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Reproducible Research: Peer Assessment 1

David Haworth

Loading and preprocessing the data

# Import the plyr and stringr libraries for use within the report
library(plyr)
library(stringr)

# Functions for replacing NAs in step count numeric vector with median or mean values
calc_median <- function(x) replace(x, is.na(x), median(x, na.rm=TRUE))
calc_mean <- function(x) replace(x, is.na(x), mean(x, na.rm=TRUE))

# Function to aggregate the total steps by day for provided data
aggregate_daily_steps <- function(x) setNames(
    aggregate(
        x$steps, 
        by = list(x$date), 
        sum
    ), 
    c("Date","Steps")
)

# Function to aggregate the average steps by interval for provided data
calc_avg_steps_for_interval <- function(x) setNames(
    aggregate(
        x$steps, 
        by=list(
            sub("([[:digit:]]{2,2})$", ":\\1",
                str_pad(
                    x$interval, 
                    4, 
                    side="left", 
                    pad="0"
                )
            )
        ), 
        ave
    ), 
    c("Interval", "Steps")
)

# Download the zip file containing the activity data, unzip the data file, and load it
download.file(
    url = "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip", 
    destfile = "data.zip", 
    method = 'curl'
)
unzip("data.zip")
all_activity <- read.csv("activity.csv")

# Activity data with NAs removed
activities_na_removed <- na.omit(all_activity)

# Activity data with NAs filled with the median number of steps for the corresponding interval
activities_na_replaced <- ddply(all_activity, ~ interval, transform, steps = calc_median(steps))

# Add a 'daytype' column to discriminate whether each activity record occurred on a weekday or weekend
activities_na_replaced$daytype <- 
    ifelse(weekdays(as.Date(activities_na_replaced$date)) %in% c("Saturday","Sunday") , "weekend", "weekday")

What is mean total number of steps taken per day?

# Aggregate the number of steps by day for data with NAs removed
total_steps_na_removed <- aggregate_daily_steps(activities_na_removed)

# Display histogram of total daily steps
hist(
    total_steps_na_removed$Steps, 
    main = "Histogram of Total Daily Steps - NAs Removed", 
    xlab = "Total Daily Steps"
)

# Calculate the mean of all steps taken (NAs removed), rounded to three decimal places
mean_total_steps_na_removed <- format(mean(total_steps_na_removed$Steps),3)

# Calcluate the median of all steps taken (NAs removed)
median_total_steps_na_removed <- median(total_steps_na_removed$Steps)

Daily Step Total - Mean: 10766.19 Median: 10765

What is the average daily activity pattern?

# Get the average number of steps for each 5 minute interval of the day
int_avg <- calc_avg_steps_for_interval(activities_na_removed)

# Plot the average number of steps for each 5 minute interval of the day
plot(
    x=as.factor(int_avg$Interval), 
    y=int_avg$Steps, 
    type="l", 
    main="Average Steps by Time Interval", 
    xlab="Time Interval", 
    ylab="Avg Steps"
)

# Find the interval that had the highest step average
interval_with_max_steps <- int_avg[which.max(int_avg$Steps),]$Interval

# Find the highest average step value for all intervals
max_steps <- format(max(int_avg$Steps),3)

Interval with the maximum value - Interval: 08:35 Steps: 206.1698

Imputing missing values

# Calculate total number of NAs in full activity data
total_na_count <- sum(is.na(all_activity$steps))

Total number of NA values in dataset: 2304

# Aggregate the number of steps for each day for data with NAs replaced
total_steps_na_replaced <- aggregate_daily_steps(activities_na_replaced)

# Plot histogram of total daily steps
hist(
    total_steps_na_replaced$Steps, 
    main = "Histogram of Total Daily Steps - NAs Replaced w/ Median of Interval", 
    xlab = "Total Daily Steps"
)

# Calculate the mean of all steps taken (NAs replaced w/ median), rounded to three decimal places
mean_total_steps_na_replaced <- format(mean(total_steps_na_replaced$Steps),3)

# Calcluate the median of all steps taken (NAs replaced w/ median)
median_total_steps_na_replaced <- median(total_steps_na_replaced$Steps)

Daily Step Total - Mean: 9503.869 Median: 10395

# Calculate diff of mean w/ NAs removed and mean w/ NAs replaced
mean_diff <- as.numeric(mean_total_steps_na_removed) - as.numeric(mean_total_steps_na_replaced)

# Calculate diff of median w/ NAs removed and median w/ NAs replaced
median_diff <- as.numeric(median_total_steps_na_removed) - as.numeric(median_total_steps_na_replaced)

Differences from NA removed - Mean Diff: 1262.321 Median Diff: 370

Both the mean and median total steps values are higher when the NAs are removed then when they are replaced with the median value for the given interval.

Are there differences in activity patterns between weekdays and weekends?

# Get the average steps by interval for weekdays
wkday_avg <- calc_avg_steps_for_interval(activities_na_replaced[activities_na_replaced$daytype == 'weekday', ])

# Get the average steps by interval for weekends
wkend_avg <- calc_avg_steps_for_interval(activities_na_replaced[activities_na_replaced$daytype == 'weekend', ])

# Plot weekday interval averages and weekend interval averages for comparison
par(mfrow=c(1,2))
plot(
    x=as.factor(wkday_avg$Interval), 
    y=wkday_avg$Steps, 
    type="l", 
    main="Average Steps by Time Interval - Weekday", 
    xlab="Time Interval", 
    ylab="Avg Steps"
)
plot(
    x=as.factor(wkend_avg$Interval), 
    y=wkend_avg$Steps, 
    type="l", 
    main="Average Steps by Time Interval - Weekend", 
    xlab="Time Interval", 
    ylab="Avg Steps"
)

Comparison shows that weekday step activity spikes in the morning, then remains lower throughout the day, with only small spikes around lunch and mid-afternoon, before a higher spike in the evening. Weekend step activity also spikes in the morning, but activity throughout the day remains much higher in comparison to weekdays.