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.csv")
```

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

```{r}
steps_per_day <- tapply(data$steps, data$date, sum)
hist(steps_per_day)
mean(steps_per_day, na.rm = TRUE)
median(steps_per_day, na.rm = TRUE)
```


## What is the average daily activity pattern?

```{r}
steps_per_int <- tapply(data$steps, data$interval, mean, na.rm = TRUE)
plot(steps_per_int, type = "l", col = "blue", main = "daily activity pattern",
     xlab = "5-min interval", ylab = "average number of steps")
```

Which 5-minute interval, on average across all days in the dataset, contains the maximum number of steps? Minute of day of the interval start will be reported above the interval number:

```{r}
which(steps_per_int == max(steps_per_int))
```


## Imputing missing values

Total number of missing values in the dataset:
```{r}
sum(is.na(data$steps))
```

Impute missing values by filing in the daily average of the interval:

```{r}
data$steps_imputed <- data$steps
data[is.na(data$steps), "steps_imputed"] <- steps_per_int
```

Histogram, mean and median of imputed steps per interval:

```{r}
steps_per_day_imp <- tapply(data$steps_imputed, data$date, sum)
hist(steps_per_day_imp)
mean(steps_per_day_imp)
median(steps_per_day_imp)
```


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

```{r}
# this is independent of any languange environment;
# values 0..6, starting on Sunday, c.f. help
dayOfWeek <- as.POSIXlt(data$date)$wday
data_augm <- data.frame(date = data$date,
                   interval = data$interval,
                   steps = data$steps_imputed,
                   weekend = (dayOfWeek %in% c(6, 0)))


steps_per_int_wd <- tapply(data_augm$steps, list(data_augm$weekend, data_augm$interval), mean, na.rm = TRUE)

par(mfrow=c(2,1))
plot(steps_per_int_wd["TRUE", ], type = "l", col = "blue", main = "daily activity pattern: weekend",
     xlab = "5-min interval", ylab = "average number of steps")
plot(steps_per_int_wd["FALSE", ], type = "l", col = "blue", main = "daily activity pattern: workday",
     xlab = "5-min interval", ylab = "average number of steps")
```