Getting and Cleaning Data Course Project

One of the most exciting areas in all of data science right now is wearable computing - see for example this article. Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from 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

Here are the data for the project:

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

You should create one R script called run_analysis.R that does the following.

1. Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set
4. Appropriately labels the data set with descriptive variable names.
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.

Data

The script is invented to analyze the data from UCI HAR Dataset. It's supposed that archive is extracted to the working directory.

The following files from the initial dataset is used:

1. features.txt - includes the descriptions for features measured
2. train/X_train.txt - includes the measurements of the features in train set (one row - 1 measurement of 561 features)
3. test/X_test.txt - includes the measurements of the features in test set
4. train/subject_train.txt - subject for each measurement from the train set
5. test/subject_test.txt - subject for each measurement from the test set
6. train/y_train.txt - activity (from 1 to 6) for each measurement from the train set
7. test/y_test.txt - activity (from 1 to 6) for each measurement from the test set

Script

Script involves the following stages:

1. Downloads to R ids and descriptions for features being measured in experiment from file features.txt.
2. Independently loads complete data for train and test sets. Let's revoke these loading process considering train set:
   • Firstly loads the measurements from X_train.txt as a data frame
   • For these data frame column names are updated to be more user friendly using features description loaded on the previous stage (Step 4)
   • Activity labels and subjects for measurements are also loaded from files train/y_train.txt and train/subject_train.txt and added to data frame as a separated columns.

Similar steps are made for test dataset and finally 2 rows of 2 data frames are merged together to form a data frame with complete data (Step 1)

3. To extract measurements that involves only mean and standard deviation values script uses grep, that finds column names that includes "mean()" or "std()" (also columns activity and subject are added to filtered data frame, since they are important dimensions). After that all new data frame with only necessary columns is created. (Step 2)

4. To provide descriptive values for activity labels a new variable activityLabel is added to dataset, that is a factor variable with levels mentioned in file activity_labels.txt (Step 3)

5. Creates a melted data frame using activity label and subject as ids, after that mean values for all variables are calculated grouped by activity and subject using dcast() function and tidy data frame is created. (Step 5)