Reproducible Research Project 1

Borye

October 2014

About

This was the first project for the Reproducible Research course in Coursera’s Data Science specialization track. The purpose of the project was to answer a series of questions using Activity monitoring dataset.

Loading and preprocessing the data

setwd("C:/Users/bliap/Documents/R")
Data <- read.csv("./activity.csv", header = TRUE, sep = ",", na.string = "NA")
Data$date <- as.Date(Data$date)

What is mean total number of steps taken per day

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

steps_by_day <- aggregate(steps ~ date, Data, sum)     ## calculate the every day steps
plot(steps_by_day$date, steps_by_day$steps, type="h", main="Histogram of Daily Steps", xlab="Date", 
     ylab="Steps per Day", col="blue", lwd = 8)

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

paste("Mean Steps per Day =", mean(steps_by_day$steps, na.rm = TRUE))

## [1] "Mean Steps per Day = 10766.1886792453"

paste("Median Steps per Day =", median(steps_by_day$steps, na.rm = TRUE))

## [1] "Median Steps per Day = 10765"

What is the average daily activity pattern

Make a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged acrossed all days (y-axis)

steps_by_interval <- aggregate(steps ~ interval, Data, mean)
plot(steps_by_interval$interval, steps_by_interval$steps, type = "l", main = "Average Number of Steps per 5-minute in a day", xlab = "Time Interval", ylab = "Steps", col = "orange", lwd = 2)

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

paste("Maximum number of steps occurs at", steps_by_interval[which.max(steps_by_interval$steps), 1], "time interval")

## [1] "Maximum number of steps occurs at 835 time interval"

Imputing missing values

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

paste("The total number of rows with missing value is", length(which(is.na(Data$steps))))

## [1] "The total number of rows with missing value is 2304"

Devise a strategy for filling in all the missing values. I will replace NAs with the mean for the particular interval number.

Data_new <- Data                 

## fill all the NAs with the mean of the particular interval number
for (i in 1:nrow(Data_new)){
    if (is.na(Data_new$steps[i]) == TRUE){
        Data_new$steps[i] <- steps_by_interval$steps[which(steps_by_interval$interval == Data_new$interval[i])]
        Data_new$steps[i] <- Data_new$steps[i]
    }
}

Data_new$date <- as.Date(Data_new$date)

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

steps_by_day_new <- aggregate(steps ~ date, Data_new, sum)     ## calculate the every day steps
plot(steps_by_day_new$date, steps_by_day_new$steps, type="h", main="Histogram of Daily Steps", xlab="Date", 
     ylab="Steps per Day", col="red", lwd = 8)

Calculate the mean and median for the new dataset

paste("Imputed Mean Steps per Day =", mean(steps_by_day_new$steps, na.rm = TRUE))

## [1] "Imputed Mean Steps per Day = 10766.1886792453"

paste("Imputed Median Steps per Day =", median(steps_by_day_new$steps, na.rm = TRUE))

## [1] "Imputed Median Steps per Day = 10766.1886792453"

• The difference between the imputed and original mean is 0
• The difference between the imputed and original median is 1.188

Are there difference 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 ia a weekday or weekend day

weekdays <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
Data_new$week_weekend = as.factor(ifelse(is.element(weekdays(as.Date(Data_new$date)),weekdays), "Weekday", "Weekend"))
steps_by_interval_day <- aggregate(steps ~ interval + week_weekend, Data_new, mean)
library(lattice)
xyplot(steps_by_interval_day$steps ~ steps_by_interval_day$interval|steps_by_interval_day$week_weekend, main="Average Steps per Day by Interval",xlab="Interval", ylab="Steps",layout=c(1,2), type="l")