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Codebook for datatidy.txt

About the RAW data

The raw data (UCI HAR Dataset) used is from the experiments 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.

The original files are in: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
More explanation about the raw data in: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

About the tidy data

To get the tidy data, the raw data passed for the some transformations:
To make the tidy data, i ignored the "/Inertial Signals/" folders.
I ignored the "MeanFreq" data, only using the "mean()" and "std()" data.
The data are in longform format, i think this is better for understood.
The tidy data is grouped by subject and activity, and have the means of the values collected by the sensors.
The data meets the tidy data principles from http://vita.had.co.nz/papers/tidy-data.pdf.
More info commented in the: "run_analysis.R" file in https://github.com/diegocgaona/Get_Data_Course_Assignment , and the README.md file.

About the variables:

Column: subject
An identifier of the subject who carried out the experiment
Storage mode: integer
Measurement: subjects ids
Min: 1
Max: 30

Column: activity
The activity labels
Storage mode: character
Measurement: nominal

6 Labels:

LAYING  
SITTING  
STANDING  
WALKING  
WALKING_DOWNSTAIRS

Column: variable
Mean and std variable names from the sensors
Storage mode: character
Measurement: nominal
Labels:

1	'tBodyAcc_Mean_X'
2	'tBodyAcc_Mean_Y'  
3	'tBodyAcc_Mean_Z'  
4	'tBodyAcc_std_X'  
5	'tBodyAcc_std_Y'  
6	'tBodyAcc_std_Z'  
7	'tGravityAcc_Mean_X'  
8	'tGravityAcc_Mean_Y'  
9	'tGravityAcc_Mean_Z'  
10	'tGravityAcc_std_X'  
11	'tGravityAcc_std_Y'  
12	'tGravityAcc_std_Z'  
13	'tBodyAccJerk_Mean_X'  
14	'tBodyAccJerk_Mean_Y'  
15	'tBodyAccJerk_Mean_Z'  
16	'tBodyAccJerk_std_X'  
17	'tBodyAccJerk_std_Y'  
18	'tBodyAccJerk_std_Z'  
19	'tBodyGyro_Mean_X'  
20	'tBodyGyro_Mean_Y'  
21	'tBodyGyro_Mean_Z'  
22	'tBodyGyro_std_X'  
23	'tBodyGyro_std_Y'  
24	'tBodyGyro_std_Z'  
25	'tBodyGyroJerk_Mean_X'  
26	'tBodyGyroJerk_Mean_Y'  
27	'tBodyGyroJerk_Mean_Z'  
28	'tBodyGyroJerk_std_X'  
29	'tBodyGyroJerk_std_Y'  
30	'tBodyGyroJerk_std_Z'  
31	'tBodyAccMag_Mean'  
32	'tBodyAccMag_std'  
33	'tGravityAccMag_Mean'  
34	'tGravityAccMag_std'  
35	'tBodyAccJerkMag_Mean'  
36	'tBodyAccJerkMag_std'  
37	'tBodyGyroMag_Mean'  
38	'tBodyGyroMag_std'  
39	'tBodyGyroJerkMag_Mean'  
40	'tBodyGyroJerkMag_std'  
41	'fBodyAcc_Mean_X'  
42	'fBodyAcc_Mean_Y'  
43	'fBodyAcc_Mean_Z'  
44	'fBodyAcc_std_X'  
45	'fBodyAcc_std_Y'  
46	'fBodyAcc_std_Z'  
47	'fBodyAccJerk_Mean_X'  
48	'fBodyAccJerk_Mean_Y'  
49	'fBodyAccJerk_Mean_Z'  
50	'fBodyAccJerk_std_X'  
51	'fBodyAccJerk_std_Y'  
52	'fBodyAccJerk_std_Z'  
53	'fBodyGyro_Mean_X'  
54	'fBodyGyro_Mean_Y'  
55	'fBodyGyro_Mean_Z'  
56	'fBodyGyro_std_X'  
57	'fBodyGyro_std_Y'  
58	'fBodyGyro_std_Z'  
59	'fBodyAccMag_Mean'  
60	'fBodyAccMag_std'  
61	'fBodyBodyAccJerkMag_Mean'  
62	'fBodyBodyAccJerkMag_std'  
63	'fBodyBodyGyroMag_Mean'  
64	'fBodyBodyGyroMag_std'  
65	'fBodyBodyGyroJerkMag_Mean'  
66	'fBodyBodyGyroJerkMag_std'

Column: value
The data from the sensors
Storage mode: numeric
Unit: Measurements

More info in README.md