PA1_template.md

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Reproducible Research: Peer Assessment 1	html_document keep_md true

Loading and preprocessing the data

1. Load the data

## Import the necessary libraries
library(data.table)
library(ggplot2)
library(plyr)

## Read the CSV file
data <- read.csv(file = "activity.csv")

2. Transform the data into a format suitable for analysis

stepsData <- as.data.table(x = data)

What is mean total number of steps taken per day?

1. Calculate the total number of steps taken per day

totalSteps <- aggregate(formula = steps ~ date, FUN = sum, data = stepsData)

A portion of the totalSteps is as follows

##         date steps
## 1 2012-10-02   126
## 2 2012-10-03 11352
## 3 2012-10-04 12116
## 4 2012-10-05 13294
## 5 2012-10-06 15420
## 6 2012-10-07 11015

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

qplot(x = steps, data = totalSteps, geom = "histogram", main = "Total Steps Per Day")

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

stepsMean <- mean(x = totalSteps$steps, na.rm = TRUE)
stepsMedian <- median(x = totalSteps$steps, na.rm = TRUE)

The mean of the total number of steps taken per day is 10766.19 & the median of the total number of steps taken per day is 10765.

What is the average daily activity pattern?

1. Make a time series plot of the 5-minute interval and the average number of steps taken, averaged across all days

## Calculate the average number of steps taken in the 5-minute interval
averageSteps <- ddply(.data = stepsData, .variables = "interval", .fun = summarise, steps = mean(steps, na.rm = TRUE))

## Create the plot
qplot(x = interval, y = steps, data = averageSteps, geom = "line", xlab = "5-Minute Interval(HHMM)", ylab = "Average Number of Steps", main = "Time Series of Avg. Steps Against 5-Minute Interval")

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

## Find the index of the maximum of steps
index <- which.max(x = averageSteps$steps)

## Get the interval located at the index
maxInterval <- stepsData$interval[index]

The interval with maximum number of steps is 835.

Imputing missing values

1. Calculate and report the total number of missing values in the dataset

countNA <- sum(is.na(stepsData[,steps]))

Total number of missing values in the dataset is 2304.

2. Fill in all of the missing values in the dataset with the average 5-minute interval values and create a new dataset with all the missing data filled in

We are replacing the NA values with the average 5-minute interval values. We use ceiling here to round off the average 5-minute interval values. The new dataset with the missing values filled in is named completeStepsData

## Get the steps column from the dataset
stepCount <- data.frame(stepsData[,steps])

## Replacing NA values with the average 5-minute interval values
stepCount[is.na(stepCount),] <- ceiling(tapply(X = stepsData[,steps], INDEX = stepsData[,interval], FUN = mean, na.rm = TRUE))

## Create a new dataset combining the columns
completeStepsData <- cbind(stepCount, data[,2:3])
colnames(completeStepsData) <- colnames(data)

A portion of the new dataset completeStepsData with all the missing data filled in is as follows

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

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

Total number of steps taken each day(with missing values filled in)

newTotalSteps <- aggregate(formula = steps ~ date, FUN = sum, data = completeStepsData)

A portion of the newTotalSteps is as follows

##         date steps
## 1 2012-10-01 10909
## 2 2012-10-02   126
## 3 2012-10-03 11352
## 4 2012-10-04 12116
## 5 2012-10-05 13294
## 6 2012-10-06 15420

Histogram of the total number of steps taken each day(with missing values filled in)

qplot(x = steps, data = newTotalSteps, geom = "histogram", main = "Total Steps Per Day With Missing Data Filled In")

Calculate the mean and median of the total number of steps taken per day(with missing values filled in)

newStepsMean <- mean(x = newTotalSteps$steps, na.rm = TRUE)
newStepsMedian <- median(x = newTotalSteps$steps, na.rm = TRUE)

The mean of the total number of steps taken per day is 10784.92 & the median of the total number of steps taken per day is 10909.00.

Are there differences in activity patterns between weekdays and weekends?

## Create a new data frame with a column for the type of the day
day.name <- weekdays(as.POSIXct(completeStepsData$date))
day.type <- ifelse(day.name == "Saturday" | day.name == "Sunday", "weekend", "weekday" )
weeklySteps <- cbind(completeStepsData, day.type)

## Calculate the average steps for 5-minute intervals for weekend and weekday
newAverageSteps <- ddply(.data = weeklySteps, .variables = c("interval", "day.type"), .fun = summarise, steps = mean(steps))

## Plotting
qplot(x = interval, y = steps, data = newAverageSteps, geom = "line", xlab = "5-Minute Interval(HHMM)", ylab = "Average Number of Steps", main = "Time Series of Avg. Steps Against 5-Minute Interval", facets = day.type ~ .)