This repository contains the solution to the peer graded project assignment of the Getting and Cleaning Data course. Following is a brief description of the files in this repository:

• README.md - this file
• CodeBook.md - brief explanation of the columns in the output data file - ml_data_2014-04-26_10-31-22.csv
• ml_data_2014-04-26_10-31-22.csv - tidy data set created using the R script run_analysis.R
• run_analysis.R - data processing R script, version 1
• run_analysis_v2.R - data processing R script, version 2

The only difference between run_analysis.R and run_analysis_v2.R scripts consists in the way the main data files (X_train.txt and X_test.txt) are being loaded from disk into a data frame object. Version 2 of the script improves the overall performance of the script by about 5x The rest of the processing tasks are identical in the 2 scripts.

The script performs the tasks described in the project assignment specifications for the "Getting and Cleaning Data" class

#####Requirements:

• the Samsung data directories and files are in the same directory with this script (directory "UCI HAR Dataset" and it's content);
• R working directory is set to the location of the run_analysis.R script;
• the computer running R has sufficient memory (RAM) for loading and processing the data files and enough disk space (less than 250kB) for saving the tidy data set;

#####General structure: The script contains one function which encapsulates the entire data processing logic followed by a single statement that calls the function and stores in a variable (ml.data.list) the output of the function - a list with 2 named elements:

• data - first elemnt of the list is a data frame containing the filtered data set loaded from the original files. This first element requires ~5.5MB of RAM
• data.mean - second element of the list, also a data frame, contains the tidy data set The tidy data set was also used to generate a disk file in comma-separated format The naming convention used for the output file is "ml_mean_YYYY-MM-DD_HH-MI-SS.csv" where YYYY-MM-DD_HH-MI-SS is the date and time of running the script. This way, each time the script is sourced (or the function data_filter() is called) a new file will be created on disk.

#####Data processing steps in data_filter() function The script performs the following data processing steps, all of which have been commented in the R script:

• retrieve the current date and time and store them in a character variable to be used for generating the output file name;
• processes the input argument of the function - dir_name - which has a default value and represents the name the top directory that stores the data. The default value is "UCI HAR Dataset". The function expects that directory to be in R's current working directory;
• generate a character vector having as elements the file names and locations (relative to dir_name) of the files to be loaded from disk. The last element of the vector is name of the output file;
• read all data files from disk and store them in data frame variables. The small explanatory files activity_labels.txt and features.txt are loaded with read.table() function while the training and test data files (X, y and subject) are loaded using scan() in one large data frame - ml.data
• from ml.data, there will be retained only the columns that have the name containing mean() or std() character sequences. This filtering will result in a much smaller ml.data data frame with only 68 columns - 66 feature columns (out of the original 561), 1 activity column and 1 subject column;
• compute the feature averages for each distinct combination of activity and subject values and store the result in a new data frame - agg.ml.data. This task could be achieved using a single line in the script but I chose to implement it in several steps (commented in the script) for readability
• add a new column to both data frames (ml.data and agg.ml.data) named activity.labels and populated with the descriptive activity names found in the activity labels explanatory file. The numeric column activity has been kept in the data frames as well since it only takes very little memory and can be processed much easier than a text variable in computations performed outside R (using code written in C++ or C)
• write the agg.ml.data data frame in a comma-separated ascii file (see naming convention above)

The file CodeBook.md in the repository contains a brief explanation of each column name in the agg.ml.data data frame (and the output data file).

###run_analysis_v2.R

As mentioned above, the run_analysis_v2.R script performs the same tasks as run_analysis.R script except for loading the X_train.txt and X_test.txt files, which is done using laf_open_fwf() function in the LaF package (Large aSCII Files).

###Performance improvement

Following are the execution times (elapsed time in seconds) of the two scripts:

• run_analysis.R execution time (elapsed) = 10.56 sec
• run_analysis_v2.R execution time (elapsed) = 2.53 sec

The performance improvement is ~5 times just by using a different package to read large data files which I found to be the single largest performance bottleneck.