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The code book describes the variables, the data, and any transformations or work performed to clean up the data. It has been separated in two (2) parts. Part 1, "Original Data", describes the data used in this project. Part 2, "Data Manipulation" describes the procedure followed in order to fulfil the project's requirements and create the tidy datasets.

Original Data

The data used for this project represent data collected from the accelerometers of the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

The data were downloaded from:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

The project data are experiment data regarding a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.

The files used in this project are:

  • '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', 'test/subject_test.txt': Each file corresponds to the respective dataset, i.e. test or training. Each row identifies the subject who performed the activity for each window sample. Their range is from 1 to 30.

Data manipulation with run_analysis.R

1. Load Data (script lines 12-24)

The content of the files presented above are loaded to respective variables. Those variables, along with the files they load, are:

  • x_test, x_train: 'X_test.txt', 'X_train.txt', respectively
  • y_test, y_train: 'y_test.txt', y_train.txt', respectively
  • s_test, s_train: 'subject_test.txt', 'subject_train.txt', respectively
  • features: 'features.txt'
  • activity_labels: 'activity_labels.txt'

2. Merge Data - Fix variable/activity names (script lines 27-69)

  • Each test/train dataset is merged in one dataset - x, y or s - using the 'rbind' command of R. The resulting variables are:

    • x_merged: merges x_test, x_train
    • y_merged: merges y_test, y_train
    • s_merged: merges s_test, s_train

  • In 'y_merged' the activity labels are replaced with the actual activity names, i.e. '1' --> 'WALKING', '2' --> 'WALKING_UPSTAIRS', etc.

  • Suitable, descriptive names for each column of the x/y/s datasets are placed.

  • 'x/y/s_merged' datasets are merged into one dataset called 'sensor_data_merged' with R's 'cbind' command. The variable names of 'sensor_data_merged' are changed so that no parentheses are present in the names.

  • The columns of interest, i.e. those that refer to mean or standard deviation values, plus those that contain the subject IDs and activity names are identified and their indices place in variable 'wanted_columns'.

  • Using the 'wanted_columns' and 'sensor_data_merged' variables, 'sensor_data_wanted' variable is created, containing only the columns of interest.

3. Create independent, tidy dataset (script lines 72-80)

  • Using plyr library's 'ddply' command, the 'sensor_data_wanted_avg' variable is created, which contains an independent, tidy dataset with the average value of each activity and subject.
  • The dataset of 'sensor_data_wanted_avg' is written in a txt file named 'tidy_sensor_data_avg.txt'.
  • 'tidy_sensor_data_avg.txt', as well as the variable 'sensor_data_wanted_avg' which contains its data, have 68 columns and 180 rows (30 subjects * 6 activities). It is structured as follows:
    subject_ID activity tBodyAccmeanX ... rest of the variables
    1 LAYING 0.2215982
    1 SITTING 0.2612376
    1 STANDING 0.2789176
    1 WALKING 0.2773308
    1 WALKING_DOWNSTAIRS 0.2891883
    1 WALKING_UPSTAIRS 0.2554617
    2 LAYING 0.2813734
    2 SITTING 0.2770874
    ... rest of the subject_IDs