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Getting and Cleaning Data Course Project

SUMMARY

This work has been performed as final project for the Coursera'a Getting and Cleaning Data Course (https://www.coursera.org/learn/data-cleaning). The data provided represents data collected from the accelerometers from the Samsung Galaxy S smartphone (http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones). A detailed explanation of the experiments performed and of the collected data can be found in the README.txt of the data package (https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip).

The aim of this codebook is to describe the data, the variables and any transformations performed to clean up the data. The script performing these operation is called run_analysis.R and the output dataset is stored in a file called tidy_dataset.txt

VARIABLES DESCRIPTION

From the provided data the mean and the standard deviation for the following features are selected:

  • tBodyAcc-XYZ

  • tGravityAcc-XYZ

  • tBodyAccJerk-XYZ

  • tBodyGyro-XYZ

  • tBodyGyroJerk-XYZ

  • tBodyAccMag

  • tGravityAccMag

  • tBodyAccJerkMag

  • tBodyGyroMag

  • tBodyGyroJerkMag

  • fBodyAcc-XYZ

  • fBodyAccJerk-XYZ

  • fBodyGyro-XYZ

  • fBodyAccMag

  • fBodyAccJerkMag

  • fBodyGyroMag

  • fBodyGyroJerkMag

where in general t stands for time, f for frequency and X(Y,Z) corresponds to a physical direction. Moreover, the different measuerements are identified by the subject and type of activity. A detailed description of the variables themselves is provided in the "README.txt" file of the provided data.

PERFORMED TRANSFORMATIONS

  • Data download

In a preliminary step the data are download and unzipped. To save space, the downloaded .zip folder is removed. The unzipped directory contains, among other files, two directories: test and train.

  • Data aggregation

In the first step the training and the test sets are merged to create a combined data set. To achieve this goal both the files named "subject_test.txt" "X_test.txt" "y_test.txt" in the test directory and the files named "subject_train.txt" "X_train.txt" "y_train.txt" in the test directory are binded by column respectively in the test and train datasets; the two datasets are then binded by rows. The dataset column names are then assigned according to the file "features.txt".

  • Data Pre-Selection

In the second step only the variables regarding the measurements on the mean and standard deviation are selected

  • Activities

In the third step the activities are labelled according to the "activity_labels.txt" file.

  • Variable names cleaning

In the forth step the dataset column names are transformed in order to achieve more clarity (e.g. Acc --> Acceleration, t-->time, etc.).

  • Tiding the dataset

In the fifth step a second tidy data set with the average of each variable for each activity and each subject is created and stored in the file "tidy_dataset.txt".

  • Clean up

The intermediate R-objects are removed from memory.