Getting and Cleaning Data Course Project

Enrique Martin Lopez

Based on the data set [1], from which the following descriptions and codebook are adapted.

[1] 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 This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited. Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.

Part of the description from the "README.txt" in [1] that is relevant to the presented work:

"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."

"From the original data set, the training and test sets were merged (using the script 'run_analysis.R') to create one data set 'tidy_dataset.txt'. This data set specifies, for each activity, and each subject participating in the experiment, the average of the different features described in the codebook 'codebook.md'.

The data set provided includes the following files:

'README.md'
'run_analysis.R': Script used to create the data set from the original data set [1]. For it to run and load the right files, it needs to be in the same directory as the data folder 'UCI HARD Dataset'.
'tidy_dataset.txt': The data set produced. Each row contains the name of an activity, the subject number, and the values of 66 features described in 'codebook.md'
'codebook.md': Shows information about the features included in the data set 'tidy_dataset.txt'

What the analysis file 'run_analysis.R' does (for further insight, see the actual commented code):

Merges the training and the test sets to create one data set.
Extracts only the measurements on the mean and standard deviation for each measurement by selecting the measurements whose naim contain mean() or std().
Merges the labels for the training and test sets in a data array with one column. Then it substitutes the labels by the activity names and binds the resulting column to the data set created in step 2.
Labels the data set with descriptive variable names taken from codebook of the original data set.
Creates a second independent data set with the average of each variable for each activity and each subject by previously adding a column with the subject identifiers. Finally, it exports this second independent data set to 'tidy_dataset.txt'.

Notes:

Features are normalized and bounded within [-1,1].