Analysis on personal movement data

Introduction

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.

This analysis makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day.

Loading and preprocessing the data

library(ggplot2)
data<- read.csv("activity.csv")
data$date<-as.Date(data$date)

What is mean total number of steps taken per day?

# Calculate the total number of steps taken per day
perDay<-aggregate(data$steps, list(data$date), sum)

# Make a histogram of the total number of steps taken each day
ggplot(data, aes(x=date, y=steps))+geom_bar(stat = "identity")

## Warning: Removed 2304 rows containing missing values (position_stack).

#ggplot(perDay, aes(x=Group.1, y=x))+geom_bar(stat = "identity")

# mean and median of the total number of steps taken per day
mean(perDay$x, na.rm = TRUE)

## [1] 10766.19

median(perDay$x, na.rm = TRUE)

## [1] 10765

What is the average daily activity pattern?

perInterval<-aggregate(data$steps, list(data$interval), mean, na.rm=TRUE)

# Make a time series plot
ggplot(perInterval, aes(x=Group.1, y=x))+geom_line()

# Which 5-minute interval, contains the maximum number of steps?
perInterval[which(perInterval$x== max(perInterval$x)),1] 

## [1] 835

Imputing missing values

# Calculate and report the total number of missing values in the dataset
sum(is.na(data$steps))

## [1] 2304

# Create a new dataset that is equal to the original dataset 
imputeData<-data

# Devise a strategy for filling in all of the missing values in the dataset
imputeData[is.na(imputeData$steps),1]<- as.integer(perInterval$x)

# Make a histogram of the total number of steps taken each day
ggplot(imputeData, aes(x=date, y=steps))+geom_bar(stat = "identity")

# Calculate and report the mean and median
perDayImputed<-aggregate(imputeData$steps, list(imputeData$date), sum)
mean(perDay$x, na.rm = TRUE)

## [1] 10766.19

median(perDay$x, na.rm = TRUE)

## [1] 10765

Do these values differ from the estimates from the first part of the assignment? Yes

What is the impact of imputing missing data on the estimates of the total daily number of steps? It has increased

Are there differences in activity patterns between weekdays and weekends?

weekdays1 <- c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday')

# Create a new factor variable in the dataset with two levels 
imputeData$wDay <- factor((weekdays(imputeData$date) %in% weekdays1),levels=c(FALSE, TRUE), labels=c('weekend', 'weekday'))

perIntervalImpute<-aggregate(imputeData$steps, list(imputeData$interval, imputeData$wDay), mean)

# Make a panel plot containing a time series plot
ggplot(perIntervalImpute , aes(x=Group.1, y=x, color = Group.2,group=Group.2))+geom_line()+facet_wrap(~Group.2,nrow = 2)