#Codebook
##What variables I used Used variables are from X_test.txt and X_train.txt.
##Names of columns and why In the final tidy dataset, column 1 is the subject label, column 2 is the activity label, column 3-88 are extracted from the raw data set according to features.txt. Only means and standard deviation of the measurements are selected. the names of columns are:
[1] "subject"
[2] "activity"
[3] "tBodyAcc-mean()-X"
[4] "tBodyAcc-mean()-Y"
[5] "tBodyAcc-mean()-Z"
[6] "tBodyAcc-std()-X"
[7] "tBodyAcc-std()-Y"
[8] "tBodyAcc-std()-Z"
[9] "tGravityAcc-mean()-X"
[10] "tGravityAcc-mean()-Y"
[11] "tGravityAcc-mean()-Z"
[12] "tGravityAcc-std()-X"
[13] "tGravityAcc-std()-Y"
[14] "tGravityAcc-std()-Z"
[15] "tBodyAccJerk-mean()-X"
[16] "tBodyAccJerk-mean()-Y"
[17] "tBodyAccJerk-mean()-Z"
[18] "tBodyAccJerk-std()-X"
[19] "tBodyAccJerk-std()-Y"
[20] "tBodyAccJerk-std()-Z"
[21] "tBodyGyro-mean()-X"
[22] "tBodyGyro-mean()-Y"
[23] "tBodyGyro-mean()-Z"
[24] "tBodyGyro-std()-X"
[25] "tBodyGyro-std()-Y"
[26] "tBodyGyro-std()-Z"
[27] "tBodyGyroJerk-mean()-X"
[28] "tBodyGyroJerk-mean()-Y"
[29] "tBodyGyroJerk-mean()-Z"
[30] "tBodyGyroJerk-std()-X"
[31] "tBodyGyroJerk-std()-Y"
[32] "tBodyGyroJerk-std()-Z"
[33] "tBodyAccMag-mean()"
[34] "tBodyAccMag-std()"
[35] "tGravityAccMag-mean()"
[36] "tGravityAccMag-std()"
[37] "tBodyAccJerkMag-mean()"
[38] "tBodyAccJerkMag-std()"
[39] "tBodyGyroMag-mean()"
[40] "tBodyGyroMag-std()"
[41] "tBodyGyroJerkMag-mean()"
[42] "tBodyGyroJerkMag-std()"
[43] "fBodyAcc-mean()-X"
[44] "fBodyAcc-mean()-Y"
[45] "fBodyAcc-mean()-Z"
[46] "fBodyAcc-std()-X"
[47] "fBodyAcc-std()-Y"
[48] "fBodyAcc-std()-Z"
[49] "fBodyAcc-meanFreq()-X"
[50] "fBodyAcc-meanFreq()-Y"
[51] "fBodyAcc-meanFreq()-Z"
[52] "fBodyAccJerk-mean()-X"
[53] "fBodyAccJerk-mean()-Y"
[54] "fBodyAccJerk-mean()-Z"
[55] "fBodyAccJerk-std()-X"
[56] "fBodyAccJerk-std()-Y"
[57] "fBodyAccJerk-std()-Z"
[58] "fBodyAccJerk-meanFreq()-X"
[59] "fBodyAccJerk-meanFreq()-Y"
[60] "fBodyAccJerk-meanFreq()-Z"
[61] "fBodyGyro-mean()-X"
[62] "fBodyGyro-mean()-Y"
[63] "fBodyGyro-mean()-Z"
[64] "fBodyGyro-std()-X"
[65] "fBodyGyro-std()-Y"
[66] "fBodyGyro-std()-Z"
[67] "fBodyGyro-meanFreq()-X"
[68] "fBodyGyro-meanFreq()-Y"
[69] "fBodyGyro-meanFreq()-Z"
[70] "fBodyAccMag-mean()"
[71] "fBodyAccMag-std()"
[72] "fBodyAccMag-meanFreq()"
[73] "fBodyBodyAccJerkMag-mean()"
[74] "fBodyBodyAccJerkMag-std()"
[75] "fBodyBodyAccJerkMag-meanFreq()"
[76] "fBodyBodyGyroMag-mean()"
[77] "fBodyBodyGyroMag-std()"
[78] "fBodyBodyGyroMag-meanFreq()"
[79] "fBodyBodyGyroJerkMag-mean()"
[80] "fBodyBodyGyroJerkMag-std()"
[81] "fBodyBodyGyroJerkMag-meanFreq()"
[82] "angle(tBodyAccMean,gravity)"
[83] "angle(tBodyAccJerkMean),gravityMean)"
[84] "angle(tBodyGyroMean,gravityMean)"
[85] "angle(tBodyGyroJerkMean,gravityMean)"
[86] "angle(X,gravityMean)"
[87] "angle(Y,gravityMean)"
[88] "angle(Z,gravityMean)"
##Unites of data if known The data has been scaled by dividing by the range, and when you divide units by the same units, the units are cancelled. Therefore, the data in the final data sets are free of units. Please see (http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) for further information.
##Reference or link to original data info The data was downloaded from (https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip)