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<p>#Reproducible Research - Peer Assessment 1</p>
<p>##How to start and quick overview</p>
<ol>
<li>Download the data (“repdata_data_activity.zip”) from the link in the description of this project into the WD and unzip it in order to yield the CSV-file “activity.csv”</li>
<li>Read the data into a variable in R:</li>
</ol>
<pre><code class="r">activity <- read.csv("activity.csv")
</code></pre>
<ol>
<li><p>The variable “activity” should contain now 17568 rows/observations and 3 columns called “steps”, “date” and “interval”. Each of the 61 unique days is divided into 288 intervals of 5-minutes and in each of those intervals the number of steps is either counted or not-available (NA).</p></li>
<li><p>The format of the column “interval” is a bit problematic, since it's just hour and minute glued together, i.e. 8:40am - 8:45am would become “840” and 1:15pm - 1:20pm would become “1315”. Since later a time series should be produced, there would always be strange looking “gaps” when a new hour begins, since e.g. “800” follows “755”. I introduce a new column interval_minutes, which contains the n-th minute of each day, from 0 to 1435 (=24 * 60 - 5 = 1440 - 5).</p></li>
</ol>
<pre><code class="r">days <- unique(activity$date)
interval_minutes <- seq(0,length(unique(activity$interval)) - 1) * 5
activity <- cbind(activity,interval_minutes)
</code></pre>
<p>##What is mean total number of steps taken per day?</p>
<ol>
<li>creating a vector (“sum_steps_per_day”) containing the total number of steps taken on each day. If there is one NA value occurring during one day, the sum of steps during this day will result in NA:</li>
</ol>
<pre><code class="r">sum_steps_per_day <- rep(-1,length(days))
for (i in 1:length(days)){
sum_steps_per_day[i] <- sum(activity$steps[activity$date == days[i]], na.rm = F)
}
</code></pre>
<ol>
<li>Making a histogram of the sum of the steps per day. NA values in the vector will not contribute to the histogram</li>
</ol>
<pre><code class="r">hist(sum_steps_per_day, main = "", breaks = 8, xlab = "Sum of steps on one day")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p>
<ol>
<li>Print mean and median of the number of steps taken per day, ignoring NA values:</li>
</ol>
<pre><code class="r">paste("Mean:",as.character(round(mean(sum_steps_per_day,na.rm = T),2)))
</code></pre>
<pre><code>## [1] "Mean: 10766.19"
</code></pre>
<pre><code class="r">paste("Median:",as.character(median(sum_steps_per_day,na.rm = T)))
</code></pre>
<pre><code>## [1] "Median: 10765"
</code></pre>
<p>##What is the average daily activity pattern?</p>
<ol>
<li>In order to produce a time series averaged over all days of the step values in each of the time intervals, I create a vector of length 288 containing the averaged number of steps taken in each interval. The NA values are not taken into account in this case:</li>
</ol>
<pre><code class="r">intervals <- unique(activity$interval_minutes)
average_per_interval <- rep(-1.,length(unique(activity$interval_minutes)))
for (i in 1:length(unique(activity$interval_minutes))) {
average_per_interval[i] <- mean(activity$steps[activity$interval_minutes == intervals[i]], na.rm = T)
}
</code></pre>
<ol>
<li>Make time series plot of steps in corresponding intervals across all days:</li>
</ol>
<pre><code class="r">plot(x=intervals,y=average_per_interval, type = "l", xlab = "# 5-minute interval of a day", ylab = "average number of steps")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-8"/> </p>
<ol>
<li>Find the 5-minute interval in which the maximum number of steps occurs on average:</li>
</ol>
<pre><code class="r">intervals[which.max(average_per_interval)]
</code></pre>
<pre><code>## [1] 515
</code></pre>
<p>So the 5-minute interval where on average the maximum number of steps are made is from 8:35am to 8:40am.</p>
<p>##Imputing missing values</p>
<ol>
<li>find out number of rows with one or more NA values:</li>
</ol>
<pre><code class="r">paste("Number of Rows with NA:",as.character(sum(is.na(activity$steps) | is.na(activity$interval) | is.na(activity$interval_minutes) | is.na(activity$date))))
</code></pre>
<pre><code>## [1] "Number of Rows with NA: 2304"
</code></pre>
<ol>
<li><p>the strategy of filling in the NA values is to fill in the mean value of the corresponding 5-minute interval.</p></li>
<li><p>creating a new dataset “completed_activity” that is equal to the original dataset but with the missing values filled in:</p></li>
</ol>
<pre><code class="r">completed_activity <- activity
na_vec <- is.na(activity$steps) | is.na(activity$interval) | is.na(activity$interval_minutes) | is.na(activity$date)
intervals_na <- completed_activity$interval_minutes[na_vec]
indices_intervals_na <- intervals_na / 5 + 1
average_values_na <- average_per_interval[indices_intervals_na]
completed_activity$steps[na_vec] <- average_values_na
</code></pre>
<ol>
<li>Histogram of total number of steps taken each day calculated from new dataset:</li>
</ol>
<pre><code class="r">sum_steps_per_day_new <- rep(-1,length(days))
for (i in 1:length(days)){
sum_steps_per_day_new[i] <- sum(completed_activity$steps[completed_activity$date == days[i]])
}
hist(sum_steps_per_day_new, main = "", breaks = 8, xlab = "Sum of steps on one day from completed dataset")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-12"/> </p>
<ol>
<li>Print mean and median of the number of steps taken per day from the new dataset:</li>
</ol>
<pre><code class="r">paste("Mean:",as.character(round(mean(sum_steps_per_day_new),2)))
</code></pre>
<pre><code>## [1] "Mean: 10766.19"
</code></pre>
<pre><code class="r">paste("Median:",as.character(round(median(sum_steps_per_day_new),2)))
</code></pre>
<pre><code>## [1] "Median: 10766.19"
</code></pre>
<p>The mean did not change but the median did slightly. Originally the median could only have an integer value, but due to the introduction of mean values in an interval across the days in the “step”-column, this can and did occur.</p>
<p>##Are there differences in activity patterns between weekdays and weekends?</p>
<ol>
<li>Add a new column to the dataset containing info if it is a weekday or a day of the weekend:</li>
</ol>
<pre><code class="r">weekday <- weekdays(as.Date(completed_activity$date))
weekdayorend <- factor(ifelse(weekday == "Saturday" | weekday == "Sunday", "weekend", "weekday"))
completed_activity <- cbind(completed_activity,weekdayorend)
</code></pre>
<ol>
<li>Aggregate the data by day type and 5-minute interval to create average number of steps per 5-minute interval. Create a panel plot with the resulting two time series of averaged steps in corresponding 5-minute intervals across the days:</li>
</ol>
<pre><code class="r">mean_steps <- aggregate(completed_activity$steps, by = list(completed_activity$interval_minutes, completed_activity$weekdayorend), FUN = mean)
names(mean_steps) <- c("interval_minutes","weekdayorend","steps")
library(lattice)
xyplot(steps ~ interval_minutes | weekdayorend, data = mean_steps,type = "l",layout = c(1,2), xlab = "# 5-minute interval of a day",ylab = "average number of steps")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-15"/> </p>
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