---
title: "run_analysis"
author: "OP"
date: "27/03/2021"
output: html_document
---

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

#==================================================================
Human Activity Recognition Using Smartphones Dataset
Summary dataframe
Version 1.0
#==================================================================
Code: O.P.
Based on data from:
Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.
Smartlab - Non Linear Complex Systems Laboratory
DITEN - Università degli Studi di Genova.
Via Opera Pia 11A, I-16145, Genoa, Italy.
activityrecognition@smartlab.ws
www.smartlab.ws
#==================================================================

# Detail about the experiment

Details can be found here:
http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. 

#Raw data

Raw data can be downloaded here:
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip  

# Installation
Download the Raw Data, extract it in the source code folder.
Details about execution and an execution example can be found in the markdown file: run_analysis.Rmd

you can use the function generateTidy() to generate a tidyDF.

## Variables

The raw data contains 561 variables (features) with time and frequency domain variables.
Out of these 561 variables, only the ones related to mean and std are extracted in the dataframe (80 variables in total).
The data frame presents the following columns:
- an id
- subject (identificatio of the volunteer)
- activity
- one column per variable

the list of variables can be found in run_analysis_variables.txt file

## Instructions

This code generates a dataframe based on:
[Details](http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) 
The source dataset can be downloaded from:
[Archive Dataset](https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip)  

Data should be extracted from the ZIP file in the same folder than the run_analysis.R code
and keep the same folder structure than stored in the ZIP file.

This code creates a dataframe that contains both test and training data, only for the variables
that represent mean or std values
It also creates a summary dataframe with the means of all of these variables, per subject and per activity

Note that dplyr and data.table packages must be installed.

you can use the function generateTidy() to generate a tidyDF.
This function calls two other functions:
createARDF : creates the general Dataframe
createTidyDF: creates the tidy Dataframe

Here is the sample code to execute, that creates both dataframes:


```{r echo=FALSE, results=''}
source("run_analysis.R")
initializeLibs()
sampleDF <- createARDF() # creates the main Dataframe
tidyDF <- createTidyDF(sampleDF) # creates the tidy Dataframe
library(knitr)
kable(head(sampleDF), format = "simple", caption = "Full dataframe")
kable(head(tidyDF), format = "simple", caption = "Tidy dataframe: Mean per suject and activity")
```