{"action":"opened","issue":{"url":"https://api.github.com/repos/aarthivemuri/Coursera-Getting-and-Cleaning-Data/issues/1","labels_url":"https://api.github.com/repos/aarthivemuri/Coursera-Getting-and-Cleaning-Data/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/aarthivemuri/Coursera-Getting-and-Cleaning-Data/issues/1/comments","events_url":"https://api.github.com/repos/aarthivemuri/Coursera-Getting-and-Cleaning-Data/issues/1/events","html_url":"https://github.com/aarthivemuri/Coursera-Getting-and-Cleaning-Data/issues/1","id":101517048,"number":1,"title":"Codebook","user":{"login":"aarthivemuri","id":11841083,"avatar_url":"https://avatars.githubusercontent.com/u/11841083?v=3","gravatar_id":"","url":"https://api.github.com/users/aarthivemuri","html_url":"https://github.com/aarthivemuri","followers_url":"https://api.github.com/users/aarthivemuri/followers","following_url":"https://api.github.com/users/aarthivemuri/following{/other_user}","gists_url":"https://api.github.com/users/aarthivemuri/gists{/gist_id}","starred_url":"https://api.github.com/users/aarthivemuri/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/aarthivemuri/subscriptions","organizations_url":"https://api.github.com/users/aarthivemuri/orgs","repos_url":"https://api.github.com/users/aarthivemuri/repos","events_url":"https://api.github.com/users/aarthivemuri/events{/privacy}","received_events_url":"https://api.github.com/users/aarthivemuri/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-08-17T21:47:07Z","updated_at":"2015-08-17T21:47:07Z","closed_at":null,"body":"\r\n---\r\n##Codebook##\r\n\r\nAuthor: *Aarthi Vemuri*\t\r\n\r\nDate: *August 17, 2015*\r\n\r\n------\r\n\r\n#### DATA DESCRIPTION\r\n\r\nThe features selected for this data come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz. \r\n\r\nSubsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag). \r\n\r\nFinally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals). \r\n\r\nThese signals were used to estimate variables of the feature vector for each pattern:  \r\n'-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.\r\n\r\n- tBodyAcc-XYZ\r\n- tGravityAcc-XYZ\r\n- tBodyAccJerk-XYZ\r\n- tBodyGyro-XYZ\r\n- tBodyGyroJerk-XYZ\r\n- tBodyAccMag\r\n- tGravityAccMag\r\n- tBodyAccJerkMag\r\n- tBodyGyroMag\r\n- tBodyGyroJerkMag\r\n- fBodyAcc-XYZ\r\n- fBodyAccJerk-XYZ\r\n- fBodyGyro-XYZ\r\n- fBodyAccMag\r\n- fBodyAccJerkMag\r\n- fBodyGyroMag\r\n- fBodyGyroJerkMag\r\n\r\nThe set of variables that were estimated from these signals that are present in the dataset are: \r\n\r\n- mean(): Mean value\r\n- std(): Standard deviation\r\n\r\n------\r\n\r\n#### DATA VARIABLES\r\n\r\n---\r\n\r\n**Categorical Variables:**\r\n\r\nSubject_id - ID of the test subject \r\nActivity - Type of the activity performed when the measurements were taken\r\n\r\n**Continuous variables:**\r\n\r\n      - tBodyAcc-mean()-X\r\n- tBodyAcc-mean()-Y\r\n- tBodyAcc-mean()-Z\r\n- tBodyAcc-std()-X\r\n- tBodyAcc-std()-Y\r\n- tBodyAcc-std()-Z\r\n- tGravityAcc-mean()-X\r\n- tGravityAcc-mean()-Y\r\n- tGravityAcc-mean()-Z\r\n- tGravityAcc-std()-X\r\n- tGravityAcc-std()-Y\r\n- tGravityAcc-std()-Z\r\n- tBodyAccJerk-mean()-X\r\n- tBodyAccJerk-mean()-Y\r\n- tBodyAccJerk-mean()-Z\r\n- tBodyAccJerk-std()-X\r\n- tBodyAccJerk-std()-Y\r\n- tBodyAccJerk-std()-Z\r\n- tBodyGyro-mean()-X\r\n- tBodyGyro-mean()-Y\r\n- tBodyGyro-mean()-Z\r\n- tBodyGyro-std()-X\r\n- tBodyGyro-std()-Y\r\n- tBodyGyro-std()-Z\r\n- tBodyGyroJerk-mean()-X\r\n- tBodyGyroJerk-mean()-Y\r\n- tBodyGyroJerk-mean()-Z\r\n- tBodyGyroJerk-std()-X\r\n- tBodyGyroJerk-std()-Y\r\n- tBodyGyroJerk-std()-Z\r\n- tBodyAccMag-mean()\r\n- tBodyAccMag-std()\r\n- tGravityAccMag-mean()\r\n- tGravityAccMag-std()\r\n- tBodyAccJerkMag-mean()\r\n- tBodyAccJerkMag-std()\r\n- tBodyGyroMag-mean()\r\n- tBodyGyroMag-std()\r\n- tBodyGyroJerkMag-mean()\r\n- tBodyGyroJerkMag-std()\r\n- fBodyAcc-mean()-X\r\n- fBodyAcc-mean()-Y\r\n- fBodyAcc-mean()-Z\r\n- fBodyAcc-std()-X\r\n- fBodyAcc-std()-Y\r\n- fBodyAcc-std()-Z\r\n- fBodyAcc-meanFreq()-X\r\n- fBodyAcc-meanFreq()-Y\r\n- fBodyAcc-meanFreq()-Z\r\n- fBodyAccJerk-mean()-X\r\n- fBodyAccJerk-mean()-Y\r\n- fBodyAccJerk-mean()-Z\r\n- fBodyAccJerk-std()-X\r\n- fBodyAccJerk-std()-Y\r\n- fBodyAccJerk-std()-Z\r\n- fBodyAccJerk-meanFreq()-X\r\n- fBodyAccJerk-meanFreq()-Y\r\n- fBodyAccJerk-meanFreq()-Z\r\n- fBodyGyro-mean()-X\r\n- fBodyGyro-mean()-Y\r\n- fBodyGyro-mean()-Z\r\n- fBodyGyro-std()-X\r\n- fBodyGyro-std()-Y\r\n- fBodyGyro-std()-Z\r\n- fBodyGyro-meanFreq()-X\r\n- fBodyGyro-meanFreq()-Y\r\n- fBodyGyro-meanFreq()-Z\r\n- fBodyAccMag-mean()\r\n- fBodyAccMag-std()\r\n- fBodyAccMag-meanFreq()\r\n- fBodyBodyAccJerkMag-mean()\r\n- fBodyBodyAccJerkMag-std()\r\n- fBodyBodyAccJerkMag-meanFreq()\r\n- fBodyBodyGyroMag-mean()\r\n- fBodyBodyGyroMag-std()\r\n- fBodyBodyGyroMag-meanFreq()\r\n- fBodyBodyGyroJerkMag-mean()\r\n- fBodyBodyGyroJerkMag-std()\r\n- fBodyBodyGyroJerkMag-meanFreq()\r\n"}}