ACTIVITY MONITORING ANALYSIS

Loading and preprocessing the data

First we load the data and define the classes of our column variables. Lets look at the structure of our data.

activity <- read.csv("activity.csv", header = T, colClasses = c("numeric", "Date", 
    "integer"))
str(activity)

## 'data.frame':    17568 obs. of  3 variables:
##  $ steps   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ date    : Date, format: "2012-10-01" "2012-10-01" ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...

What is mean total number of steps taken per day?

• Make a histogram of the total number of steps taken each day. The ddplyr function sums the steps splitted by the variable date & creates a new data frame.

library(ggplot2)
library(plyr)
perDay <- ddply(activity, "date", summarise, totalSteps = sum(steps, na.rm = T))
qplot(totalSteps, data = perDay, binwidth = 500)

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

with(perDay, mean(totalSteps, na.rm = T))

## [1] 9354

with(perDay, median(totalSteps, na.rm = T))

## [1] 10395

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)

perInterval <- ddply(activity, "interval", summarise, meanSteps = mean(steps, 
    na.rm = T))
qplot(interval, meanSteps, data = perInterval, geom = "line")

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

with(perInterval, interval[which.max(meanSteps)])

## [1] 835

Imputing missing values

Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.

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

with(activity, sum(is.na(steps)))

## [1] 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.

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

Here I will use the impute function from the Hmisc package. This will fill the missing values with the mean of the values of this variable (steps).

library(Hmisc, warn.conflicts = F)

## Loading required package: grid
## Loading required package: lattice
## Loading required package: survival
## Loading required package: splines
## Loading required package: Formula

completeActivity <- transform(activity, steps = impute(steps, mean))
byDay <- ddply(completeActivity, "date", summarise, totalSteps = sum(steps, 
    na.rm = T))

• 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. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?

When imputing missing value, our mean and median changed

qplot(totalSteps, data = byDay, binwidth = 500)

with(byDay, mean(totalSteps, na.rm = T))

## [1] 10766

with(byDay, median(totalSteps, na.rm = T))

## [1] 10766

Are there differences in activity patterns between weekdays and weekends?

For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.

• 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.

completeActivity$weekends <- weekdays(completeActivity$date) %in% c("Saturday", 
    "Sunday")
completeActivity$weekends <- as.factor(completeActivity$weekends)
levels(completeActivity$weekends) <- 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).

perInterval <- ddply(completeActivity, .(weekends, interval), summarise, meanSteps = mean(steps))
qplot(interval, meanSteps, data = perInterval, geom = "line", facets = . ~ weekends)