PA1_template.Rmd

# RR Assignment 1: Analyses of activity monitoring data
========================================================

## Loading and preprocessing the data  
```{r}
data <- read.csv("activity.csv")
data$date <- as.Date(data$date)
```

## What is mean total number of steps taken per day?  
```{r fig.width=7, fig.height=6}
stepsPerDay <- aggregate(data$steps, list(day=data$date), sum, na.rm=TRUE)
colnames(stepsPerDay) <- c("day", "steps")
hist(stepsPerDay$steps, col="red", xlab="Total steps per day", main = "Histogram - total steps per day")
meanStepsPerDay <- mean(stepsPerDay$steps)
medianStepsPerDay <- median(stepsPerDay$steps)
```
### Summary of total number of steps taken per day 
Mean = `r meanStepsPerDay`  
Median = `r medianStepsPerDay`

## What is the average daily activity pattern?
```{r fig.width=7, fig.height=6}
stepsPerInterval <- aggregate(data$steps, list(interval=data$interval), mean, na.rm=TRUE)
colnames(stepsPerInterval) <- c("interval", "steps")
plot(stepsPerInterval$interval, stepsPerInterval$steps, type="l", 
     main = "Average steps in each 5-minute interval",
     xlab = "5-minute interval in a day", ylab = "Average steps taken")
maxStepsIndex <- which.max(stepsPerInterval$steps)
maxInterval <- stepsPerInterval$interval[maxStepsIndex]
maxStepsPerInterval <- stepsPerInterval$steps[maxStepsIndex]
```
### Interval with max steps on average
#### The 5-minute interval "`r maxInterval`" has the maximum number of steps (i.e. `r maxStepsPerInterval`), on average across all the days in the dataset.  

## Imputing missing values
```{r}
numMissing <- dim(data[is.na(data$steps),])[1]
```
#### Total number of missing values: `r numMissing`  
Fill in the missing values in the dataset with the mean for that 5-minute interval (as computed above).

```{r fig.width=7, fig.height=6}
# Merge original data with 5-minute interval means, based on "interval" field
merged <- merge(data, stepsPerInterval, by.x = "interval", by.y = "interval")

# Inside "merged", set data.steps = stepsPerInterval.steps where data.steps == NA 
merged[is.na(merged$steps.x),"steps.x"] <- merged[is.na(merged$steps.x), "steps.y"]

# discard the last column
imputedData <- merged[, 1:3]
colnames(imputedData)[2] <- "steps"

imputedStepsPerDay <- aggregate(imputedData$steps, list(day=imputedData$date), sum)
colnames(imputedStepsPerDay) <- c("day", "steps")
hist(imputedStepsPerDay$steps, col="red", xlab="Total steps per day", main = "Histogram from imputed data - total steps per day")
meanImputedStepsPerDay <- mean(imputedStepsPerDay$steps)
medianImputedStepsPerDay <- median(imputedStepsPerDay$steps)
```
### Summary of total number of steps taken per day (from imputed data)
Mean = `r meanImputedStepsPerDay`  
Median = `r medianImputedStepsPerDay`  
  
Imputing NA-data leads to an increase in the total steps per day, as expected.

## Weekdays vs weekends
```{r  fig.width=7, fig.height=6}
# determine weekday/weekend
days <- weekdays(imputedData$date)
imputedData$typeOfDay <- ifelse (days %in% c("Saturday", "Sunday"), "weekend", "weekday")

# comute mean(steps) group by (interval, weekday)
imputedStepsPerIntervalWeekday <- aggregate(imputedData$steps, list(interval=imputedData$interval, typeOfDay=imputedData$typeOfDay), mean)
colnames(imputedStepsPerIntervalWeekday)[3] <- "steps"

library(lattice)
xyplot(steps ~ interval | typeOfDay, imputedStepsPerIntervalWeekday, layout = c(1,2), type="l", xlab="Interval", ylab= "Number of steps")
```