PA1_template.Rmd

---
title: 'Reproducible Research: Peer Assessment 1'
author: "JE-"
date: "November 16, 2014"
output:
  html_document:
    fig_caption: yes
    keep_md: yes
---
```{r, echo=FALSE}
rm(list=ls())
setwd("C:/Main/Data Science (Coursera 2014)/Reproducible Research/RepData_PeerAssessment1")
library(ggplot2)
library(reshape2)
library(lattice)
##-------------------------------------------------------------------
```

## Loading and preprocessing the data
```{r}
unzip("activity.zip")
data <- read.csv("activity.csv",colClasses = c("integer", "Date", "integer"))
```

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

- Make a histogram of the total number of steps taken each day
```{r}
totalSteps <- tapply(data$steps,data$date,sum)
hist(totalSteps, main="Histogram of total number of steps per day", 
    xlab="Date",col=c("blue"))
```

- Calculate and report the mean and median total number of steps taken per day
```{r}
meanSteps <- mean(totalSteps,na.rm=T)
medianSteps <- median(totalSteps,na.rm=T)
meanSteps
medianSteps
```
The mean total number of steps taken per day is `r meanSteps`, and the median total number of steps taken per day is `r medianSteps`.

## What is the average daily activity pattern?
- Make a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)

```{r}
stepsByInterval<-tapply(data$steps,data$interval,mean,na.rm=T)
plot(stepsByInterval,type = 'l',xlab = "5-minute interval", ylab = "Average steps taken")
```

- Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
```{r}
maxInterval <- names(stepsByInterval[which.max(stepsByInterval)])
```
The **`r maxInterval`th** interval contains the maximum number of steps across all days.

## Imputing missing values
```{r}
sumMissing <- sum(is.na(data$steps))
```
The total number of missing values is **`r sumMissing`**.

```{r}
data2 <- data 
indexes = as.integer(rownames(stepsByInterval))
for (i in 1:dim(data2)[1]) {
  if (is.na(data2[i,][1])) {
    data2[i,][1] <- stepsByInterval[which(data2$interval[i] == indexes)]
  }
}
head(data2)
sum(is.na(data2$steps))

totalSteps2 <- tapply(data2$steps,data2$date,sum)
hist(totalSteps2, main="Histogram of total number of steps per day (no missing cases)", 
    xlab="Date",col=c("blue"))
```

- Calculate and report the mean and median total number of steps taken per day
```{r}
meanSteps2 <- mean(totalSteps2,na.rm=T)
medianSteps2 <- median(totalSteps2,na.rm=T)
meanSteps2
medianSteps2
```
The mean total number of steps taken per day is `r meanSteps`, and the median total number of steps taken per day is `r medianSteps`.

The mean number of steps taken per day stayed the same (because we've imputed the mean value for each missing day).
The median value changed a bit, and is now equal to the mean.

## Are there differences in activity patterns between weekdays and weekends?
```{r}
data2$dayNames <- factor(format(data2$date, "%A"))
levels(data2$dayNames) <- list(weekday = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"),
                               weekend = c("Saturday", "Sunday"))

AvgStepsByInterval <- aggregate(data2$steps, list(interval = as.numeric(as.character(data2$interval)), 
                                                  dayNames = data2$dayNames), FUN = "mean")
names(AvgStepsByInterval)[3] <- "meanOfSteps"
xyplot(AvgStepsByInterval$meanOfSteps ~ AvgStepsByInterval$interval | AvgStepsByInterval$dayNames, 
       layout = c(1, 2), type = "l", 
       xlab = "Interval", ylab = "Number of steps")
```