PA1_template.md

title	Reproducible Research: Peer Assessment 1
output	html_document pdf_document keep_md true default

Loading and preprocessing the data

I begin by loading the necessary packages and importing the data.

library(dplyr)
library(ggplot2)
data = read.csv(unz("activity.zip", "activity.csv"), colClasses = c("integer", "Date", "integer"))

What is mean total number of steps taken per day?

Here is the distribution of the total number of steps taken in a day.

dailysteps = summarize(group_by(data,date), numsteps = sum(steps,na.rm = TRUE))
qplot(dailysteps$numsteps)

## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

The mean number of steps taken in a day is:

mean(dailysteps$numsteps)

## [1] 9354.23

and the median number of steps is:

median(dailysteps$numsteps)

## [1] 10395

What is the average daily activity pattern?

Below is a plot of the average steps taken in each five minute interval across all days.

intervalsteps = summarize(group_by(data,interval), avesteps = mean(steps,na.rm = TRUE))
qplot(interval,avesteps, data = intervalsteps, geom = "path")

The maximum average number of steps occurs during this five minute interval:

intervalsteps$interval[max(intervalsteps$avesteps)==intervalsteps$avesteps]

## [1] 835

Imputing missing values

There are:

sum(is.na(data$steps))

## [1] 2304

intervals with missing values.

To impute these missing values I use the average number of steps taken in the five minute interval across all days when that interval is not missing.

data_imp = data %>% 
    group_by(interval) %>% 
    mutate(imputesteps = mean(steps ,na.rm = TRUE))

data_imp$steps[is.na(data_imp$steps)] = data_imp$imputesteps[is.na(data_imp$steps)]
data_imp = select(data_imp,-imputesteps)

Now that the data is imputed we can view the histogram for the total steps taken in a day.

dailysteps_imp = summarize(group_by(data_imp,date), numsteps = sum(steps,na.rm = TRUE))
qplot(dailysteps_imp$numsteps)

## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

Note that the imputation has increased the estimates of the average and median number of steps taken on each day:

mean(dailysteps_imp$numsteps)

## [1] 10766.19

median(dailysteps_imp$numsteps)

## [1] 10766.19

Are there differences in activity patterns between weekdays and weekends?

Unsurprisingly, there are differences between weekday and weekends in terms of steps taken in any five minute interval.

data_imp$weekday = weekdays(data_imp$date)
data_imp$weekend = "Weekday"
data_imp$weekend[data_imp$weekday == "Saturday" | data_imp$weekday == "Sunday"] = "Weekend"
data_imp$weekend = as.factor(data_imp$weekend)

intervalsteps = summarize(group_by(data_imp,interval,weekend), avesteps = mean(steps,na.rm = TRUE))
qplot(interval,avesteps, data = intervalsteps, facets = weekend ~ ., geom = "path")