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<h2>Loading and preprocessing the data</h2>
<ol>
<li>Load the data (i.e. <code>read.csv()</code>)</li>
</ol>
<pre><code class="r">activity<-read.csv("activity.csv",stringsAsFactors=FALSE)
</code></pre>
<ol>
<li>Process/transform the data (if necessary) into a format suitable for your analysis</li>
</ol>
<pre><code class="r"># Convert the date column from a character type to an appropriate date format
activity$date<-strptime(activity$date,"%Y-%m-%d")
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<ol>
<li>Make a histogram of the total number of steps taken each day</li>
</ol>
<pre><code class="r">library(plyr)
library(ggplot2)
agg1<-ddply(activity,.(date),summarize,steps=sum(steps,na.rm=T))
ggplot(agg1,aes(x=steps))+scale_y_continuous(breaks=seq(0,10,by=2))+
ylab("Number of days")+xlab("Steps per day")+ggtitle("Daily Steps")+geom_histogram(binwidth=1000)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk task2p1"/> </p>
<ol>
<li>Calculate and report the <strong>mean</strong> and <strong>median</strong> total number of steps taken per day</li>
</ol>
<pre><code class="r">with(agg1,data.frame(mean=mean(steps),median=median(steps)))
</code></pre>
<pre><code>## mean median
## 1 9354.23 10395
</code></pre>
<h2>What is the average daily activity pattern?</h2>
<ol>
<li>Make a time series plot (i.e. <code>type = "l"</code>) 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">agg2<-ddply(activity,.(interval),summarize,mean_steps=mean(steps,na.rm=TRUE))
ggplot(agg2,aes(x=interval,y=mean_steps))+ggtitle("Average Steps by Interval")+
ylab("Average number of steps")+xlab("Interval")+geom_line(colour="steelblue")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk task3p1"/> </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">agg2[agg2$mean_steps==max(agg2$mean_steps),]
</code></pre>
<pre><code>## interval mean_steps
## 104 835 206.1698
</code></pre>
<h2>Imputing missing values</h2>
<ol>
<li>Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with <code>NA</code>s)</li>
</ol>
<pre><code class="r">table(is.na(activity$steps))["TRUE"]
</code></pre>
<pre><code>## TRUE
## 2304
</code></pre>
<ol>
<li>Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.</li>
</ol>
<p><strong>Strategy:</strong> I'll use mean for 5-minute intervals which have been computed in the previous task.</p>
<ol>
<li>Create a new dataset that is equal to the original dataset but with the missing data filled in.</li>
</ol>
<pre><code class="r">activity_bis<-activity
# I'll use the data frame agg2 prepared in the previous task
activity_bis<-merge(agg2,activity_bis,by="interval",all=TRUE)
activity_bis$steps<-with(activity_bis,ifelse(is.na(steps),mean_steps,steps))
activity_bis$mean_steps<-NULL
</code></pre>
<ol>
<li>Make a histogram of the total number of steps taken each day and Calculate and report the <strong>mean</strong> and <strong>median</strong> total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?</li>
</ol>
<pre><code class="r">agg3<-ddply(activity_bis,.(date),summarize,steps=sum(steps))
ggplot(agg3,aes(x=steps))+scale_y_continuous(breaks=seq(0,10,by=2))+
ylab("Number of days")+xlab("Total Steps per day")+ggtitle("Daily Steps (NAs removed)")+geom_histogram(binwidth=1000)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk task4p3"/> </p>
<pre><code class="r">with(agg3,data.frame(mean=mean(steps),median=median(steps)))
</code></pre>
<pre><code>## mean median
## 1 10766.19 10766.19
</code></pre>
<p><strong>Answer:</strong> The above numbers differ, they'e now larger as because NAs were replaced with certain numbers.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<ol>
<li>Create a new factor variable in the dataset with two levels – “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.</li>
</ol>
<pre><code class="r"># To be sure that the names of days are in English
Sys.setlocale("LC_TIME", "English")
</code></pre>
<pre><code>## [1] "English_United States.1252"
</code></pre>
<pre><code class="r">activity$dow<-as.factor(ifelse(weekdays(activity$date,abbreviate=T) %in% c("Sat","Sun"),"weekend","weekday"))
# Confirm by displaying first rows
head(activity)
</code></pre>
<pre><code>## steps date interval dow
## 1 NA 2012-10-01 0 weekday
## 2 NA 2012-10-01 5 weekday
## 3 NA 2012-10-01 10 weekday
## 4 NA 2012-10-01 15 weekday
## 5 NA 2012-10-01 20 weekday
## 6 NA 2012-10-01 25 weekday
</code></pre>
<ol>
<li>Make a panel plot containing a time series plot (i.e. <code>type = "l"</code>) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).</li>
</ol>
<pre><code class="r">agg4<-ddply(activity,.(dow,interval),summarize,mean_steps=mean(steps,na.rm=TRUE))
ggplot(agg4,aes(x=interval,y=mean_steps,colour=agg4$dow))+ggtitle("Average Steps by Interval")+
ylab("Average number of steps")+xlab("Interval")+facet_grid(dow~.)+geom_line()
</code></pre>
<p><img 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" 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