CodeBook.Rmd

---
title: "Human Activity Recognition (HAR) Using Smartphones - Codebook"
author: Wagner Pinheiro
date: February, 2017
output: 
  html_document: 
    fig_height: 12
    fig_width: 12
    keep_md: yes
---


In this CodeBook is demonstrated all the functions necessary to tidy the dataset and all the results after running the source code.


## HAR.download()

Before we can tidy the data, we need to download and unzip the dataset using the HAR.download function.

Source code:
```{r}
source("run_analysis.R")
HAR.download

```

Running the HAR.download():
```{r}
HAR.download()
```

## HAR.load()

This function load a defined dataset passed as parameter ("train" or "test"), 

```{r}
HAR.load
```

```{r}
test_data <- HAR.load("test")
dim(test_data)
```

In the ***HAR.load()*** function we merge the features with the activity names, to: **3. Uses descriptive activity names to name the activities in the data set**:

```{r, eval=FALSE}
y <- merge(y, activity_labels, by.x = "activity_id", by.y ="activity_id", all.x=TRUE)
```


## HAR.loadAndMerge()

For load and merge the train and test data, we call the HAR.loadAndMerge function:

```{r}
HAR.loadAndMerge
```

Running the HAR.loadAndMerge to **1. Merges the training and the test sets to create one data set**:

```{r}
full_dataset <- HAR.loadAndMerge()
str(full_dataset)
```


## HAR.extractMeanAndStdFields()

For **2. Extracts only the measurements on the mean and standard deviation for each measurement**, we use the auxiliary function HAR.extractMeanAndStdFields to ***grep*** only the variables with 'mean' and 'std' in they names. This auxiliary function is used in the previous executed ***HAR.load*** function.

Source code:
```{r}
HAR.extractMeanAndStdFields
```

Running HAR.extractMeanAndStdFields:
```{r}
partial_dataset <- HAR.extractMeanAndStdFields(full_dataset)
str(partial_dataset)
```

## HAR.describeFeatures()

This function replace abbreviated features names, and is used in the ***HAR.load()*** function, for ***4. Appropriately labels the data set with descriptive variable names.***

Source code:
```{r}
HAR.describeFeatures
```

## HAR.calcAvg()

Calculate the averages values for each variable.

Source code:
```{r}
HAR.calcAvg
```

Running HAR.calcAvg:
```{r}
avgs_dataset <- HAR.calcAvg(partial_dataset)
str(avgs_dataset)
```

## HAR.saveData()

Save the tidy dataset to a file.

Source code:
```{r}
HAR.saveData
```

Running:
```{r}
HAR.saveData(data=avgs_dataset, filename="./data/tidy_data_avgs.txt")
file.info("./data/tidy_data_avgs.txt")
```

[Click here to see the resulting tidy dataset](./data/tidy_data_avgs.txt)

## HAR.plotAvgs()

Plotting the results of averages coloured by subject:
```{r averages_plot}
HAR.plotAvgs(avgs_dataset)
```