################################################################# #Summary #################################################################

The script returns a data.table and saves this table to disk according to the specifications of the Getting and Cleaning Data project course assignment. The final output is a file called "tidy_output.txt". A description of the output variables are discussed in CODEBOOK.md and for convenience, duplicated below in this readme file.

###Input data for this analysis comes from the Samsung activity study:

Human Activity Recognition Using Smartphones Dataset Version 1.0

Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws

Additional detail on the study data parameters may be found at this URL: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

In summary, the Samsung study records acceleration and gyroscopic movement data obtained from a Samsung Galaxy S II device while subjects are requested to perform various physical activity (such as walking, sitting, standing). Acceleration and position are thus measured in space and recorded in XYZ coordinates.

For detailed information on the primary data methodology and study design, the interested reader is referred to the URL above.

This analysis examines key features of the Samsung data, specifically identifying mean and standard deviation observations associated with the Samsung spatial measurements. The analysis output reports the mean values of these key feature measurements, aggregated by-subject and by-activity in the tidy_output.txt file. This file is space-delimited and adheres to format expectations for tidy data with one observation per row and one variable per column.
Total data table dimensions are 180 rows by 68 columns.

Specific steps to perform this analysis are completed using the run_analysis.R script, which is further discussed below

################################################################# #Script: run_analysis.R ################################################################# ##The script defines 4 functions: ###(1) setup_files() : Retrieves .zip archive, saves to disk and extracts ###(2) load_data() : Reads extracted data sets from disk into a named list of data.frames ###(3) combine_data() : Combines the individual data.frames of interest into a unified, named, labeled data.frame ###(4) extract_data() : Subsets the combined data.frame with columns of interest and creates a summary table of means ###(5) run() : Calls the above functions in their sequential order and writes the summary table to disk

##Specific actions performed in detail, by script line number are as follows: ##(1) setup_files() * Line 21-22: Defines directory names and .zip file * Line 23-33: Downloads .zip file using specified URL and extracts to disk * Line 36-38: Generates a directory structure from .zip extract folder, from which generates vectors of file names and their associated file path * Line 41-43: Generates and returns a list() of filename::pathname pairs

##(2) load_data() * Line 57-61: Retrieves file names and paths from setup_files() and defines the specific data sets of interest (by file name) * Line 66-79: For each data set of interest, data file is read-in using read.table() according to file path obtained via list defined by setup_files() * Line 75: The data.frame read is saved to a name::value list object according to dataset_name::data.frame * Line 77-78: Time status report is printed for read-in of dataset * Line 81-83: Total load time is reported and returns the dataset_name::data.frame list

##(3) combine_data() * Line 98-99: Column names for unified dataset are defined (combining subject_id,activity_id and defined values from features dataset and a variable to distinguish training/test data) * Line 102-103: The test datasets and rain datasets are respectively combined by column using cbind according to: subject-data + x-data + y-data + custom is_train variable * Line 107-108: The respective combined datasets' column names are defined according to the previously created col_name vector * Line 111 : The two test and train datasets are merged using rbind * Line 114 : Using the activity_id column read from the y-data present in the merged dataset, the labels are associated with the dataset by adding a new activity_label column * Line 116 : The combined, named, labelled data.frame is returned.

##(4) extract_data() * Line 129 : a subset of the combined data.frame is defined to include columns "subject_id","activity_label" and all column titles partially matching "mean()" or "std()" * Line 130 : a summary dataset is defined by taking the column-wise mean of all numeric columns and grouping by the columns "subject_id","activity_label" * Line 131 : the summary dataset is returned

##(5) run() * Line 144 : extracted data is obtained from extract_data() * Line 147 : to make data more readable, they're rounded to 6 decimal places and saved to a "tidy_data" data.frame * Line 145-164: I played around with writing the output text file padded by 31 spaces (max length of longest column name) to make it more readable...Didn't do it, but left in commented-out lines... * Line 149 : File "tidy_output.txt" writes out "tidy_data" data.frame to disk using write.table * Line 150 : Function completes. ################################################################# #Data Dictionary For: tidy_output.txt #################################################################

The data file tidy_output.txt defines a space-delimited data table of 180 rows by 68 columns.

###Input data for this analysis comes from the Samsung activity study:

Human Activity Recognition Using Smartphones Dataset Version 1.0

Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws

Additional detail on the study data parameters may be found at this URL: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

In summary, the Samsung study records acceleration and gyroscopic movement data obtained from a Samsung Galaxy S II device while subjects are requested to perform various physical activity (such as walking, sitting, standing). Acceleration and position are thus measured in space and recorded in XYZ coordinates.

This study calculates the mean of variables specified in the Human Activity Recognition Using Smartphones study as specified below.

The data dictionary for this table is as follows.

###Variables 3-68 are the calculated mean for given variable as defined in the Human Activity Recognition Using Smartphones Dataset grouped by subject id, by activity label

##DATA DICTIONARY Col Variable Name Type Values Units ===================================================================== 1 subject_id numeric 0..30 Activity Participant ID 2 activity_label string Defined Category: Activity Category . LAYING Catigorical Unit . SITTING Catigorical Unit . STANDING Catigorical Unit . WALKING Catigorical Unit . WALKING_UPSTAIRS Catigorical Unit . WALKING_DOWNSTAIRS Catigorical Unit 3 1-tBodyAcc-mean()-X numeric -1.000000..1.000000 Gravitational Units (g) 4 2-tBodyAcc-mean()-Y numeric -1.000000..1.000000 Gravitational Units (g) 5 3-tBodyAcc-mean()-Z numeric -1.000000..1.000000 Gravitational Units (g) 6 4-tBodyAcc-std()-X numeric -1.000000..1.000000 Gravitational Units (g) 7 5-tBodyAcc-std()-Y numeric -1.000000..1.000000 Gravitational Units (g) 8 6-tBodyAcc-std()-Z numeric -1.000000..1.000000 Gravitational Units (g) 9 41-tGravityAcc-mean()-X numeric -1.000000..1.000000 Gravitational Units (g) 10 42-tGravityAcc-mean()-Y numeric -1.000000..1.000000 Gravitational Units (g) 11 43-tGravityAcc-mean()-Z numeric -1.000000..1.000000 Gravitational Units (g) 12 44-tGravityAcc-std()-X numeric -1.000000..1.000000 Gravitational Units (g) 13 45-tGravityAcc-std()-Y numeric -1.000000..1.000000 Gravitational Units (g) 14 46-tGravityAcc-std()-Z numeric -1.000000..1.000000 Gravitational Units (g) 15 81-tBodyAccJerk-mean()-X numeric -1.000000..1.000000 Gravitational Units (g) 16 82-tBodyAccJerk-mean()-Y numeric -1.000000..1.000000 Gravitational Units (g) 17 83-tBodyAccJerk-mean()-Z numeric -1.000000..1.000000 Gravitational Units (g) 18 84-tBodyAccJerk-std()-X numeric -1.000000..1.000000 Gravitational Units (g) 19 85-tBodyAccJerk-std()-Y numeric -1.000000..1.000000 Gravitational Units (g) 20 86-tBodyAccJerk-std()-Z numeric -1.000000..1.000000 Gravitational Units (g) 21 121-tBodyGyro-mean()-X numeric -1.000000..1.000000 Angular Velocity (radians/second) 22 122-tBodyGyro-mean()-Y numeric -1.000000..1.000000 Angular Velocity (radians/second) 23 123-tBodyGyro-mean()-Z numeric -1.000000..1.000000 Angular Velocity (radians/second) 24 124-tBodyGyro-std()-X numeric -1.000000..1.000000 Angular Velocity (radians/second) 25 125-tBodyGyro-std()-Y numeric -1.000000..1.000000 Angular Velocity (radians/second) 26 126-tBodyGyro-std()-Z numeric -1.000000..1.000000 Angular Velocity (radians/second) 27 161-tBodyGyroJerk-mean()-X numeric -1.000000..1.000000 Angular Velocity (radians/second) 28 162-tBodyGyroJerk-mean()-Y numeric -1.000000..1.000000 Angular Velocity (radians/second) 29 163-tBodyGyroJerk-mean()-Z numeric -1.000000..1.000000 Angular Velocity (radians/second) 30 164-tBodyGyroJerk-std()-X numeric -1.000000..1.000000 Angular Velocity (radians/second) 31 165-tBodyGyroJerk-std()-Y numeric -1.000000..1.000000 Angular Velocity (radians/second) 32 166-tBodyGyroJerk-std()-Z numeric -1.000000..1.000000 Angular Velocity (radians/second) 33 201-tBodyAccMag-mean() numeric -1.000000..1.000000 Gravitational Units (g) 34 202-tBodyAccMag-std() numeric -1.000000..1.000000 Gravitational Units (g) 35 214-tGravityAccMag-mean() numeric -1.000000..1.000000 Gravitational Units (g) 36 215-tGravityAccMag-std() numeric -1.000000..1.000000 Gravitational Units (g) 37 227-tBodyAccJerkMag-mean() numeric -1.000000..1.000000 Gravitational Units (g) 38 228-tBodyAccJerkMag-std() numeric -1.000000..1.000000 Gravitational Units (g) 39 240-tBodyGyroMag-mean() numeric -1.000000..1.000000 Angular Velocity (radians/second) 40 241-tBodyGyroMag-std() numeric -1.000000..1.000000 Angular Velocity (radians/second) 41 253-tBodyGyroJerkMag-mean() numeric -1.000000..1.000000 Angular Velocity (radians/second) 42 254-tBodyGyroJerkMag-std() numeric -1.000000..1.000000 Angular Velocity (radians/second) 43 266-fBodyAcc-mean()-X numeric -1.000000..1.000000 Gravitational Units (g) 44 267-fBodyAcc-mean()-Y numeric -1.000000..1.000000 Gravitational Units (g) 45 268-fBodyAcc-mean()-Z numeric -1.000000..1.000000 Gravitational Units (g) 46 269-fBodyAcc-std()-X numeric -1.000000..1.000000 Gravitational Units (g) 47 270-fBodyAcc-std()-Y numeric -1.000000..1.000000 Gravitational Units (g) 48 271-fBodyAcc-std()-Z numeric -1.000000..1.000000 Gravitational Units (g) 49 345-fBodyAccJerk-mean()-X numeric -1.000000..1.000000 Gravitational Units (g) 50 346-fBodyAccJerk-mean()-Y numeric -1.000000..1.000000 Gravitational Units (g) 51 347-fBodyAccJerk-mean()-Z numeric -1.000000..1.000000 Gravitational Units (g) 52 348-fBodyAccJerk-std()-X numeric -1.000000..1.000000 Gravitational Units (g) 53 349-fBodyAccJerk-std()-Y numeric -1.000000..1.000000 Gravitational Units (g) 54 350-fBodyAccJerk-std()-Z numeric -1.000000..1.000000 Gravitational Units (g) 55 424-fBodyGyro-mean()-X numeric -1.000000..1.000000 Angular Velocity (radians/second) 56 425-fBodyGyro-mean()-Y numeric -1.000000..1.000000 Angular Velocity (radians/second) 57 426-fBodyGyro-mean()-Z numeric -1.000000..1.000000 Angular Velocity (radians/second) 58 427-fBodyGyro-std()-X numeric -1.000000..1.000000 Angular Velocity (radians/second) 59 428-fBodyGyro-std()-Y numeric -1.000000..1.000000 Angular Velocity (radians/second) 60 429-fBodyGyro-std()-Z numeric -1.000000..1.000000 Angular Velocity (radians/second) 61 503-fBodyAccMag-mean() numeric -1.000000..1.000000 Gravitational Units (g) 62 504-fBodyAccMag-std() numeric -1.000000..1.000000 Gravitational Units (g) 63 516-fBodyBodyAccJerkMag-mean() numeric -1.000000..1.000000 Gravitational Units (g) 64 517-fBodyBodyAccJerkMag-std() numeric -1.000000..1.000000 Gravitational Units (g) 65 529-fBodyBodyGyroMag-mean() numeric -1.000000..1.000000 Angular Velocity (radians/second) 66 530-fBodyBodyGyroMag-std() numeric -1.000000..1.000000 Angular Velocity (radians/second) 67 542-fBodyBodyGyroJerkMag-mean() numeric -1.000000..1.000000 Angular Velocity (radians/second) 68 543-fBodyBodyGyroJerkMag-std() numeric -1.000000..1.000000 Angular Velocity (radians/second)