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PA1_template.html
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<title>Loading and preprocessing the data</title>
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<h2>Loading and preprocessing the data</h2>
<pre><code class="r">Sys.setlocale(category = "LC_ALL", locale = "en_US.utf8")
</code></pre>
<pre><code>## [1] "LC_CTYPE=en_US.utf8;LC_NUMERIC=C;LC_TIME=en_US.utf8;LC_COLLATE=en_US.utf8;LC_MONETARY=en_US.utf8;LC_MESSAGES=zh_CN.UTF-8;LC_PAPER=zh_CN.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=zh_CN.UTF-8;LC_IDENTIFICATION=C"
</code></pre>
<pre><code class="r">library(dplyr)
library(lattice)
unzip("activity.zip")
activity <- read.csv("activity.csv")
activity_df <- tbl_df(activity)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<pre><code class="r">activity_perday <- summarise(group_by(activity_df, date), steps = sum(steps))
barplot(activity_perday$steps, names.arg = activity_perday$date,
ylab = "Number of Steps",
main = "Total Number of Steps Taken per Day"
)
</code></pre>
<p><img 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" alt="plot of chunk steps_perday"/> </p>
<p>Mean of above distribution is <strong>1.0766 × 10<sup>4</sup></strong> while the meadian is <strong>10765</strong>.</p>
<h2>What is the average daily activity pattern?</h2>
<pre><code class="r">activity_interval <- summarise(group_by(activity_df, interval), steps = mean(steps, na.rm = TRUE))
plot(activity_interval$interval, activity_interval$steps,
type = "l",
ylab = "Number of Steps",
xlab = "Time Intervals",
main = "Average Daily Steps by Time(5 Mins per interval)"
)
</code></pre>
<p><img 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" alt="plot of chunk average_interval"/> </p>
<p>Across all days, the maximum of average number of steps happened on interval <strong>835</strong>, which is <strong>206.1698</strong>.</p>
<h2>Imputing missing values</h2>
<p>The total number of rows with NAs is <strong>2304</strong>.
Use average steps of a given interval to fill the missing value.</p>
<pre><code class="r">activity_filled <- activity_df
for (x in 1:nrow(activity_filled)){
if(is.na(activity_filled[x,]$steps)){
mark <- activity_filled[x,]$interval
activity_filled[x,]$steps <- filter(activity_interval, interval == mark)$steps
}
}
rm(x, mark)
</code></pre>
<p>Below is what the histgram of the new filled dataset.</p>
<pre><code class="r">activity_filled_perday <- summarise(group_by(activity_filled, date), steps = sum(steps))
barplot(activity_filled_perday$steps, names.arg = activity_filled_perday$date,
ylab = "Number of Steps",
main = "Total Number of Steps Taken per Day(Filled)"
)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk filled_data"/> </p>
<p>Mean of above distribution is <strong>1.0766 × 10<sup>4</sup></strong> while the meadian is <strong>1.0766 × 10<sup>4</sup></strong>. There should be no big change from original dataset.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<pre><code class="r">activity_week <- cbind(activity_filled, week = NA)
for (x in 1:nrow(activity_week)){
activity_week[x,]$week <- ifelse(test = (weekdays(strptime(activity_week[x,]$date, format = "%Y-%m-%d"), TRUE) %in% c("Sat", "Sun")),
yes = "weekend",
no = "weekday")
}
rm(x)
activity_week_interval <- summarise(group_by(activity_week, interval, week), steps = mean(steps))
xyplot(steps ~ interval | week, data = activity_week_interval,
layout = c(1, 2),
type = "l",
ylab = "Number of Steps",
xlab = "Time Intervals",
main = "Average Daily Steps by Time(5 Mins per interval)"
)
</code></pre>
<p><img 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" alt="plot of chunk weekend"/> </p>
<p>Above chart suggests there indeed some differences between weekdays and weekend.</p>
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