PA1_template.md

title	output	keep_md
Reproducible Research: Peer Assessment 1	html_document	true

Loading and preprocessing the data

Extract the zip file and load the data with classes of the columns defined.

unzip("./activity.zip")
activity <- read.csv("./activity.csv", na.strings = "NA", 
                     colClasses = c("numeric", "Date", "integer"))
head(activity, 3)

##   steps       date interval
## 1    NA 2012-10-01        0
## 2    NA 2012-10-01        5
## 3    NA 2012-10-01       10

tail(activity, 3)

##       steps       date interval
## 17566    NA 2012-11-30     2345
## 17567    NA 2012-11-30     2350
## 17568    NA 2012-11-30     2355

Convert the time interval into actual minute value.

activity$minute <- sapply(activity$interval, function(x) {x%/%100*60+x%%100})
head(activity, 3)

##   steps       date interval minute
## 1    NA 2012-10-01        0      0
## 2    NA 2012-10-01        5      5
## 3    NA 2012-10-01       10     10

tail(activity, 3)

##       steps       date interval minute
## 17566    NA 2012-11-30     2345   1425
## 17567    NA 2012-11-30     2350   1430
## 17568    NA 2012-11-30     2355   1435

What is mean total number of steps taken per day?

A histogram of the total number of steps taken each day.

eachday <- aggregate(steps ~ date, data = activity, FUN = sum)
hist(eachday$steps)

mean total number of steps taken per day (ignoring missing values).

mean(eachday$steps)

## [1] 10766

median total number of steps taken per day (ignoring missing values).

median(eachday$steps)

## [1] 10765

What is the average daily activity pattern?

A time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)

eachtime <- aggregate(steps ~ minute + interval, data = activity, FUN = mean)
plot(eachtime$minute, eachtime$steps, type = "l")

The 5-minute interval, on average across all the days in the dataset, that contains the maximum number of steps.

eachtime[eachtime$steps >= max(eachtime$steps), ]

##     minute interval steps
## 104    515      835 206.2

Imputing missing values

Total number of missing values in the dataset.

sum(!complete.cases(activity))

## [1] 2304

Fill in all the missing values by the median for that 5-minute interval. I have chosen that because it is fun to do something that is not suggested also not forbidden.

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

newdata <- activity
newdata$steps <- with(newdata, do.call(c, tapply(steps, minute, function(y) {
    ym <- median(y, na.rm=TRUE)
    y[is.na(y)] <- ym
    y
})))

A histogram of the total number of steps taken each day of new dataset.

eachdaynew <- aggregate(steps ~ date, data = newdata, FUN = sum)
hist(eachdaynew$steps)

mean total number of steps taken per day of new dataset.

mean(eachdaynew$steps)

## [1] 9504

median total number of steps taken per day of new dataset.

median(eachdaynew$steps)

## [1] 9069

Are there differences in activity patterns between weekdays and weekends?

Create a new factor variable in the dataset with two levels - "weekday" and "weekend".

newdata$daytype <- as.factor(sapply(weekdays(newdata$date), function(x) {
    if(grepl("^S", x)) "weekend"
    else "weekday"
}))

A panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).

library(lattice)
bydaytype <- aggregate(steps ~ minute + daytype, data = newdata, FUN = mean)
xyplot(steps ~ minute | daytype, data = bydaytype, type = "l", layout = c(1, 2))