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Code Book for Getting and Cleaning Data Course Project

Thomas Fischer February 02, 2018

Source Data

Training and test data sets can be downloaded here. The zip archive contains a folder named UCI HAR Dataset with all the raw data. Hereby a list with only the data relevant for this project:

  • 'features.txt': List of all features.
  • 'activity_labels.txt': Links the class labels with their activity name.
  • 'train/X_train.txt': Training set.
  • 'train/y_train.txt': Training labels.
  • 'test/X_test.txt': Test set.
  • 'test/y_test.txt': Test labels.
  • 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
  • 'test/subject_test.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

Transformations by script run_analysis.R:

The script is following the instructions for this coursera assignment:

  1. Merges the training and the test sets to create one data set.

    Mergeing test/X_test.txt and train/X_train.txt.
    Adding column headers from features.txt.
    Adding the labels as new column from train/y_train.txt and test/y_test.txt.
    Adding activity id's from train/subject_train.txt and test/subject_test.txt.

  2. Extracts only the measurements on the mean and standard deviation for each measurement.

    Features containing either the string 'std()', 'mean()' or 'meanFreq()' are extracted together with label 'activity_id' and identifier for the subjects 'subject_id'.
    From 561 original measurement features 482 features were removed.

  3. Uses descriptive activity names to name the activities in the data set

    The descriptive activity names from activity_labels.txt were merged into the dat set.

  4. Appropriately labels the data set with descriptive variable names.

    Nothing transformed. In my opinion there is no need to further transform the variable names, because there is no gain in ending in column names like:
    "frequencyDomainBodyBodyAccelerometerJerkMagnitudeStandardDeviation"

  5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

    The data set finally has been agregated by 'activity_id' and 'subject_id' and for each aggregation pair the mean for all measurement variables is calculated.
    The final data set is saved to tidy_data.txt.

Variables in tidy_data.txt

Aggreated data by 'activity_id' and 'subject_id'. The mean of all the measurements per aggregation is calculated. Please refer to the original README.txt file for detailed information.