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run_analysis.R

Description

The run_analysis.R script performs data preparation and analysis on the UCI HAR Dataset, following a set of five steps outlined in the course project's instructions.

Steps

  1. Download the dataset

    The dataset is downloaded from an online source and extracted into a folder named "UCI HAR Dataset".

  2. Assign data to variables

    Various data files are assigned to specific variables:

    • features: features.txt (561 rows, 2 columns)
      • Contains the selected features from the accelerometer and gyroscope signals.
    • activities: activity_labels.txt (6 rows, 2 columns)
      • Lists the activities performed and their corresponding codes.
    • subject_test: test/subject_test.txt (2947 rows, 1 column)
      • Contains the test data of volunteer test subjects.
    • x_test: test/X_test.txt (2947 rows, 561 columns)
      • Includes the recorded features test data.
    • y_test: test/y_test.txt (2947 rows, 1 column)
      • Holds the test data of activity codes.
    • subject_train: train/subject_train.txt (7352 rows, 1 column)
      • Contains the train data of volunteer subjects.
    • x_train: train/X_train.txt (7352 rows, 561 columns)
      • Includes the recorded features train data.
    • y_train: train/y_train.txt (7352 rows, 1 column)
      • Holds the train data of activity codes.

  3. Merge training and test sets

    The script merges the training and test datasets to create one dataset:

    • X (10299 rows, 561 columns) combines x_train and x_test using the rbind() function.
    • Y (10299 rows, 1 column) combines y_train and y_test using rbind().
    • Subject (10299 rows, 1 column) combines subject_train and subject_test using rbind().
    • Merged_Data (10299 rows, 563 columns) merges Subject, Y, and X using cbind().

  4. Extract mean and standard deviation measurements

    A subset of the merged dataset, named TidyData, is created by selecting columns related to mean and standard deviation measurements:

    • Includes columns for subject, code, and relevant mean and standard deviation (std) measurements.

  5. Label activities and variables descriptively

    The script replaces activity codes in TidyData with descriptive activity names from the activities variable. Variable names in TidyData are also appropriately labeled:

    • code column is renamed to activities.
    • Abbreviations and prefixes in variable names are expanded for clarity.

  6. Create a second tidy dataset with averages

    The script generates a second independent tidy dataset, FinalData, by summarizing TidyData. The dataset calculates the average of each variable for each activity and subject, after grouping by subject and activity.

    • FinalData has 180 rows and 88 columns.

  7. Export FinalData

    FinalData is exported as a text file named "FinalData.txt".