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---
title: "Wearable computing study case - README"
output: 
  html_document: 
    keep_md: yes
    number_sections: yes
    toc: yes
    toc_depth: 5
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


I will keep it simple, since I think the codebook provided is very detailed.

## Contents of the repo
### README.Rmd

The text file you are reading, aimed to "explain how all of the scripts work and how they are connected".

### Codebook.Rmd

The complete description of:

* Data: source, purpose and general description.
* Variables: selection for the assignment, description, units and metadata.
* Data transformation process: merge, variables selection and the tidy data dataframe (the 'FinalReport' requested).

### run_analysis.R

The code for the transformation of the original datasets described in section 'Data transformation' of the file 'Codebook.Rmd'.

The script accomplishes the following:

* **First Part** 

1.Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set
4. Appropriately labels the data set with descriptive variable names.

* **Second Part**

5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

#### Using the code

**R working directory** should be set to the folder resulting from decompressing the file in the following link:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

**All outputs, including the 'FinalReport.txt' will be stored in this folder.**

## Original sources

### Complete description of the study

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

### Coursera assignment

https://www.coursera.org/learn/data-cleaning/peer/FIZtT/getting-and-cleaning-data-course-project

### Original study citation

Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012