library(lubridate) library(dplyr) library(ggplot2) library(lattice) ```

Code for reading in the dataset and/or processing the data

Load data

if (!file.exists(“activity.csv”) ) { dlurl <- ‘http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip’
download.file(dlurl,destfile=‘repdata%2Fdata%2Factivity.zip’,mode=‘wb’)
unzip(‘repdata%2Fdata%2Factivity.zip’) }

Read data

data <- read.csv(“activity.csv”)

Histogram of the total number of steps taken each day

steps_by_day <- aggregate(steps ~ date, data, sum) hist(steps_by_day$steps, main = paste(“Total Steps Each Day”), col=“green”,xlab=“Number of Steps”)

Mean and median number of steps taken each day

rmean <- mean(steps_by_day$steps) rmean

rmedian <- median(steps_by_day$steps) rmedian

Time series plot of the average number of steps taken

steps_by_interval <- aggregate(steps ~ interval, data, mean) plot(steps_by_interval\(interval,steps_by_interval\)steps, type=“l”, xlab=“Interval”, ylab=“Number of Steps”,main=“Average Number of Steps per Day by Interval”)

max_interval <- steps_by_interval[which.max(steps_by_interval$steps),1] max_interval

Code to describe and show a strategy for imputing missing data

NATotal <- sum(!complete.cases(data)) NATotal

Histogram of the total number of steps taken each day after missing values are imputed

StepsAverage <- aggregate(steps ~ interval, data = data, FUN = mean) fillNA <- numeric() for (i in 1:nrow(data)) { obs <- data[i, ] if (is.na(obs\(steps)) { steps <- subset(StepsAverage, interval == obs\)interval)\(steps } else { steps <- obs\)steps } fillNA <- c(fillNA, steps) }

Panel plot comparing the average number of steps taken per 5-minute interval across weekdays and weekends

new_activity <- data new_activity$steps <- fillNA

StepsTotalUnion <- aggregate(steps ~ date, data = new_activity, sum, na.rm = TRUE) hist(StepsTotalUnion\(steps, main = paste("Total Steps Each Day"), col="blue", xlab="Number of Steps") #Create Histogram to show difference. hist(steps_by_day\)steps, main = paste(“Total Steps Each Day”), col=“green”, xlab=“Number of Steps”, add=T) legend(“topright”, c(“Imputed”, “Non-imputed”), col=c(“blue”, “green”), lwd=10)

Mean

rmeantotal <- mean(StepsTotalUnion$steps) rmeantotal

Median

rmediantotal <- median(StepsTotalUnion$steps) rmediantotal

rmediandiff <- rmediantotal - rmedian rmediandiff

rmeandiff <- rmeantotal - rmean rmeandiff

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