Peer Assesment 1

Loading and preprocessing the data

mainData <- read.csv("activity.csv", sep=",")
head(mainData)

##   steps       date interval
## 1    NA 2012-10-01        0
## 2    NA 2012-10-01        5
## 3    NA 2012-10-01       10
## 4    NA 2012-10-01       15
## 5    NA 2012-10-01       20
## 6    NA 2012-10-01       25

hist(mainData$steps)

What is mean total number of steps taken per day?

1. Total number of steps calculated for each day and saved in the variable

stepsPerDay <- tapply(mainData$steps, mainData$date, sum, na.rm=TRUE)
head(stepsPerDay)

## 2012-10-01 2012-10-02 2012-10-03 2012-10-04 2012-10-05 2012-10-06 
##          0        126      11352      12116      13294      15420

2. Histogram of total number of steps taken each day .

## Loading required package: grid
## Loading required package: lattice
## Loading required package: survival
## Loading required package: splines
## Loading required package: Formula
## 
## Attaching package: 'Hmisc'
## 
## The following objects are masked from 'package:base':
## 
##     format.pval, round.POSIXt, trunc.POSIXt, units

qplot(stepsPerDay, xlab="Total steps for day", ylab="Frequency of steps",  binwidth=1000)

3. Calculating here the mean and median of total number of steps taken each day.

meanByDay <- mean(stepsPerDay)
medianByDay <- median(stepsPerDay)

Mean of total number of steps taken each day is 9354.2295082.
Mean of total number of steps taken each day is 10395.

What is the average daily activity pattern?

averageStepsPerTimeBlock <- aggregate(x=list(meanSteps=mainData$steps), by=list(interval=mainData$interval), FUN=mean, na.rm=TRUE)

head(averageStepsPerTimeBlock)

##   interval meanSteps
## 1        0 1.7169811
## 2        5 0.3396226
## 3       10 0.1320755
## 4       15 0.1509434
## 5       20 0.0754717
## 6       25 2.0943396

1. Make a time series plot

qplot(interval, meanSteps, data=averageStepsPerTimeBlock, geom=c("line"), xlab="Average no of steps taken", ylab="5-min interval")

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

missingValues <- length(which(is.na(mainData$steps)))

Total number of missing value is 2304.

2. 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.

Hmisc package impute function is used here.

dataImputed <- mainData
dataImputed$steps <- impute(mainData$steps, fun=mean)

4. Make a histogram of the total number of steps taken.

stepsPerDayImputed <- tapply(dataImputed$steps, dataImputed$date, sum)
qplot(stepsPerDayImputed, xlab='Total steps per day (Imputed)', ylab='Frequency using', binwidth=100)

Calculate and report the mean and median

stepsPerDayMeanImputed <- mean(stepsPerDayImputed)
stepsPerDayMedianImputed <- median(stepsPerDayImputed)

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 weekday and weekend

dataImputed$dateType <-  ifelse(as.POSIXlt(dataImputed$date)$wday %in% c(0,6), 'weekend', 'weekday')

2. Make a time series plot

avgdataImputed <- aggregate(steps ~ interval + dateType, data=dataImputed, mean)
ggplot(avgdataImputed, aes(interval, steps)) + 
    geom_line() + 
    facet_grid(dateType ~ .) +
    xlab("5-minute interval") + 
    ylab("avarage number of steps")