PA1_template.md

Reproducible Research: Peer Assessment 1

Loading and preprocessing the data

activity<- read.csv("activity.csv")
activity$date<-as.Date(activity$date)

What is mean total number of steps taken per day?

aggActivity <- aggregate(steps ~ date, activity, sum)

hist(aggActivity$steps, ylab  = "frequency", main="total number of steps per day", xlab="number of steps", breaks=10)

Mean of steps per day:

meanOfStepsPerDay <-mean(aggActivity$steps)

## [1] 10766.19

Median of steps per day:

medianOfStepsPerDay <- median(aggActivity$steps)

## [1] 10765

What is the average daily activity pattern?

intervalActivity <- aggregate(steps ~ interval, activity, mean)
plot(intervalActivity$interval, intervalActivity$steps, type="l",xlab="time of the day (in minutes)", ylab="number of steps", main= "daily activity pattern")

###Interval with the most number of steps:

maxInterval <-intervalActivity$interval[which(intervalActivity$steps==max(intervalActivity$steps))]
maxInterval

## [1] 835

Imputing missing values

number of missing values:

nrow(activity[is.na(activity$steps),])

## [1] 2304

Repacing the missing values by the mean for that interval:

cleanedActivity <-activity
for(i in 1:nrow(activity)){
    if(is.na(activity$steps[i])){
        cleanedActivity$steps[i]<-intervalActivity$steps[which(intervalActivity$interval==activity$interval[i])]
    }
}

CleanedaggActivity <- aggregate(steps ~ date, cleanedActivity, sum)

hist(CleanedaggActivity$steps, ylab  = "frequency", main="total number of steps per day", xlab="number of steps", breaks=10)

Mean of steps per day for cleaned data:

CleanedmeanOfStepsPerDay <-mean(CleanedaggActivity$steps)

## [1] 10766.19

Median of steps per day for cleaned data:

CleanedmedianOfStepsPerDay <- median(CleanedaggActivity$steps)

## [1] 10766.19

Because the missing values have been replaced by the mean for that interval, the total mean per day doesn't change but the median is closer to the mean.

Are there differences in activity patterns between weekdays and weekends?

Sorting which dates are weekdays and which are weekend:

cleanedActivity$weekday <- weekdays(cleanedActivity$date)
cleanedActivity$weekend[cleanedActivity$weekday %in% c("Saturday","Sunday")]<-"weekend"
cleanedActivity$weekend[!(cleanedActivity$weekday %in% c("Saturday","Sunday"))]<-"weekday"
cleanedActivity$weekend <- factor(cleanedActivity$weekend)

Getting the average by interval for weekdays and weekends:

WeekendintervalActivity <- aggregate(steps ~ interval, cleanedActivity[cleanedActivity$weekend=="weekend",], mean)
WeekendintervalActivity$weekend<-"weekend"
WeekintervalActivity <- aggregate(steps ~ interval, cleanedActivity[cleanedActivity$weekend=="weekday",], mean)
WeekintervalActivity$weekend<-"weekday"
weekIntervals<- rbind(WeekintervalActivity,WeekendintervalActivity)
weekIntervals$weekend <- factor(weekIntervals$weekend)

library(lattice)

xyplot(steps ~ interval|weekend, data=weekIntervals, layout=c(1,2),type="l",main="Comparison of average steps by interveal between work days and weekends")