PA1_template.md

title	output
Reproducible Research: Peer Assessment 1	html_document keep_md true

Loading and preprocessing the data

if (!file.exists('activity.zip')) {
  download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip", 'activity.zip')
}
if (!file.exists('activity.csv')) {
  unzip('activity.zip')  
}
dataset <- read.csv('activity.csv')

What is mean total number of steps taken per day?

Lets aggregate step data by days and look at our anonynus user steps distribution

totalStepsByDay <- aggregate(dataset$steps, by=list(dataset$date), FUN=sum, na.rm=TRUE)
hist(totalStepsByDay$x, 
     xlab = 'Steps',
     ylab = 'Date',
     main = '',
     col = 'steelblue3'
)

The mean steps count for two months is 9354 and median is 10395.

What is the average daily activity pattern?

meanStepsByInterval <- aggregate(dataset$steps, by=list(interval = dataset$interval), FUN=mean, na.rm=TRUE)
plot(meanStepsByInterval,
    xlab = '5 minute intervals',
    ylab = 'Average steps through days',
    col = 'steelblue3',
    type = "l"
)

The most active interval is 835.

Imputing missing values

There were total 2304 observations with missing values in this data set. Let's fill the NA data with the 5 mintue intervals mean to make our calculations more accurate.

for(i in 1:nrow(dataset)) {
  if (is.na(dataset[i, ]$steps)) {
    meanStepsIndex <- which(meanStepsByInterval$interval == dataset[i, ]$interval);
    dataset[i, ]$steps <- round(meanStepsByInterval[meanStepsIndex, ]$x)
  }
}
summary(dataset)

##      steps                date          interval     
##  Min.   :  0.00   2012-10-01:  288   Min.   :   0.0  
##  1st Qu.:  0.00   2012-10-02:  288   1st Qu.: 588.8  
##  Median :  0.00   2012-10-03:  288   Median :1177.5  
##  Mean   : 37.38   2012-10-04:  288   Mean   :1177.5  
##  3rd Qu.: 27.00   2012-10-05:  288   3rd Qu.:1766.2  
##  Max.   :806.00   2012-10-06:  288   Max.   :2355.0  
##                   (Other)   :15840

Now we will check our totals on the new data set:

totalStepsByDay <- aggregate(dataset$steps, by=list(dataset$date), FUN=sum, na.rm=TRUE)
hist(totalStepsByDay$x, 
     xlab = 'Steps',
     ylab = 'Date',
     main = '',
     col = 'steelblue3'
)

In the new data set mean steps is 10766 and median is 10762.

We see, that our new results look more accurate

Are there differences in activity patterns between weekdays and weekends?

Lets mark when our observations was done on weekends or weekdays

dataset$dayType <- ifelse(as.POSIXlt(dataset$date)$wday %in% c(0,6), 'weekend', 'weekday')

Lets compare weekends to weekdays activity:

meanStepsByIntervalAndDayType <- aggregate(steps ~ interval + dayType, data=dataset, mean)
library(ggplot2)
ggplot(meanStepsByIntervalAndDayType, aes(interval, steps)) + 
    geom_line() + 
    theme_bw() +
    facet_grid(dayType ~ .) +
    xlab('5 minute intervals') + 
    ylab('Average steps through days')

As we weekends patters does not differ very much. I can assume, that this person has more lazy mornings and a little more active daytime in the weekends.