Adding files to repo

README.md

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-# cleaning-data-assignment
+# Readme.md 
+
+The tidy data can be read into R with the following code:
+read.table("tidy_data_set.txt", header=TRUE)
+
+## Description of run_analysis.R script
+
+<< add description of script >>
+
+There are some interesting threads on the course discussion board about wide vs. narrow formats for tidy data. I chose to use the wide format, aligning to these principles:
+
+1) Each column represents a variable or measure or characteristic.
+2) Each variable is in one column.
+3) Each observation of the variable is in a different row.
+
+## Special instructions for running run_analysis.R
+
+run_analysis.R is written to work with the Windows file system. You'll need to modify the file paths in read.table to run on Mac/Linux systems.
+
+
+## Description of tidy data
+
+The codebook.md file contains the specific description of the tidy data file contents. 
+
+The codebook still has the specific description of the tidy data file contents (and you mention that it exists and it's role in the ReadMe)

UCI HAR Dataset/README.txt

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+==================================================================
+Human Activity Recognition Using Smartphones Dataset
+Version 1.0
+==================================================================
+Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.
+Smartlab - Non Linear Complex Systems Laboratory
+DITEN - Universit� degli Studi di Genova.
+Via Opera Pia 11A, I-16145, Genoa, Italy.
+activityrecognition@smartlab.ws
+www.smartlab.ws
+==================================================================
+
+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. See 'features_info.txt' for more details. 
+
+For each record 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.
+
+The dataset includes the following files:
+=========================================
+
+- 'README.txt'
+
+- 'features_info.txt': Shows information about the variables used on the feature vector.
+
+- 'features.txt': List of all features.
+
+- 'activity_labels.txt': Links the class labels with their activity name.
+
+- 'train/X_train.txt': Training set.
+
+- 'train/y_train.txt': Training labels.
+
+- 'test/X_test.txt': Test set.
+
+- 'test/y_test.txt': Test labels.
+
+The following files are available for the train and test data. Their descriptions are equivalent. 
+
+- 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30. 
+
+- 'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and 'total_acc_z_train.txt' files for the Y and Z axis. 
+
+- 'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration. 
+
+- 'train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second. 
+
+Notes: 
+======
+- Features are normalized and bounded within [-1,1].
+- Each feature vector is a row on the text file.
+
+For more information about this dataset contact: activityrecognition@smartlab.ws
+
+License:
+========
+Use of this dataset in publications must be acknowledged by referencing the following publication [1] 
+
+[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.

UCI HAR Dataset/activity_labels.txt

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+1 WALKING
+2 WALKING_UPSTAIRS
+3 WALKING_DOWNSTAIRS
+4 SITTING
+5 STANDING
+6 LAYING