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<title>Reproducible Research: Peer Assessment 1</title>
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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<pre><code class="r">library(ggplot2)
library(plyr)
library(lattice)
data <- read.csv("activity.csv")
dailytotals <- aggregate(data$steps, by=list(data$date), FUN=sum)
colnames(dailytotals) <- c("Date","Steps")
dailytotals$Date <- as.Date(dailytotals$Date)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>First, we'll look at a histogram of the total number of steps per day through the two-month period covered by the data set.</p>
<pre><code class="r">par(las=2)
ggplot(dailytotals, aes( x = factor(Date), y = Steps)) + geom_histogram(stat= "identity") + theme(axis.text.x = element_text(size=8, angle=45, hjust=1))
</code></pre>
<pre><code>## Warning: Removed 8 rows containing missing values (position_stack).
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/> </p>
<p>For this two-month period, the mean number of steps is 10766 and the median number of steps is 10765.</p>
<h2>What is the average daily activity pattern?</h2>
<pre><code class="r">goodrows <- complete.cases(data)
data1 <- data[goodrows,]
avgint <- aggregate(data1$steps, by=list(data1$interval), FUN=mean)
colnames(avgint) = c("Interval", "Average")
plot(avgint$Interval, avgint$Average, type="l", col="blue", main="Average Steps per 5-minute Interval Time Series", xlab="Interval", ylab="Interval Average")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-3"/> </p>
<pre><code class="r">indexMax = which(avgint$Average == max(avgint$Average))
intervalMax = avgint[indexMax, "Interval"]
</code></pre>
<p>The 835 time interval is when the maximum average steps occurs in a day across the two-month data set.</p>
<h2>Imputing missing values</h2>
<p>There are 2304 missing values in the data set.</p>
<p>Here is a summary of a new data set where the missing steps values have been replaced with the average of the steps for the respective 5-minute time interval.</p>
<pre><code class="r">summary(datareplace)
</code></pre>
<pre><code>## steps date interval
## Min. : 0.0 2012-10-01: 288 Min. : 0
## 1st Qu.: 0.0 2012-10-02: 288 1st Qu.: 589
## Median : 0.0 2012-10-03: 288 Median :1178
## Mean : 37.4 2012-10-04: 288 Mean :1178
## 3rd Qu.: 27.0 2012-10-05: 288 3rd Qu.:1766
## Max. :806.0 2012-10-06: 288 Max. :2355
## (Other) :15840
</code></pre>
<p>Histogram of the total number of steps taken each day.</p>
<pre><code class="r">dailytotals2 <- aggregate(datareplace$steps, by=list(datareplace$date), FUN=sum)
colnames(dailytotals2) <- c("Date","Steps")
dailytotals2$Date <- as.Date(dailytotals2$Date)
</code></pre>
<pre><code class="r">par(las=2)
ggplot(dailytotals2, aes( x = factor(Date), y = Steps)) + geom_histogram(stat= "identity") + theme(axis.text.x = element_text(size=8, angle=45, hjust=1))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-7"/> </p>
<pre><code class="r">meanDaily2 <- mean(dailytotals2$Steps)
medianDaily2 <- median(dailytotals2$Steps)
</code></pre>
<p>For this two-month period, using the data set where missing values have been replaced by their respective interval averages, the mean number of steps is 10766 and the median number of steps is 10766. The mean and median values have been rounded to an integer, and as these values show, the difference in mean and median between the original and the replacement data sets is very small. The median value increased by 1 or only by 0.0093 percent. </p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<pre><code class="r">datareplace$partOfWeek <- ifelse(weekdays(as.Date(datareplace$date), abbreviate=TRUE) %in% c("Sat", "Sun"), "weekend", "weekday")
avgint2 <- aggregate(datareplace$steps, by=list(datareplace$interval, datareplace$partOfWeek), FUN=mean)
colnames(avgint2) = c("Interval", "PartOfWeek", "Average")
xyplot(Average ~ Interval | PartOfWeek, data=avgint2, layout=c(1,2), xlab="Interval", ylab="Number of steps", type="l")
</code></pre>
<p><img 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" 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