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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<p>Load data file.</p>
<pre><code class="r">D <- read.csv("activity.csv", colClasses = c("integer", "Date", "integer"))
str(D)
</code></pre>
<pre><code>## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Date, format: "2012-10-01" "2012-10-01" ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
</code></pre>
<pre><code class="r">head(D)
</code></pre>
<pre><code>## steps date interval
## 1 NA 2012-10-01 0
## 2 NA 2012-10-01 5
## 3 NA 2012-10-01 10
## 4 NA 2012-10-01 15
## 5 NA 2012-10-01 20
## 6 NA 2012-10-01 25
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>A histogram of the total number of steps taken each day.</p>
<pre><code class="r">D.step <- tapply(D$steps, D$date, sum, na.rm = TRUE)
hist(D.step, xlab = "Total number of steps taken each days", ylab = "Number of days",
breaks = 10)
</code></pre>
<p><img 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alt="plot of chunk meanstep"/> </p>
<pre><code class="r">D.sum <- summary(D.step)
str(D.sum)
</code></pre>
<pre><code>## Classes 'summaryDefault', 'table' Named num [1:6] 0 6780 10400 9350 12800 21200
## ..- attr(*, "names")= chr [1:6] "Min." "1st Qu." "Median" "Mean" ...
</code></pre>
<p>The mean and median total number of steps taken
per day are respectively 9350 and 10400.</p>
<h2>What is the average daily activity pattern?</h2>
<p>Time series plot of the average steps taken in each 5-minute interval across all the days.</p>
<pre><code class="r">D.interval <- tapply(D$steps, D$interval, mean, na.rm = TRUE)
str(D.interval)
</code></pre>
<pre><code>## num [1:288(1d)] 1.717 0.3396 0.1321 0.1509 0.0755 ...
## - attr(*, "dimnames")=List of 1
## ..$ : chr [1:288] "0" "5" "10" "15" ...
</code></pre>
<pre><code class="r">plot(D.interval, type = "l", xlab = "5-minute interval", ylab = "average of steps taken")
</code></pre>
<p><img 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" alt="plot of chunk dailyactivityplot"/> </p>
<p>Which 5-minute interval on avarage contains the maximum?</p>
<pre><code class="r">D.asc <- D.interval[order(-D.interval)]
D.maxintvl <- D.asc[1]
</code></pre>
<p>The 5-minute interval of 835 contains 206.1698 as the maximum value.</p>
<h2>Imputing missing values</h2>
<pre><code class="r">D.na <- sum(is.na(D$steps))
</code></pre>
<p>The total number of missing values is 2304.</p>
<p>Assign median of 5-minute interval to missing values of data. </p>
<pre><code class="r">D.medianintvl <- tapply(D$steps, D$interval, median, na.rm = TRUE)
D.filter <- D
for (i in names(D.medianintvl)) {
D.filter$steps <- replace(D.filter$steps, which(is.na(D.filter$steps)),
D.medianintvl[[i]])
}
</code></pre>
<p>The histogram of the total number of steps taken each days.</p>
<pre><code class="r">D.step2 <- tapply(D.filter$steps, D.filter$date, sum)
hist(D.step2, xlab = "Total number of steps taken each days", ylab = "Number of days",
breaks = 10)
</code></pre>
<p><img 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alt="plot of chunk histosumoffiltered"/> </p>
<p>The mean and median total number of steps taken per days.</p>
<pre><code class="r">D.mean <- tapply(D$steps, D$date, mean)
D.median <- tapply(D$steps, D$date, median)
D.mean2 <- tapply(D.filter$steps, D.filter$date, mean)
D.median2 <- tapply(D.filter$steps, D.filter$date, median)
hist(D.mean, breaks = 10)
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAC4lBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACYDwXjAAAA9nRSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZGVmZ2lqa2xtbm9wcXJzdHV3eHp8fX5/gIGCg4SFhoeIiYqLjI2Oj5CSk5SVlpiZmpucnZ6foKGio6Smp6ipqqusra6wsbKztLW2uLm6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV19jZ2tvc3d7f4OHi4+Tl5ufo6err7O3u7/Dx8vP09fb3+Pn6+/z9/v8Ht3VSAAAACXBIWXMAAAsSAAALEgHS3X78AAAOsElEQVR4nO3deXgU5R3A8XdJICAiwUQNBjXGVNZi1WKsicUEDdXWYj0Kaq1WK5Zar1oPtNVaK1jwpJUqpVXbiHggmipCKKcaIGrKYdQVAx6NSQwbzsD7f2cnm0CWbNw32cnMzu/7eR6Tzcy8v5nkC7vJwzxRKQAAAAAAAAAAAAAAAAAAAAAAkLrStS6w3i3QUwbq1qRNHb1xz032gzqtdXjNnWlJm4wk2Rc+o+adfZurdFFvpj6ll5bYD+p0qGaL1nN6MwxO2Be+0+Zehl+gL257UKfHKfX9XXtH9WYaHBDzVF+ydOv/Xj7R6q71ZWrwjI3hNVcFlBr2r4Y3f6CrVI6uL66aoMat3Nr4xmhVoD+4pe7jm4vWhCvz24a1L4gsv8/eYodX5Xpa5IPOC/JerK8vH6Hap1mzL6rZ+vIR7nwZ5LHCr6uurm5uC39EeO+LS/Tmg877SN+TF1ioP3llu75ZBZbqLWub7fDhTXrCUdtaF6/SoUCB1jvXW//VhvUL9qyOBedV60dOsTe1hb9Fz4t80GnBwVt2z6/QHw5un5ajW5tX79FPuPaVEMYK38YO/z1de7R6eF6B/VRfousy1dm6sV+J/nho4DE7vJ6SlVFa8VuVuVtnWx2Daon+Z+AsXWvP6lhgPYFc2Da+LfxVemXkg04LbtKzs7MX6Uvap1mzT1Y/0++69YWQpvNTfVZI6/cePK7tNf4G/RcVSXf0zfpRpb5lh9/ez9p00gOLtmqdU6DrlHpGX2sFDdmzOhbEhr9FPxf5oNOCJ9r+vP2ufVqOblDqhOgfITgu5jU+48p5zXrHKDv8jXbHTfrY2/XDkSb2a7y15butDQ/9+H+R8KFIx6s7wncsiA1frqdGPui04B/6kXGW49un2bODhO8rncNf+OQlKqNCX2+FH6NK9SdD1Vjd1O98XXuwmt4Rfob1rdpRe7sI37EgJvw5u/Z+My0vL63Tgin6SaV+OWN0+zTC963O4cv09lde3Lnn26pSLxwdWKQ3Ldhmfa/Wf6PetHpvR/hb9daXP9X6yAPCdyzYP3yoZrO2GmfryDcF+y3IbtDPLdAfDGmfRvi+FfNUf9lbX7WssX4GH78lfL4a8nBti/3jXF5F87uXdYQf/Exz7U3L9dUHhN+3YL/wWresnZJ2YHh1QkXjF0/ldkwjvPcceWGZsp7CK9y+DvSx43bq+6/ZqK9x+zrQ10orG8Jrrw24fRkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5Ll/lpnJbl8wkmNlvplKty8YybHY4ePhUYQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThk+CwTLevwBzhe6fiCJW7fM+uhcPdvhBThO8dnafKnx484MHn3b4QU4TvHSv8ByOVym5y+0JMEb539Bnp889VqnSD2xdiivC9s2TTjsZqVdI42e0LMUX43hpQUKxOG7Pv46Gn2oqGuXdJiSB8spVOs71wr9sX0j3CO+SSX7l9Bd0jfO8E28XuILy/vapb6myxOwjvc0/M7Ho74X2u7LautxNeKMILRXihCC8U4YUivFCEF4rwQhFeKMILRXihCC8U4YUivFCEF4rwQhFeKMILRXihCC8U4YUivFCEF4rwQhFeKMILRXihCC8U4YUivFCEF8pv4bdXmVngyFWnAL+Fb3R4vm8QXijCC0V4oQgvFOGFIrxQhBeK8EIRXijCC0V4oQgvFOGFIrxQhBeK8EIRXijCC0V4oQgvFOGFIrxQhBeK8EIRXijCC0V4oQgvFOGFIrxQhBeK8EIR3gceLUkzXkN4H/j9O5/95ex0szWE94X83/zns7+e099gBeF9IXPinIaaFXUXJL6C8D5we2X43zceq9TYTxNfQ3gf+NvFQ+z3gy9KfA3hfSBjUom64tcDjNYQ3gcerzpZFb8122gN4X3gy3zrTX6T0RrC+0Dt6dab4lqjNYT3gUvrH7314forjdYQ3g9G3vX4PSeZLSG8UIT3gbIV6yPi7A0c0q+LrYT3gY+nnRi0dLXroDs37tSt79+dEbuD8D6wZVDcXX9/vTSrf9aY+Qf8kE94H7jtjrj/Ht90pP1uSH3sDsL7wLLwVxvivMavnWS/m7gmdgfhfSDYpqtdhaF1c2c/W7P51NgdhPeFtOGBOHvSyybdMblsv9tzzp1rWzm1Ty6sxwifgNzFLY2FS49N7OCMYbaf3+DsNfUW4RPw2mMDQ+nTFxqt4aneB7ZlqpDKbulqVzAY5xsAwvvAO+Ot8D98r6tdr+qWOlvsDsL7QGn9vObnvziny31PzOx6DeH9IOvquybldL2r7LautxNeKML7wKo2RmsI7wNFRUXFEysnGK0hvF9kv2t0OOH94tvNRocT3gciL/BVux8xWkN4HyiKCMb7Z5quEV4owvtAXVO7xNcQ3geue+OM4cVv3JSZmZn4GsL7QF3kxrrcT4zWEN4HNpVYb0o2G60hvA9Mbph67dSGOP8aEwfh/eC0GeWPnc2Pc07O96j4N1vGQ3gfMLrZMorwPsDNlkLDd3OzZVyE94FubraMi/A+0N3NlvEQ3g+6udkyHsL7wLujzNcQ3gfumnPAL7z4WoT3gcqmnbXxfwdO1wjvAye2MVpD+JQXzlTq8oNNVxE+5WkrfFOe6SrCpzzCRxA+QYRPefqswsLwjwoLC41WET7l1bczWkV4oQgvFOGFIrxQhBeK8EIRXijCC0V4oQgvFOGFIrxQhBeK8EIRXijCC0V4oQgvFOGFIrxQhBeK8EIRXijCC0V4oQgvFOGFIrxQhBeK8EIRXijCC0V4oQgvFOGFkh6+apyZkYbzPUt6+G3TzFQazvcs6eHFvjQQ3gzh+wjhHUJ4M4TvI4R3COHNEL6PEN4hhDdD+D5CeIcQ3gzh+wjhHUJ4M4TvI4R3COHNpGT4wzIP3EZ4M6kWvuIIlbt8z66Fw2N3EN5MqoXXear86cEDHny+Y8ugfNuNN/btheTkm1lqOJ/wnVnhPxipVHZTx5Zxs2yvT+3bC6mdZabBcD7hO9NnpM8/V6nSDbE7+vqp3mtP3X4Pv2TTjsZqVdI4OXYH4c2kWnilBhQUq9PGHLCZ8GZSL3wchDdD+B4ivEcQ3gzhe4jwHkF4M4TvIcJ7BOHNEL6HCO8RhDdD+B4ivEcQ3gzhe4jwHkF4M4TvIcJ7RG/Dh14347U7agjfQ6n+N5jwPUT4FEV4M4SPInyKIrwZwkcRPkUR3gzhowifoghvhvBRhE9RhDdD+CjCpyjCmyF8FOFTFOHNED6K8CmK8GZ8G3614R01nxuez2shCR8l7W8w4aMI3z3CR3ktDOETRHgzhI/yWhjCJ4jwZggf5bUwhE8Q4c0QPsprYQifIMKbIXyU18IQPkGEN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alt="plot of chunk differencefromfilter"/> </p>
<pre><code class="r">hist(D.mean2, breaks = 10)
</code></pre>
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alt="plot of chunk differencefromfilter"/> </p>
<p>When we compare two histogram of median total number of steps taken per days, we found that higher distribution around steps of 0 indicates that most of the missing values are treated as 0 for median value. Imputing missing values changes the distribution to higher density near 0.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>Add a new column for weekdays with two levels of “weekday” and “weekend”.</p>
<pre><code class="r">D.filter$week <- !(weekdays(D.filter$date, abbreviate = TRUE) %in% c("土",
"日"))
D.filter$week <- factor(D.filter$week, levels = c(TRUE, FALSE), labels = c("weekday",
"weekend"))
head(D.filter)
</code></pre>
<pre><code>## steps date interval week
## 1 0 2012-10-01 0 weekday
## 2 0 2012-10-01 5 weekday
## 3 0 2012-10-01 10 weekday
## 4 0 2012-10-01 15 weekday
## 5 0 2012-10-01 20 weekday
## 6 0 2012-10-01 25 weekday
</code></pre>
<p>The time series plot of 5-minute interval and steps taken all the weekdays and weekends.</p>
<pre><code class="r">library(lattice)
D.aggr <- aggregate(steps ~ interval + week, D.filter, mean)
head(D.aggr)
</code></pre>
<pre><code>## interval week steps
## 1 0 weekday 2.02222
## 2 5 weekday 0.40000
## 3 10 weekday 0.15556
## 4 15 weekday 0.17778
## 5 20 weekday 0.08889
## 6 25 weekday 1.31111
</code></pre>
<pre><code class="r">xyplot(steps ~ interval | week, data = D.aggr, type = "l", xlab = "5-minute interval",
ylab = "average steps taken")
</code></pre>
<p><img 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" alt="plot of chunk weekdayplot"/> </p>
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