PA1_template.md

Reproducible Research: Peer Assessment 1

Loading and preprocessing the data

1. Load the data (i.e. read.csv())

if(!file.exists('activity.csv')){
  unzip('activity.zip')
}

## Warning in unzip("activity.zip"): error 1 in extracting from zip file

activityData <- read.csv('activity.csv')

2. Process/transform the data (if necessary) into a format suitable for your analysis

#activityData$interval <- strptime(gsub("([0-9]{1,2})([0-9]{2})", "\\1:\\2", activityData$interval), format='%H:%M')

---

What is mean total number of steps taken per day?

stepsByDay <- tapply(activityData$steps, activityData$date, sum, na.rm=TRUE)

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

qplot(stepsByDay, xlab='Total steps per day', ylab='Frequency using binwith 500', binwidth=500)

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

stepsByDayMean <- mean(stepsByDay)
stepsByDayMedian <- median(stepsByDay)

• Mean: 9354.2295082
• Median: 10395

---

What is the average daily activity pattern?

averageStepsPerTimeBlock <- aggregate(x=list(meanSteps=activityData$steps), by=list(interval=activityData$interval), FUN=mean, na.rm=TRUE)

1. Make a time series plot

ggplot(data=averageStepsPerTimeBlock, aes(x=interval, y=meanSteps)) +
  geom_line() +
  xlab("5-minute interval") +
  ylab("average number of steps taken") 

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

mostSteps <- which.max(averageStepsPerTimeBlock$meanSteps)
timeMostSteps <-  gsub("([0-9]{1,2})([0-9]{2})", "\\1:\\2", averageStepsPerTimeBlock[mostSteps,'interval'])

• Most Steps at: 8:35

---

Imputing missing values

1. Calculate and report the total number of missing values in the dataset

numMissingValues <- length(which(is.na(activityData$steps)))

• Number of missing values: 2304

2. Devise a strategy for filling in all of the missing values in the dataset.

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

activityDataImputed <- activityData
activityDataImputed$steps <- impute(activityData$steps, fun=mean)

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

stepsByDayImputed <- tapply(activityDataImputed$steps, activityDataImputed$date, sum)
qplot(stepsByDayImputed, xlab='Total steps per day (Imputed)', ylab='Frequency using binwith 500', binwidth=500)

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

stepsByDayMeanImputed <- mean(stepsByDayImputed)
stepsByDayMedianImputed <- median(stepsByDayImputed)

• Mean (Imputed): 1.0766189 × 10⁴
• Median (Imputed): 1.0766189 × 10⁴

---

Are there differences in activity patterns between weekdays and weekends?

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.

activityDataImputed$dateType <-  ifelse(as.POSIXlt(activityDataImputed$date)$wday %in% c(0,6), 'weekend', 'weekday')

2. Make a panel plot containing a time series plot

averagedActivityDataImputed <- aggregate(steps ~ interval + dateType, data=activityDataImputed, mean)
ggplot(averagedActivityDataImputed, aes(interval, steps)) + 
  geom_line() + 
  facet_grid(dateType ~ .) +
  xlab("5-minute interval") + 
  ylab("avarage number of steps")