Skip to content

Latest commit

 

History

History
30 lines (15 loc) · 2.2 KB

CodeBook.md

File metadata and controls

30 lines (15 loc) · 2.2 KB
Raw

Codebook for Getting and Cleaning Data Course Project

This is the code book for the tidy data set created by the run_analysis.R script for this course project. See the file README.md for documentation of the script and the study design.

The submission data is written to the file submit_data.txt. The format is that produced by write.table with the row.names = FALSE argument.

The data has 7 columns

  1. subject: this is the identifier for the subject under observation. There are 30 subjects numbered from 1 to 30.

  2. activity: this contains text desrcibing the type of activity the subject performed. Possible values are WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, and LAYING.

  3. sensor: the code for the sensor metric of the measurement. There are 17 different sensors.

  4. measure: indicates if the data is the mean ("mean") or standard deviation ("std"). This data set is a subset of the original data and only contains these two summary statistics.

  5. dimension: Many of the sensor metrics have three values, one for each spatial dimention which is denoted by "x", "y", and "z" in this column. The nine 'magnitude' sensors (names ending with "Mag") only provide one value and these have "" in this column.

  6. mean: this is the mean value of the data for the subject, activity, sensor, measure, and dimension

    • The original data has all values normalised and bounded to [-1,1] and we have not altered the values.
    • The gyroscope ("Gyro") measures angular velocity while the accelerometer provided tri-axial and total acceleration.
    • In the original data a Fast Fourier Transform (FFT) was applied to some of the signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag.

  7. n: the number of observations used to calculate the mean.

For more information about the underlying data, see the Human Activity Recognition Using Smartphones Data Set on the UCI Machine Learning Repository.