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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">#it is assumed that 'activity.zip' file is in working directory
unzip(zipfile="activity.zip")
data <- read.csv("activity.csv", na.strings = "NA", header = TRUE)
#tranform date variable to the date data type
data$date = as.Date(as.character(data$date), "%Y-%m-%d")
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<pre><code class="r">#calculate the number of steps taken per day
daysteps<-tapply(data$steps, data$date, sum, na.rm=TRUE)
#create histogram
par(mar=c(5,4,2,2))
hist(daysteps, xlab="Number of steps per day", main=NULL)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">mean1<-mean(daysteps)
print(mean1)
</code></pre>
<pre><code>## [1] 9354
</code></pre>
<pre><code class="r">median1<-median(daysteps)
print(median1)
</code></pre>
<pre><code>## [1] 10395
</code></pre>
<p>The mean of total number of steps taken per day was <em>9354.2295</em>, and the median total number of steps taken per day was <em>10395</em>.</p>
<h2>What is the average daily activity pattern?</h2>
<pre><code class="r">#Make a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
data2<-aggregate(steps~interval, data=data, FUN=mean, na.rm=TRUE)
par(mar=c(5,4,2,2))
plot(data2$interval, data2$steps, type='l', ylab= "Average No of Steps", xlab= "Five-minute time interval")
</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">#Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
maxinterval<-data2[data2$steps==max(data2$steps),'interval']
maxsteps<-data2[data2$steps==max(data2$steps),'steps']
</code></pre>
<p>The <em>835</em>-th five-minute interval contained the maximal <em>206.1698</em> average number of steps.</p>
<h2>Imputing missing values</h2>
<p>There are a number of days/intervals where there are missing values (coded as NA).
The presence of missing days may introduce bias into some calculations or summaries of the data.</p>
<pre><code class="r">#Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
NoMissing <- is.na(data$steps)
totNA <- sum(NoMissing)
</code></pre>
<p>The 2304 number of missing values were substitued using the mean value of particular five-minute interval.
The new dataset that is equal to the original dataset but with the missing data filled in was created.
I found <a href="http://stackoverflow.com/questions/20273070/function-to-impute-missing-value/">here</a>, how to do that, in easy way.
Then the histogram of the total number of steps taken each day was created and
the mean and median total number of steps taken per day was calculated.</p>
<pre><code class="r">cleandata<-data
cleandata$steps[is.na(cleandata$steps)] <- ave(cleandata$steps, cleandata$interval,
FUN = function(x)
mean(x, na.rm = TRUE))[c(which(is.na(cleandata$steps)))]
a<-tapply(cleandata$steps,cleandata$date,sum)
par(mar=c(5,4,2,2))
hist(a, xlab="Number of steps per day", main=NULL)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p>
<pre><code class="r">mean(a)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">median(a)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<p>We can see that these values differ from the estimates calculated in the first part of the assignment. They are both higher now. Moreover, the distribution of the data is much symmetrical now, since the median and mean values are identical.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<pre><code class="r">#since I'm using non english system I overwrite local language settings to get the days in english.
Sys.setlocale("LC_TIME", "English")
</code></pre>
<pre><code class="r">cleandata$weekend<-sapply(cleandata$date,function(x){
if (weekdays(x)=="Sunday" | weekdays(x) == "Saturday") {"weekend"}
else {"weekday"}
})
data3<-aggregate(steps~interval+weekend, data=cleandata, FUN=mean)
require(lattice)
xyplot(steps~interval | weekend, type='l', data=data3, layout = c(1, 2), ylab= "Average No of Steps", xlab= "Five-minute time interval")
</code></pre>
<p><img src="data:image/png;base64,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" 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