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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>
<p>We have the data already present, but in zipped form,
so it will have to be unzipped first.
Let's do that in a directory “tmp”.</p>
<pre><code class="r">filezip <- "activity.zip"
filecsv <- "activity.csv"
tmpdir <- "tmp"
if (!file.exists(filecsv)) unzip(filezip, exdir = tmpdir, file = filecsv)
filecsv <- paste(tmpdir, filecsv, sep = "/")
</code></pre>
<p>The data is now in file tmp/activity.csv, so let's get it into a data table (for easier processing by group). Curiously the colClasses argument does not seem to do anything?</p>
<pre><code class="r">if (!("data.table" %in% rownames(installed.packages()))) install.packages("data.table")
library(data.table)
data <- fread(filecsv, colClasses = c("integer", "Date", "integer"), na.strings="NA")
data$date <- as.Date(data$date)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>As a first approximation, we'll just ignore it when values are not available, effectively taking them to be 0.</p>
<pre><code class="r">total_per_day <- tapply(data$steps, data$date, sum, na.rm = TRUE)
median_total_per_day <- median(total_per_day)
mean_total_per_day <- mean(total_per_day)
hist(total_per_day)
abline(v = median_total_per_day)
abline(v = mean_total_per_day, col = "blue")
</code></pre>
<p><img 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alt="plot of chunk unnamed-chunk-3"/> </p>
<p>The median (black) and the mean (blue) are 10395 and 9354.2295.</p>
<h2>What is the average daily activity pattern?</h2>
<p>Here the na.rm values are ignored, but in the context of average
that means that they are assumed to have that same average value.</p>
<pre><code class="r">average_daily_activity <- data[, mean(steps, na.rm = TRUE), by=interval]
max_average_daily_activity <- max(average_daily_activity$V1)
where_max_average_daily_activity <- data[which.max(average_daily_activity$V1)]$interval
plot(average_daily_activity, type="l")
abline(h = max_average_daily_activity)
abline(v = where_max_average_daily_activity)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>The maximum average daily activity is 206.1698 at 835. It seems unfortunate that min() does not provide both values at once…</p>
<h2>Imputing missing values</h2>
<pre><code class="r">num_total <- nrow(data)
num_complete <- sum(complete.cases(data))
num_incomplete <- num_total - num_complete
</code></pre>
<p>Unfortunately there are those missing values: 2304 out of 17568.</p>
<p>It's easy enough to add a column where the missing values are replaced by the average
for that interval and redo the histogram calculations based on that:</p>
<pre><code class="r">data[, imputed := ifelse(is.na(steps), mean(steps, na.rm = TRUE), steps), by=interval]
</code></pre>
<pre><code class="r">total_imputed_per_day <- tapply(data$imputed, data$date, sum)
median_total_imputed_per_day <- median(total_imputed_per_day)
mean_total_imputed_per_day <- mean(total_imputed_per_day)
hist(total_imputed_per_day)
abline(v = median_total_imputed_per_day)
abline(v = mean_total_imputed_per_day, col = "blue")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-7"/> </p>
<p>Compare the medians: 10395 and 1.0766 × 10<sup>4</sup>.</p>
<p>Compare the means: 9354.2295 and 1.0766 × 10<sup>4</sup>. Shouldn't they be the same?</p>
<p>I don't immediately know what to make of that.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>As a first solution let's compute some needed values in new columns, although that does not seem very elegant.</p>
<pre><code class="r">data[, sort_of_day := ifelse((weekdays(date) %in% c("Saturday", "Sunday")), "weekend", "weekday")]
data[, mean_imputed_per_interval_and_sort_of_day := mean(imputed), by="interval,sort_of_day"]
</code></pre>
<pre><code class="r">library(lattice)
xyplot(data$mean_imputed_per_interval_and_sort_of_day ~ data$interval | data$sort_of_day,
type = "l", layout = c(1, 2), xlab = "Interval", ylab = "Number of steps")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-9"/> </p>
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