Coursera Getting and cleaing data project:

1. Repository content:

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• tidy_data.txt

the required data set.

• Code book.md

details on the data set and all variables.

• Readme.md

details on the original data collection and the files in the repository.

• run_analysis.R

the R script file containing who the data was transformed to tidy_data.csv

2.The study:

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More on the study and data sources can be found in Click here

Data Set Information:

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.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

Check the README.txt file for further details about this dataset.

A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: Click here

Attribute Information:

For each record in the dataset it is provided:

• Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.

• Triaxial Angular velocity from the gyroscope.

• A 561-feature vector with time and frequency domain variables.

• Its activity label.

• An identifier of the subject who carried out the experiment.

3.Data transformation:

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• Downloaded the dataset zip file.

• Unzipped the file

• Read the Readme.md file with provided with the data.

• Read the activity and features file of the study.

• Filered the features to contains only columns names that has ( mean and std).

• Read the tables for both training and test folders.

• Changed the activity values from y_test.txt and y.train.txt to the activity names.

• Selected the required columns with the help of features table.

• Cleaned the columns names from features file (removed spaces and others described fully in the run_analysis.R)

• Created two tables for test and train with subjects and activity names.

• Combined the tables for training and testing.

• Grouped the resulting table by subject first then activity.

• Calculated means of all variables.

• Exported a tidy data file as tidy_data.txt