RepData_PeerAssessment1/PA1_template.html at master · ale210/RepData_PeerAssessment1 · GitHub

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<body>
<p>Download the data if it doesn&#39;t exist already</p>

<pre><code class="r">zipFilename &lt;- &quot;activity.zip&quot;
if(!file.exists(zipFilename)) {
    download.file(&quot;https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip&quot;, zipFilename, method=&quot;curl&quot;)
}

uncompressedFilename &lt;- &quot;activity.csv&quot;
if(!file.exists(uncompressedFilename)){
    unzip(zipFilename)
}

rawData &lt;- read.csv(uncompressedFilename, stringsAsFactors=FALSE)
</code></pre>

<p>transform $date to the proper data format:</p>

<pre><code class="r">rawData &lt;- read.csv(&quot;activity.csv&quot;, stringsAsFactors=FALSE)
rawData$date &lt;- as.Date(rawData$date)
</code></pre>

<p>compute the total # of steps per day:</p>

<pre><code class="r">totalStepsPerDay &lt;- aggregate(steps ~ date, data = rawData, FUN=&quot;sum&quot;)
head(totalStepsPerDay)
</code></pre>

<pre><code>##         date steps
## 1 2012-10-02   126
## 2 2012-10-03 11352
## 3 2012-10-04 12116
## 4 2012-10-05 13294
## 5 2012-10-06 15420
## 6 2012-10-07 11015
</code></pre>

<p>make a histogram of the total number of steps taken each day:</p>

<pre><code class="r">hist(totalStepsPerDay$steps, main=&quot;Total steps taken in a day&quot;, xlab=&quot;Steps&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>

<p>calculate mean/median of the number of steps per day</p>

<pre><code class="r">steps_mean &lt;- format(mean(totalStepsPerDay$steps), digits=7)
steps_mean_formatted &lt;- format(steps_mean, digits=7)
steps_median &lt;- median(totalStepsPerDay$steps)
</code></pre>

<p>the mean number of steps per day is 10766.19<br/>
the median number of steps per day is 10765</p>

<p>compute the average # of steps per interval, averaged across all days:</p>

<pre><code class="r">averageStepsPerInterval &lt;- aggregate(steps ~ interval, data = rawData, FUN=&quot;mean&quot;)
head(averageStepsPerInterval)
</code></pre>

<pre><code>##   interval     steps
## 1        0 1.7169811
## 2        5 0.3396226
## 3       10 0.1320755
## 4       15 0.1509434
## 5       20 0.0754717
## 6       25 2.0943396
</code></pre>

<p>plot:</p>

<pre><code class="r">plot(averageStepsPerInterval$interval, averageStepsPerInterval$steps, type=&quot;l&quot;, main=&quot;Average number of steps for interval&quot;, xlab=&quot;interval (minutes)&quot;, ylab=&quot;Number of steps&quot;)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-7"/> </p>

<pre><code class="r">max_steps &lt;- max(averageStepsPerInterval$steps)
max_interval &lt;- averageStepsPerInterval[averageStepsPerInterval[,2] == max_steps,]
max_interval_num &lt;- max_interval[1]
max_interval_mean_steps &lt;- max_interval[2]
</code></pre>

<p>The inteval with the maximum number of steps on average is interval 835, with an average of 206.1698113 steps</p>

<p>Calculate the number of rows with missing data:</p>

<pre><code class="r">num_data_with_na &lt;- length(which(is.na(rawData$steps)))
num_data_with_na
</code></pre>

<pre><code>## [1] 2304
</code></pre>

<p>Create a new dataset with the missing data imputed as the average # of steps for that
interval, averaged across all days</p>

<pre><code class="r">dataWithIntervalMeans &lt;- merge(rawData, averageStepsPerInterval, by=&quot;interval&quot;)
dataWithIntervalMeans$stepsImputed &lt;- ifelse(
    is.na(dataWithIntervalMeans$steps.x),
    dataWithIntervalMeans$steps.y,
    dataWithIntervalMeans$steps.x)
</code></pre>

<pre><code class="r">totalStepsPerDay &lt;- aggregate(dataWithIntervalMeans$stepsImputed ~ date, data = dataWithIntervalMeans, FUN=&quot;sum&quot;)
names(totalStepsPerDay)[2] &lt;- &quot;totalSteps&quot;
hist(totalStepsPerDay$totalSteps, main=&quot;Total steps taken in a day (imputing missing data)&quot;, xlab=&quot;Steps&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p>

<pre><code class="r">imputed_mean &lt;- format(mean(totalStepsPerDay[,2]), digits=10)
imputed_median &lt;- format(median(totalStepsPerDay[,2]), digits=10)
</code></pre>

<p>the mean number of steps per day is 10766.18868<br/>
the median number of steps per day is 10766.18868
Interestingly, they are the same.</p>

<p>Create a new factor varible that indicates if the date is a weekend</p>

<pre><code class="r">weekend_days &lt;- c(&quot;Saturday&quot;, &quot;Sunday&quot;)
dataWithIntervalMeans$isWeekend &lt;- factor(
    weekdays(dataWithIntervalMeans$date) %in% weekend_days,
    labels=c(&quot;Weekday&quot;, &quot;Weekend&quot;))
sums &lt;- aggregate(
    dataWithIntervalMeans$stepsImputed,
    list(
        isWeekend = dataWithIntervalMeans$isWeekend,
        interval=dataWithIntervalMeans$interval),
    mean)
</code></pre>

<p>Now let&#39;s analyse the data with that separation</p>

<pre><code class="r">library(lattice)
xyplot(
  x ~ interval | isWeekend,
  data=sums,
  type=&quot;l&quot;,
  ylab=&quot;Number of Steps&quot;,
  layout=c(1,2))
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-14"/> </p>

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