Reproducible Research: Peer Assessment 1

Calling ggplot2 function

library(ggplot2)

Loading and preprocessing the data

activity_data <- read.csv("./activity.csv", na.strings = "NA")

Change class of dates in activity data from character to Date

activity_data$date <- as.Date(activity_data$date, format = "%Y-%m-%d")
original_data <- activity_data

What is mean total number of steps taken per day?

Group the steps' data by date using sum

group_data <- aggregate(x = activity_data$steps, 
                        by = list(activity_data$date), 
                        FUN = sum, na.rm = TRUE)

Plotting a histogram of Total Number of Steps taken each Day

with(group_data, 
     hist(x = x, 
          breaks = 10, 
          xlim = c(0,25000), 
          main = "Histogram of Total Number of Steps taken each Day", 
          xlab = "Total Number of Steps taken per Day"))

Calculating mean and median of the total number of steps taken per day

Mean <- mean(group_data$x, na.rm = TRUE)
Mean

## [1] 9354.23

Median <- median(group_data$x, na.rm = TRUE)
Median

## [1] 10395

What is the average daily activity pattern?

Group the steps' data by interval using mean

group_interval <- aggregate(x = activity_data$steps, 
                        by = list(activity_data$interval), 
                        FUN = mean, na.rm = TRUE)

Plotting the time series graph for Average Daily Activity Pattern

with(group_interval,
     plot(x = Group.1, 
          y = x, type = "l", xlim = c(0,2500), 
          main = "Average Daily Activity Pattern", 
          ylab = "Average Number of Steps Taken", 
          xlab = "5 Min Intervals"))

Which interval has maximum number of steps?

group_interval[which.max(group_interval$x),]$Group.1

## [1] 835

Imputing missing values

Number of rows where no. of steps' data is missing

nrow(activity_data[is.na(activity_data$steps),])

## [1] 2304

Looping through na_data for assigning values - I am here using the mean for that respective interval

na_data <- activity_data[is.na(activity_data$steps),]
for(i in 1:2304) {
        na_data$steps[i] <- group_interval[group_interval$Group.1 == na_data[i,]$interval,]$x
}

Replacing data with means of same interval group

activity_data$steps[is.na(activity_data$steps)] <- na_data$steps

Making new group, for steps' new data, by date using sum

new_group <- aggregate(x = activity_data$steps, 
                       by = list(activity_data$date), 
                       FUN = sum)

Plotting a histogram of Total Number of Steps taken each Day using Filled Missing Data

with(new_group, 
     hist(x = x, 
          breaks = 10, 
          xlim = c(0,25000), 
          main = "Histogram of Total Number of Steps taken each Day", 
          xlab = "Total Number of Steps taken per Day"))

Calculating mean and median of the total number of steps taken per day of Filled Missing Data

Mean2 <- mean(new_group$x)
Mean2

## [1] 10766.19

Median2 <- median(new_group$x)
Median2

## [1] 10766.19

Comparing with older data

oldgroup_data <- aggregate(x = original_data$steps, 
                           by = list(original_data$date), 
                           FUN = sum, na.rm = TRUE)

Mean <- mean(oldgroup_data$x)
Mean

## [1] 9354.23

Median <- median(oldgroup_data$x)
Median

## [1] 10395

Are there differences in activity patterns between weekdays and weekends?

Adding a variable of day using weekdays function

original_data$day <- weekdays(original_data$date)

Changing day variable values to two factor weekday or weekend

for (i in 1:17568) {
        if (original_data$day[i] != "Saturday") {
                original_data$day[i] <- "Weekday"
        } else if (original_data$day[i] != "Sunday") {original_data$day[i] <- "Weekend"}
        else {original_data$day[i] <- "Weekend"}
}

Coercing day data into factors

original_data$day <- as.factor(original_data$day)

Grouping by day and interval and taking mean

weekday_group <- aggregate(x = original_data$steps, 
                           by = list(original_data$day, original_data$interval), 
                           FUN = mean,
                           na.rm = TRUE)

Plotting a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis)

g <- ggplot(data = weekday_group, aes(x = Group.2, y = x))
g + geom_line() + 
        facet_grid(Group.1~.) + 
        labs(title = "Activity Patterns between Weekdays & Weekends") +
        xlab("5 Min Intervals") + 
        ylab("Average Number of Steps Taken") +
        coord_cartesian(xlim = c(0,2500))

Thank You