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<title>Reproducible Research: Peer Assessment 1</title>
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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<p>Please make sure you set the “activity.zip” is in the working directory.</p>
<pre><code class="r">## initiate ggplott2 for later use
## unzip data and read it in
library(ggplot2)
unzip("activity.zip")
data<-read.csv("activity.csv")
</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">##sum the number of steps per day
steps<-tapply(data$steps,data$date,sum)
##output histograms
hist(steps,main="Histogram of Number of Steps Taken Each Day",xlab="Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Histogram_1"/>
2. Calculate and Report the mean and median total number os steps taken per day.</p>
<pre><code class="r">## find mean/median while ignoriing missing observations
mean_steps<-mean(steps,na.rm=T)
median_steps<-median(steps,na.rm=T)
mean_steps
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">median_steps
</code></pre>
<pre><code>## [1] 10765
</code></pre>
<p>The mean number of steps is 1.0766 × 10<sup>4</sup> and the median is 10765.</p>
<h2>What is the average daily activity pattern?</h2>
<ol>
<li>Make a time series plot of the 5-minute interval (x-axis) and the average
number of steps taken, averaged across all days(x-axis).</li>
</ol>
<pre><code class="r">## average the steps accross all days
interval_steps<-tapply(data$steps,data$interval,mean,na.rm=T)
##modify the variable to make is suitable for plotting
data_2<-data.frame(interval_steps)
data_2$intervals<-rownames(data_2)
row.names(data_2)=NULL
## plot the line plot
plot(data_2$intervals,data_2$interval_steps,type="l",
main="Average Steps vs. Interval",
xlab="Interval Identifier",
ylab="Average Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Line_Plot_1"/> </p>
<ol>
<li>Which 5-minute interval, on average accross all days in the dataset,
contains the maximum number of steps?</li>
</ol>
<pre><code class="r">## basic code for finding and printing the max
max_steps<-max(data_2$interval_steps)
max_interval<-data_2[data_2$interval_steps==max_steps,2]
max_steps
</code></pre>
<pre><code>## [1] 206.2
</code></pre>
<pre><code class="r">as.numeric(max_interval)
</code></pre>
<pre><code>## [1] 835
</code></pre>
<p>Thus, the interval with the highest average steps is 835 with
206.1698 number of steps.</p>
<h2>Imputing missing values</h2>
<ol>
<li>Calculate and report the total number of missing values in the dataset.</li>
</ol>
<pre><code class="r">##returns True for "NA's" and False for complete observations
na_vector<-is.na(data$steps)
## summing the bool vector gives the number of True instances, as True=1 and
## False=0
number_na<-sum(na_vector)
number_na
</code></pre>
<pre><code>## [1] 2304
</code></pre>
<p>The number of missin values in this data set is 2304.</p>
<ol>
<li><p>Devise a strategy for filling in all the missing values in the data.
This strategy does not need to be sophisticated. I used means for the five
minute intervals to replace the missing values. I matched the five minute interval
and replaced the corresponind steps in the new data set with the average mean
for accross all days for that 5-minute interval.</p></li>
<li><p>Create a new dataset that is equal to the original dataset
but with the missing data filled in. I also prinited the first ten values of the
data set to show that the NA's are gone. </p></li>
</ol>
<pre><code class="r">## initialize a new data frame
data_3<-data
## iterate across all rows
for (i in 1:length(na_vector)) {
## check for NA's
if (na_vector[i]==T) {
## replace the NA with the correspondin mean of the time interval
data_3$steps[i]<-interval_steps[[as.character(data_3$interval[i])]]
## round as steps are considered integers
data_3$steps[i]<-round(data_3$steps[i],0)
}
}
## show a bit of data to demonstrate that the NA are indeed gone
data_3[1:10,]
</code></pre>
<pre><code>## steps date interval
## 1 2 2012-10-01 0
## 2 0 2012-10-01 5
## 3 0 2012-10-01 10
## 4 0 2012-10-01 15
## 5 0 2012-10-01 20
## 6 2 2012-10-01 25
## 7 1 2012-10-01 30
## 8 1 2012-10-01 35
## 9 0 2012-10-01 40
## 10 1 2012-10-01 45
</code></pre>
<ol>
<li>Make a histogram of the total number of steps taken each day and Calculate and
report the mean and median total number of steps taken per day.</li>
</ol>
<pre><code class="r">##sum the number of steps per day
steps<-tapply(data_3$steps,data_3$date,sum)
##output histograms
hist(steps,main="Histogram of Number of Steps Taken Each Day",xlab="Steps")
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAAh1BMVEX9/v0AAAAAADkAAGUAOTkAOY8AZo8AZrU5AAA5ADk5AGU5OQA5OY85ZrU5j9plAABlADllAGVlZgBlZjlltbVltf2POQCPOTmPOWWPjzmPtY+P27WP29qP2/21ZgC1tWW124+1/rW1/tq1/v3ajzna/rXa/tra/v39tWX924/9/rX9/tr9/v2PHjMIAAAALXRSTlP//////////////////////////////////////////////////////////wCl7wv9AAAACXBIWXMAAAsSAAALEgHS3X78AAAP+klEQVR4nO2da2PixhlGIzsb3NQLm6aQbbomaU1qbv//91VzEQgzLBqQ/Eh6z/kQb9bieJiDLrBm+GEPJvlBPQDQQHijEN4ohDcK4Y1CeKMQ3iiENwrhjUJ4oxDeKIQ3CuGNQnijEN4ohDcK4Y1CeKMQ3iiENwrhjUJ4oxDeKIQ3CuGN8mHht7PH1/1+t3h8DX+qWM7vkG6eiocXLy/mQZ7c7OI3kjKvKz69NRncblF4JvH/T+/be5y3JP6USwOMW90zLw0QhD/56+Vdd7C6dTlXZag7wx+GEmZ+0mRwHYYvpteHfAeqPb7cvYrHVz9v5aSt4v0s///xP7NPb7vFw6/l/KzDLK2Ln8q/f10d94Kwvb+13zPdXE293E/htlTUb1T+5R9xy1XYl+IPSMrKsX16Kwf539rgwvHkDzfmfRx7dcfc1vt9HKq7b6VpnriVH2b4AYfN/U8tv1lu9efTcSv3h7X737CZv0/rlh8IovDhUf3pf2Ful3Gv8ZP/45ML7777V9jobV1UxKmJ29fDP/4jRK+FP94o7pflpsu4L9Uyn8lcWPcI29UHN90fJHHsMWEIX/2du28rd+t3t5rEOYi3qjYP3z0McFKbKXe4qTZzxuX5ceIuPjB8lcHdsbCb7MPRdPPk95WHl81TeZdXYUIm1c0eX92D38Vwm7i/O2xfO9SXe8z0XfjjjUrdNP6AeEY4/oBzWYjlJj8M7niTqb+YOIy9Ehxq+vtWDmT/7lbr+DCJc1D9jxtg+WPKn+K3qkQx/CoMJ2w2aXa2ykAU3v9fNbdrH6G8o/4P23Cof6lu5MJP4jE0/O1h+3r41+XDt9PwxxuFWVu6HxBPspUqJduH/TUk2ddu4iSrYnoYeyD2CkMtH0F/d5rTH1Tt6bXw1T0Lx+93Wx3Dh812i/LoV11GtITyHF/E3exSeL/NIfzx4ZAOv539dB4+3Cg7fDzcp8Mfxl5tWfaqhlo9KE5vdUwaDw71e+Z4t1V1qK82268efn9q+VpP+XRuGec2dah38xom+Tx88lDvbljEK6tV8T58/VDvOIZPHOpX1RX94VAfbhIP9YexV4Jyg2qo4Rw/rd8qFb7aPN7haSL8uqhMYTdp+RSvCn94LC8vXNy5+7ku0nv8YfvT8O4pwWv83ln4k4u7mioh26cGV12IlX84fLvauvRWQ43n+dfarU6ShgPBvNr8eHGX2Gp6sLq7dnJd0QLKQ308nPsdJjydK+9z9XTOdSlnbxrPACfhD9ufhA/PgNy0/fP8UF89nVuGB0At/Jls/25wh5tUkurb1cbu7+JQ/UD8mft4q0T4anO/WalKbTU/ToD7Q8un+B6+ZLtu/cHdDq1fV+ewav2FvF6Fj4/1bl+yuhVl+A5+dq/Ch5Na2we1lhCGX3Xwyn2/wsOHQXijEN4ohDcK4Y1CeKMQ3iiENwrhjUJ4oxDeKIQ3CuGNQnijEN4ohDcK4Y1CeKMQ3iiENwrhjUJ4oxDeKIQ3CuGNQnijEN4ohDcK4Y1CeKMQ3iiENwrhjWIhfNEq6nvTEibC91YmhPBKmRDCK2VCrobf/vJ6/MSGYUL4BE3Cu/b7zeePGE4nED5Bk/Cb57ew5w8Uwie4Hn728O2r2+OfB3usJ3yCBhd3/tMc1sKFXO+F8Am4qlfKhNwSfmgvYhE+wfXwm6diuqx/CMuBocwB4RNcDb9bzPdL98kC5xd3Q5kDwido9ALOepp8OjeUOSB8gkZ7vIM9vgOZkOvn+O3MlV9xju9AJuSep3NDmQPCJyC8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCGi1w6GDxow5kQhovd7Y+/6SCocwB4RM0WuCw/rXGUOaA8AnY45UyIU0WOOQc35VMCFf1SpkQ1qtXyoSwXr1SJoT16pUyIaxXr5QJYb16pUwI69UrZUJ4OqeUCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmZBGq165VU3Pl7IdzBwQPkGT8H6ls83ns28NZQ4In6BJeL/SGevcdSATcj387OHb11fWuetEJqTBxd1uUUz2a9a560AmhKt6pUwIy5YrZUIaLVv+8MLFXScyIY0WMd4tpoTvQiak4adQLSeE70AmpOmy5asffyZ86zIhTZYtn7oviXXLhzIHhE/A0zmlTAjhlTIhhFfKhBBeKRNCeKVMCOGVMiGEV8qEEF4pE0J4pUwI4ZUyIYRXyoQQXikTQnilTAjhlTIhhFfKhBBeKRNCeKVMCOGVMiGEV8qEEF4pE0J4pUwI4ZUyIYRXyoQQXikTQnilTAjhlTIhhFfKhBBeKRNCeKVMCOGVMiGEV8qEEF4pE0J4pUxICL+dTW647VDmgPAJqj1+XfhFzbIYyhwQPkHtUL9bFMU857ZDmQPCJ6jCh2UME2uaue84WMu2A5mQ6hyf+ByCQFzubL8+32Ioc0D4BA0XOGRJ005kQmL4dXl2XyWv7tjjO5QJiYf6L6755nzxSve9Gef4rmRCQviwWyd26u8ylDkgfIJ4qPe7dWKnTsJ69SOA9eqVMiGsV6+UCTlc1V+4gGO9+i5lQqoXcC6+Vst69R3KhMTwiZdqK1ivvjuZkHioX05vuO1Q5oDwCapD/cVz/HcYyhwQPgG/gaOUCSG8UiYkht8tik9/fcn8FZyhzAHhE1Sv1U83z2+8Vv/RMiGHp3Nl+O89qUsxlDkgfIL6Hr9ij/9gmZDjOb4oMrsPZg4In4CreqVMCOGVMiG8cqeUCanv8avMF+yHMgeET1APz9O5j5YJqYdfc6j/YJmQk3N81jvnhjMHhE/AVb1SJoTwSpmQk0N95hO6ocwB4RPEPX41qf6TwVDmgPAJ6r9sydO5j5YJOfzr3J49/uNlQur/Ope7ANJQ5oDwCbiqV8qEEF4pE8IvWyplQvhlS6VMCL9sqZQJ4ZctlTIh/LKlUiaEq3qlTMj198dfZihzQPgE8Rz/W+7K1Y6hzAHhE/BbtkqZEM7xSpkQwitlQlz4717auW+6M0Hiud5Q5oDwCarwm+cLT+LLb/oHxubz2beGMgeET9AkvP8eCxx2IBNyPfzs4dtXt8efbzCUOSB8Ah/++79ju1sUk+S7bIYyB4RPwFW9UibklvCsVz8C2OOVMiGEV8qEXP/4sctXfkOZA8InuL7HhzdbpBjKHBA+QYND/fbSb98OZQ4In4BzvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQvoavmiTVgfWpkxIb8P31EX4PeEHDeGVMiGEV8qEEF4pE0J4pUwI4ZUyIYRXyoQQXikTQnilTAjhlTIhhFfKhFwPv3m6tM4t4QfM1fC7xdx/TXz+KOEHzPUFDuM69R+8Xj3hO4Y9XikTcv0cf/mjyQg/YLiqV8qE9HW9esJ3DHu8UiaE8EqZkL6uV0/4junrevWE75i+rldP+I7hHK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgivlAkhvFImhPBKmRDCK2VCCK+UCSG8UiaE8EqZEMIrZUIIr5QJIbxSJoTwSpkQwitlQgifK+vtRyHmQfjRyPIg/GhkeRB+NLI8CD8aWR6EH40sD8KPRpYH4Ucjy6PRJ1S4ZU3PP6eA8P2S5dEkvP9Uks3ns28RvleyPJqE3zy/nXwmzYXXnXr7mlZ/W/U6/Ozh21e3xz9f+0ya3k5JbwfW6/Bu+episl9f/0ya3k5JbwfW8/AXIXyvZHkQfjSyPAg/GlkehB+NLA/Cj0aWB+FHI8uD8KOR5UH40cjyIPxoZHkQfjSyPAg/GlkehB+NLA/Cj0aWB+FHI8uD8KOR5UH40cjyIPxoZHkQfjSyPAg/GlkehB+NLA/Cj0aWB+FHI8uD8KOR5UH40cjyIPxoZHkQXirTvd2Q8EZlhDcqI7xRGeGNyghvVEZ4ozLCG5UR3qiM8EZlhDcqI7xRGeGNyghvVEZ4ozLCG5UR3qjsevjNk/9XfpY0HZfsavjdYu6/rs8XrCf8gGWNPqig/nW//5hlyyGbdsN/Z4+HAXP9HO8+l6RInuNhwNxzVQ8DhvBGIbxRCG8UwhuF8EYhvFEIbxTCG4XwRiG8UVoML/63KZCFb0/Fv8d3LiO8URnhjcoIb1RGeKMywhuVEd6ojBdwjEJ4oxDeKIQ3CuGNQnijEN4ohDcK4Y1CeKO0FX47K+59H/Wq8O/JjabTL3lsfn59L7hd52XtjM0tLjJva2RRduvIWgrv3kW/mtznWM5rptMveazdTCQ9N+i8rJ2xbb+87Dd/e2lnZFF288haCu/Wy/C7xu3sfnupmU6/ZImWD7+Xt0h68nVB1s7Y1q7Fct7OyKLs5pG1FH7z/OYfg3fgF2CYV6bTL7mjKe900nOLzsnaG9ulId0qu3lkLYV3C6XcGd4dt8rHbzSdfslVla2Snlt0/lHU1th2i2l7I3Oym0fWnz3es5z3co9va2zb2XTf2si87OaR9ecc77lwBsy0bNo7x5+Ev1e2eXJXYi2NLMhuHllrV/XTe6/q3fFp9/U1mk6/ZOLudNJzi646b9w/tpiqnZFF2c0j69fz+IeXFp54d/Q8/v6xrfz7XebtjKyS3ToyXrkzCuGNQnijEN4ohDcK4Y1CeKMQ3iiENwrhjUJ4oxDeKIQ3CuGNQnijEN4ohDcK4Y1CeKMQ3iiWw7t3oTy282vhw8Nw+O3Mvdn00xvhjRF+CfvLv2flbr+d+fca//Iv/7vKawOfoWw4/G4RfvfcPQCW/h0I29mnt/WjfyPKve/57j2Gw/s9u9zB3XuOvry4dza5g//ut5c23gTYe0yH3/v36Lrw7s3GD6H4cu4Wm3gYe3vD4f3SAu6dpuFdhvuwykRcaWB97xvC+o7h8P6q3r2pOJzjy9Tb2cR9cdEJP2LcAb48pO8W/qq+/NP2y6/+GL/kqt4WJq7qIoSvQXgYPYQ3CuGNQnijEN4ohDcK4Y1CeKMQ3iiENwrhjUJ4oxDeKIQ3CuGNQnij/B/EYnOfV9CmhwAAAABJRU5ErkJggg==" alt="plot of chunk Histogram_2"/> </p>
<pre><code class="r">## calculate new mean/median - see above
mean_steps_2<-mean(steps)
median_steps_2<-median(steps)
mean_steps_2
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">median_steps_2
</code></pre>
<pre><code>## [1] 10762
</code></pre>
<p>Yes the new values do have some impact on the estimates of the total daily number
of steps. </p>
<pre><code class="r">## just a little table to see how the values have changed
comp_table<-data.frame(c(mean_steps,median_steps),c(mean_steps_2,median_steps_2))
rownames(comp_table)<-c("mean","median")
colnames(comp_table)<-c("old","new")
comp_table
</code></pre>
<pre><code>## old new
## mean 10766 10766
## median 10765 10762
</code></pre>
<ul>
<li>the new mean is slightly lower then bofore: old -1.0766 × 10<sup>4</sup> vs new
1.0766 × 10<sup>4</sup>.<br/></li>
<li>the median is also lower: old 10765 vs new 1.0762 × 10<sup>4</sup></li>
<li>getting rid of the missing values did not change the skewness, as the
median is lower than the the mean in both cases.That is in both cases
the distribution is negatively skewed. </li>
</ul>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>1.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.</p>
<pre><code class="r">## convert to date objects
data_3$date<-weekdays(as.Date(data_3$date))
## create a numeric vector 0->weekday, 1->weekend
day_factor<-as.numeric((data_3$date=="Saturday")|(data_3$date=="Sunday"))
## initiate the factor
day_factor<-factor(day_factor,labels=c("Weekday","Weekend"))
## write in the factor into the data frame
data_3$date<-day_factor
## find the means steps across the factor levels, seperated by interval
data_4<-aggregate(steps~date+interval,mean,data=data_3)
</code></pre>
<p>2.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). </p>
<pre><code class="r">## use ggplot to plot the two line plots on top of each other
ggplot(data=data_4, aes(x=interval, y=steps, group=1)) + geom_line()+facet_grid(date~.)
</code></pre>
<p><img 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" alt="plot of chunk Line_Plot_2"/> </p>
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