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

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<h2>Loading and preprocessing the data</h2>

<p>The raw data was provided as a zip archive, so first we clear the local data
directory and unzip it:</p>

<pre><code class="r">unlink(&quot;data&quot;, recursive = TRUE, force = TRUE)
unzip(&quot;activity.zip&quot;, exdir = &quot;data&quot;)
dir(&quot;data&quot;)
</code></pre>

<pre><code>## [1] &quot;activity.csv&quot;
</code></pre>

<p>The zip file contained a csv file with data, so we load it:</p>

<pre><code class="r">DF &lt;- read.csv(&quot;data/activity.csv&quot;)
str(DF)
</code></pre>

<pre><code>## &#39;data.frame&#39;:    17568 obs. of  3 variables:
##  $ steps   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ date    : Factor w/ 61 levels &quot;2012-10-01&quot;,&quot;2012-10-02&quot;,..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...
</code></pre>

<p>The date field needs to be converted and the interval treated as an ordered factor:</p>

<pre><code class="r">DF$date &lt;- as.Date(DF$date)
DF$interval &lt;- as.ordered(DF$interval)
str(DF)
</code></pre>

<pre><code>## &#39;data.frame&#39;:    17568 obs. of  3 variables:
##  $ steps   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ date    : Date, format: &quot;2012-10-01&quot; &quot;2012-10-01&quot; ...
##  $ interval: Ord.factor w/ 288 levels &quot;0&quot;&lt;&quot;5&quot;&lt;&quot;10&quot;&lt;&quot;15&quot;&lt;..: 1 2 3 4 5 6 7 8 9 10 ...
</code></pre>

<p>Now the data looks good.</p>

<h2>What is mean total number of steps taken per day?</h2>

<p>First, we remove the data that is NA, then sum the steps taken grouped by date:</p>

<pre><code class="r">DFcomplete &lt;-DF[complete.cases(DF), ]
stepsPerDay &lt;- with(DFcomplete, tapply(steps, date, sum))
meanStepsPerDay &lt;- mean(stepsPerDay)
medianStepsPerDay &lt;- median(stepsPerDay)
hist(stepsPerDay, breaks = 10, col = &quot;gray&quot;, main = &quot;Total Steps per Day&quot;,
    xlab = &quot;Total Steps&quot;)
abline(v = meanStepsPerDay, col = &quot;red&quot;)
legend(&quot;topright&quot;, &quot;mean&quot;, pch = &quot;|&quot;, col = &quot;red&quot;)
</code></pre>

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

<p>The mean total number of steps per day is
10766.19 and the median is
10765.</p>

<h2>What is the average daily activity pattern?</h2>

<p>Using the data from above with all NAs removed, we calculate the mean number of
steps taken grouped by interval:</p>

<pre><code class="r">steps &lt;- with(DFcomplete, tapply(steps, interval, mean))
stepsPerInterval &lt;- data.frame(interval = as.numeric(names(steps)), steps)
maxSteps &lt;- stepsPerInterval[which.max(steps), ]
with(stepsPerInterval,
    plot(interval, steps, type = &quot;l&quot;, lwd = 2, main = &quot;Average Daily Activity&quot;,
    xlab = &quot;Interval&quot;, ylab = &quot;Average Steps Taken&quot;, xaxp = c(0, 2400, 6)))
abline(v = maxSteps$interval, col = &quot;red&quot;)
legend(&quot;topright&quot;, &quot;max&quot;, pch = &quot;|&quot;, col = &quot;red&quot;)
</code></pre>

<p><img 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" alt="plot of chunk stepsPerInterval"/> </p>

<p>On average across all days, interval 835 had the most steps
with 206.17.</p>

<h2>Inputing missing values</h2>

<p>Let&#39;s see how many observations have missing values:</p>

<pre><code class="r">incompleteCount &lt;- nrow(DF) - nrow(DFcomplete)
incompleteCount
</code></pre>

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

<p>There are 2304 incomplete observations.</p>

<p>Now, we fill in the missing values using the median number of steps taken for
the interval in question:</p>

<pre><code class="r">medians &lt;- with(DFcomplete, tapply(steps, interval, median))
missingIndex &lt;- is.na(DF$steps)
missingValues &lt;- medians[DF$interval[missingIndex]]
DFpatched &lt;- DF
DFpatched$steps[missingIndex] &lt;- missingValues
</code></pre>

<p>Using the new patched data, we again look at the total number of steps taken
grouped by date:</p>

<pre><code class="r">stepsPerDay &lt;- with(DFpatched, tapply(steps, date, sum))
meanStepsPerDay &lt;- mean(stepsPerDay)
medianStepsPerDay &lt;- median(stepsPerDay)
hist(stepsPerDay, breaks = 10, col = &quot;gray&quot;, main = &quot;Total Steps per Day&quot;,
    sub = &quot;(using patched data)&quot;, xlab = &quot;Total Steps&quot;)
abline(v = meanStepsPerDay, col = &quot;red&quot;)
legend(&quot;topright&quot;, &quot;mean&quot;, pch = &quot;|&quot;, col = &quot;red&quot;)
</code></pre>

<p><img 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" alt="plot of chunk patchedStepsPerDay"/> </p>

<p>For this patched data, the mean total number of steps per day is
9503.87 and the median is
10395.</p>

<p>Compared to the first part of this assignment, the mean and medium have gone
noticeably down. It seems that this change is sensitive to the choice of method
for filling in missing values.  Because we used the median for each interval
and the missing data typically consisted of entire days without observations,
the effect was to add several days consisting entirely of median values. A day
like this consists of 1141 steps, which added several days to the
first bar of the histogram (under 2000 steps) and skewed the mean and median
toward zero.</p>

<h2>Are there differences in activity patterns between weekdays and weekends?</h2>

<p>We add a factor column to our patched data which indicates whether the
observation was for a weekday or weekend:</p>

<pre><code class="r">dowMap &lt;- c(Sun = 2, Mon = 1, Tue = 1, Wed = 1, Thu = 1, Fri = 1, Sat = 2)
dows &lt;-dowMap[weekdays(DFpatched$date, abbreviate = TRUE)]
DFpatched$dow &lt;- factor(dows, labels = c(&quot;weekday&quot;, &quot;weekend&quot;))
str(DFpatched)
</code></pre>

<pre><code>## &#39;data.frame&#39;:    17568 obs. of  4 variables:
##  $ steps   : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ date    : Date, format: &quot;2012-10-01&quot; &quot;2012-10-01&quot; ...
##  $ interval: Ord.factor w/ 288 levels &quot;0&quot;&lt;&quot;5&quot;&lt;&quot;10&quot;&lt;&quot;15&quot;&lt;..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ dow     : Factor w/ 2 levels &quot;weekday&quot;,&quot;weekend&quot;: 1 1 1 1 1 1 1 1 1 1 ...
</code></pre>

<p>Now, we plot the data split by weekday/weekend:</p>

<pre><code class="r">library(lattice)
steps &lt;- with(DFpatched, tapply(steps, list(dow, interval), mean))
stepsPerInterval &lt;- rbind(
    data.frame(interval = as.numeric(names(steps[&quot;weekday&quot;, ])),
        steps = steps[&quot;weekday&quot;, ], dow = &quot;weekday&quot;),
    data.frame(interval = as.numeric(names(steps[&quot;weekend&quot;, ])),
        steps = steps[&quot;weekend&quot;, ], dow = &quot;weekend&quot;))
with(stepsPerInterval, xyplot(steps ~ interval | dow, layout = c(1, 2),
    type = &quot;l&quot;, lwd = &quot;2&quot;, xlab = &quot;Interval&quot;, ylab = &quot;Average Steps Taken&quot;,
    xlim = -50:2450, scales = list(x = list(tick.number = 12))))
</code></pre>

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

<p>From the plot, it appears that the main difference between weekday and weekend
observations is the number of steps taken during the mid-day intervals. These
are higher on weekends and lower on weekdays; this may be a result of typical
work schedules.</p>

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