Reproducible Research: Peer Assessment 1

Author: Alexander Barrantes Herrera

Date: March 14, 2019

Loading and preprocessing the data

1. Load the data (i.e. read.csv())
if(!file.exists('activity.csv')){
    unzip('activity.zip')
}
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): 1.076618910^{4}
  • Median (Imputed): 1.076618910^{4}

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")