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Introduction

The script run_analysis.Rperforms the 5 steps described in the course project's definition.

  • First, all the similar data is merged using the rbind() function. By similar, we address those files having the same number of columns and referring to the same entities.
  • Then, only those columns with the mean and standard deviation measures are taken from the whole dataset. After extracting these columns, they are given the correct names, taken from features.txt.
  • As activity data is addressed with values 1:6, we take the activity names and IDs from activity_labels.txt and they are substituted in the dataset.
  • On the whole dataset, those columns with vague column names are corrected.
  • Finally, we generate a new dataset with all the average measures for each subject and activity type (30 subjects * 6 activities = 180 rows). The output file is called averages_data.txt, and uploaded to this repository.

Variables

  • x_train, y_train, x_test, y_test, subject_train and subject_test contain the data from the downloaded files.
  • x_data, y_data and subject_data merge the previous datasets to further analysis.
  • features contains the correct names for the x_data dataset, which are applied to the column names stored in mean_and_std_features, a numeric vector used to extract the desired data.
  • A similar approach is taken with activity names through the activities variable.
  • all_data merges x_data, y_data and subject_data in a big dataset.
  • Finally, averages_data contains the relevant averages which will be later stored in a .txt file. ddply() from the plyr package is used to apply colMeans() and ease the development.