PA1_template.Rmd

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


## Loading and preprocessing the data

Extract the zip file and load the data with classes of the columns defined.

```{r}
unzip("./activity.zip")
activity <- read.csv("./activity.csv", na.strings = "NA", 
                     colClasses = c("numeric", "Date", "integer"))
head(activity, 3)
tail(activity, 3)
```

Convert the time interval into *actual* minute value.

```{r}
activity$minute <- sapply(activity$interval, function(x) {x%/%100*60+x%%100})
head(activity, 3)
tail(activity, 3)
```


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

A histogram of the total number of steps taken each day.

```{r}
eachday <- aggregate(steps ~ date, data = activity, FUN = sum)
hist(eachday$steps)
```

**mean** total number of steps taken per day (ignoring missing values).
```{r}
mean(eachday$steps)
```

**median** total number of steps taken per day (ignoring missing values).
```{r}
median(eachday$steps)
```


## What is the average daily activity pattern?

A time series plot of the 5-minute interval (x-axis) 
and the average number of steps taken, averaged across all days (y-axis)

```{r}
eachtime <- aggregate(steps ~ minute + interval, data = activity, FUN = mean)
plot(eachtime$minute, eachtime$steps, type = "l")
```

The 5-minute interval, on average across all the days in the dataset,
that contains the maximum number of steps.

```{r}
eachtime[eachtime$steps >= max(eachtime$steps), ]
```


## Imputing missing values

Total number of missing values in the dataset.
```{r}
sum(!complete.cases(activity))
```

Fill in all the missing values by the median for that 5-minute interval.
I have chosen that because it is fun to do something that is not suggested also not forbidden.

Create a new dataset that is equal to the original dataset but with the missing data filled in.

```{r}
newdata <- activity
newdata$steps <- with(newdata, do.call(c, tapply(steps, minute, function(y) {
    ym <- median(y, na.rm=TRUE)
    y[is.na(y)] <- ym
    y
})))
```

A histogram of the total number of steps taken each day of new dataset.

```{r}
eachdaynew <- aggregate(steps ~ date, data = newdata, FUN = sum)
hist(eachdaynew$steps)
```

**mean** total number of steps taken per day of new dataset.
```{r}
mean(eachdaynew$steps)
```

**median** total number of steps taken per day of new dataset.
```{r}
median(eachdaynew$steps)
```


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

Create a new factor variable in the dataset with two levels - "weekday" and "weekend".

```{r}
newdata$daytype <- as.factor(sapply(weekdays(newdata$date), function(x) {
    if(grepl("^S", x)) "weekend"
    else "weekday"
}))
```

A panel plot containing a time series plot of the 5-minute interval (x-axis) 
and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).

```{r}
library(lattice)
bydaytype <- aggregate(steps ~ minute + daytype, data = newdata, FUN = mean)
xyplot(steps ~ minute | daytype, data = bydaytype, type = "l", layout = c(1, 2))
```