps_for_each_interval)
activity.data$steps &lt;-
    replace(activity.data$steps,
            is.na(activity.data$steps),
            round(activity.data.plus.estimate$estimate[is.na(activity.data$steps)], digits=0))
</code></pre>

<h3>Step 4: Make a histogram of total number of steps per day</h3>

<pre><code class="r">total_nr_of_steps_per_day2 &lt;- aggregate(activity.data$steps,
                                        by=list(activity.data$date),
                                        FUN=sum)
hist(total_nr_of_steps_per_day2$x,
     main=&quot;Histogram of total number of steps per day&quot;,
     xlab=&quot;Total number of steps per day&quot;)
</code></pre>

<p><img 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" alt="plot of chunk histogram_missing_vals_imputed"/> </p>

<p>Show both the <strong>mean</strong> and the <strong>median</strong> for the total number of steps per day (across all the days observed):</p>

<pre><code class="r">mean(total_nr_of_steps_per_day2$x, na.rm=TRUE)
</code></pre>

<pre><code>## [1] 10889
</code></pre>

<pre><code class="r">median(total_nr_of_steps_per_day2$x, na.rm=TRUE)
</code></pre>

<pre><code>## [1] 11015
</code></pre>

<p>If you count the total number of steps per day with NA values <em>replaced</em>, the mean and median of the daily <strong>sums</strong> turn out to be (slightly) higher than without the replacement, which is to be expected (NA will not contribute to a sum, while the replacement values will).</p>

<h2>Are there differences in activity patterns between weekdays and weekends?</h2>

<pre><code class="r"># Add a weekdays factor variable:

#   I used ```format()``` and ```as.POSIXct()``` because they are not locale-specific and
#   are available in the base-package. I preferred this approach to ```weekdays()```, which is
#   suggested by the assignment but is locale-dependent, so it is not guaranteed to work on
#  your system, depending on your locale (or any changes to it)

# Create a vector that for each observation contains the day of the week as a number between
# 0 and 6 inclusive (where 0=sunday, ... , 6=saturday)
day.number &lt;- format(strptime(activity.data$date, format=&#39;%Y-%m-%d&#39;), format=&#39;%w&#39;)

# Now map the day.number to weekday.or.weekend factor variable as required,
# and bind it into activity.data
library(plyr) # Use plyr package for recoding the values
activity.data &lt;-
   cbind(activity.data,
         weekday.or.weekend = as.factor(mapvalues(day.number,
                                                  from=0:6,
                                                  to=c(&quot;weekend&quot;, &quot;weekday&quot;, &quot;weekday&quot;, &quot;weekday&quot;,
                                                       &quot;weekday&quot;, &quot;weekday&quot;, &quot;weekend&quot;))))
# Use lattice to make a plot like in the example of R.D. Peng.
pattern.weekday.or.weekend &lt;- aggregate(steps ~ interval + weekday.or.weekend,
                                        activity.data,
                                        FUN=mean)
library(lattice)
xyplot(steps ~ interval | weekday.or.weekend, data = pattern.weekday.or.weekend, layout = c(1,2),
       panel = function(x, y) {
         panel.grid(h = 1, v = 2)
         panel.xyplot(x, y, type = &quot;l&quot;)
       },
       main=&quot;Panel plot: weekend versus weekday&quot;)
</code></pre>

<p><img 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" alt="plot of chunk panel_plot"/> </p>

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