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<h2>Reproducible Research Peer Assessment 1</h2>
<p>Mark Morscher</p>
<h3>Background</h3>
<p>For this Peer Assessment assignment, I will be loading a dataset containing two months of information generated by someone's activity moinitoring device. The data contains the # of steps taken during five minute intervals over these two months. A variety of analysis, charts and answers to questions will be processed and presented.</p>
<h1></h1>
<h3>Code and Results</h3>
<p>Load the data (available in this repository). The 'date' is a Factor variable, so convert it to a usable “date”. The structure of the resulting data set is presented.</p>
<pre><code class="r">data = read.csv("activity.csv")
data$date = as.Date(data$date, "%Y-%m-%d")
str(data)
</code></pre>
<pre><code>## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Date, format: "2012-10-01" "2012-10-01" ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
</code></pre>
<p>Here are a few characteristics about the data, notably a Histogram, mean and median of steps per day.</p>
<pre><code class="r"># Calculate Total Steps per day and plot the histogram.
dailyTotalSteps = aggregate(data$steps, list(Date = data$date), FUN = sum, na.rm = TRUE)
hist(dailyTotalSteps$x, breaks = 10, main = "Distribution of # of Steps per Day",
xlab = "# Steps")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">
# Calculate and print out the Mean and Median Steps per day
cat("Mean # of Steps per Day (ignoring NAs): ", mean(dailyTotalSteps$x))
</code></pre>
<pre><code>## Mean # of Steps per Day (ignoring NAs): 9354
</code></pre>
<pre><code class="r">cat("Median # of Steps per Day (ignoring NAs): ", median(dailyTotalSteps$x))
</code></pre>
<pre><code>## Median # of Steps per Day (ignoring NAs): 10395
</code></pre>
<p>Let's look at the distribution across each 5-minute interval, by daily average and which interval has the maximum average # of steps.</p>
<pre><code class="r"># Calculate Average Steps per interval and plot the line chart.
intervalAverageSteps = aggregate(data$steps, list(Interval = data$interval),
FUN = mean, na.rm = TRUE)
plot(intervalAverageSteps$Interval, intervalAverageSteps$x, type = "l", main = "Average # of Steps per 5-minute Interval",
xlab = "Interval", ylab = "Avg Steps")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>
<pre><code class="r">
cat("The Interval with the Maximum Average # of Steps (ignoring NAs) is ", intervalAverageSteps$Interval[which.max(intervalAverageSteps$x)])
</code></pre>
<pre><code>## The Interval with the Maximum Average # of Steps (ignoring NAs) is 835
</code></pre>
<p>The next step is to do something about the NAs that exist in the 'steps' data. I will choose to use the Mean value of steps for all the other non-NA data for that interval as the imputed value to insert in the new data. I would like to use Median, but that appears to be '0' too often.</p>
<pre><code class="r"># How many NA's do we have?
cat("There are ", sum(is.na(data$steps)), " NA values for 'steps' out of ",
nrow(data), "observations.")
</code></pre>
<pre><code>## There are 2304 NA values for 'steps' out of 17568 observations.
</code></pre>
<pre><code class="r">
# Create an imputed data set.
dataImputed = data
# If an observation has NA for its 'steps' variable, then use the
# appropriate median for that interval.
for (i in 1:nrow(dataImputed)) {
if (is.na(dataImputed$steps[i])) {
dataImputed$steps[i] = intervalAverageSteps$x[which(intervalAverageSteps$Interval ==
dataImputed$interval[i])]
}
}
# Calculate new values for the mean, median and Histogram of total steps per
# day. If I have time, I should turn this into a function.
dailyTotalStepsImputed = aggregate(dataImputed$steps, list(Date = dataImputed$date),
FUN = sum, na.rm = TRUE)
hist(dailyTotalStepsImputed$x, breaks = 10, main = "Distribution of # of Steps per Day, Imputed Data",
xlab = "# Steps")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-4"/> </p>
<pre><code class="r">
cat("Mean # of Steps per Day for Imputed data is ", mean(dailyTotalStepsImputed$x),
"(compared to ", mean(dailyTotalSteps$x), "when ignoring NAs)")
</code></pre>
<pre><code>## Mean # of Steps per Day for Imputed data is 10766 (compared to 9354 when ignoring NAs)
</code></pre>
<pre><code class="r">
cat("Median # of Steps per Day for Imputed data is ", median(dailyTotalStepsImputed$x),
"(compared to ", median(dailyTotalSteps$x), "when ignoring NAs)")
</code></pre>
<pre><code>## Median # of Steps per Day for Imputed data is 10766 (compared to 10395 when ignoring NAs)
</code></pre>
<p>It clearly looks like replacing the missing data has presented a noticably more active set of data (total steps higher, as you would expect). Another interesting observation is that the median and mean of the daily total steps is now the same, which bears out in the nearly normal histogram distribution.</p>
<p>Lets look at the data by weekday and weekend. We'll first add an indicator which type of day it is, and then plot the data for each to compare. I used a side-by-side comparison using qplot since I feel it does a better job highlighting the maginitude differences between weekends and weekdays.</p>
<pre><code class="r"># Add the weekday/weekend designation to the imputed data frame.
dataImputed$dateType = as.factor("weekday")
levels(dataImputed$dateType) = c("weekday", "weekend")
for (i in 1:nrow(dataImputed)) {
if (weekdays(dataImputed$date[i]) == "Saturday" || weekdays(dataImputed$date[i]) ==
"Sunday") {
dataImputed$dateType[i] = as.factor("weekend")
}
}
# Create the interval averages broken down by weekday/weekend
intervalAvgSteps = aggregate(dataImputed$steps, list(Interval = dataImputed$interval,
dateType = dataImputed$dateType), FUN = mean, na.rm = TRUE)
# Now plot the comparison
library(ggplot2)
qplot(Interval, x, data = intervalAvgSteps, facets = . ~ dateType, geom = "line",
main = "Comparison of Average Steps During Time of Day", xlab = "Interval",
ylab = "Avg Number of Steps")
</code></pre>
<p><img 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" 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