peerAssessment1.Rmd

---
title: "PeerAssessment1"
author: "Zhang Shiwei"
date: "Sunday, September 14, 2014"
output: html_document
---

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 script will clean the data and do some analysis.

Loading data
============
```{r}
theData <- read.csv("./activity.csv")
str(theData)
```

Mean total number of steps taken per day
========================================
```{r}
totalStepsTakenPerDay <- aggregate(steps ~ date,theData,sum)
hist(totalStepsTakenPerDay$steps,breaks=10)
mean(totalStepsTakenPerDay$steps)
median(totalStepsTakenPerDay$steps)
```

Average daily activity pattern
==============================
```{r}
averageStepsTakenOfFiveMinuteInterval <- aggregate(steps ~ interval,theData,mean)
plot(averageStepsTakenOfFiveMinuteInterval,type="l")
title("Average Steps Taken Of Five Minute Interval")
IntervalThatHasMaxAverageSteps <- averageStepsTakenOfFiveMinuteInterval[averageStepsTakenOfFiveMinuteInterval$steps == max(averageStepsTakenOfFiveMinuteInterval$steps),"interval"]
```

The `r IntervalThatHasMaxAverageSteps` five-minute interval has the max average steps

Imputing missing values
======================
```{r}
NumberOfNAs <- nrow(theData[is.na(theData$steps),])
```

The total number of rows with NAs is `r NumberOfNAs`

I use the mean of the interval to fill NAs:
```{r}
for(i in 1:nrow(theData)){
  if(is.na(theData$steps[i])){
    theData$steps[i] <- averageStepsTakenOfFiveMinuteInterval$steps[averageStepsTakenOfFiveMinuteInterval$interval==theData$interval[i]]
  }
}
nrow(theData[is.na(theData$steps),])
```

Total number of steps taken each day now is like following:
```{r}
totalStepsTakenPerDay <- aggregate(steps ~ date,theData,sum)
hist(totalStepsTakenPerDay$steps,breaks=10)
mean(totalStepsTakenPerDay$steps)
median(totalStepsTakenPerDay$steps)
```

We can see it slightly differs from the previous one.

Are there differences in activity patterns between weekdays and weekends?
=========================================================================
```{r}
```
I haven't finish it but there is no time left...