Personal activity monitoring

This document presents the results of the Peer Assessment - 1 of Reproducible Research (the online offering of Johns Hopkins University) in a report using a single R markdown document.

This assignment makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day.

Data

The data for this assignment is downloaded from the course web site:

• Dataset: Activity monitoring data [52K] The variables included in this dataset are:

• steps: Number of steps taking in a 5-minute interval (missing values are coded as NA)

• date: The date on which the measurement was taken in YYYY-MM-DD format

• interval: Identifier for the 5-minute interval in which measurement was taken

The dataset is stored in a comma-separated-value (CSV) file and there are a total of 17,568 observations in this dataset.

Assignment

This assignment will be described in multiple parts but ultimately, need to be completed in a single R markdown document that can be processed by knitr and be transformed into an HTML file.

Always echo = TRUE is used so that someone else will be able to read the code.

echo = TRUE
library(ggplot2)

Loading and preprocessing the data

Show any code that is needed to

1. Load the data (i.e. read.csv())
2. Process/transform the data (if necessary) into a format suitable for your analysis

data <- read.csv("activity.csv", header = TRUE, na.strings = "NA", 
                 colClasses = c("numeric", "character", "numeric"))
    data$interval <- factor(data$interval)
    data$date <- as.Date(data$date, format = "%Y-%m-%d")
    head(data)

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

    data$interval <- factor(data$interval)
    data$date <- as.Date(data$date, format = "%Y-%m-%d")
    str(data)

## '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: Factor w/ 288 levels "0","5","10","15",..: 1 2 3 4 5 6 7 8 9 10 ...

Summary

summary(data)

##      steps            date               interval    
##  Min.   :  0.0   Min.   :2012-10-01   0      :   61  
##  1st Qu.:  0.0   1st Qu.:2012-10-16   5      :   61  
##  Median :  0.0   Median :2012-10-31   10     :   61  
##  Mean   : 37.4   Mean   :2012-10-31   15     :   61  
##  3rd Qu.: 12.0   3rd Qu.:2012-11-15   20     :   61  
##  Max.   :806.0   Max.   :2012-11-30   25     :   61  
##  NA's   :2304                         (Other):17202

What is mean total number of steps taken per day?

For this part of the assignment, missing values in the dataset are ignored.

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

steps_taken_per_day <- aggregate(steps ~ date, data, sum)
colnames(steps_taken_per_day) <- c("date", "steps")
ggplot(steps_taken_per_day, aes(x = steps)) + geom_histogram(fill = "darkblue", 
       binwidth = 1000) + labs(title = "Total Steps Taken  Each Day",                                      x =  "Number of steps taken each Day", y = "Number of times (Count)") + 
      theme_bw()

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

mean_steps = mean(steps_taken_per_day$steps, na.rm = TRUE)
median_steps = median(steps_taken_per_day$steps, na.rm = TRUE)
mean_steps

## [1] 10766

median_steps

## [1] 10765

• Therefore, the mean and median are 10766 and 10765 respectively.

What is the average daily activity pattern?

1. 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).

steps_per_interval <- aggregate(data$steps, by = list(interval = data$interval),
                                FUN = mean, na.rm = TRUE)
# Convert to integers for plotting
steps_per_interval$interval <- as.integer(levels(steps_per_interval$interval)
                                          [steps_per_interval$interval])
colnames(steps_per_interval) <- c("interval", "steps")

# Time series generation
ggplot(steps_per_interval, aes(x = interval, y = steps)) + 
       geom_line(color =  "darkblue", size = 1) + 
       labs(title = "Average Daily  Activity Pattern", x = "5-minute Interval", 
       y = "Average number of steps taken") + theme_bw() + 
       theme(legend.position = "bottom")

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

max_step_interval <- steps_per_interval[which.max(steps_per_interval$steps), 
                     ]$interval
max_step_interval

## [1] 835

On average, the 835th 5-minute interval contains the maximum number of steps.

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.

1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
2. 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.
3. Create a new dataset that is equal to the original dataset but with the missing data filled in.
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. 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?

fill_na <- function(data, defaults) {
           na_indices <- which(is.na(data$steps))
           na_replacements <- unlist(lapply(na_indices, FUN = function(idx) {
           interval = data[idx, ]$interval
           defaults[defaults$interval == interval, ]$steps
           }))
           fill_steps <- data$steps
           fill_steps[na_indices] <- na_replacements
           fill_steps
           }

data_fill <- data.frame(steps = fill_na(data, steps_per_interval), 
                        date = data$date, interval = data$interval)

Histogram of the total number of steps taken each day.

full_steps_per_day <- aggregate(steps ~ date, data_fill, sum)
colnames(full_steps_per_day) <- c("date", "steps")

ggplot(full_steps_per_day, aes(x = steps)) + geom_histogram(fill = "darkblue", 
       binwidth = 1000) + labs(title = "Histogram of Full Steps Taken per Day", 
       x = "Number of Steps after populate missing values", y = "Count") +     theme_bw()

full_mean_steps = mean(full_steps_per_day$steps)
full_median_steps = median(full_steps_per_day$steps)
full_mean_steps

## [1] 10766

full_median_steps

## [1] 10766

• the mean and median are 10766 and 10765 respectively before filling in all the missing values in the dataset.
• the mean and median are 10766 and 10766 respectively after filling in all the missing values in the dataset.

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.

1. 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.

weekdays_steps <- function(data) {
    weekdays_steps <- aggregate(data$steps, by = list(interval = data$interval), 
        FUN = mean, na.rm = T)
    # Convert to integers for plotting
    weekdays_steps$interval <- as.integer(levels(weekdays_steps$interval)[weekdays_steps$interval])
    colnames(weekdays_steps) <- c("interval", "steps")
    weekdays_steps
}

data_by_weekdays <- function(data) {
    data$weekday <- as.factor(weekdays(data$date))
    weekend_data <- subset(data, weekday %in% c("Saturday", "Sunday"))
    weekday_data <- subset(data, !weekday %in% c("Saturday", "Sunday"))

    weekend_steps <- weekdays_steps(weekend_data)
    weekday_steps <- weekdays_steps(weekday_data)

    weekend_steps$dayofweek <- rep("weekend", nrow(weekend_steps))
    weekday_steps$dayofweek <- rep("weekday", nrow(weekday_steps))

    data_by_weekdays <- rbind(weekend_steps, weekday_steps)
    data_by_weekdays$dayofweek <- as.factor(data_by_weekdays$dayofweek)
    data_by_weekdays
}

data_weekdays <- data_by_weekdays(data_fill)

2. 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). The plot should look something like the following, which was creating using simulated data:

Your plot will look different from the one above because you will be using the activity monitor data. Note that the above plot was made using the lattice system but you can make the same version of the plot using any plotting system you choose.

ggplot(data_weekdays, aes(x = interval, y = steps)) + geom_line(color = "red", 
       size = 1) + facet_wrap(~dayofweek, nrow = 2, ncol = 1) + 
       labs(x = "Interval", y = "Number of steps") + theme_bw()