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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<p>Unzip data file:</p>
<pre><code class="r">unzip("activity.zip")
</code></pre>
<p>Load csv file:</p>
<pre><code class="r">library(data.table)
setwd("C:\\Users\\Igor\\git\\RepData_PeerAssessment1")
d <- read.csv("activity.csv", colClasses = c("integer", "Date", "integer"))
</code></pre>
<p>Convert data to data.table</p>
<pre><code class="r">dt <- data.table(d)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>Histogram of the total number of steps taken each day:</p>
<pre><code class="r">ccase <- complete.cases(dt)
dt_complete <- dt[ccase]
steps_per_day <- dt_complete[, sum(steps), by = date]
hist(steps_per_day$V1, main = "Total number of steps taken each day", xlab = "Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>Mean of total number of steps taken per day:</p>
<pre><code class="r">mean1 <- mean(steps_per_day$V1)
mean1
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<p>Median of total number of steps taken per day:</p>
<pre><code class="r">median1 <- median(steps_per_day$V1)
median1
</code></pre>
<pre><code>## [1] 10765
</code></pre>
<h2>What is the average daily activity pattern?</h2>
<p>Time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis):</p>
<pre><code class="r">avg_steps_per_interval <- dt_complete[, mean(steps), by = interval]
with(avg_steps_per_interval, plot(interval, V1, type = "l"))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-7"/> </p>
<p>This interval, on average across all the days in the dataset, contains the maximum number of steps:</p>
<pre><code class="r">with(avg_steps_per_interval, interval[which.max(V1)])
</code></pre>
<pre><code>## [1] 835
</code></pre>
<h2>Imputing missing values</h2>
<p>Total number of missing values in the dataset:</p>
<pre><code class="r">sum(!ccase)
</code></pre>
<pre><code>## [1] 2304
</code></pre>
<p>To fill missing values we will use mean for each 5-minute interval:</p>
<pre><code class="r">dtm <- merge(dt[!ccase], avg_steps_per_interval, by = "interval")
</code></pre>
<p>Create a new dataset that is equal to the original dataset but with the missing data filled in:</p>
<pre><code class="r">dts <- data.table(steps = round(dtm$V1), date = dtm$date, interval = dtm$interval)
dt_filled <- rbind(dts, dt_complete)
</code></pre>
<p>Histogram of the total number of steps taken each day:</p>
<pre><code class="r">steps_per_day_filled <- dt_filled[, sum(steps), by = date]
hist(steps_per_day_filled$V1)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p>
<p>Mean of total number of steps taken per day:</p>
<pre><code class="r">mean2 <- mean(steps_per_day_filled$V1)
mean2
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<p>Median of total number of steps taken per day:</p>
<pre><code class="r">median2 <- median(steps_per_day_filled$V1)
median2
</code></pre>
<pre><code>## [1] 10762
</code></pre>
<p>New mean and median are <strong>different</strong>.</p>
<p>The imapct for mean is <strong>0.5493</strong> and for median is <strong>3</strong></p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>Calculate average number of steps per interval per weekend/weekday:</p>
<pre><code class="r">wd <- weekdays(dt_filled$date, abbreviate = T)
dt_filled$weekpart <- factor(wd == "Sat" | wd == "Sun", labels = c("weekday",
"weekend"))
avg_steps_per_interval_weekpart <- dt_filled[, mean(steps), by = list(interval,
weekpart)]
</code></pre>
<p>Panel plot containing 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 weekday days or weekend days (y-axis):</p>
<pre><code class="r">library(lattice)
xyplot(V1 ~ interval | weekpart, data = avg_steps_per_interval_weekpart, layout = c(1,
2), type = "l", xlab = "Interval", ylab = "Number of steps")
</code></pre>
<p><img 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" 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