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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<pre><code class="r"># Load Libraries
library(ggplot2)
library(lattice)
# Load the activity data
activity <- read.csv("activity/activity.csv")
# Format the Date column
activity[, 2] <- as.Date(activity[, 2], format = "%Y-%m-%d")
# Eliminate rows with NA values
a <- activity[complete.cases(activity), ]
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<pre><code class="r">
# Summarize steps per day
Steps_per_Day <- tapply(a$steps, format(a$date, "%Y-%m-%d"), sum)
# Plot Steps per day
qplot(Steps_per_Day)
</code></pre>
<pre><code>## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">
# Mean Steps per day
mean(Steps_per_Day, na.rm = T)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">
# Median Steps per day
median(Steps_per_Day, na.rm = T)
</code></pre>
<pre><code>## [1] 10765
</code></pre>
<h2>What is the average daily activity pattern?</h2>
<pre><code class="r"># Summarize steps per interval
Average_Steps_per_Interval <- tapply(a$steps, a$interval, mean)
# Get list of intervals
intervals <- unique(a$interval)
# plot intervals
plot(intervals, Average_Steps_per_Interval, type = "l")
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-3"/> </p>
<pre><code class="r">
# Max Steps
max(Average_Steps_per_Interval)
</code></pre>
<pre><code>## [1] 206.2
</code></pre>
<pre><code class="r">
# Time Interval
names(Average_Steps_per_Interval[match(max(Average_Steps_per_Interval), Average_Steps_per_Interval)])
</code></pre>
<pre><code>## [1] "835"
</code></pre>
<h2>Imputing missing values</h2>
<pre><code class="r">
# Number of NA's
sum(is.na(activity$steps))
</code></pre>
<pre><code>## [1] 2304
</code></pre>
<pre><code class="r">
### Strategy: 1.Replace missing values with interval averages. 2.If there are
### still missing values, replace them with the date average
# Create a new data table for replacing missing tables
b <- activity
# Create intervals and days tables
intervals <- as.data.frame(as.table(Average_Steps_per_Interval))
days <- as.data.frame(as.table(Steps_per_Day))
# 1.
for (i in 1:nrow(b)) {
# replace NA with average interval
if (is.na(b$steps[i])) {
# Get interval
inter <- as.character(b$interval[i])
# Match interval
index <- match(inter, intervals[, 1])
#
b$steps[i] <- Average_Steps_per_Interval[index]
}
}
# 2.
for (i in 1:nrow(b)) {
# if still NA replace with average of date
if (is.na(b$steps[i])) {
# Get interval
inter <- as.character(b$date[i])
# Match interval
index <- match(inter, days[, 1])
#
b$steps[i] <- Steps_per_Day[index]
}
}
# New Summarry Data set
Steps_per_Day_All <- tapply(b$steps, format(b$date, "%Y-%m-%d"), sum)
# Plot new summary set
qplot(Steps_per_Day_All)
</code></pre>
<pre><code>## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>
<pre><code class="r">
# Mean
mean(Steps_per_Day_All, na.rm = T)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">
# Median
median(Steps_per_Day_All, na.rm = T)
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">
# By replacing the missing values with averages of other factors, we have
# not moved the mean, but rather moved the median in towards the mean. We
# have made the distribution less skewed.It has increased the total number
# of steps per day on average because it has added values where there were
# none before
</code></pre>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<pre><code class="r">
# Add day name to data frame
b$dayType <- weekdays(b$date)
# Convert day name to day type
for (i in 1:nrow(b)) {
if (b$dayType[i] == "Saturday" | b$dayType[i] == "Sunday") {
b$dayType[i] <- "Weekend"
} else {
b$dayType[i] <- "Weekday"
}
}
# Find the mean of steps per interval for weekend
c <- subset(b, dayType == "Weekend")
DFc <- aggregate(steps ~ interval + dayType, data = c, FUN = mean)
# Find the mean of steps per interval for weekday
d <- subset(b, dayType == "Weekday")
DFd <- aggregate(steps ~ interval + dayType, data = d, FUN = mean)
# Combine weekend and weekday summary data
DF <- rbind(DFc, DFd)
# Plot summary data
xyplot(DF$steps ~ DF$interval | DF$dayType, ylab = "Number of Steps", xlab = "Interval",
type = "l", layout = (c(1, 2)))
</code></pre>
<p><img 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" 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