An analysis on personal activity monitoring device data

Synopsis

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 report 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

Load the data

temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip", temp, mode="wb", method="curl")
unzip(temp, "activity.csv")
activity <- read.table("activity.csv", sep=",", header=T)

Process/transform the data (if necessary) into a format suitable for your analysis

totalSteps <- aggregate(steps ~ date, data = activity, sum, na.rm = TRUE)

What is mean total number of steps taken per day?

Make a histogram of the total number of steps taken each day

hist(totalSteps$steps,col="blue",main="Histogram of Total Steps taken per day",xlab="Total Steps taken per day",cex.axis=1,cex.lab = 1)

Calculate and report the mean and median total number of steps taken per day

mean_steps <- mean(totalSteps$steps)
median_steps <- median(totalSteps$steps)

• The mean total number of steps taken per day is 1.0766 × 10⁴ steps
• The median total number of steps taken per day is 10765 steps

What is the average daily activity pattern?

Make a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)

steps_interval <- aggregate(steps ~ interval, data = activity, mean, na.rm = TRUE)
plot(steps ~ interval, data = steps_interval, type = "l", xlab = "Time Intervals (5-minute)", ylab = "Mean number of steps taken (all Days)", main = "Average number of steps Taken at 5 minute Intervals",  col = "blue")

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

maxStepInterval <- steps_interval[which.max(steps_interval$steps),"interval"]

• 835 interval contains the maximum number of steps

Imputing missing values

Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)

missing_rows <- sum(!complete.cases(activity))

• The total number of missing rows is 2304

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.

## This function returns the mean steps for a given interval
getMeanStepsPerInterval <- function(interval){
    steps_interval[steps_interval$interval==interval,"steps"]
    }

Create a new dataset that is equal to the original dataset but with the missing data filled in.

complete.activity <- activity

## Filling the missing values with the mean for that 5-minute interval
flag = 0
for (i in 1:nrow(complete.activity)) {
    if (is.na(complete.activity[i,"steps"])) {
        complete.activity[i,"steps"] <- getMeanStepsPerInterval(complete.activity[i,"interval"])
        flag = flag + 1
        }
    }

• Total of 2304 missing values were filled.

Make a histogram of the total number of steps taken each day.

total.steps.per.days <- aggregate(steps ~ date, data = complete.activity, sum)
hist(total.steps.per.days$steps, col = "blue", xlab = "Total Number of Steps", 
     ylab = "Frequency", main = "Histogram of Total Number of Steps taken each Day")

Calculate and report the mean and median total number of steps taken per day.

showMean <- mean(total.steps.per.days$steps)
showMedian <- median(total.steps.per.days$steps)

• Mean total number of steps taken per day is 1.0766 × 10⁴
• Median total number of steps taken per day is 1.0766 × 10⁴

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

• The mean value is the same as the value before imputing missing data, but the median value has changed.

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

• The mean value is the same as the value before imputing missing data since the mean value has been used for that particular 5-min interval. The median value is different, since the median index is now being changed after imputing missing values.

Are there differences in activity patterns between weekdays and weekends?

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.

complete.activity$day <- ifelse(as.POSIXlt(as.Date(complete.activity$date))$wday%%6 == 
                                    0, "weekend", "weekday")
complete.activity$day <- factor(complete.activity$day, levels = c("weekday", "weekend"))

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

steps.interval= aggregate(steps ~ interval + day, complete.activity, mean)
library(lattice)
xyplot(steps ~ interval | factor(day), data = steps.interval, aspect = 1/2, 
       type = "l")