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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>Extract the original activity.zip file, to get activity.csv</p>
<pre><code class="r">unzip("activity.zip")
</code></pre>
<p>Load the data</p>
<pre><code class="r">data <- read.csv("activity.csv", na.string="NA", colClasses=c("integer", "Date", "integer"))
</code></pre>
<p>Process/transform the data</p>
<pre><code class="r">data$date <- as.Date(data$date, format = "%Y-%m-%d")
data$interval <- formatC(data$interval, width = 4, format = "d", flag = "0")
datetime <- strptime(paste(data$date,data$interval), "%F %H%M")
data <- cbind(data,datetime)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>Histogram of the total number of steps taken each day</p>
<pre><code class="r">data_steps_sum <- with(data, aggregate(list(Sum_Steps = steps), by=list(Date = date), FUN=sum))
plot(data_steps_sum, type = "h", main = "Total Steps Taken Each Day", xlab = "Date", ylab = "Total Steps")
</code></pre>
<p><img 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alt="plot of chunk unnamed-chunk-4"/> </p>
<p>Mean total number of steps taken per Day</p>
<pre><code class="r">data_steps_mean <- with(data, aggregate(list(Mean_Steps = steps), by=list(Date = date), FUN=mean))
print(data_steps_mean)
</code></pre>
<pre><code>## Date Mean_Steps
## 1 2012-10-01 NA
## 2 2012-10-02 0.4375
## 3 2012-10-03 39.4167
## 4 2012-10-04 42.0694
## 5 2012-10-05 46.1597
## 6 2012-10-06 53.5417
## 7 2012-10-07 38.2465
## 8 2012-10-08 NA
## 9 2012-10-09 44.4826
## 10 2012-10-10 34.3750
## 11 2012-10-11 35.7778
## 12 2012-10-12 60.3542
## 13 2012-10-13 43.1458
## 14 2012-10-14 52.4236
## 15 2012-10-15 35.2049
## 16 2012-10-16 52.3750
## 17 2012-10-17 46.7083
## 18 2012-10-18 34.9167
## 19 2012-10-19 41.0729
## 20 2012-10-20 36.0938
## 21 2012-10-21 30.6285
## 22 2012-10-22 46.7361
## 23 2012-10-23 30.9653
## 24 2012-10-24 29.0104
## 25 2012-10-25 8.6528
## 26 2012-10-26 23.5347
## 27 2012-10-27 35.1354
## 28 2012-10-28 39.7847
## 29 2012-10-29 17.4236
## 30 2012-10-30 34.0938
## 31 2012-10-31 53.5208
## 32 2012-11-01 NA
## 33 2012-11-02 36.8056
## 34 2012-11-03 36.7049
## 35 2012-11-04 NA
## 36 2012-11-05 36.2465
## 37 2012-11-06 28.9375
## 38 2012-11-07 44.7326
## 39 2012-11-08 11.1771
## 40 2012-11-09 NA
## 41 2012-11-10 NA
## 42 2012-11-11 43.7778
## 43 2012-11-12 37.3785
## 44 2012-11-13 25.4722
## 45 2012-11-14 NA
## 46 2012-11-15 0.1424
## 47 2012-11-16 18.8924
## 48 2012-11-17 49.7882
## 49 2012-11-18 52.4653
## 50 2012-11-19 30.6979
## 51 2012-11-20 15.5278
## 52 2012-11-21 44.3993
## 53 2012-11-22 70.9271
## 54 2012-11-23 73.5903
## 55 2012-11-24 50.2708
## 56 2012-11-25 41.0903
## 57 2012-11-26 38.7569
## 58 2012-11-27 47.3819
## 59 2012-11-28 35.3576
## 60 2012-11-29 24.4688
## 61 2012-11-30 NA
</code></pre>
<p>Median total number of steps taken per Day</p>
<pre><code class="r">data_steps_median <- with(data, aggregate(list(Median_Steps = steps), by=list(Date = date), FUN=median, na.rm=TRUE))
print(data_steps_median)
</code></pre>
<pre><code>## Date Median_Steps
## 1 2012-10-01 NA
## 2 2012-10-02 0
## 3 2012-10-03 0
## 4 2012-10-04 0
## 5 2012-10-05 0
## 6 2012-10-06 0
## 7 2012-10-07 0
## 8 2012-10-08 NA
## 9 2012-10-09 0
## 10 2012-10-10 0
## 11 2012-10-11 0
## 12 2012-10-12 0
## 13 2012-10-13 0
## 14 2012-10-14 0
## 15 2012-10-15 0
## 16 2012-10-16 0
## 17 2012-10-17 0
## 18 2012-10-18 0
## 19 2012-10-19 0
## 20 2012-10-20 0
## 21 2012-10-21 0
## 22 2012-10-22 0
## 23 2012-10-23 0
## 24 2012-10-24 0
## 25 2012-10-25 0
## 26 2012-10-26 0
## 27 2012-10-27 0
## 28 2012-10-28 0
## 29 2012-10-29 0
## 30 2012-10-30 0
## 31 2012-10-31 0
## 32 2012-11-01 NA
## 33 2012-11-02 0
## 34 2012-11-03 0
## 35 2012-11-04 NA
## 36 2012-11-05 0
## 37 2012-11-06 0
## 38 2012-11-07 0
## 39 2012-11-08 0
## 40 2012-11-09 NA
## 41 2012-11-10 NA
## 42 2012-11-11 0
## 43 2012-11-12 0
## 44 2012-11-13 0
## 45 2012-11-14 NA
## 46 2012-11-15 0
## 47 2012-11-16 0
## 48 2012-11-17 0
## 49 2012-11-18 0
## 50 2012-11-19 0
## 51 2012-11-20 0
## 52 2012-11-21 0
## 53 2012-11-22 0
## 54 2012-11-23 0
## 55 2012-11-24 0
## 56 2012-11-25 0
## 57 2012-11-26 0
## 58 2012-11-27 0
## 59 2012-11-28 0
## 60 2012-11-29 0
## 61 2012-11-30 NA
</code></pre>
<h2>What is the average daily activity pattern?</h2>
<p>Time series plot of the 5-minute interval and the average number of steps taken, averaged across all days</p>
<pre><code class="r">data_interval_mean <- with(data, aggregate(list(Mean_Steps = steps), by=list(Interval = interval), FUN=mean, na.rm=TRUE))
data_interval_mean_plot <- with(data_interval_mean, plot(Interval, Mean_Steps, type="l"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-7"/> </p>
<p>Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?</p>
<pre><code class="r">data[order(data_interval_mean$Mean_Steps, decreasing=TRUE)[1],]$interval
</code></pre>
<pre><code>## [1] "0835"
</code></pre>
<h2>Imputing missing values</h2>
<p>Total number of missing values in the dataset</p>
<pre><code class="r">missing_steps = matrix(is.na(data$steps))
missing_steps_count = sum(missing_steps)
</code></pre>
<p>New dataset that is equal to the original dataset but with the missing data filled in</p>
<pre><code class="r">new_data = data
new_data[missing_steps,]$steps = data_interval_mean$Mean_Steps
</code></pre>
<p>Histogram of the total number of steps taken each day</p>
<pre><code class="r">new_data_steps_sum <- with(new_data, aggregate(list(Sum_Steps = steps), by=list(Date = date), FUN=sum))
plot(new_data_steps_sum, type = "h", main = "Total Steps Taken Each Day - after missing values were imputed", xlab = "Date", ylab = "Total Steps")
</code></pre>
<p><img 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alt="plot of chunk unnamed-chunk-11"/> </p>
<p>New mean total number of steps taken per Day</p>
<pre><code class="r">new_data_steps_mean <- with(new_data, aggregate(list(Mean_Steps = steps), by=list(Date = date), FUN=mean, na.rm=TRUE))
print(new_data_steps_mean)
</code></pre>
<pre><code>## Date Mean_Steps
## 1 2012-10-01 37.3826
## 2 2012-10-02 0.4375
## 3 2012-10-03 39.4167
## 4 2012-10-04 42.0694
## 5 2012-10-05 46.1597
## 6 2012-10-06 53.5417
## 7 2012-10-07 38.2465
## 8 2012-10-08 37.3826
## 9 2012-10-09 44.4826
## 10 2012-10-10 34.3750
## 11 2012-10-11 35.7778
## 12 2012-10-12 60.3542
## 13 2012-10-13 43.1458
## 14 2012-10-14 52.4236
## 15 2012-10-15 35.2049
## 16 2012-10-16 52.3750
## 17 2012-10-17 46.7083
## 18 2012-10-18 34.9167
## 19 2012-10-19 41.0729
## 20 2012-10-20 36.0938
## 21 2012-10-21 30.6285
## 22 2012-10-22 46.7361
## 23 2012-10-23 30.9653
## 24 2012-10-24 29.0104
## 25 2012-10-25 8.6528
## 26 2012-10-26 23.5347
## 27 2012-10-27 35.1354
## 28 2012-10-28 39.7847
## 29 2012-10-29 17.4236
## 30 2012-10-30 34.0938
## 31 2012-10-31 53.5208
## 32 2012-11-01 37.3826
## 33 2012-11-02 36.8056
## 34 2012-11-03 36.7049
## 35 2012-11-04 37.3826
## 36 2012-11-05 36.2465
## 37 2012-11-06 28.9375
## 38 2012-11-07 44.7326
## 39 2012-11-08 11.1771
## 40 2012-11-09 37.3826
## 41 2012-11-10 37.3826
## 42 2012-11-11 43.7778
## 43 2012-11-12 37.3785
## 44 2012-11-13 25.4722
## 45 2012-11-14 37.3826
## 46 2012-11-15 0.1424
## 47 2012-11-16 18.8924
## 48 2012-11-17 49.7882
## 49 2012-11-18 52.4653
## 50 2012-11-19 30.6979
## 51 2012-11-20 15.5278
## 52 2012-11-21 44.3993
## 53 2012-11-22 70.9271
## 54 2012-11-23 73.5903
## 55 2012-11-24 50.2708
## 56 2012-11-25 41.0903
## 57 2012-11-26 38.7569
## 58 2012-11-27 47.3819
## 59 2012-11-28 35.3576
## 60 2012-11-29 24.4688
## 61 2012-11-30 37.3826
</code></pre>
<p>New median total number of steps taken per Day</p>
<pre><code class="r">new_data_steps_median <- with(new_data, aggregate(list(Median_Steps = steps), by=list(Date = date), FUN=median, na.rm=TRUE))
print(new_data_steps_median)
</code></pre>
<pre><code>## Date Median_Steps
## 1 2012-10-01 34.11
## 2 2012-10-02 0.00
## 3 2012-10-03 0.00
## 4 2012-10-04 0.00
## 5 2012-10-05 0.00
## 6 2012-10-06 0.00
## 7 2012-10-07 0.00
## 8 2012-10-08 34.11
## 9 2012-10-09 0.00
## 10 2012-10-10 0.00
## 11 2012-10-11 0.00
## 12 2012-10-12 0.00
## 13 2012-10-13 0.00
## 14 2012-10-14 0.00
## 15 2012-10-15 0.00
## 16 2012-10-16 0.00
## 17 2012-10-17 0.00
## 18 2012-10-18 0.00
## 19 2012-10-19 0.00
## 20 2012-10-20 0.00
## 21 2012-10-21 0.00
## 22 2012-10-22 0.00
## 23 2012-10-23 0.00
## 24 2012-10-24 0.00
## 25 2012-10-25 0.00
## 26 2012-10-26 0.00
## 27 2012-10-27 0.00
## 28 2012-10-28 0.00
## 29 2012-10-29 0.00
## 30 2012-10-30 0.00
## 31 2012-10-31 0.00
## 32 2012-11-01 34.11
## 33 2012-11-02 0.00
## 34 2012-11-03 0.00
## 35 2012-11-04 34.11
## 36 2012-11-05 0.00
## 37 2012-11-06 0.00
## 38 2012-11-07 0.00
## 39 2012-11-08 0.00
## 40 2012-11-09 34.11
## 41 2012-11-10 34.11
## 42 2012-11-11 0.00
## 43 2012-11-12 0.00
## 44 2012-11-13 0.00
## 45 2012-11-14 34.11
## 46 2012-11-15 0.00
## 47 2012-11-16 0.00
## 48 2012-11-17 0.00
## 49 2012-11-18 0.00
## 50 2012-11-19 0.00
## 51 2012-11-20 0.00
## 52 2012-11-21 0.00
## 53 2012-11-22 0.00
## 54 2012-11-23 0.00
## 55 2012-11-24 0.00
## 56 2012-11-25 0.00
## 57 2012-11-26 0.00
## 58 2012-11-27 0.00
## 59 2012-11-28 0.00
## 60 2012-11-29 0.00
## 61 2012-11-30 34.11
</code></pre>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>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">day_type <- factor(weekdays(new_data$date) %in% c("Saturday", "Sunday"), labels = c("weekday", "weekend"))
new_data = cbind(new_data, day_type)
</code></pre>
<p>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">new_data_interval_mean_weekday <- with(new_data[new_data$day_type=="weekday",], aggregate(list(Mean_Steps = steps), by=list(Interval = interval), FUN=mean, na.rm=TRUE))
new_data_interval_mean_weekend <- with(new_data[new_data$day_type=="weekend",], aggregate(list(Mean_Steps = steps), by=list(Interval = interval), FUN=mean, na.rm=TRUE))
plot(new_data_interval_mean_weekday, type="l", col="red")
lines(new_data_interval_mean_weekend, col="green")
legend(x = 235, legend="red - weekday, green - weekend")
</code></pre>
<p><img 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" 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