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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<p>The dataset used for this study is the number of steps that an individual has taken each day, measured in 5 minute intervals, for two months.
The data is in CSV format - dates are converted into a datetime (YYYY-MM-DD) after the data is read in to R.</p>
<pre><code class="r">fileName <- "activity.csv"
data <- read.csv(fileName)
data$date <- as.Date(data$date, "%Y-%m-%d")
</code></pre>
<h2>What is the mean total number of steps taken per day?</h2>
<p>The loaded data is aggregated to get total steps for each day of the two month period.</p>
<pre><code class="r">daily <- aggregate(data$steps, list(data$date), sum)
names(daily) <- c("date", "steps")
</code></pre>
<p>This is then plotted on a histogram to show the distribution of daily steps that the individual has taken.</p>
<pre><code class="r">hist(daily$steps, breaks = 10, xlab = "Steps", main = "Total daily steps", col = "slategrey")
</code></pre>
<p><img 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" alt="plot of chunk daily_hist"/> </p>
<p>The mean and median number of daily steps can be calculated. NAs have been removed.</p>
<pre><code class="r">mean(daily$steps, na.rm = TRUE)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">median(daily$steps, na.rm = TRUE)
</code></pre>
<pre><code>## [1] 10765
</code></pre>
<h2>What is the average daily activity pattern?</h2>
<p>The average number of steps taken at each 5 minute interval across each day of the study is calculated to show a typical pattern of activity.</p>
<pre><code class="r">pattern <- aggregate(data$steps, list(data$interval), mean, na.rm = TRUE)
names(pattern) <- c("interval", "avgSteps")
</code></pre>
<p>This pattern is visualised using a line chart.</p>
<pre><code class="r">plot(pattern$interval, pattern$avgSteps, type = "l", xlab = "interval", ylab = "average steps")
</code></pre>
<p><img 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" alt="plot of chunk pattern_plot"/> </p>
<p>The interval with the highest average number of steps can be found.</p>
<pre><code class="r">pattern$interval[pattern$avgSteps == max(pattern$avgSteps)]
</code></pre>
<pre><code>## [1] 835
</code></pre>
<h2>Imputing missing values</h2>
<p>Some days have missing values and no steps recorded. We can use the average pattern caclulated above to fill in these missing values and assume a “typical” day.</p>
<p>First, the number of records that have missing values are identified.</p>
<pre><code class="r">missing <- data[is.na(data$steps), ]
nrow(missing)
</code></pre>
<pre><code>## [1] 2304
</code></pre>
<p>These missing records are joined to the typical pattern dataset by interval to get an appropriate number of steps.
The replaced values are then appended to the original dataset (minus the missing values).</p>
<pre><code class="r">dfSteps <- merge(missing, pattern, by = "interval")[, c(1, 3, 4)]
colnames(dfSteps)[3] <- "steps"
data2 <- rbind(data[!is.na(data$steps), ], dfSteps[, c("steps", "date", "interval")])
</code></pre>
<p>Now we're interested to see if the new dataset has a different distribution and/or mean and median.
As above, show the distribution of daily steps and calculate the mean and median.</p>
<pre><code class="r">daily2 <- aggregate(data2$steps, list(data2$date), sum)
names(daily2) <- c("date", "steps")
hist(daily2$steps, breaks = 10, xlab = "Steps", main = "Total daily steps",
col = "slategrey")
</code></pre>
<p><img 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" alt="plot of chunk repaired_summary"/> </p>
<pre><code class="r">mean(daily2$steps)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">median(daily2$steps)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<p>As you can see, the “typical” or “average” day that we substituted the missing records for yeilds the average number of steps per day across the two month period.
Therefore, the frequency of the 10000 band has increased for each of the days we replaced with a typical day's steps. Also, both the mean and median reflect the global mean number of steps.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>In order to determine whether there is a difference in activity between weekdays and weekends, the date of each measurement must be flagged as either being a weekday or weekend.
The weekdays() function is used to create a new column in our repaired dataset.</p>
<pre><code class="r">data2$weekend <- ifelse(weekdays(data$date, TRUE) %in% c("Sat", "Sun"), "weekend",
"weekday")
</code></pre>
<p>A typical weekday and weekend were determined using the same method as earlier - taking an average across each day for every interval.</p>
<pre><code class="r">weekend <- aggregate(data2$steps, c(list(data2$weekend), list(data2$interval)),
mean)
names(weekend) <- c("weekend", "interval", "avgSteps")
</code></pre>
<p>The lattice plotting system is used to visualise a typical weekday and weekend. There are no significant differences.</p>
<pre><code class="r">library(lattice)
xyplot(avgSteps ~ interval | weekend, weekend, type = "l", layout = c(1, 2),
ylab = "average steps")
</code></pre>
<p><img 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" alt="plot of chunk weekend_plot"/> </p>
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