Title: Reproducible Research Peer Assignment 1

Loading and preprocessing the data

setwd("e:\\module5")
activity <- read.csv("activity.csv", colClasses = c("numeric", "character", "numeric"))
head(activity)

##   steps       date interval
## 1    NA 2012-10-01        0
## 2    NA 2012-10-01        5
## 3    NA 2012-10-01       10
## 4    NA 2012-10-01       15
## 5    NA 2012-10-01       20
## 6    NA 2012-10-01       25

names(activity)

## [1] "steps"    "date"     "interval"

activity$date <- as.Date(activity$date, "%Y-%m-%d")

What is mean total number of steps taken per day?

1.Calculate the total number of steps taken per day

StepsTotal <- aggregate(steps ~ date, data = activity, sum, na.rm = TRUE)
head(StepsTotal)

##         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

2.Make a histogram of the total number of steps taken each day

hist(StepsTotal$steps, main = "Total steps by day", xlab = "day", col = "red")

3.Calculate the mean and median of the total number of steps taken per day

The mean and median of the total number of steps taken per day respectively

mean(StepsTotal$steps); median(StepsTotal$steps)

## [1] 10766.19

## [1] 10765

What is the average daily activity pattern?

1.Make 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 days (y-axis)

Get mean of steps and plot time series.

time_series <- tapply(activity$steps, activity$interval, mean, na.rm = TRUE)

plot(row.names(time_series), time_series, type = "l", xlab = "5-min interval", 
    ylab = "Average across all Days", main = "Average number of steps taken", 
    col = "red")

2.Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?

max_interval <- which.max(time_series)
names(max_interval)

## [1] "835"

Imputing missing values

1.Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)

activity_NA <- sum(is.na(activity))
activity_NA

## [1] 2304

2.Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.

Replace NA with mean for that 5-minute interval

StepsAverage <- aggregate(steps ~ interval, data = activity, FUN = mean)
fillNA <- numeric()
for (i in 1:nrow(activity)) {
    obs <- activity[i, ]
    if (is.na(obs$steps)) {
        steps <- subset(StepsAverage, interval == obs$interval)$steps
    } else {
        steps <- obs$steps
    }
    fillNA <- c(fillNA, steps)
}

3.Create a new dataset that is equal to the original dataset but with the missing data filled in.

new_activity <- activity
new_activity$steps <- fillNA

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. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?

StepsTotal2 <- aggregate(steps ~ date, data = new_activity, sum, na.rm = TRUE)
hist(StepsTotal2$steps, main = "Total steps by day", xlab = "day", col = "red")

The mean and median of the total number of steps taken per day respectively

mean(StepsTotal2$steps); median(StepsTotal2$steps)

## [1] 10766.19

## [1] 10766.19

There are no difference in mean and only slight difference in median

Are there differences in activity patterns between weekdays and weekends?

For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.

1.Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.

day <- weekdays(activity$date)
daylevel <- vector()
for (i in 1:nrow(activity)) {
    if (day[i] == "Saturday") {
        daylevel[i] <- "Weekend"
    } else if (day[i] == "Sunday") {
        daylevel[i] <- "Weekend"
    } else {
        daylevel[i] <- "Weekday"
    }
}
activity$daylevel <- daylevel
activity$daylevel <- factor(activity$daylevel)

stepsByDay <- aggregate(steps ~ interval + daylevel, data = activity, mean)
names(stepsByDay) <- c("interval", "daylevel", "steps")

2.Make a 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). See the README file in the GitHub repository to see an example of what this plot should look like using simulated data.

library(lattice)
xyplot(steps ~ interval | daylevel, stepsByDay, type = "l", layout = c(1, 2), xlab = "Interval", ylab = "Number of steps")