Introduction

The script run_analysis.R performs the 5-step procedure from the Coursera's Getting and Cleaning Data project assignment.

At the beginning the dataset is read to tables from the data files saved on hard drive.
Data with the same columns and referring to the same entities is merged using rbind() function.
Only columns with the mean and standard deviation measurements are extracted. They are given the correct names, taken from features.txt.
The activity IDs are replaced by the activity names taken from activity_labels.txt.
The dataset is labeled with descriptive variable names.
A new dataset is created with all the average measurements for each subject and activity type

Variables

x_train, y_train, subject_train, x_test, y_test, and subject_test contain the data from the downloaded files.
x_data, y_data and subject_data merge the previous datasets for 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.