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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<p>Note: This program assumes that the data is located in the working directory</p>
<p>The data is initially loaded and appended with factor and index columns.</p>
<pre><code class="r">activities <- read.csv("activity.csv")
# Rename the date column to datefactor to indicate that it is a factor
# variable
names(activities)[2] <- "datefactor"
# Create a Date column with the Date class type
date <- as.Date(as.character(activities$datefactor))
activities <- cbind(activities, date)
# Create a column to indicate the day of the week
dayofweek <- weekdays(activities$date)
dayofweekfactor <- as.factor(dayofweek)
activities <- cbind(activities, dayofweekfactor)
# Create a daytype factor column to indicate a weekday or weekend
weekendTF <- weekdays(activities$date) == "Sunday" | weekdays(activities$date) ==
"Saturday"
weekendday <- dayofweek
weekendday[weekendTF] <- "weekend"
weekendday[weekendTF == FALSE] <- "weekday"
daytypefactor <- as.factor(weekendday)
activities <- cbind(activities, daytypefactor)
# Create a factor column for the intervals. This will be used to split the
# data.
intervalfactor <- as.factor(activities$interval)
activities <- cbind(activities, intervalfactor)
# Create a column that indexes the 5-minute interval
intervalindex <- as.numeric(intervalfactor)
activities <- cbind(activities, intervalindex)
# tidy up the data set
activities <- data.frame(steps = activities$steps, date = activities$date, datefactor = activities$datefactor,
dayofweekfactor = activities$dayofweekfactor, daytypefactor = activities$daytypefactor,
interval = activities$interval, intervalfactor = activities$intervalfactor,
intervalindex = activities$intervalindex)
# Inspect the resulting data set
head(activities)
</code></pre>
<pre><code>## steps date datefactor dayofweekfactor daytypefactor interval
## 1 NA 2012-10-01 2012-10-01 Monday weekday 0
## 2 NA 2012-10-01 2012-10-01 Monday weekday 5
## 3 NA 2012-10-01 2012-10-01 Monday weekday 10
## 4 NA 2012-10-01 2012-10-01 Monday weekday 15
## 5 NA 2012-10-01 2012-10-01 Monday weekday 20
## 6 NA 2012-10-01 2012-10-01 Monday weekday 25
## intervalfactor intervalindex
## 1 0 1
## 2 5 2
## 3 10 3
## 4 15 4
## 5 20 5
## 6 25 6
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>The data is split by date. Then a vector is created to hold the total steps by date.</p>
<pre><code class="r">activities_steps_splitbydate <- with(activities, split(steps, datefactor))
activities_steps_splitbydate_total <- sapply(activities_steps_splitbydate, sum)
</code></pre>
<p>This histogram shows the frequency distribution of steps per day.</p>
<pre><code class="r">hist(activities_steps_splitbydate_total, xlab = "Steps per day", main = "Histogram of Steps per day",
breaks = 10)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk stepsperdayhistogram"/> </p>
<pre><code class="r">
activities_steps_splitbydate_total_df <- data.frame(total = activities_steps_splitbydate_total)
print(activities_steps_splitbydate_total_df)
</code></pre>
<pre><code>## total
## 2012-10-01 NA
## 2012-10-02 126
## 2012-10-03 11352
## 2012-10-04 12116
## 2012-10-05 13294
## 2012-10-06 15420
## 2012-10-07 11015
## 2012-10-08 NA
## 2012-10-09 12811
## 2012-10-10 9900
## 2012-10-11 10304
## 2012-10-12 17382
## 2012-10-13 12426
## 2012-10-14 15098
## 2012-10-15 10139
## 2012-10-16 15084
## 2012-10-17 13452
## 2012-10-18 10056
## 2012-10-19 11829
## 2012-10-20 10395
## 2012-10-21 8821
## 2012-10-22 13460
## 2012-10-23 8918
## 2012-10-24 8355
## 2012-10-25 2492
## 2012-10-26 6778
## 2012-10-27 10119
## 2012-10-28 11458
## 2012-10-29 5018
## 2012-10-30 9819
## 2012-10-31 15414
## 2012-11-01 NA
## 2012-11-02 10600
## 2012-11-03 10571
## 2012-11-04 NA
## 2012-11-05 10439
## 2012-11-06 8334
## 2012-11-07 12883
## 2012-11-08 3219
## 2012-11-09 NA
## 2012-11-10 NA
## 2012-11-11 12608
## 2012-11-12 10765
## 2012-11-13 7336
## 2012-11-14 NA
## 2012-11-15 41
## 2012-11-16 5441
## 2012-11-17 14339
## 2012-11-18 15110
## 2012-11-19 8841
## 2012-11-20 4472
## 2012-11-21 12787
## 2012-11-22 20427
## 2012-11-23 21194
## 2012-11-24 14478
## 2012-11-25 11834
## 2012-11-26 11162
## 2012-11-27 13646
## 2012-11-28 10183
## 2012-11-29 7047
## 2012-11-30 NA
</code></pre>
<p>Finally, calculate the mean and median steps per day. Ignore any NAs.</p>
<pre><code class="r">activities_steps_splitbydate_mean = format(mean(activities_steps_splitbydate_total_df$total,
na.rm = TRUE), digits = 6, nsmall = 2)
activities_steps_splitbydate_median = median(activities_steps_splitbydate_total_df$total,
na.rm = TRUE)
</code></pre>
<p>The mean steps per day is 10766.19. The median steps per day is 10765. The distribution is fairly symmetric.</p>
<h2>What is the average daily activity pattern?</h2>
<p>Split the data by time interval and calculate the mean for each interval.</p>
<pre><code class="r"># Split by interval
activities_steps_splitbyinterval <- with(activities, split(steps, intervalfactor))
# Calculate each interval's mean.
activities_steps_splitbyinterval_mean <- sapply(activities_steps_splitbyinterval,
mean, na.rm = TRUE)
# Inspect the data.
head(activities_steps_splitbyinterval_mean)
</code></pre>
<pre><code>## 0 5 10 15 20 25
## 1.71698 0.33962 0.13208 0.15094 0.07547 2.09434
</code></pre>
<p>The name of each value in the mean vector corresponds to the time of day (e.g., 0=midnight, 100=1:00 AM, 1300=1:00 PM, etc.).</p>
<p>Plot the average number of steps as a time series. Since, the time series must be over contiguous segments, the x-axis is represented by the interval index.</p>
<pre><code class="r">plot(activities_steps_splitbyinterval_mean, type = "l", main = "Average Steps by Time Interval",
xlab = "Time Interval", ylab = "Average Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk averagestepsbytimeinterval"/> </p>
<p>Find the interval that is represented by the peak in the above graph.</p>
<pre><code class="r">maxindex_steps_splitbyinterval_mean <- which(activities_steps_splitbyinterval_mean ==
max(activities_steps_splitbyinterval_mean))
maxtimeinterval <- names(maxindex_steps_splitbyinterval_mean)[1]
</code></pre>
<p>The most activity occurred at the 104 interval which corresponds to the 835 time interval.</p>
<h2>Imputing missing values</h2>
<p>Find the number of records that are missing steps.</p>
<pre><code class="r"># Total number of missing values
missingsteps <- is.na(activities$steps)
missingstepscount <- sum(missingsteps)
</code></pre>
<p>There are 2304 observations in which the step count is missing.</p>
<p>The step count will be imputed by using the average number of steps in the same time interval in the other days of the study. A column titled 'imputedsteps' will be added to the data frame.</p>
<pre><code class="r"># Create the imputed steps based on the average steps in each interval
imputedsteps <- activities$steps
activities <- cbind(activities, imputedsteps)
activities$imputedsteps[missingsteps] <- activities_steps_splitbyinterval_mean[activities$intervalfactor[missingsteps]]
# Analyze the impact of the imputed steps by splitting across days again.
activities_imputedsteps_splitbydate <- with(activities, split(imputedsteps,
datefactor))
activities_imputedsteps_splitbydate_total <- sapply(activities_imputedsteps_splitbydate,
sum)
</code></pre>
<p>This histogram shows the frequency distribution of steps per day, including the imputed steps.</p>
<pre><code class="r">hist(activities_imputedsteps_splitbydate_total, xlab = "Imputed Steps per day",
main = "Histogram of Imputed Steps per day", breaks = 10)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk imputedstepsperdayhistogram"/> </p>
<pre><code class="r">
activities_imputedsteps_splitbydate_total_df <- data.frame(total = activities_imputedsteps_splitbydate_total)
print(activities_imputedsteps_splitbydate_total_df)
</code></pre>
<pre><code>## total
## 2012-10-01 10766
## 2012-10-02 126
## 2012-10-03 11352
## 2012-10-04 12116
## 2012-10-05 13294
## 2012-10-06 15420
## 2012-10-07 11015
## 2012-10-08 10766
## 2012-10-09 12811
## 2012-10-10 9900
## 2012-10-11 10304
## 2012-10-12 17382
## 2012-10-13 12426
## 2012-10-14 15098
## 2012-10-15 10139
## 2012-10-16 15084
## 2012-10-17 13452
## 2012-10-18 10056
## 2012-10-19 11829
## 2012-10-20 10395
## 2012-10-21 8821
## 2012-10-22 13460
## 2012-10-23 8918
## 2012-10-24 8355
## 2012-10-25 2492
## 2012-10-26 6778
## 2012-10-27 10119
## 2012-10-28 11458
## 2012-10-29 5018
## 2012-10-30 9819
## 2012-10-31 15414
## 2012-11-01 10766
## 2012-11-02 10600
## 2012-11-03 10571
## 2012-11-04 10766
## 2012-11-05 10439
## 2012-11-06 8334
## 2012-11-07 12883
## 2012-11-08 3219
## 2012-11-09 10766
## 2012-11-10 10766
## 2012-11-11 12608
## 2012-11-12 10765
## 2012-11-13 7336
## 2012-11-14 10766
## 2012-11-15 41
## 2012-11-16 5441
## 2012-11-17 14339
## 2012-11-18 15110
## 2012-11-19 8841
## 2012-11-20 4472
## 2012-11-21 12787
## 2012-11-22 20427
## 2012-11-23 21194
## 2012-11-24 14478
## 2012-11-25 11834
## 2012-11-26 11162
## 2012-11-27 13646
## 2012-11-28 10183
## 2012-11-29 7047
## 2012-11-30 10766
</code></pre>
<p>Finally, calculate the mean and median steps per day. This time there are no NAs to ignore.</p>
<pre><code class="r">activities_imputedsteps_splitbydate_mean = format(mean(activities_imputedsteps_splitbydate_total),
digits = 6, nsmall = 2)
activities_imputedsteps_splitbydate_median = format(median(activities_imputedsteps_splitbydate_total),
digits = 6, nsmall = 2)
</code></pre>
<p>The mean steps per day is 10766.19. The median steps per day is 10766.19. The mean did not change, but now the median is more in line with the mean resulting in a distribution that is now even more symmetric.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>subset the data into two data frames, one for weekend dates and the other for weekend dates and then split each by the time interval. </p>
<pre><code class="r"># Split each subset by the time interval.
activities_imputedsteps_splitbyinterval_weekend <- with(subset(activities, daytypefactor ==
"weekend"), split(imputedsteps, intervalfactor))
activities_imputedsteps_splitbyinterval_weekday <- with(subset(activities, daytypefactor ==
"weekday"), split(imputedsteps, intervalfactor))
# Calculate the mean within each data frame.
activities_imputedsteps_splitbyinterval_weekend_mean <- sapply(activities_imputedsteps_splitbyinterval_weekend,
mean)
activities_imputedsteps_splitbyinterval_weekday_mean <- sapply(activities_imputedsteps_splitbyinterval_weekday,
mean)
</code></pre>
<p>Plot the average number of steps in each time interval for each data frame.</p>
<pre><code class="r">par(mfrow = c(2, 1))
plot(activities_imputedsteps_splitbyinterval_weekend_mean, type = "l", main = "weekend",
xlab = "Time Interval", ylab = "Average Imputed Steps", ylim = c(0, 250))
plot(activities_imputedsteps_splitbyinterval_weekday_mean, type = "l", main = "weekday",
xlab = "Time Interval", ylab = "Average Imputed Steps", ylim = c(0, 250))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk imputedstepspertimeinterval"/> </p>
<p>Notice that the weekend activity has peaks throughout the day, but the weekday activity tends to consolidate more activity earlier in the day.</p>
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