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<h1>Peer Assignment 1 </h1>
<pre><code class="r">setwd("C:/Users/ece.o/OneDrive/Coursera/Johns Hopkins University Data Science/Reproducible Research/RepData_PeerAssessment1")
</code></pre>
<h2>Loading and preprocessing the data</h2>
<pre><code class="r">data <- read.csv("activity.csv")
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<ol>
<li>Total number of steps taken per day</li>
</ol>
<pre><code class="r">dailySteps <- tapply(data[,1], data[,2], sum, na.rm = TRUE)
</code></pre>
<ol>
<li>Histogram of the total number of steps taken each day</li>
</ol>
<pre><code class="r">hist(dailySteps)
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-4"/> </p>
<ol>
<li>Mean of the total number of steps taken per day is 9354 and median is 10395</li>
</ol>
<h2>What is the average daily activity pattern?</h2>
<ol>
<li>Time series plot of the 5-minute interval and the average number of steps taken, averaged across all days</li>
</ol>
<pre><code class="r">intSteps <- tapply(data[,1], data[,3], mean, na.rm = TRUE)
plot(names(intSteps), intSteps, type = "l", xlab = "5-Minute Interval", ylab = "# of Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p>
<ol>
<li>Interval 835 contains the maximum number of steps.</li>
</ol>
<h2>Imputing missing values</h2>
<ol>
<li>The total number of rows with NAs is 2304.</li>
<li>Fill missing data with interval mean</li>
</ol>
<pre><code class="r">filled <- data
for (i in 1:nrow(filled)) {
if (is.na(data[i,1])) {
filled[i, 1] = intSteps[names(intSteps) == filled[i, 3]]
}
}
</code></pre>
<ol>
<li></li>
</ol>
<pre><code class="r">dailySteps2 <- tapply(filled[,1], filled[,2], sum)
hist(dailySteps2)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-7"/> </p>
<p>Mean of the total number of steps taken per day is 10766 and median is 10766, too.</p>
<p>The frequencies in the middle part of the histogram increased because the empty values are now replaced with average values. The mean and median are slightly higher, but not too high that can distort the analysis in a bad way.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<ol>
<li>Convert to PosIX first, then apply <code>weekdays()</code>.</li>
</ol>
<pre><code class="r">filled$weekday <- as.factor(weekdays(as.POSIXct(filled[,2])))
filled$weekDE <- ifelse((filled[,4] == "Saturday") | (filled[,4] == "Sunday"), "weekend", "weekday")
filled$weekDE <- as.factor(filled$weekDE)
</code></pre>
<ol>
<li>Group the data using <code>data.table</code>.</li>
</ol>
<pre><code class="r">library(data.table)
filled <- data.table(filled)
weekMean <- filled[, lapply(.SD, mean), by = "interval,weekDE"]
</code></pre>
<p>Create line chart.</p>
<pre><code class="r">library(lattice)
xyplot(steps ~ interval|weekDE,
data = weekMean,
type = "l",
xlab = "Interval",
ylab = "Number of steps",
layout=c(1,2))
</code></pre>
<p><img 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" 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