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<h2>Loading and preprocessing the data</h2>
<p>The following libraries will be used to complete this analysis:</p>
<pre><code class="r">library(dplyr)
library(lubridate)
library(ggplot2)
</code></pre>
<p>We begin by loading the tracker data. This data is in a clean CSV, and we load it
straight from the compressed data file provided. We also parse the dates into proper
dates at this point.</p>
<pre><code class="r">data <- read.csv(unz("activity.zip", "activity.csv"), stringsAsFactors = FALSE)
data$date <- ymd(data$date)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>We are interested in the number of steps taken every day. In this case, it's acceptable to
ignore missing values, so we do so. For the days where there are NO values, we will still
count “zero steps per day.” While this is less accurate than “ignoring those days entirely”
would be, it is also more stable, because the addition/change of a single data point is not
likely to perturn the final result.</p>
<p>We compute a basic data frame of days to steps-per-day, and show what the data look like in
a histogram. We also compute the median and the mean.</p>
<pre><code class="r"># Function to compute steps-per-day aggregate
aggregateStepsPerDay <- function(raw_data) {
raw_data %>%
select(date, steps) %>%
group_by(date) %>%
summarise_each(funs(sum(., na.rm = TRUE)))
}
# Compute number of steps per day.
steps_per_day <- aggregateStepsPerDay(data)
# Compute the mean and median
mean_steps_per_day <- mean(steps_per_day$steps)
median_steps_per_day <- median(steps_per_day$steps)
# Plot histogram of number of steps per day.
ggplot(data = steps_per_day, aes(x=steps)) + geom_histogram(binwidth = 2000) +
labs(x = "Number of Steps", y = "Count") +
labs(title = "Histogram of Distribution of Steps Taken Per Day")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk steps_per_day"/> </p>
<p>The median and mean are:</p>
<ul>
<li>Mean steps per day: 9354.23</li>
<li>Median steps per day: 10395</li>
</ul>
<h2>What is the average daily activity pattern?</h2>
<p>We now compute the average activity pattern across all days, by interval. This will show, on average,
at what time of the day our subject is most active.</p>
<pre><code class="r"># Compute average steps per interval
avg_steps_per_interval <- data %>%
select(interval, steps) %>%
group_by(interval) %>%
summarise_each(funs(mean(., na.rm = TRUE)))
# Compute interval with the max activity on average.
max_avg_activity_interval_value <- max(avg_steps_per_interval$steps)
max_avg_activity_interval <- avg_steps_per_interval[which.max(avg_steps_per_interval$steps), ]$interval
# Plot the result, showing average time over the day
ggplot(data = avg_steps_per_interval, aes(interval, steps)) + geom_line() +
labs(x = "5-Minute Time Interval", y = "Average Activity (steps)") +
labs(title = "Average Activity Over Time Intervals")
</code></pre>
<p><img 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" alt="plot of chunk average_daily_activity_pattern"/> </p>
<p>The interval with the max activity is 835, with 206.17 steps.</p>
<h2>Imputing missing values</h2>
<p>There are a number of missing values in the data set. In this section, we look into the situation, and see how robust our
previous analysis was, where we ignored all missing values.</p>
<p>The data set contains 2304 missing values.</p>
<p>We will impute the missing values by substituting in the mean for that 5-minute interval, which we've
already calculated above.</p>
<pre><code class="r"># Impute the values, as follows:
# 1) Left-join with the average steps per interval data, on interval. This will split "steps" into
# "steps.x" and "steps.y"
# 2) Create a new value "steps", which will be equal to "steps.x" if it's defined, and "steps.y" if
# "steps.x" is NA.
# 3) Drop the interstitial "steps.x" and "steps.y" variables.
imputed_data <- left_join(data, avg_steps_per_interval, by = "interval") %>%
mutate(steps = ifelse(is.na(steps.x), steps.y, steps.x)) %>%
select(steps, date, interval)
</code></pre>
<p>Now, recalculate the total steps per day, along with the mean and median, so we can compare to the old results.</p>
<pre><code class="r"># Compute number of steps per day.
imputed_steps_per_day <- aggregateStepsPerDay(imputed_data)
# Compute the mean and median
imputed_mean_steps_per_day <- mean(imputed_steps_per_day$steps)
imputed_median_steps_per_day <- median(imputed_steps_per_day$steps)
# Plot histogram of number of steps per day.
ggplot(data = imputed_steps_per_day, aes(x=steps)) + geom_histogram(binwidth = 2000) +
labs(x = "Number of Steps", y = "Count") +
labs(title = "Histogram of Distribution of Steps Taken Per Day (with imputed missing values)")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk imputed_steps_per_day"/> </p>
<p>The new median and mean are:</p>
<ul>
<li>Imputed mean steps per day: 10766.19</li>
<li>Imputed median steps per day: 10766.19</li>
</ul>
<p>Recall the original values, for comparison:</p>
<ul>
<li>Original mean steps per day (from earlier): 9354.23</li>
<li>Original median steps per day (from earlier): 10395</li>
</ul>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>To separate out weekends from weekdays, we add a factor to represent whether the day in question is a weekday.</p>
<pre><code class="r">weekdayFactor <- function(date) {
as.factor(
ifelse(
weekdays(date) %in% c("Saturday", "Sunday"),
"Weekend",
"Weekday"
)
)
}
imputed_data$weekday <- weekdayFactor(imputed_data$date)
</code></pre>
<p>To quickly visualize the differences (if any) between weekday and weekend activity, we plot average activity
across all days, for each interval, split by weekday/weekend levels.</p>
<pre><code class="r"># Compute average steps per interval, by weekday
weekday_avg_steps_per_interval <- imputed_data %>%
select(interval, weekday, steps) %>%
group_by(interval, weekday) %>%
summarise_each(funs(mean))
# Plot the result, showing average time over the day
ggplot(data = weekday_avg_steps_per_interval, aes(interval, steps)) + geom_line() +
facet_grid(weekday ~ .) +
labs(x = "5-Minute Time Interval", y = "Average Activity (steps)") +
labs(title = "Average Activity Over Time Intervals")
</code></pre>
<p><img 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dWZCVH1WJaGCv7qhOAp13RwqX/H0Zds7j5+fdQ0Yw4sd/oxE7bXZm5nf9HaUkh5YPlVt5ZYZMtmo2r1aqUD8H9xlCXn7uPXe00zZmVpbbnFn5mwqxpzW669pZDkPH+MXFp+0a0ldsbXutn6ZlURV+42BjATDlRkWRrqpb7BsdbbqosY/Ib6zAR2g47Bgt86pV7tQyIGv5ZViz9XnGVpqOB1VbnjuusH+FVNmAnxRViWhgpeV5U7rrt+gGc32LC7YhkseLFX7pYEMRPumrIsDRW82Ct3EW2ZCangFdNSj8AnruXPJlXucgB+fgdmAjvQnT6Bj/RRaal78GMJiRa8YmYSSsYPmZZ6BH6ZO3+2PAEfHl4mLDw8XLTgZ7GnHbNIZlrqEfi59vzZ8uYlzUGfcodFfKmf1pOVwop3pk/gpxq/4s+XF+AHl9twxFvE4Nl97KDTRaalHoEfCx7y58sL8GEJN28eETH4Cf1YKSWZUbD0CfxgkKDKUvfgT/eo0/20iMH/MZCVUjaWaalH4PuAE6osdQ/eb8K+CbVEDH70IFaK1z6mpR6B7wJ2qrLUPfia6L/aIgY/Yigrpfp2pqUegW8NeGZEJCx1D77r7KPh7S5fFi34oey5hP0373hCt9Qj8I2NF6iyzIO2+uq4tAF/KQXBo5kWiJCmYaNZKQ3XedDPeX0C7+80kT9fXoDf2O/kgcvaXOrxkKZ4NNMCEdJ04B+slOYriq6kW+oReB8fdoVEYal78P6zOy6row14PKQpHs2UCml6Nz4+/sknjr79y93Hry+aZsyBZdg0VkrHFebhtM1/v2ltKaQcW1Zs103XlvzCwDe62eOmn3aVu7gUBI9mSoU0vRIbG3v/C0fff3D38eurphm/f9facmA4K6VnOBiZO0sh/cippfvYQF1b8gsD33tq03DtwePRTKmQpnp9qe85jZUSEgro72306VJfJKIif768uNSX7d2n71atwePRTAtESNPgWayUge1AA7qlHoG33FVUlaWuwc+7ee5mwrKWWoPHo5kWiJCm7PBmcGgAoA+nyzvw98o91s4y0/iqSQZ/Rp2Dr7JoddvyIVqe8XzSW/Ad5rNSxlQo5Ey3zDPwO8B47SyfgzTZXRWWugZ/tq+T786bN0UMvi0rkiWcZVlJlkmzzDPwU12qamd5XwZdzqmw1P09PnF1c6+RIgYftISVshW0NKZFMs878O3bl9HOMtEGVt3FnzFvRstenCpi8C2Ws1Iuge70Tjh5B778XFftLM8Wh4Hj0/ktpTh32lqyw5TDlyZhtrROOHkH3mavyp5U/JY7fODGsg499/BU8CTwWlsGcroslxrvdIlmmWfgzc+YCWRkWS5rji6uTylj23kr+7yXwGttyZ6RAv0phJc4S7PMM/DG19lz1QtYTg7BPzIP9XGswbaUwGtryZpMGtX5ux7HaJZ5BT4dpLKnLBewHKAIs3uzENtSAq+tpX80N5E+91yegX8G0s2TBXIyLVsvpNbSjVk/GQm8hhkP/UWt1dzGTaVPMK0B+I1X8A8twT80yrRX1XeSEtOy1hbFqmM8y1ICr5lKUF1phxXezk2lTymvAXh/otVXS/B3zDgTGLMUckhpeWUKhKWPKzbZ82FK4DXMaL+H+Iw1Aru5qXWjaJbC4MsSAa+1BJ9kDcscVZvPZ5XScn0VmG55U7HJrpJK4DXMaEpW6fxswT5uauPVNEth8PZEMEwtwSc4wCp71ObziFBaLvSAGyopk9ivliTwmuV7CYhJxjaXGAkOcpPpU5AJg39pRITK0xL8BWfot1VtvqKzlJZTnGG9xcokdhdRCbxm+VLADOwjveyqheAIN7k97Y2dMPibgHibpyX4MyVhQ1W9pQlZTFBaDrfJtKFVBdlDPSXwnF0RfXjyJYJReFrlV9HgGDeZ3jlDGPxJR+OXWpWSAH/cgzO5IVMvwQilZS+TpEK0EZPswKsSeM6u3o158p0HfdFlquMBeAyc5Cb3mkSzFAS/zZd4LNMS/EFP2HmOumz3wAClZVsQRY+KsL0ay1J/wb/nKPsrdx+/PmqaMTubsyuwJk++M6AjupzQ6P372+AiN3nQBLWWLK1qVea0VqX8+gVbHvR+32eGumw3QYjSMhCM7EZLO1WWz1ITaVTKFyI44909ePLtBeh14I7NWaxXC8+z9LDhNEvBM35ySKX9WpWSOONjfGEoezAHQ6dAe6VlNVB3Ki3tEisomx6f8Vz33wI+XebAky8a1EBvAnhnWpuL3OQxoTRLQfCDR/rGaFVKAny0PxwVpjrT2Tr7QTOlZRlQgv7++I4py1ICz1KCqQlPFInVduXgZetEbHXrS27yxN40S0HwPacGRGlVSgL8+gZwEjsoA03RxaJBXaVlUROTHbTUl+Ap01ICz1JMVZMU1p5naHXe0wm2ZA+TVYo+IZEw+DYRRGcOLcGvagJX1FGdaYnVSpmv0tLMmVkLpbXi4ZYSeJamdHO4wtzjtB3CGYHmhx0esfMqNK8jzVIQfKO1xGszLcEvbQUfFbqhMtMUMMulosLyGfABifRkF2bVRALP3tNxlgezQfyZEfqQ/keIucdM1Tb0aJfC4H1jOs/WqpQE+Ih2ELZX3cF6EAjzKaWwvG0eyIy/58l8SyOBZ++puMd3W/kHtB1xIOTY5MEjt3i9UG1DD2wsDL784V6TtSolAX52Fwh3uGeqytQFBDV3UlheLtqGOc99bWY3Agk8a0e66d3GUwC94r7FuPbUGr0mqbWkx7AXBu8SRzyWaQkei7OW4XJAVabGoHI/G4XlSffgkozkZiuYlhJ4phKtYJfG4DhtzyzvIj1Ld5qj1nJrLZqlIHjrxJGDtSolAX4i1ng4opuqTNVAodkyheX+Sv2Zs2V1YQ75k8CzdlxyguEyxiv3/iPNPW2bL1druZPWOioIPsModWLfkxFagx8dii4uWz9VkcndGmwDin5C22qOYLbOh45hWkrgmTrpDs8BsIm2p/kyf2BUaYday/20N9+C4B8ZvZoZPK+x1uCJ5sEaK1VksvECl4yfUVuRDSd2YSSTPW4VlvkJPjtb/8Af8ISvHIzQ+2EIVRnyjRkNZLKLai2PlqVZCoG/YYNW0CfW1Ro8cc4uVPEo/wIEgceW96jNJUG3LjPS1zZkWuYXePnG1oXNzR1ab5Qj/Mon8DHVIVxZdy565bQ4ROxxP7GniCt4qtbyDK0iJQj+nCtc0WxITa3B98UDGaVaXufNc8tigBUsokib3YWVfqQsYzO/wK+rNP18+s+fGRdmeEXqFfgo9EyEfdA/se0Ehwv4HqukjLNV7NVbojUDpaUQ+EMV4Pr6Pb21Bt9jOr7RYRxvnjPFR3vAkheoTbwmSNetQowHwfwCv/tfau3f3XoB/hHZW2VlU4jfTdONUqcXv4WuPwVpEDb0Um+ZZE2zVAn++smEe6jZthrwT7/WnujJy46toUoEeLLb3C7sUT5hETvPrqoz/GF5xePI8GGs9FemjJHy+XiPf/pv9urN3/mp/37w093S8M8F7dDFxBpBF2SZMNTzEYTXsLmGOjVTb5liojydVIMv7WJnAmT2RRvDPVXql4LwsJmqGjpLBPh2xMD8V6770d+nLzvP2ka7JkDv/dRmvwnsDO70Z9R8BD/b/F24X9Uh+gI+2JS4gs7oji7mGIOaRdE/cTv/NHi0NLojtJ96S/r0JCrBp5mkQvjsztWTd+GRcj4u6EkKYjQrJQG+JdnvamRXtJie7DzhndBFbYVhF87VJCCKvpV/4Asn/yqW/tyR2vyxIDLySj5Gtqw23QZ//zGuP7pYDkoWKof9dwKCXm1Ba2Hw+jUBS9l9paUq8OeKKVbPlCxdGMLNgB0IW4UI8FQf7ivWT+BQN3aeUaHoopECbtBidoZ2jOim+Qfe7sUNb+S1DbX59iC6yL/Ilpk210PxFy1DsYfljSAY+GFbqZX6LW6liaWN8q2ZSvDrlO26l4sUsYJwtX05zUpJgK+/gdysuQJ2LczOE4J1+2ulaJfl9scNmcywzDfwgyp4bE6r1ZHaTI3atfcfKrLlt+zs7A+vOcqSc/fx629NM8pJy9tWfz0uuhdd6TcRXewGq0BzfP+dkqX6aGJZ7JLSMos3R8rrsaGKjVvWpqavXy8KsrylUSm/4Ja1t5KbS+q8bmzOztNqCbroFEFt1tzGzjBsJMPys0bf/FrDv6UW4H/s3fvfkyVZ1GbGA+T+Tiqy5eaZM2fGIb9T8ZURZIsXWtXstwzdug5SbPoRCamzHmtyfLlEgQzPHH413qrYemcEwH/IooFBm7Qoo+d1cuWTVVoN8IOVWv8QugiLoDa9L7APnztUi+/SVtrU6n89uZD6k77j+4r8i2yJNbZnVpuF9Y5Bty4YPfNkR/9Vb+mlqE5jl/r73AzHwTZ7ZR3+uYkMPIUTBs5uo1EpiUu91S1qu9PYEoDdK6TcYXQRqnhd786ZmmIeY768/LvUP/J2rF6k6iNqMz4RSduZf5Eto/3RxXG7O7Ax1ikqtQtsMomVU70lbex01ueHptyAQ7tBmVDaZmcnkwdw+IiLDnwhLp6zX73i4B/KFHl3u1mAm6w8jlgL7UjF03vRS6x0uIoxXiD/wNee+B35PrEOtZm9K3rb3/kX2ZKYG7xHMKxDvp+JYceHU29JG4ya9fksSOFk2AjAftrmtX6FbsEBE2Hx45ycMLO9FWsPDj5OGfLqVVkjB1Zf30wZ1kw/sT+1bcn+YcCtfvCy8uVTPtbq36GLt7aIKv1e8Evxuvs9+2M+O3Jk2XKZ0vLzn4DbnL4M2DAvA/YJsPss2INqZ9kUq0iZ6C67O4n+KyHAx9DC2PxZpDTzF7NpjRFmP6s7uZ1hxAmacsATLmgIqd35B77nenSxrouegCd7S4b7lDuUI8tOc5WWnyMAN5zkbB9W0GvnC7B9BNxA9eBopQhXs8nmvPvRerPpeXHwC+n1gdTKzMHafRvjk5lHtKfSjTj3kLPF4eRqsCR5pcg/8ANlVdtVBa2Cg4P1ATw5fWS6p9FpFTnVW4ZMUVp+5htJPb4/a4f7KdhsFUwxI0/BatRsdmds/oT113jhtTSqORAHP5oxmKIWc8B0Fze8RWdlM3L7FjvYEf42eERZaEcWLf/AxyikD+DHDSA+94N4FTnVW4aNSqb6QGR97gy4YXIGj2TtKH8E1t2IAidf+7uQ5+qd4uiVIWSCK943i3rngoNvO49+OCvaXpAxHuY2qh65fZk1Ygri/T/6OEFzMmhLfnbE+O+vX/zM8wG84mXWZv4IoEKWo0NbUO9Bsz7XMeZO295zKmtH5b3Qdwd6IDEoO0PWAF5Fs7yogb1HnxpsgfeXcSSv/yj450fdGb2+2yxkuDUCeLOgoh5wgjuN9CujRx3NXwGy3Tf/wGc2tLRPqpumJ+AH8L/k1thySoifjGy1zfpc2o0d4Jon5rVvDKyIXnYPlcK3kkFVPHJJlxpYJ+4oH4C9JITmJEcU/FYr0zT64d2Yvfz9QFvs44AXuU3vC0bJ5kZT8ACQIRzyD3zT4V/df4xrrCfgOfONaGk5t2M56nEtZl9hf/bpzRMIFX1uRG/zMJ0IW3HCqCRcWBxOK34H2zplBrDHy3RQiDgKBT+XMRkC55fqBfDReyecySb6rX7cMrqeqw3iANlin3/gC31E3JF3lnoCnlYrz5HlshZFChN3zwz3EeYd2Dd0CGuwb/voLwFvZGmBXwqiS1rDKdYxNkRo1IcAf0f00PiI0xjsRT8KfrA386c5cghjszTAv/I8MIrEt9c24pbR82AlsAuQsXDyD3zVoyj4Y5X1BLz6GCPCllEBsirEybYJ9AJD2nLG0HuyK/rok791Evq5oCW2taApeDnMqMV0MtEOYJMIJVnDGxXboFV7+a2nrVm3CtYs9k7G+KX/Guhmiz9KLmzNLWONbSXBCkAGesk/8BcdO9l0LHpaT8BzQ1JrZ3nEyaj5EnzNv2pTo1mgJLuO6Mp+tG+3AJpgI7US8Iadib3NkkNAOapnb1XHEhAPcwUfN/FJhvLKi9kNS6uYveZtiuNR9O/L7s91x5oNZ7JiHWEKXGNnPxmQwzvzD7z8/ZY5m16r6mP7u8H7b86d5U1gSwyEPO2wsqrlMiBbxcpgw27M6zorDeDVNXfs7crwoc7n2wAZVXEPqmaHLo9hsTkyBrmchsZ9irDa3vcR1TjyST/TZA3RRHsLNW6QoXw8patthMxrACBH+eUX+B8/3H6g+miDqNLvBe+jarptDS0zZO598N7PXYbtdC4aBVqw2ms4QYRhr0lnHfGV3lh3+X7jPQ/UB4Ca72B4B1l6HNxDTEGzxC7M2FM5VILQZezgbVFliIaj5+CFopRpPkN5mg1Q9RwNmrUGtYmN/AJvYgJMMKlotvvt4Dm3YG0tS/rgIQTvWCbFmrjvMo9oyUy+Yc0+IHTs1E74ymYs2mHXWQ3WewNFz71HyaC6K9zsT2ztLxwE6rMOf2KEou7qY+qCk78no5UyqWgk7DUZcjS4a6EeNU3I57x8fJxTiTw/wJfivL7W0rJ2I7y33h9t4AVQKXHgDh9m8uFy7AMm9AkgKuCpZg+xLnI9ppcqbKNMNre0Vg6+Tn3mdJh9vM2AZFgdeF1xxshfs6WX8oDtuY48TynjA5wGuzmRQz+k7tXEZ7HzubTs1BGrT71AASWCmuhzlRMzeT37jIUrmtqQHSv8NkDYMHLcAAef0srkIp2MMvCu3pjkn5+zD4flQBS0NesB4zHycS6MUs51r8WeMQnVbPfyU2VedqQlB/wpTodsuqWACmz3artr6vMJWo4Oi2gD4Upv7CG8IXqOGjOr9TO7sg/Y6yJ7Q6yN74k9ai1rbtKW1upSbofxQ8UjG98Uo/XA5GSLoBXo3d5pinLsHlnKriCKe8Qy44bLQaDJE8KSA35BZc4RdEv1ynn36nwGLzj1g5Dl86er0QesKmhl/hXAuutaX51BTw4bxT4gHlBxS46V2PSy4oE97kbDaQFV7kObG/i4aEx84MPr99hH3K8vOk3ZQ81ESFqmNTzFPWIj6LENhDQkTCnwNxSdrkc5cQ+hWapXzrtXc8FzwyfmXWTLt6zAojYAACAASURBVEYvcm0ZU+f9Mee/3r//BLCIkuUHgVtkwsOW7/9qu4SdPdOoEWn5xsGol/uFG0aOu5fTMxS/NHg8ucobhjI6YEYwsXa1aIM6GpTyAJh4Fgy6ZXOObrmpApXc0/g172G6jmzJ6l6dv2f8E6DqpZzmlrurwiCsAT3LAntP105mMePQQzxhqXFGMPiTk79wL8pywV5j2yvPjVhPYJ4He1ANebyzSZ92DVxCrp4vQvWnU1fK42D5DTACTq+M/V+pM36B4nEjkNONT9hSoZx3r85f8PvKCuTTwPJomeuFsFcsWUWwV7xp08caFzV2CRy2Jq4leFBz9DNOfq9pSktrkAwHsjrq1dimeKPHCz7VxPoqtX6JCuGhrpSXwZ7nYAJMr4z9nijw4xXddSsBnv5/ApYKaQz+lKJj9c9TegA+dHDuLeNcBuFj0rPcierx5Q4vnx1fPCDAwdg0nm9q3xGHlZZlwRNOeuCaRlQ7Mv/88ZN4amPqSpkMrkBrtOZxwuaaEvxARWheR2f+1kvdgp/nvTT5E4J8Sl7qPV8PwJflmW5EW8trlrZ4t62syqzxik/KHOC027Es6xlzo5i1W+BFlYofvLalTHNIg+5YXS60oRJ8J2rg5kujZuz50YUtFdL8Uv9ucV2zokXN6i5+x8/994K3TMq95T2yNTSrNnu8Yo1wOwHLrpyGPWysmxPVhVo34DFVxwbXPSmxRgG+kYzsynPDZhBPM68Glri063r15s1/Kqj/ZvDPAbeBRGvLZ+Szc1Z79sQxTbt4cbMzLEc5c1OHDFME1dUd+Dn4PSemyAMKvE8F8kHzYIVFTXNkiauARr1KshTKp4nldKJHM3e0bBfPFgKWETwx8icEmVHXf92BJ9U2mALvNtJuEf41K5qcdYKpC7h5xQz+dAldWnJKHmYyhC8jzTLGh5s6x7M4tapz8HfsT5DgbW4cL9cIazseOyDdMmmdLTevmMHvqiqUL1fgJ/I1oDIs0xO5qavNFb8GnYOHi8u8eYmd3S/Rx4m0QQ6rIOw8C9ba2B1wHzt1DX7EJTU3+N8MnhUDLpeWnJJHAB6ugpZJRtToiDwAn+m/JA48xIJcY1sH3Vve9YuGff8oCZgh25OO6x78jKrOQ+LYg7zzCfycjkL5cgV+Q85awaspYlHqHjycMSAaHIBwG3Gte9q7iE0cnOtvUY4xZg8GtciLS/3zpfWcQ0+rejH7O8GP0SAWTS7A32b3w9LMcpMi0HgegN/cdBaYC+EI6n++u85zuNuoRuAaWp7ic4yq5QX4j3v7Onj5Fz+c/+D7qHgVnTNL3VPKA/DnKvS16Tk9stYG2r6bYGB3Wifu6wB0Ka578IsaWDVblYYgF4rlP/g23JEvubAsEOCfFGrUvkrF0pb0qIeZ1qvHDFigGDwYVXVksmmmzsF3PfAZXcoR+cH8B+/LfXeWC8sCAf6zuf1uS1MbZuDT0ckLTWSKq9+IUPhKdl/MvWxfWQm32IoNvLxMkdSgansesnbfiV7QnFqvHgmha5yIe9mmxvNNMZhzy4IBfsJ+GMvXvHCG6r632yUN73b+O3rZ/p7IlunbohUhgLM++I+ynx2QW0u6Cgh4Vb1s0803E5eBptgAkeYrCMuXZ1YO6fgUwucq5kLKZcudUGTLt8Qn443Kq008nWd4ZgGlUs7A7TJf90V3iM2s1ebAQ8bfosqUoYCHM2oW8ug89/g9C6wbYv9R8FrM5PaeMpcGYe4x8LJdi0jegzQG73vclxAjBxXZUgX4s57pMLLJ/Cirs7SdQUb0R8/J+Nvl00aBE9effkzsSh59RJl+oYhnZl8AbEC7ZPz38o9H1IjUk9x2Sq4MBjx60p9a0KWcDB/Nsb3cXsu6/RcexrrpDGo30PGPobbnonimYNUYfNL7JEKMHFRkyyuxsbH3v3D0b62qLQq3qgwqFe8b0rFxQLUybs1renisKVnbu3V/QiE2Rft26Njee9C8vvVcgVOdnug+19a2Ll3Rz24d2zdq5LisVEmrdhUiosqZAYdy/kFBVeTc7+HVVw3zffn+XdOcGlv+yAfL1y+w5T+OxVZTew6Brqe+fJnoWPY1N7cWl/rgw185l3oqsuXd+Pj4J584+vZ+9/rdnz5tyNw/e/7K6J1Hzset2rvl6vuIPUeWzCZ1avPsxUuWz/4Ly/6/hJh56K69n97Ez0U/F65c9eefpz8d37k6MwlLfnX77O61869yv4ZfXzTN+O83MVnuXvuZWn279h90+frIC55s2jTg1LHve4rVXpt/kS11aVngL/U5sNSqcvd6QwvXQYw9+RfZUpeWEngB8F9PDi9eiXO5pySBV0pU4De0ta69UE1IcAm8UqIC3zzyL9XUJfAMiQq8kCTwSokIPH8DjgSeXyICn/T+It5+c1ECr4FEBD5Hr2U//qNpYd9qmvHjx4Jg+c8HfbfU4WtZSaKRdsGPJIlGTPDfNl1CYpb/qyKvJBGJFRHD9w6SULO/ytzZHziSy7n7+PVR04wGa/klS6eW754zpA58YSz1uZ1K8FKtXikR1eoRpAw2b2JCGQm8BhIV+N2OI5aMdtwmgddAogKPpM4ZNPOuSu4SeJrEBR7Vl72dJPAaSFzgvx3pbuU+SgKvgUQE/vupvnaVzC+omX9MAq+UiMA7lpl6H3F+rZq7BJ4mEYEv7zLurgRe04wiAv/r5riSPhYXpUu9RhIReFQ/Lw8pUnqsBF4DiQs8qu8n+0jgNZDowKuTBF4pwwLPjYafdxMV6Lkl70QFemWpxUQF0hlvsGe8Ps0fr0tLCXwBmj9el5YS+AI0f7wuLSXwBWj+eF1aSuAL0PzxurSUwBeg+eN1aSmBV1+rx+ePV8ldAk+TuMALSAKvlKjAu+Pyarj4C8IrCbxSogK/pV5scmz99aeCwiTwQhIVeDcsIAYsj2QXl8ALSVzgsdiGiS7IGwm8oEQFfkvR8WvGF11/0WWhBF5IogKP3JvSZ9JN5FECP3cJPE3iAo9LGkKliUQF/lFYcHBwW2cJvAYSFfiafce1ivE7hq79OLR1fSY+R8HvmaiALgm8biy1AG/+Xt4E+V9ddC31OAI34nMUCE1UIIHXnWX+tdxdRnw/ZBdG1/56h3zchM9RIDBRgQReh5b5F8vWLH1a9frN8PXM9U/wOQqoiQrO7927Nzmbox8/uPv49a+mGQuI5Xd9t9SmVp/x9ceO1Z/QlV/nol8TcxRQExWkosemfeTo6zfuPn7JNc2YB5bfvhqgpRbgqzyg1h7s+0nOUSBNVMAvUV3q5/T9Rq4dWR4ZuQOfo0CaqIBfogLfwM6sTIUKFRBVksArJSrw9wlJ4DWQqMBLAyoMFLw0oMJAwUsDKgwUvDSgwkDBSwMqDBS8NKDCQMFLAyoMFDzfpMISeH6JCjzfpMISeH6JCjzfpMISeH6JDLw0qbCmGUUFXppU2EDBS5MKGyh4TNJEBZpJXOCliQoMEbwGExX8xVGWnLuPX+81zZiVpXNLeR5Y8vwxcmn5RbeWupyo4C1HX7K5+/j1QdOMX74UBMtsub5bahzSVJqoQBtLEV3qpYkKtLEUEXhEmqjAYMEj0kQFmmYUHXh1ksArJYHXaWEx6QT8tUcMSwm8oYBvHcGwlMAbCviAcIalBN5QwFeeybCUwIsZfEJoumLdbRrDUgIvZvDbwDjFut0khqUEXszgo0BbavWV8XiGpQRezOBXgSbU6kMwhmEpgRcz+CUmdajVBDCSYSmBFzP4uQ4+1OpJMJRhKYEXM/jpHhWo1d0gjGEpgRcz+Am+JanVKDCAYSmB1xj8pRTkx4LIyCsFKKTpqCaO1Ooi0IdhKYHXEPzP6NkpyNuD6FoBCmk6uIsltToNhDAsJfAagv/181wKkhq1a+8/VEjTd6jVa27HLs373P2jaUbNO8ixLAcMMfofuTreqIdOLFVL8w5y+WWZw0t9XAqS8QC5v5MKabp3xYoVV//j6Ncv7r5cKseWA+aAT+TqRMteOrFUrV8/9d0y5+BRfV9BhTQtAJf6TnNkd8nV0CIdGJbSpV4b8PGJSNrOAhTSNGiR7TVytZdbG4alBF4b8Nm7orf9XYBCmjZZWSyOXA32bMWwlMCL+Tm+3gaPY+Rq++rNGJa/CXzo9VxZSuBzZum3tfJecrVlvUCG5e8Bf99kL3enBF7TjDkG773Lbyu5Gti8AcNSA/DH03ksVYsP/BqwnSenBF5D5Ri858GG68jVgI4BDEsNwBc/wWOpWnzgW4EtPDkl8Boqx+BLHW+1hFytGVKbYakBeOvDPJaqxQP+tqVnFE9OCbyGyjF4l7jOVN/aqmE1GJbC4NON9vJYqhYP+JmtA9fw5JTAa6icgneTXRkynFyvMMaHnqQB+AcghmupRjzgw0a1WM6TM5/BJx2c0kzc4GUgaWEQue4+rQrDUhj8VRDNtVQjHvBdZ7aN4MmZn+BjZ7Ry9eqxSNTgXwJwb68XuVFsfkWGpTD4E2A9x1KdeMA3X955jlAp1VrqHjxwnnJF7Jf6FAAe37AiNwqvLM+wFAa/G6zkWKoTD/haW7rPECqlWss8OOOnNi7fPuK8qMEnAZCeYUq+pbHa6MGwFAYfBZZwLNWJB7znwd6ThUqp1jJP7vGJi92MRA3+IjCBsOwRYkO2w51hKQx+MZjPsVQnHvDO5wf+IVRKtZa6B5+wrlclv1F7RA3+hFNnCAPx6zXMAIeLMyyFwc8AsziW6sQD3uL2kJE8OfMTvIXzlHix3+P3YxW7/mPx9afgrDPDUhD8n95gCscS0x/r+Q/ggn8O0kYOFiqlOuUB+LX9qpZsPmGHqMH/6Ycu5hD9Lx4YxxdhWAqCHwKIgXecUgaFcDNj4oK/VQiOG8CTM5/v8ZdX96ksavCRDdHFtur4+u1CCfYMS0HwA4H5SI4lpkY1+Q/ggo8rBif3EiqlOuUB+KsTgqdcE/mlfgnW9eIy0cE6wS7RhmEpCL63ZaXBHEtMfjaZvAdwwR/0hDO7CZVSnfIAfLNRtXq10gH49xxlf+Xu49dHTTNmZ+fIcn4wuvjLJA1bT3B+UEg7y17hE0M5lpiqgFu8B3z9QnyerUvt2eb/PqKLUCnVibIUlkaWWGTLWjdb36wq8jN+Ym9sWeIUtjztdteUYSl4xncOnxLCscRU2oLvVavyjI8CVHev8E5wYRuenPl5xjc41npbdZGDHzkEW9bdgC1jy6YYMywFwbeLmNWVY4nJqfYE3gMo8MsVo/RCR8PlLYRKqU55AH7rlHq1D4kc/AC88SQEj4Sx3ysVvKJbCoJvtXR+e44lJuthrXkPoMDP8bK+R6y1WAbXBPLklCp3GiqH4Lvh7eTT8erV9urPwEu6pSD4JqvwyiGnlJnGf3rwZFeCn9i3XSixVnE/jKonVEp10uPKHdddf8ATr0Q3+mPLaP+X4BndUhB8g/WrmnAsUT0Dd2XP+Q6gwA8bFmd1B1+zuQG31ubJmZ/gDaFy13gVtkQfplGtr/8KpNItBcH7b95Qn2OJ6o4pLHmc7wAKfO+JsFN/bOWBaQbcUU2olOokVe5yZOm/GVs+McbOz9WNoXEK3VIQfM2Y6NocS1QJ9rDJUr4DKPCd5sCLVsnoyjkXCPdVFiqlOkmVuxxZVse7TkEn7OlqWUtododuKQjeZ9f2ahxLVOeKw1ED+Q6gwDdbAWEX7ElyDwr9cAWenPkJfiwhUYOvQgxmqIWd+BFtoeUtuqUg+Er7ibOVXcoj5eH6Ojz5FeDrbEIvC4WuQRjZAMLjpYVKqU55AD48vExYeHi4qMF7HsI/umCxTOd2gjaJdEtB8OViD5dnWeJ1ul0+8EJhvgMo8N670UXnPhDOQx8Hz5YQKqU65cVLmoM+5Q6L/FLvQdTBxmMxUGZ2g/YJdEtB8O4nT5RiWu6vhS031YHp5sk8B1Dgy2DD9a5aJsI/0CreRSdlhtuxfKVUpzwAP7jchiPeIgdf8gz+sRZ7STc5BBaJp1sKgneNi3NlWq7Hu2uubIreRHbwHECBd7qALYNDYL9x6DXfQZlhPBVtMT/BhyXcvHlE5OAJAvA41udqXH9Y7DzdUhB80cvEG3yl5SK84WYBegEPnsJzAAXe8ia2TCwU33YBhEnWygwBVNVAarnTUDkE73AV/0iRpUM4ahAsfo5uKQje/tp1W6bldDx22vSe6I2jM88BJPhXRkR7Qc/uddejT/1mivR0y2owfWQmu5TqJLXc5cjSmqzN2V2FcOhw6H6KbikI3upWsgXTchR+vx49CMLdlXgOIMGnGhHvBG5YOu2D8KGRIj0WeMJE8JBdSnWSWu5yZEk9uHvvgDB0DPRorwxerwF407sPTJiW/fE+PAPQO/dd03TuAQT4iMOFyO1e2OvZp0CRc4qnGzwGLrFLqU5Sy12OLI0eEp/t5kHYdyIsD27TLIXAZxo9eoJDU1p2xoPmdcX63haN4x5BgK8xy5bcTrK6h17fwVMqPXBoEbgZhI0ocC13l1IQPKhlQYlsqfibjwxF77hT4aBCl2iWQuBfgmfEax2lZTMZtmyJtdfW5xkES4D3nqDo1Ik/9ptQDcUZNvut4ALg0Sp/wS/EpA14PLIlHtSyoES2VFxllzcjztPStHcrguDR0z0TPGJY+oMMdFkXG/E+aBj3CAK85/BijL02N8iVU84PjTNHA6Nm+Qt+yJAw37bagMcjW+JBLanIlvoOXtHl5mB5CDvMh7DqHpqlEHjsaNk9hqUXwM5hn13oYgVP/woCfJn+boy9rtRNYVY79ArSQwYC8717dZKWtfq4FAQPaklFtjz5559/3vzG0X//cffx67umGXNmmW6hWMn+1nHdt28Nd2thiR1tlcawdAf/Q5fl4tHFDReeUv7AlqX6lGPsrXSBXGmz5pspbOEDmmjzH/+haU6NLPFBk7Gx27RsuUPB40EtqciWaQ8ePEj/wNHXr9x9/PqsacacWd6zIVf+tnzwoeXaDx9ardXC8r71hw92dxiW9uAxunS6gS5ey55zjviWjS1dOlRk7K2x78OVJ1ghHBM+2N6t2gPU1+I/TlhqIo0sMfCBgYFNlmoNHg9qWVAiW15VvErx3A8D16K1ctpYdcFLPdbWijfyZkRlEHsyTYywhgEL/Nmg/H7OEcSl3rEJ8w18/amRTbGeQOcLZ0LncJsloHb+XupP96jT/bTW4PGglgUlsuUFxWC5lkvwKhn6SKe0FAKPvV3BG3kPg0oE5MfA7oriWaEdN+ABAd7Gn9nnJqhGTT/s9eD8lmjt0npxSoca+Qveb8K+CbXE/Rx/RjE7xeDhsNYWCIfTZqXBwD+iv65jC3uf6nYaXYmpMd+hNVY1T7JyQStq92X4OJpJPThHEOAtfPwYe4OdylXAJj5riz5WVAIn4E4fWikTnXh77yktdQ++JvqvtrjBH1N0ho1oDatvR2HRxrFh4COCoGphPSjweKjrmsCUftZjnsG4Yu4nsbFYePKfPpwjCPAm5Rnx9OAAI6di2LOf0ykIa4BkuLcKrZTRgC8aGs1S9+C7zj4a3u7yZTGDP+hJre2ujPfGmUcLXI6Bn8WIdckS1mcK78mxEDsqrq7rukPlyx9RXEcSLV+xj8DBZ4ASDRl7RwHTQv0hvGKD1hTqy17BAxVppZxowd9FX2GZB2311XGJGfyeqtTaNRvoeRDClU2UiRj4yb5qnLB+V3gg3GnEdWJTkb41Ku+DcD8ZQ8n2CvsIHPwzULgpY+9UAEA3CJdgD/7NS+A9t5SlbNeErxMuzVKKc5cDyxgF1wzTe1i3mGhaH3cM/Fi+jpCUsHtxtR3oyiiyZtCyTmC17UqT2pyLNA7+EbBghEeHESj4tugTxVR0vUNN9BbiQSul5xgvqE4SeO0tXz2M9ldseMRi9TR6V2cM/DC+/nCUsKgKeATkAeSzQM/i7WtPvY312sTVdyz7CBz8XQDaMfauQcGjV5qSRzGLNhCecleWMtN8Y3m2C9NSAq+t5dPiJp71FbsDV2ETFpyghT/CwIfaqXHaiNbRAjaiK13JB7cRxr3qm0+Go0OJzYUt2Ufg4G8CwOyksRU4gAC0SoC9NwgdhPfOVpQyRXa8lNr/jgRea8tbhVaCxord/cdibTFXaJ1jMfC9ZGqc1jWCsBEW+rrlCmLHTDC8CRgKe5DT0B91vx7JPAIHnwBAd8be/cAXVIOr8CF0CTeI1gWqlOddiG59KiX/lOKkNoNCEnhSCQ47gPKcnN3J9jp6GaaBxsB3BWqeooOHQ9gUi5hVdxOxYyWYHARCYOBqYvOJyZTqzCNw8JcA6M3Ye8qmlaUn7KmYwxzryEeVcpf35aJq/zvyT9fAQ7U5KEngScUVO0m72W7zLXQLwhfKThE4+HbgLlSlW9a3IQxaAslu8qhiwIIOoA30oqaccPdmvoYjwJ8DzKlM4ZN53T3cYBlFCy8Wiocq5cqm19TdbTDwF8EFtTkoSeBJnXC/QbvZXnLE37BaKLvgYOBbANVNd4OxwdXtsEZ2snc+PA7WBoP6sCjVm6MlsGQegoM/AQA7wNmQOkWTzdOoLSwUD1XKKSG3C0F1kn86A3arzUFJAk/qUIWnQNmq+sIEHyrrdFFpiZa8ETijyueRDZYVD3ZPkU4AMb2AT7oxNeZ2DABPGMfg4I8AMILldTLSan0txRaGmirlwLH31VUzMPBHiYC6gpLAk9pdFVrQZhEugd/Oy8YqdmDg/cFBVT6T8Smr8AjE1GUiFRwZCErfUpyiUcaWzAsGDn4/AJznvIfGfZTxLbFQPFQp20Qww3RwJP+0H/B14edKAk/qz5rQNVS5PwDvhlVNOTMQBt6XmImARy+c8FlJek/C3sZR57Xp+aHAUfmu91Ybj1jGQTj4HQBMhCy9AKV2Kjawnj1UKWtsTQNp9JyprOZA+acdgC9CIlcSeFJR9WBlWr+4ELwblmJmIgJ8ZVPWA5lCi4gYhlgQnQcmVFS7cU9HF5XRx8L5RTMOwsFvBWAax04mU94UsHOcLOUr6xvMaA1wCKtHl/zTFqC+NZ+SBJ7Uqiaw/mjl/qnm2LKNcroIDHw558X8Lq88CKSD0Ss0vd49oQaIpT14t1rEOAoD/2KZjAh9zJANrUUeC8VDlvKiI9mtj9Jde9b4a/mn9cYq4miyJIEntTgItqdFJYvCh7D1nKq0REvuVonLCNfGssSdd8QQ5jjnaUGWW2itf72ZYc8w8HNldkSwc4achyjXsT7bZClXouc3fdD+msKVajAPlH9aWdRj1ib+UjL028G/4Uj+hbuPXx80zZgDy/md32zYr9x/oTC2HDZKseOL/M0b5+Zt+F2qLyc+x4W9eRPrqdx/KtpleVnl5rhQxlGY5TjgCpZy/MpsV66/Bs+oUoaOffPGPpFKuNO02Lb93swDs+VLqzi0+oO/lAxp9Lc0BPAzezL2/y8OW07up9iBUbI/47qHz+SQ8ytiBcu/oyY9qcJU2u8gvBvjMMxyICgHVnEML0LahvFjqpQ1t71543QlnSjgUrueT97EVmAemC2f19S4JvP3xa/fDp57PdGPS/2EfjypczsqLdGSF7rdmfdaH0g9QU3rCeE6Rn3LdwjtDd9S5nsa7FLfEXiDteqLaHqXLGV6IfRBsfi56tgzZUKAG9bp/wTrnY3807TuJo58Q3PZku7xpHgnCKD1xMDAG6eMCuXJFWdD1bRnd4FwIeMtq38fWperKGYnKwx8IPAX6E2FtQsQpcRnTih1ojR6C4+0Hoi3Jse5MPPKP03o5wiaqjfEJYEnFTaKJzWqrtLyM1bNWsTX666z4jczvwOEMxjTEtTrSqtk764KL5dRbmLgq4HGvLPJ0mSdSJZyKdZIVOGw80oIg2YSaVccmXnln0YOLgtqQWFJ4En14TSjoNqi7IKDgkefqHd6czMlWCrCoqGPBnDMEHpiYGtl7w7s/f5eI+V7Hwx8KdBWZasQKfsEspQhWBEr77OZB/Fe/5jo8TNwy0+DR/kBTygsCTypbnzzvcUon5ZQ8Hdl8FIRbqbQrorVFeg5GTqentiiMS02bbwjjALK+CoYeAfzbmAPVCvHeLKU3tgvpNp2kylUKEaiPZcu+ad+E5oB1vWfVxJ4Uh3m8aTuUt6gUfCJ1vCZEfeNfIV9itW1aMWuWzg9sY0/ra53xwwuBsrWOxT8KxPPAeCA+iI6XSBK+dIMa7upvR50diM690GiWY8u+ace04NtraCwJPCkFNOH07Vf2bUx6/MTS0dFmBy63E8oVrE6QRBjVuCOPs2UG89B2lSgvLKg4B8atxgJjqovomscUcoTeMtQvQhQzfiVJ/m2KI3VNUT+qfOcwT6AJ/wGWxJ4UlRHGYYOKe+WWZ+vAVcIS5zlZHJRBrvA6gQNN9ITgyvQq4Omd4bLlP1tUPAJ9oPCjU6qL6LbaaKUxHCOwInACTwoRf7YMo0eMfLKP7VeNKWd7L56R0wSeFIBG3lSjynr4FmfT4HSZDBCpgor35DFVEef3BkdIUJK0p/uHONDKig79qHgschqpjxRUugqfZwoZXd88tEWg4ERiFdEYjNjRk6Uf2q2Mvkiz2WJIwk8qRp8deuTyob2rM+7XcZiAWs5mayTFKvYkIwKjJ9G/yL01pRSx1s3UD7Lo+A31IHQ8hJUq3KxRCkr4ZXAtt0AALF218hERfwM0vJTg/XY16h3xCSBJ1VlL0/qOeUso1mfN2D1c6pWRZNM2RHvAHprcGVwDLOkTydWdXe9rsoHBRT80MEQFkmCaoXez7FSPpfhvSg7t0DB/6kIkcqIvomBr70Frf7vUu+ISQJPqvwRnlTay/SszwuxF93+0ew89DfkR8uij930mNdwuDG9PScgquqIKmSQIxx8AHp+Ct2QK+/DSxlLhLXuXtsYgKWK72QEYcTAY7/MRgKNwJgk8KQY0Qwp0RrGsj7jk4s1JLtiPNszm6w6PwMvFJlOu6F3XUZ1awzoS9sKGuk2pzx8bk0EtpJ/m8QwdAAAEhpJREFUzrRRN/CalM8uvJRziMpCH88iwGicot5emnlVl3/C7kWdwqGgJPCknPh6JV+3VaxmfR46HP1ovgKDPtrPtJQZWad7aKScR/KSE0xnPktNBKG0rQuFTaLc4TVA/Hrkn6+pmIOSId8YvJSdiVba0GIVgUtPRZdLMtI6JfmbMujDYShf8zNLEnhCmeZ3eFJvKptCsj7jvTLaRpzwM/fsG5mseHxPNlfmv2EDHxm9pTtMA4wW3O2yY84wFgTjG/LPeypDYdXaipeyPNHOM9SiAQhopJj91JvZmVre0OgMc1y/KkngCT2U8fVepUUUzvochHWc6jJ7ZhPiQl2V/JNfs6XlN4W3LBmlnAWY88HfT7CHmx2K4Se6/DPvOx+26mzCSvlE9hjfGgU6g16eivE0Nbcx8sqrgIvEKwMhSeAJXeQdckYEpyUsPwdgr09DpozvT+wIIN+mXqIdmQoy4oswSjmf3Xn6diEY0cYRf3SXfx4+BAqr/gaslAfIYbITwBCjWTaKMTkBzHe68tJF7jKHd6tSHoP/sSAy8kpBCGm6n/eiS2sKz/qMRz0Y+Mew4cQOqo33DK2PXTp4csqNUcrFgDW9aIox/GNgB7zdVv6Z1plTtQLttr9XBj6fB8bPOgwUg6UDmaFS5U6X0eWRcsKueQz+7UF0URBCmq7jm9kTPlfW2LNWWWHDaIYN70eSDJ4Zi78io7XuYX1gD3oySrmSPb7hGXjZa9JKfEC2/LP3TiispmAoatmeHHz9rNQseAso3g63XEZ8ks+EcmzIH4wvDAWVx+BTo3bt/YcKaQqfP3+eyZ2rWvNpxD9pmlHzacQpy7nd+VJfgwxq9UuxRW/RjwmDe8whdgwZ174D9nnUi3aA9d09NRiljALhTMu34EXQqocWr95jc347XtegiEEgALX0OEluJj99/8akNpXYsV489nHM7AK+KTd6gS6fGb+8KOSq0d8y5+AzHiD3d1IhTY9s2LDh+neOfv7k7uPXD00zam85fgJf6r/gLbV6y+kb9jE3rPM6Ysf0MLtG39+N/n68Ju0Ap5TdgYxS7gHLWZ5mmbWPfPeOxUr5r8kbDYrYCdh8+/7W9BNtl2szaq03wIvj1cz3K/b5wQgr5b+mS/2EXDX6W+aqcvd9BRXSVJ8v9V1n8yYr33PNILpbzOpKzEMK4eziJp7wtDncSu/oVOLsimaMUm4F7Nu4TaLbSTgMm4FQnibT4DEetjezuwH3MKa48FHMNd4Lf82bbPbMB+8G+pgYkFuhprpoPbjy+FIfn4ik7SwIIU39+QdHWVBt4mmuxLuXiHb+5HCF5cCnCNwDnm+gT/9c9uj8DoxS7gDs9/xF4gslwX1l0TV5kkbRKzq7ljsGpzDCZrRsT60NBGPQ5WZfeMYWe11ziwjO2dmoJBRQHoPP3hW97e8CENI0wfIeb7IN9QIlvB5R8uUtfMgK2WYwxCRjA7i1ihYTDVbeN60no5R7OaOWix82egFfWqGY5Cc16RwHg739YmAQ48LRT/EzGI63DA4PJYfRXSJiNIYDVidMrqTneFyh3fiTqTkHnzudIkq+rlH5w8SufWCr7F4EiGO0ltSIGRPKKOUhTq95j7VYi0+TRSj4GN6ZR9nq2bj5SljyBH3XiunU2vU+2G+g0Wq0uu+GXrSOE6H6DgMLIVcJPKbHNuf4k4teJj5nBZBBjDf7lyCDI5wGCU7nJ4GDtEEX2GwUg0YySnkcbGBZVpyMPf/NRX8u8hVtNCli727dZt0ze6kidSHmgV+Ftjs9hHuIt/0vxhtlCLhK4DHNUXXqkT1dnjkdIsFvr0Z1uLll/6riviFgywz63bfJql6TGaU8AzazLH16Ye1qV2zToXxaX6iBBowcOnI7T7duQmsbQarnRfsecDPZwy8DPBZwlcBD5ShnrshJR2fUo8KW76+kiIvzHNaL7A5WTKTF0YBBizrPYZTyPGA2pkNYOxB7rw/dD0P5YE4sDD7duju11wSVr122YQM2iDvSHfsDK6jLD+9ktnRJ4FHFuKu6MBKdGp8WPUKBP1raSNnvomN4S5NZo8No+TuHt1zKKOUVwG6ca1ABP897j4byLvM0K+SSoGZ8fYBxYZ1+XpEzVy0uM4Pqyln4qoCnBB5VQxXD3vH+bqimNVRMVHDGnva+fVw//5Kjh9BjF4VMqbCdUcobYB9kqpkVPuIi2gfKG63XrJCb/Z25nXtJYcGxHxkTP9zMWo5UhH2e3sBMSeAhvGiTqiq5IvYS/InjUQX4S8bKN7UwKqBi3X7YRJIKDfRzy2CUMhkwe0pA2AYsxD4emz+QV2H/KFToYGELlT3lsRlRFG+GL5pTr4SoZw+VksCjV12+EdKE8C6YU7AnZBL8dWCvTL3g5BLSMYT+FmY4GM8s5QPA7o/dmRw5479OzpiuWo0uAN5XSLjuySA8qXhLu4bqOegj9PpHAg8fWserTMa6Lj4ujNWZSfB3AC2mTbpMNt3Jld7YOw7EMEuZCth9+UKMiQr3pK5ZpvytRhylWKkuIRYGa7ci0L4iiHEdoXAoEng4o7HqZKyDyyQ8nQSf6VeWllyhZ+K4BvRRztPATWYp0wB7uMSAcsTnSecME7Ux62hS16RvehcqW40V4JsIhTmUwGeUVHNV9A88nuqA1+ypWagS6Cf4DfbTwFx7VilfgYusLEPJHhWvimzTMNC0+v+4QwLEO37jUoBvJ9TFQwK/qbya86k+mDahKWHJU3KulvqzSym7yspykerO27Gl+tkmaFL3H3c7Baco+u4rwHfnG/WtqaVC4gXfeR30V3duBIL+9sSYRs3A3z/HLqXFDRVZ4UrThqqS2FL3H/fZDocMpzYU4LFQizm2VEi04DMtzKLtn6nJ0Az4WJOWGoHHxCzl7BcqssE7RpoEKeKxZKrJCth2AbWhAD8S68aZfueiygGZBgz+/hCYbN7Gbqi6PK2AjOwBkVPwauStSR9bQcvuU7GTnpQC/CTXyiWsgayosaoBWoYK/lUGPAJij3qskyWqy9YWUNNW5AH4pareEGhlOTIMFlY87SnA39qw+/SNx4wx3JpbKiRC8F2GwBjQcV2Dl+rHF3YCYBBpqXvwcp1YhndMNVbcTjiTEbmfgPz67eC5XTk172X7UdOMPL1sI4cztkDL9+vNqk3vJWDZ3Uy2SKWlCmlcyq9fdGG5scHF4qotvQ/kwFKhFyI448c1p21MLzyuMpzn6dJrooBlz6Iu5P1Tb8/4/Z5LlRHVOGd8gKroiQZzqR+k7Ayb0dft0lV7OKGtrO5aAcveZSLJHg16C/6SzE4ZxYUDvuXSHFgqJAbwPRQdG58287kDXxg/HjTSzPq4gOUAReB4vQX/iB4SlQM+eGYOLBUSA/jWzuTKHZ+mWHBJp/Pdp5cAjwQswxRtLHoLHtLfAnHAh45dtTCzN0+HPYMB34Acxn7GeSzeRFt9W6slvkWELIcpYlbpL3iGJRv82IG9u6YAnpfzBgO+OjGLz6HC5Aww7cMDolrVELIcpWgFL6DgZ3UNahrPM+uN4YAvB/BOkhUWktuTu1fZ07ezkOWx/QrLggl+aUs/38OgOTenwYB3BudfzIavFL1PY7zdTh05pIVlwQS/sU5pj83O9tybvMGAt7Q5uNlo8Ugr6h3sLQs84pTYwe+pYmO/KMiL2zhsKODTjby2dDayMFW+Ai+MT7ssdvAnnIHx+N4z2ubMsuCCv0nVZ1OMm06w6WBqrBzodmirdpYFE3yKiZWs45jT3FnnL9Snj7UZyT9Cq+CC72VCdpm/ZttDNjB+otdwVk6xg4d+pYpWmZtuwRlYs8HYXxnxnNsllFCBBZ9mF+0egndJP1Mi9SS6spw9K7DowU+uWcEyEvpuZe+f2y4wUHGax4Pl7HRcBRb8jkrwgX8ANr5of8XcWhZQ8PcO1gZ7YKj/A9b+UYOf+wdRfUUPMOJvKlVgwU8LRgvfrXQ8hMEh/DlFDx7CznUfw8e12POY9pwOH1frQvbvjjQO4ByGqUCATyiWCW+Q/xEKfCe8L/T0wnvibVSMHjUA8LhGDGLtaLYSwocVyXHas6vZ83Y1LhDgV4MLsAQZyYYCX5EYnLbVrmr/nFjSVcDBL27F2lF9B7q4U4aYOX3YEDveLlq5BK9BZMtjkzT5bzHHqCh+owdbu0cvgmFgUSIgQ0xg4Df1zfQjhw/Dcz5XNbFUpwIOfi+7D3/J09gyqSQ+3V6XWQ34puTJLXgNIlv2daCNSUntoGJsKOP/7zMX/0hb6lVklEtxT+jvberiajUnDvs9ZGWl1PezOVhsh4o/gwpLdSrg4K9T8a6fPocbSzyC0IKYTyHBCZuSpeG60byXxFyCpyJbqgHvLjuSeezplZsPMf6zQSRMX73rqjIGf2o6+/9/Y6mZ8/oMmDSicNWVL2A3YHrKevdIq4FrO7kUDpoT9zlrjMzE2Xug8F/KUMCnyw7ELJ/Qr6WvRY1uTh7Ne0UZkSdXeD0Io+1i97s85Tkql+AFIlv+KFO6lG0fRzcrMwcrAMwc3Ky62vq4ermZ2VWrTqqQE7qo5uPgWrp0aR98j73DoAleHj6Ful7CLHZWbmYz5Pv3tdjGow09ihdxNj28Zmyle8KxG/MwWKaw5X+/0bKOa83WYdPXHUodNm3Vu9q+xh3I/X+7lHZ02fH9e0t/noNyCZ6KbPkO/Q29fstRVtLZs4lPzp7LvIFuPHuQFHf+9dmz519n3nxwltLtePzj1iXFnsQXGW/h2bMPCIv/PX5wlu6cciuT+z28+kfDfG+/fNE0p8aW2fL8s0z8QK1lnktIQz+eXuTJlUvwBSGypbBlwb7U58wyt7V6/Y9sqYGlBF5Pn+PpksDrxlICL4GXwOvOUgKv08JiksDrxlICL4GXwOvO0tDB37qpaWH/p2nGvLC8JZxHS8uk28J5tLRMFIpdrJ2lDsHz6MKp3DpwdO6Mzi1Px+nc8uQFnVseu6xzS5WSwOdUEnhdFIMhCfxvUK7B30nSRTEYun1TOI+Wupmsc8vEOzq3vH5P55YqlWvwkgqmJPAGKgm8gSq34OldMXMv5bzluvK9lIIo/XRjilrqtpw/Dm1dn6nrUgopt+DpXTFzL+W85brx/Rk9O4XmpwtT3FK35Uw9jsCNui2lsHILnt4VM/dSzluuG99fP8+lIEo/XZjilrot51/vkI+bdFtKYeUWPNUVUzdSzluuK9+4FETppxtT1FLX5cxc/0TXpRRSbsHTJxnXich5y3Xli1JS+unGFLVEdFrOX+eiX+u8lELKLXh6V8zcSzlvua58UUpKP92Yopa6LeeDfT8RnZdSSLmu1dO6YuZeynnLdeWLUlL66cYUtdRtOY8sj4zcoetSCkl6jjdQSeANVBJ4A5V4wNcHADTH116DftjHEPAiyZedy+SHcr0BwHWIk4mQ7z6+g5hK9sphafNd4gFf8uqLF//D117Lin1HkJ8lzF+8P87ORWeY9fFjyaMfP77lZCIkgS8Q+tf8P2r1tVXQKQS50tgRO+MfBSx2LRWHXKuFYP+aAjf5ZR/L5plkVvcLCIJlqvOHY0BCDetRCKJM9d1HHqs86H6DOVWab0CQRcFIVCmLWqkS+PzXU/s2ZboTxF5bbRmAIGMiCfBWC75M8KfAoyfve8fYD8MakkdR4I13/O3r9PIaeEtLRcETxyoPum/X90FkWwSpcyjD7NK7vmES+PxXgveJRyEB+Oprq7+Lff/l8T8CvO0P5H4FGvitnRDkqyV5eaDAF0eQiYPQzae0VBQ8cazyoPtm35C/rL++tv/6NR2RjwuWwOuHsozfrbKz2/zaCml+5mZ9hABfHkEeEeATcPBzbNzd3e3/Io6gwKN0p85EkDJPaakoeOJY5UH3y6D7a5/c0Af5Mb1G41YSeD1Q4iX0Pm/6EVtFwW8KnbyKBF+BAI9W3ffi4Dd1RJD/kn4RR3HA01JR8BUo8ORu7PRHFg5veRLZWf1vJEYCrweKt7/0/o/G+CoK/l2xCpkM8A9lye8bYOA/vi56/N24APIoDnhaKg08dRAO/nFx1+/I6vrZb/zaSuDzX7/Wl7PtQFzAUfBIE7RORgf/a4R1lX0o+K428tNelo2ek0dxwCPKVCV4xUE4eKQSWhv4p2nhOkedt0ngJRUwSeANVBJ4A5UE3kAlgTdQSeANVBJ4A5UE3kAlgTdQSeANVBJ4A9X/ARcXay9lHNCVAAAAAElFTkSuQmCC" alt="plot of chunk average_daily_activity_pattern_weekday_weekend"/> </p>
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