After "run_analysis.R" was run you will have two new variables in your workspace: final_data and grouped_data. All of the auxillary variables, which were created within the script are removed in the end of the script.
##Columns Both final_data and grouped_data are data.frames with 81 columns (they are following the "wide data" format). Both have the 'Subject' (i.e. the person id, from 1 to 30) as the first column and 'Activity' (which is one of WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) as the second column. The next 79 column come from the initial dataset. Following is the description from features_info.txt provided with the original dataset.
The features selected for this database 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.
Subsequently, 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).
Finally 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).
These signals were used to estimate variables of the feature vector for each pattern:
'-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.
- tBodyAcc-XYZ
- tGravityAcc-XYZ
- tBodyAccJerk-XYZ
- tBodyGyro-XYZ
- tBodyGyroJerk-XYZ
- tBodyAccMag
- tGravityAccMag
- tBodyAccJerkMag
- tBodyGyroMag
- tBodyGyroJerkMag
- fBodyAcc-XYZ
- fBodyAccJerk-XYZ
- fBodyGyro-XYZ
- fBodyAccMag
- fBodyAccJerkMag
- fBodyGyroMag
- fBodyGyroJerkMag
The set of variables that were estimated from these signals are:
- mean(): Mean value
- std(): Standard deviation
- meanFreq(): Weighted average of the frequency components to obtain a mean frequency
- final_data contains 10299 rows - all of those are individual measurements from the original dataset (train set and test set combined).
- grouped_data contains 180 rows - those are means for each of the columns grouped by all of the activities and subjects (6 activities over 30 subject, thus 180 rows). Calculated from final_data.
To see the transformations which were applied to the initial data set, please refer to the comments in the run_analysis.R