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<h2>Reproducible Research: Peer Assessment 1</h2>
<h3>Loading and preprocessing the data</h3>
<p>Also loading in some packages</p>
<pre><code class="r">unzip("activity.zip")
data <- read.csv("activity.csv", na.strings = "NA")
data$date <- as.Date(data$date, "%Y-%m-%d")
library("ggplot2")
library("plyr")
</code></pre>
<h3>What is mean total number of steps taken per day?</h3>
<pre><code class="r">daily_sum <- ddply(data, .(date), summarize, steps = sum(steps, na.rm = T))
daily_median = median(daily_sum$steps, na.rm = T)
daily_mean = mean(daily_sum$steps, na.rm = T)
qplot(x = steps, data = daily_sum, binwidth = 750)
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAAqFBMVEUAAAAAADoAAGYAOpAAZrYzMzM6AAA6AGY6kNtmAABmADpmAGZmZjpmtv9/f39/f5V/f6t/lcF/q9aQOgCQOjqQkDqQ2/+Vf3+Vq9aVweurf6urlZWrlcGr1v+2ZgC2///BlX/BlZXBlavBq8HBwdbB6//Wq3/Wq5XW///bkDrb///l5eXrwZXr///y8vL/tmb/1qv/25D/68H//7b//9b//9v//+v///8f31jUAAAACXBIWXMAAAsSAAALEgHS3X78AAANc0lEQVR4nO3cDXvathrG8Wxdzpb21HnpWXtok75tYbQnJHQQ/P2/2bEFcfCQJfmRjJD1v69eCY3R7Qf/MDhb0pOSZJmT2AOQOAE+0wCfaYDPNMBnGuAzDfCZBvhMA3ymAT7TAJ9pgM80wGcaEfz/bLHf4yn3zvfsUTpMa+KjAi9tTXxU4KWtiY8KvLQ18VGBl7YmPirw0tbERwVe2pr4qMBLWxMfFXhpa+KjAi9tTXxU4KWtiY8KvLQ18VGBl7YmPirw0tbERwVe2pr4qMBLWxMfFXhpa+KjAi9tTXxUd/jV+1lZTovzW/W35gbwIUuPEH5RXMzKxeW8+lP/7ekG8EFLjw9+/ef666y8m2xO/OcbRZXuV4ltTndivTOJEcNLvYK/UZ/K5xt1rM+4Xfj+z82OHONp5F16fGf8Fn7vjAc+aOmxwgvf44GP2+oPv7mYX13P+13VAx+3Ndr38cDHbQU+ZGviowIvbU18VOClrYmPCry0NfFRgZe2Jj4q8NLWxEcFXtqa+KjAS1sTHxV4aWviowIvbU18VOClrYmPCry0NfFRgZe2Jj4q8NLWxEcFXtqa+KjAS1sTHxV4aWviowIvbU18VOClrYmPCry0NfFRgZe2Jj4q8NLWxEcFXtqa+KjAS1sTHxV4aWviowIvbU18VOClrYmPCry0NfFRgZe2Jj4q8NLWxEcFXtqa+KjAS1sTHxV4aWviowIvbU18VOClrYmPCnyTU/cx3UtVgAc+SivwxgAPPPDAe5aqAA98lFbgjQEeeOCB9yxVAR74KK3AGwN839zbsntErXd2jX23PXM6yJh1go8aoJUzvglnPPDAA+9ZqgI88FFagTcGeOCBB96zVAV44KO0Am8M8MADD7xnqQrwwEdpBd4Y4IEHHnjPUhXggY/SCrwxwAMPPPCepSrAAx+lFXhjgAceeOA9S1WABz5KK/DGAA888MB7lqoAD3yUVuCNAR544IH3LFUBHvgorcAbAzzwwAPvWaoCPPBRWoE3BnjggQfes1QFeOCjtAJvDPDAAw+8Z6kK8MBHaQXeGOCBBx54z1IV4IGP0uoPf1dUmdS3pkVxfgu8W9KHr7L+Y15//HL7/CXrjoF3zvHC/6XEVx/eFVf1jfolwLpm94ha7xwtp2mMOUys8IurzaeLWXl3s/2a9RnHGe+coz3j/3p+iV9MgHfLCOBX1/MGnTPeNSOA//u/W/3p9uoeeIeMAF4b646Bdw7w1gDvVQp8E+CBBx54z1IV4IGP0gq8McADDzzwnqUqwAMfpRV4Y4AHHnjgPUtVgAc+SivwxgAPPPDAe5aqAA98lFbgjQEeeOCB9yxVAR74KK3AGwM88MAD71mqAjzwUVqBNwZ44IEH3rNUBXjgo7QCbwzwwAMPvGepCvDAR2kF3hjggQceeM9SFeCBj9IKvDHAAw888J6lKsADH6UVeGOABx544D1LVYAHPkrrUPD3tuweUeudXWPfbc+cDjJmneCjBmjljG/CGQ888MB7lqoAD3yUVuCNAR544IH3LFUBHvgorcAbAzzwzvBuS4AH3qnVLcBbA7xmNfBPAR544IE3lwJvDPDOAd4a4L1KgW8CPPDAA28uBd4Y4J0DvDXAe5UC3wR44IEH3lwKvDHAOwd4a4D3KgW+CfDAAw+8uRR4Y4B3DvDWAO9VCnwT4IEHHnhz6Ujhl68+NR+B15WOEn55drLJi+/Ad5SOEt7hXAd+nPCOse4YeOccCfyDeqn/mff4rtKRwi/PXnPGG0vHCs97vKV0pPDlx9+AN5aOFH77DR3v8Z2lI4V3jHXHwDsHeGuA9yo1wvNSbysdKfwm32xXeNYdA++cY4Lf/aZuWhTnt5sbm8/Ajxf+4fmlfv1lq724nFd/gB8p/PY9/vk/360+vCuu6ht3k3L1flbdKKponizt7B4e652HTucwp50RlHU3B3oYgWO5ql9czMq7m+pG9WH9dbb9qvUZd1RnfOcw3fDdpW6PzF6mH9WcQ387t5iUO2c88KOEf3zT/jmMGl2d8Qm/xwO/WW2Cf3xTfyf3bUe+uqqvzvXrecJX9cBvVpvgR/kzd8BvVvc744Fvl44Ufu89Hvh/lI4V3i3WHQMPPPA9RjVn2B+2fF1+++kt8F2lI4VfvqzNf/zKVX1X6UjhH9/U/5n+gav6ztKRwm/+L43t5zCAHx+8W6w7Bh544HuMag7w1gCvWQ28bgPwwAMP/H4p8MYADzzwPUY1B3hrgNesBl63AXjggQd+vxR4Y4AHHvgeo5oDvDXAa1YDr9sAPPDAA79fCrwxwAMPfI9RzQHeGuA1q4HXbQAeeOCB3y8F3hjggQe+x6jmAG8N8JrVwOs2GNIqBd4Y4IEHvseo5gBvDfCa1cDrNgAPPPDAAw888MAD3xngxwl/b8vu47be2TX23fYcxhHeray7WTj2fqQHYLuaM163wZBWqdsj0653GNWc+Ge8dcfAAw98j1HNAd4a4DWrgddtAB544IEHHnjggQe+M8ADD3yPUc0B3hrgNauB120AHnjggQceeOCBB74zwAMPfI9RzQHeGuA1q4HXbQAeeOCBBx544IEHvjPAAw98j1HNAd4a4DWrgddtAB544JOCbx3roPDWZ4RlGPOjAt4a4J3vCbxxGOCBBx544IEHHnjggQe+O8ADD7z5UQFvDfDO9wTeOAzwwAMPPPDAAw888MAD3x3gU4dffy6Ky3l9a1oU57fA5wK/uKrIb+pnwJfb569adwx86vB17mr41Yd3xVX9t6KKdcnu47bvIEhOO/fptEESyTBHEzv8QnkvLmabZ0Ad6zMuwhO+daw54zWre8JPr56fAhPgc4Fff755RueMzwd+Wr+lT1bX8/rW0wkP/Pjh9bHuGHjggTc/KuCtAd75nsAbhwEeeOCBBx544IEHHnjguwM88MCbHxXw1gDvfE/gjcMADzzwwAMPPPDAW+Elz4+gB8BWCrx+CfDAAw888MADDzzwXQEeeOAPdABspcDrlwAPPPDAAw888MAD3xXggQf+QAfAVgq8fgnwwAMPPPDAAw888F0BHnjgD3QAbKXA65cADzzwwAMPPPDAA98V4IEH/kAHwFYKvH4J8A65t6WFECrm3Z527tNpgySOZWEe/b3tAFhXc8ZrlwjiWHagA2ArBV6/BHjggQceeOCBBx74rgAPPPAHOgC2UuD1S4AHHviB4Yc78IeBl4wpOExNStPMrXsCbxzGd/+SMQWHqQnwuubWFuA1q4EPtX/JmILD1AR4XXNrC/Ca1cCH2r9kTMFhagK8rrm1BXjNauBD7V8ypuAwNQFe19zaArxmNfCh9i8ZU3CYmgCva25tAV6zGvhQ+5eMKThMTYDXNbe2AK9ZDXyo/UvGFBymJsDrmltbgNesBj7U/iVjCg5TE+B1za0twGtWAx9q/5IxBYepCfC65tYW4DWrgQ+1f8mYgsPUBHhdc2sL8JrVwIfav2RMwWFqAryuubUFeM1q4EPtXzKm4DA1AV7X3NoCvGY18KH2LxlTcJiaAK9rbm0BXrMa+FD7l4wpOExNgNc1t7YAr1kNfKj9S8YUHKYmwOuaW1uA16wGPtT+JWMKDlMT4HXNrS3Aa1YDH2r/kjEFh6kJ8Lrm1hbgNauBD7V/yZiCw9QEeF1zawvwmtXAh9q/ZEzBYWoCvK65tQV4zep+8NPi/LZ9A3ifMQWHqckh4ReX8+rP7g3gvcYUHKYmh4S/m5Sr97PdG0UVy6sESSA2+Jty/XW2e6OO03PTLYP8i56H+3dCvUuP9Z803T/jgQ9aeqzwwvf4bI6md+mxwm8u5lfX835X9dkcTe/So4XXZuARvUuB160GXtia+KjAS1sTHxV4aWviowIvbU18VOClrYmPCry0NfFRgZe2Jj4q8NLWxEcFXtqa+KjAS1sTHxV4aWviowIvbU18VOClrYmPCry0NfFRQ8BbM8hP5Q3zo365jgp8MqXAH39rAqMCn0xpCvDk6AN8pgE+0wCfaYDPNEPA7/7uhX9XXbZp3P3oE/W7YPuVfsXb0qDTrj8XxeU8+KgqA8C3ftvKM+svt03j7h+v+YqLmabSr1iVhp52cVUZ34QedZMB4Fu/X+mZ1Yd3xdW2cfejR+X6z/rXfvcrvYo3pQNMW97dBB51myHgd3+j2jOL6jyqHrpq3P3oVarg9yo9i+u1A0xbnfTBR1U58jO+zmIS+Nk+wBlfNhJhp51eleFHVTny9/jFpH4FCfz+pk7O0G+cqjTwtOvPN6XumuFI3+NDX9VPQl/RqpMz9KXytjTotNP6352ZJHNVT1II8JkG+EwDfKYBPtMAr/Lj10+xRzhwgFcBPrs8nJz8/Gl59vShXL76/eSnt9uvxx5uwOQOv3z1qfz2izrjP/5W31qevfj+UD0LNl8fb7KHf/m2/lTB17cq7uXZ6/LxP2+3Xx9vcocvf/yrfmWv4c9OTqqbSvzj6+3Xx5vs4as8vPhew79Sb+k1fHXGb78ed7Ahkzt8jbuBr9/jq1vLs1/qT9uvxx5vuOQOX35UV++Pb9RVffXivnz5b/Ua/5Gr+rwy+qu6bYD/R4Anow7wmQb4TAN8pgE+0wCfaYDPNMBnmv8Du7Usyt8mXq0AAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-2"/> </p>
<p>The median steps per day is 10395 and the
mean steps per day is 9354.2295.</p>
<h3>What is the average daily activity pattern?</h3>
<pre><code class="r">average_day <- ddply(data, .(interval), summarize, steps = mean(steps, na.rm = T))
peak_steps_interval <- average_day$interval[average_day$steps == max(average_day$steps)]
qplot(interval, steps, data = average_day, geom = "line")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-3"/> </p>
<p>The most steps per interval on an average day was in interval 835.</p>
<h3>Imputing missing values</h3>
<p>There are 2304 missing values.<br/>
Let's replace them with the mean no. of steps for their interval. </p>
<pre><code class="r">filledData <- data
filledData$steps[is.na(filledData$steps)] <- average_day$steps
</code></pre>
<p>Now we'll re-evaluate our analysis of the daily activity.</p>
<pre><code class="r">new_daily_sum <- ddply(filledData, .(date), summarize, steps = sum(steps, na.rm = T))
new_daily_median = median(new_daily_sum$steps)
new_daily_mean = mean(new_daily_sum$steps)
qplot(x = steps, data = new_daily_sum, binwidth = 750)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>The median steps per day is 1.0766 × 10<sup>4</sup> and the
mean steps per day is 1.0766 × 10<sup>4</sup>.<br/>
Imputing missing data increased the daily median by 371.1887 and increased the daily mean by 1411.9592.</p>
<h3>Are there differences in activity patterns between weekdays and weekends?</h3>
<pre><code class="r">filledData$weekday = weekdays(filledData$date)
daytype <- filledData$weekday == "Saturday" | filledData$weekday == "Sunday"
daytype[daytype == T] <- "Weekend"
daytype[daytype == "FALSE"] <- "Weekday"
daytype <- as.factor(daytype)
filledData <- cbind(filledData, daytype)
qplot(interval, steps, data = filledData, facets = daytype ~ ., geom = "line",
stat = "summary", fun.y = "mean")
</code></pre>
<p><img src="data:image/png;base64,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" 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