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<h2>Loading and preprocessing the data</h2>
<h5>1. Load the data</h5>
<pre><code class="r">## Import the necessary libraries
library(data.table)
library(ggplot2)
library(plyr)
## Read the CSV file
data <- read.csv(file = "activity.csv")
</code></pre>
<h5>2. Transform the data into a format suitable for analysis</h5>
<pre><code class="r">stepsData <- as.data.table(x = data)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<h5>1. Calculate the total number of steps taken per day</h5>
<pre><code class="r">totalSteps <- aggregate(formula = steps ~ date, FUN = sum, data = stepsData)
</code></pre>
<p>A portion of the <code>totalSteps</code> is as follows</p>
<pre><code>## date steps
## 1 2012-10-02 126
## 2 2012-10-03 11352
## 3 2012-10-04 12116
## 4 2012-10-05 13294
## 5 2012-10-06 15420
## 6 2012-10-07 11015
</code></pre>
<h5>2. Make a histogram of the total number of steps taken each day</h5>
<pre><code class="r">qplot(x = steps, data = totalSteps, geom = "histogram", main = "Total Steps Per Day")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p>
<h5>3. Calculate the mean and median of the total number of steps taken per day</h5>
<pre><code class="r">stepsMean <- mean(x = totalSteps$steps, na.rm = TRUE)
stepsMedian <- median(x = totalSteps$steps, na.rm = TRUE)
</code></pre>
<p>The <strong>mean</strong> of the total number of steps taken per day is <strong>10766.19</strong> & the <strong>median</strong> of the total number of steps taken per day is <strong>10765</strong>.</p>
<h2>What is the average daily activity pattern?</h2>
<h5>1. Make a time series plot of the 5-minute interval and the average number of steps taken, averaged across all days</h5>
<pre><code class="r">## Calculate the average number of steps taken in the 5-minute interval
averageSteps <- ddply(.data = stepsData, .variables = "interval", .fun = summarise, steps = mean(steps, na.rm = TRUE))
## Create the plot
qplot(x = interval, y = steps, data = averageSteps, geom = "line", xlab = "5-Minute Interval(HHMM)", ylab = "Average Number of Steps", main = "Time Series of Avg. Steps Against 5-Minute Interval")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-7"/> </p>
<h5>2. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?</h5>
<pre><code class="r">## Find the index of the maximum of steps
index <- which.max(x = averageSteps$steps)
## Get the interval located at the index
maxInterval <- stepsData$interval[index]
</code></pre>
<p>The <em>interval with maximum number of steps</em> is <strong>835</strong>.</p>
<h2>Imputing missing values</h2>
<h5>1. Calculate and report the total number of missing values in the dataset</h5>
<pre><code class="r">countNA <- sum(is.na(stepsData[,steps]))
</code></pre>
<p><em>Total number of missing values</em> in the dataset is <strong>2304</strong>.</p>
<h5>2. Fill in all of the missing values in the dataset with the average 5-minute interval values and create a new dataset with all the missing data filled in</h5>
<p>We are replacing the NA values with the average 5-minute interval values. We use <code>ceiling</code> here to round off the average 5-minute interval values. The new dataset with the missing values filled in is named <code>completeStepsData</code></p>
<pre><code class="r">## Get the steps column from the dataset
stepCount <- data.frame(stepsData[,steps])
## Replacing NA values with the average 5-minute interval values
stepCount[is.na(stepCount),] <- ceiling(tapply(X = stepsData[,steps], INDEX = stepsData[,interval], FUN = mean, na.rm = TRUE))
## Create a new dataset combining the columns
completeStepsData <- cbind(stepCount, data[,2:3])
colnames(completeStepsData) <- colnames(data)
</code></pre>
<p>A portion of the new dataset <code>completeStepsData</code> with all the missing data filled in is as follows</p>
<pre><code>## steps date interval
## 1 2 2012-10-01 0
## 2 1 2012-10-01 5
## 3 1 2012-10-01 10
## 4 1 2012-10-01 15
## 5 1 2012-10-01 20
## 6 3 2012-10-01 25
</code></pre>
<h5>4. 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</h5>
<h6>Total number of steps taken each day(with missing values filled in)</h6>
<pre><code class="r">newTotalSteps <- aggregate(formula = steps ~ date, FUN = sum, data = completeStepsData)
</code></pre>
<p>A portion of the <code>newTotalSteps</code> is as follows</p>
<pre><code>## date steps
## 1 2012-10-01 10909
## 2 2012-10-02 126
## 3 2012-10-03 11352
## 4 2012-10-04 12116
## 5 2012-10-05 13294
## 6 2012-10-06 15420
</code></pre>
<h6>Histogram of the total number of steps taken each day(with missing values filled in)</h6>
<pre><code class="r">qplot(x = steps, data = newTotalSteps, geom = "histogram", main = "Total Steps Per Day With Missing Data Filled In")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14"/> </p>
<h6>Calculate the mean and median of the total number of steps taken per day(with missing values filled in)</h6>
<pre><code class="r">newStepsMean <- mean(x = newTotalSteps$steps, na.rm = TRUE)
newStepsMedian <- median(x = newTotalSteps$steps, na.rm = TRUE)
</code></pre>
<p>The <strong>mean</strong> of the total number of steps taken per day is <strong>10784.92</strong> & the <strong>median</strong> of the total number of steps taken per day is <strong>10909.00</strong>.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<pre><code class="r">## Create a new data frame with a column for the type of the day
day.name <- weekdays(as.POSIXct(completeStepsData$date))
day.type <- ifelse(day.name == "Saturday" | day.name == "Sunday", "weekend", "weekday" )
weeklySteps <- cbind(completeStepsData, day.type)
## Calculate the average steps for 5-minute intervals for weekend and weekday
newAverageSteps <- ddply(.data = weeklySteps, .variables = c("interval", "day.type"), .fun = summarise, steps = mean(steps))
## Plotting
qplot(x = interval, y = steps, data = newAverageSteps, geom = "line", xlab = "5-Minute Interval(HHMM)", ylab = "Average Number of Steps", main = "Time Series of Avg. Steps Against 5-Minute Interval", facets = day.type ~ .)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-16"/> </p>
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