PA1_template.Rmd

---
title: "Reproducible Research: Peer Assessment 1"
output: 
  html_document:
    keep_md: true
---


#### Loading and preprocessing the data

```{r}
data <- read.csv("activity/activity.csv", colClasses=c(NA,"Date",NA))
```



#### What is mean total number of steps taken per day?

Mean number of steps per day: `r mean(data$steps, na.rm=TRUE)`

Median number of steps per day: `r median(data$steps, na.rm=TRUE)`

Histogram of steps:

```{r}
hist(data$steps)
```



#### What is the average daily activity pattern?

Plot of average number of steps per interval:

```{r}
library(plyr)
intervalMeans <- ddply(data, .(interval), numcolwise(mean, na.rm=TRUE))
plot(intervalMeans$interval, intervalMeans$steps, type="l", xlab="5-minute interval", ylab="Number of steps")
```

```{r} 
maxInterval <- intervalMeans[which.max(intervalMeans$steps),]
```

The interval with the highest average number of steps is `r maxInterval$interval` with `r maxInterval$steps` steps.


#### Imputing missing values

```{r}
completeRows <- complete.cases(data)

```
Total number of rows with NAs: `r summary(completeRows)["FALSE"]`

We will fill missing values with averages from the intervals.

```{r}
imputeData <- transform(data, steps = ifelse(is.na(steps), ave(steps, interval, FUN = function(x) mean(x, na.rm = TRUE)), steps))
```

Histogram of imputed steps:

```{r}
hist(imputeData$steps)
```

Mean number of steps per day (with imputed): `r mean(imputeData$steps, na.rm=TRUE)`

Median number of steps per day (with imputed: `r median(imputeData$steps, na.rm=TRUE)`

These are the same as pre-imputed values.

#### Are there differences in activity patterns between weekdays and weekends?

Add weekend and weekday columns:

```{r}
days <- weekdays(imputeData$date)
imputeData$weekend <- days %in% c("Saturday", "Sunday")
head(imputeData)
```

Plot weekend and weekday average steps:

```{r}
weekendSubset <- subset(imputeData, weekend==TRUE)
weekdaySubset <- subset(imputeData, weekend==FALSE)

weekendMeans <- ddply(weekendSubset, .(interval), numcolwise(mean, na.rm=TRUE))
weekdayMeans <- ddply(weekdaySubset, .(interval), numcolwise(mean, na.rm=TRUE))

plot(weekendMeans$interval, weekendMeans$steps, type="l", xlab="5-minute interval", ylab="Number of steps", main="Weekend")

plot(weekdayMeans$interval, weekdayMeans$steps, type="l", xlab="5-minute interval", ylab="Number of steps", main="Weekday")
```