Report on personal activity monitoring device data

Synopsis

The following report makes use of data from a personal activity monitoring device(i.e. Fitbit,Nike Fuelband, Jawbone Up.
The dataset (Activity monitoring data) consists of two months of data collects from an individual during the months of October and November 2012.

The report will answer the following questions related to the individuals personal activity.
1. What is mean total number of steps taken per day?
2. What is the average daily activity pattern?
3. Are there differences in activity pattern between weekdays and weekends?

## The Code below is designed to Load and preprocess data
## The 0 values and NA values will be removed from the dataset.
url<-"http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
download.file(url, destfile = "activity.zip", mode="wb") 
unzip("activity.zip")
data <- read.csv("activity.csv")
data$interval <- sprintf("%04d", data$interval)

## What is mean total number of steps taken per day?
## A Histogram is also created with the total number of steps taken each day.
## The mean and median of dataset is also calculated
data$steps[data$steps==0] <- NA
omitdata <- na.omit(data)
omitdata$steps <-as.numeric(omitdata$steps)
omitdata$date <- as.Date(omitdata$date, format = "%Y-%m-%d")
sumdata <- aggregate(omitdata$steps, by=list(omitdata$date), FUN=sum)
hist(sumdata$x, main="Total number of steps per day",xlab="Steps per day", ylab="Frequency")

mean(sumdata$x)

## [1] 10766

median(sumdata$x)

## [1] 10765

## What is the average daily activity pattern??
## A time series plot is created of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
## The 5-minute interval, on average across all the days in the dataset, containing the maximum number of steps is displayed?
omitdata$interval <- format(omitdata$interval, format="%H%M")
averagesteps <- aggregate(omitdata$steps, list(interval = omitdata$interval),mean)
plot(averagesteps$interval,averagesteps$x,type="l", main="Average number of steps per interval", xlab="5 min intervals(hhmm)", ylab="Average number of steps" )

max(averagesteps$x)

## [1] 352.5

## Imputing missing values
data <- read.csv("activity.csv")
## Total numbers of missing values in the dataset
sum(is.na(data$steps))
data$interval <- sprintf("%04d", data$interval)
## The strategy used to fill in missing data is to update each interval with the
## mean for that 5 minute interval.
data$steps[is.na(data$steps)] <- averagesteps[match(data$interval, averagesteps$interval),2]

## Warning: number of items to replace is not a multiple of replacement
## length

data$steps[data$steps==0] <- NA
na.omit(data$steps)
## Updated histogram containing substituted values
hist(data$steps,main="Total number of steps per day",xlab="Steps per day", ylab="Frequency")

## Are there differences in activity patterns between weekdays and weekends?
data <- read.csv("activity.csv")
data$steps[is.na(data$steps)] <- 0
averagesteps <- aggregate(data$steps, list(interval = data$interval),mean)
data$date <- as.POSIXct(data$date, format="%Y-%m-%d")
week <-weekdays(data$date)
week <-as.factor(ifelse(week %in% c("Saturday","Sunday"),"Weekend","Weekday"))
updatedata <- cbind(data,week)
weekplotdata <- subset(updatedata, week=="Weekday")
weekendplotdata <- subset(updatedata, week=="Weekend")
averageweekplotdata <- aggregate(weekplotdata$steps, list(interval= weekplotdata$interval),mean)
averageweekendplotdata <- aggregate(weekendplotdata$steps, list(interval = weekendplotdata$interval),mean)
par(mfrow = c(2,1), mar = c(4,4,2,1), oma = c(0,0,2,0))
WeekdayPlot <- plot(averageweekplotdata$interval,averageweekplotdata$x,type="l", main="Average Number of steps per week", xlab="Frequency", ylab="Interval")
WeekendPlot <- plot(averageweekendplotdata$interval, averageweekendplotdata$x, type="l",main= "Average Number of steps per weekend", xlab="Frequency",ylab="Interval")