PA1_template.Rmd

Loading and preprocessing the data

setwd("C:/Raghu/Rscipts/data")
unzip(zipfile="repdata_data_activity.zip")
activityData <- read.csv("activity.csv")

What is mean total number of steps taken per day?

totalSteps <- tapply(activityData$steps, activityData$date, sum, na.rm=TRUE)
hist(totalSteps)

mean(totalSteps, na.rm=TRUE)

## [1] 9354.23

median(totalSteps, na.rm=TRUE)

## [1] 10395

What is the average daily activity pattern?

intervalAvgs <- aggregate(x=list(steps=activityData$steps), 
                          by=list(interval=activityData$interval),
                          mean, na.rm=TRUE)

plot(intervalAvgs$interval,intervalAvgs$steps, type="l",
        xlab = "5-minute Intervals", ylab="Average number of steps taken", )

Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?

intervalAvgs[which.max(intervalAvgs$steps),]

##     interval    steps
## 104      835 206.1698

Imputing missing values

1.The total number of missing values in the dataset (i.e. the total number of rows with NAs)

missing <- is.na(activityData$steps)
table(missing)

## missing
## FALSE  TRUE 
## 15264  2304

2.Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.

Create a new dataset that is equal to the original dataset.

filledActivityData <- activityData

All of the missing values are filled in with mean value for that 5-minute interval.

# Replace each missing value with the mean value of its 5-minute interval
fillMean <- function(steps, interval) {
    filled <- NA
    if (!is.na(steps))
        filled <- c(steps)
    else
        filled <- (intervalAvgs[intervalAvgs$interval==interval, "steps"])
    return(filled)
}
filledActivityData$steps <- mapply(fillMean, filledActivityData$steps, filledActivityData$interval)

4.Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day.

totalSteps <- tapply(filledActivityData$steps, filledActivityData$date, sum, na.rm=TRUE)
hist(totalSteps)

mean(totalSteps, na.rm=TRUE)

## [1] 10766.19

median(totalSteps, na.rm=TRUE)

## [1] 10766.19

Please note: The new mean and median values are slightly higher than the earlier, since all NA values are replaced with mean of respective interval.

Are there differences in activity patterns between weekdays and weekends?

1. Create a new factor variable in the dataset with two levels - ‘weekday’ and ‘weekend’ indicating whether a given date is a weekday or weekend day.

dayCat <- function (stringDay){
  if (stringDay %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))
    return("weekday")
  else if (stringDay %in% c("Saturday", "Sunday"))
    return("weekend")
}
filledActivityData$wday<- sapply(weekdays(as.Date(filledActivityData$date, na.rm = TRUE)), dayCat)
class(filledActivityData$wday)

## [1] "character"

filledActivityData$wday <- as.factor(filledActivityData$wday)
class(filledActivityData$wday)

## [1] "factor"

2. Make a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).

averages <- aggregate(steps ~ interval + wday, data=filledActivityData, mean)
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.2.1

ggplot(averages, aes(interval, steps)) + geom_line() + facet_grid(wday ~ .) +
    xlab("5-minute interval") + ylab("Average number of steps taken")