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<h2>Introduction</h2>
<p>It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.</p>
<p>Here we make use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day.</p>
<h2>Loading and preprocessing the data</h2>
<p>The data is available on [<a href="https://github.com/evgaster/RepData_PeerAssessment1">https://github.com/evgaster/RepData_PeerAssessment1</a>] in the activity.zip file. You need to make that available locally for reading into R and further processing.</p>
<pre><code class="r">unzip("activity.zip")
d <- read.csv("activity.csv")
str(d)
</code></pre>
<pre><code>## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 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 attribute <em>interval</em> seems to be represented as a single number encoding the hours and the minutes into the hour. The encoding looks like interval = hour * 100 + minutes. Later on we need to work with date and interval in various <em>time</em> ways.</p>
<pre><code class="r">d$timestamp <- as.POSIXlt(d$date)
d$timestamp$hour <- d$interval %/% 100
d$timestamp$min <- d$interval %% 100
d$time <- sprintf("%0.2d:%0.2d", d$timestamp$hour, d$timestamp$min)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>The total number of steps taken per day is calculated by summing the value of steps over all intervals of a day. </p>
<p>Note that we are ignoring missing values here. So strickly speeking these are not <em>total number of steps per day</em> but <em>total number of **reported</em>* steps per day*.</p>
<pre><code class="r">totalStepsByDay <- aggregate(steps ~ date, data = d, sum)
</code></pre>
<p>The histogram below gives an impression of how many days (y axis: Frequency) a certain number of steps (x axis: Total of reported steps per day) are reported.</p>
<pre><code class="r">hist(totalStepsByDay$steps,
main = "",
xlab = "Total of reported steps per day"
)
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-4"/> </p>
<pre><code class="r">meanOftotalStepsByDay <- as.integer(round(mean(totalStepsByDay$steps)))
medianOftotalStepsByDay <- as.integer(round(median(totalStepsByDay$steps)))
</code></pre>
<p>The mean of the total steps per day = 10766, its median = 10765.</p>
<h2>What is the average daily activity pattern?</h2>
<p>An other view on the data is the number of steps over the course of the day. The average daily activity pattern is calculated by taking the mean of the number of reported steps per 5-minute interval, over all days.</p>
<pre><code class="r">averageOfStepsByInterval <- aggregate(steps ~ time, data = d, mean)
</code></pre>
<p>The figure below gives an impression of this activity pattern (y axis: Average number of steps) over the day (x axis: Interval time).</p>
<pre><code class="r">averageOfStepsByInterval$timestamp <- as.POSIXct(averageOfStepsByInterval$time, format = "%H:%M")
plot(averageOfStepsByInterval$timestamp, averageOfStepsByInterval$steps,
type = "l",
xlab = "Interval time",
ylab = "Average number of steps"
)
</code></pre>
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" alt="plot of chunk unnamed-chunk-7"/> </p>
<pre><code class="r"># At what time of the day was this person the most active (on average over all days)?
maxAverageOfStepsIntervalTime <- averageOfStepsByInterval$timestamp[which.max(averageOfStepsByInterval$steps)]
maxAverageOfStepsInterval <- format(maxAverageOfStepsIntervalTime, format = "%H:%M")
</code></pre>
<p>The activity has its max at 08:35.</p>
<h2>Imputing missing values</h2>
<p>There are a number of days/intervals where there are missing values.</p>
<pre><code class="r">countOfMissingValues <- sum(is.na(d$steps))
</code></pre>
<p>The number of entries with missing steps value = 2304.</p>
<p>There are many approaches to handling missing values. Here we subsitute a missing value by the mean of the number of reported steps per 5-minute interval, over all days.</p>
<pre><code class="r">m <- merge(d, averageOfStepsByInterval, by = "time")
m$steps.x[is.na(m$steps.x)] <- m$steps.y[is.na(m$steps.x)]
m <- data.frame(m$steps.x, m$date, m$interval, m$timestamp.x, m$time)
names(m) <- names(d)
</code></pre>
<p>The total number of steps taken per day is calculated again by summing the value of steps over all intervals of a day. </p>
<pre><code class="r">totalStepsByDay <- aggregate(steps ~ date, data = m, sum)
</code></pre>
<p>The figure below gives an impression of how many days (y axis: Frequency) a certain number of steps (x axis: Total of steps per day) are reported or <strong>imputed</strong></p>
<pre><code class="r">hist(totalStepsByDay$steps,
main = "With imputing for missing steps data",
xlab = "Total of steps per day"
)
</code></pre>
<p><img 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alt="plot of chunk unnamed-chunk-12"/> </p>
<pre><code class="r">meanOftotalStepsByDay <- as.integer(round(mean(totalStepsByDay$steps)))
medianOftotalStepsByDay <- as.integer(round(median(totalStepsByDay$steps)))
</code></pre>
<p>The mean of the total steps per day = 10766, its median = 10766.
This hardly differs from the values obtained previously (without imputing missing values).</p>
<p>Why this is become immediatly clear when we look at where the values are missing.</p>
<pre><code class="r">table(d[is.na(d$steps),]$date)
</code></pre>
<pre><code>##
## 2012-10-01 2012-10-02 2012-10-03 2012-10-04 2012-10-05 2012-10-06
## 288 0 0 0 0 0
## 2012-10-07 2012-10-08 2012-10-09 2012-10-10 2012-10-11 2012-10-12
## 0 288 0 0 0 0
## 2012-10-13 2012-10-14 2012-10-15 2012-10-16 2012-10-17 2012-10-18
## 0 0 0 0 0 0
## 2012-10-19 2012-10-20 2012-10-21 2012-10-22 2012-10-23 2012-10-24
## 0 0 0 0 0 0
## 2012-10-25 2012-10-26 2012-10-27 2012-10-28 2012-10-29 2012-10-30
## 0 0 0 0 0 0
## 2012-10-31 2012-11-01 2012-11-02 2012-11-03 2012-11-04 2012-11-05
## 0 288 0 0 288 0
## 2012-11-06 2012-11-07 2012-11-08 2012-11-09 2012-11-10 2012-11-11
## 0 0 0 288 288 0
## 2012-11-12 2012-11-13 2012-11-14 2012-11-15 2012-11-16 2012-11-17
## 0 0 288 0 0 0
## 2012-11-18 2012-11-19 2012-11-20 2012-11-21 2012-11-22 2012-11-23
## 0 0 0 0 0 0
## 2012-11-24 2012-11-25 2012-11-26 2012-11-27 2012-11-28 2012-11-29
## 0 0 0 0 0 0
## 2012-11-30
## 288
</code></pre>
<p>This shows that only complete days are missing. Those days get the average of the other days imputed. So they become an average day. And where does an average day ends up in the histogram?</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>To see if the activity pattern differs between the typical work and weekend days we need to make that piece of information explicit.</p>
<pre><code class="r">m$dayType <- "weekday"
m$dayType[weekdays(m$timestamp) %in% c("Saturday", "Sunday")] <- "weekend"
m$dayType <- as.factor(m$dayType)
</code></pre>
<p>The average daily activity pattern is calculated by taking the mean of the number of reported steps per 5-minute interval, over the different types of days.</p>
<pre><code class="r">averageOfStepsByInterval <- aggregate(steps ~ time + dayType, data = m, mean)
</code></pre>
<p>The figure below gives an impression of the activity pattern (y axis: Number of steps) over the day (x axis: Interval) for both weekdays and weekend.</p>
<pre><code class="r">averageOfStepsByInterval$timestamp <- as.POSIXct(averageOfStepsByInterval$time, format = "%H:%M")
library(lattice)
p <- xyplot(steps ~ timestamp | dayType,
averageOfStepsByInterval,
type = "l",
xlab = "Interval",
ylab = "Number of steps",
layout = c(1, 2),
scales = list(x = list(format = "%H:%M"))
)
print(p)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-17"/> </p>
<p>In the weekend the activity is more spread out. On weekdays it peaks in the morning rush hour.</p>
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