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<h1>Peer assignment 1 </h1>
<p>This assignment is about analyzing the data from a fitbit.</p>
<h3>Load data</h3>
<pre><code class="r">df <- read.csv("activity.csv")
</code></pre>
<h3>Pre process data</h3>
<pre><code class="r">total_steps <- with(df, aggregate(steps, by = list(date), sum, na.rm =TRUE))
names(total_steps) <- c("date", "total.steps")
</code></pre>
<h3>What is mean total number of steps taken per day?</h3>
<h3>1. Make a histogram of the total number of steps taken each day</h3>
<pre><code class="r">hist(total_steps$total.steps, col = "green", xlab = "Total steps taken each day", ylab = "Frequency", main = "Total steps taken each day")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk histogram"/> </p>
<h3>2. Calculate and report the mean and median total number of steps taken per day</h3>
<pre><code class="r">mean<- mean(total_steps$total.steps)
median <- median(total_steps$total.steps)
</code></pre>
<h3>Mean total number of steps each day is 9354.2295082 and median is 10395</h3>
<h3>3. What is the average daily activity pattern?</h3>
<ol>
<li>Make a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)</li>
</ol>
<pre><code class="r">library(ggplot2)
</code></pre>
<pre><code>## Warning: package 'ggplot2' was built under R version 3.1.1
</code></pre>
<pre><code class="r">mean_steps <- with(df, aggregate(steps, by = list(interval), mean, na.rm = TRUE))
names(mean_steps)<- c("interval", "steps")
qplot(interval, steps, geom = "line", data = mean_steps, xlab = "Time interval", ylab = "Avg number of steps across all days")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Mean steps"/> </p>
<ol>
<li>Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?</li>
</ol>
<pre><code class="r">max_interval_df <- subset(mean_steps, mean_steps$steps == max(mean_steps$steps))
maxInterval<- max_interval_df[[1]]
</code></pre>
<p>Max number of steps across all days is during 835th interval.</p>
<h3>4. Imputing missing values</h3>
<p>Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.</p>
<ol>
<li>Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)</li>
</ol>
<pre><code class="r">num_na <- sum(is.na(df))
</code></pre>
<p>Number of NA rows is 2304</p>
<ol>
<li>Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.</li>
</ol>
<p>Strategy is to fill the missing values with the median steps for the respective interval across all days. </p>
<pre><code class="r">msteps <- with(df, aggregate(steps, by = list(interval), median, na.rm = TRUE))
names(msteps) <- c("interval", "steps")
mergeddf<- merge(df, msteps, by = "interval")
mergeddf<- mergeddf[order(mergeddf$date),]
</code></pre>
<ol>
<li>Create a new dataset that is equal to the original dataset but with the missing data filled in.</li>
</ol>
<pre><code class="r">for ( i in 1:nrow(mergeddf))
{
if(is.na(mergeddf[i, ]$steps.x))
mergeddf[i,]$steps.x = mergeddf[i,]$steps.y
}
</code></pre>
<ol>
<li>Make a histogram of the total number of steps taken each day and Do these values differ from the estimates from the first part of the assignment? </li>
</ol>
<pre><code class="r">total_steps<- aggregate(mergeddf$steps.x, by = list(mergeddf$date), sum)
names(total_steps)<- c("interval", "steps")
hist(total_steps$steps, col = "green", xlab = "Total steps taken each day", ylab = "Frequency", main = "Total steps taken each day with median missing values")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>Calculate and report the mean and median total number of steps taken per day.</p>
<pre><code class="r">mean<- mean(total_steps$steps)
median <- median(total_steps$steps)
</code></pre>
<h3>Mean total number of steps each day is 9503.8688525 and median is 10395.</h3>
<p>What is the impact of imputing missing data on the estimates of the total daily number of steps? </p>
<ul>
<li>Mean value differes slightly but median is the same since we used the median value for imputing missing values.<br/></li>
</ul>
<h3>Are there differences in activity patterns between weekdays and weekends?</h3>
<p>For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.</p>
<ol>
<li>Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.</li>
</ol>
<pre><code class="r">mergeddf$day <- weekdays(as.Date(mergeddf$date))
</code></pre>
<ol>
<li>Make a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis). </li>
</ol>
<pre><code class="r">par(mfrow=c(2,1))
week_days <- subset(mergeddf, mergeddf$day %in% c("Monday","Tuesday","Wednesday","Thursday","Friday"))
weekday_steps <- with(week_days, aggregate(steps.x, by = list(interval), mean))
weekend_days <- subset(mergeddf, mergeddf$day %in% c("Saturday","Sunday"))
weekend_steps <- with(weekend_days, aggregate(steps.x, by = list(interval), mean))
names(weekday_steps)<- c("interval", "avg_steps")
names(weekend_steps)<- c("interval", "avg_steps")
plot(weekday_steps$interval, weekday_steps$avg_steps , type = "l", xlab = "Time interval", ylab = "Avg number of steps across week days", col = "green")
plot(weekend_steps$interval, weekend_steps$avg_steps , type = "l", xlab = "Time interval", ylab = "Avg number of steps across weekend days", col = "red")
</code></pre>
<p><img 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" alt="plot of chunk multiple_plots"/> </p>
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