1. README.md
2. run_analysis.R
3. TidySamsungGalaxyData.txt
4. CodeBook.md

The purpose of this course project was to get and clean a data set that was generated by collecting data from the accelerometers and gyroscopes from the Samsung Galaxy S smartphone. The original source of the smartphone data is the UCI Machine Learning Repository. A detailed description of the original data set and its components can be found in CodeBook.md

In brief, the goals of this project as outlined in the Course website's project description, were to

1. Merge the training and test sets of the machine learning data set into one data set containing the measurements for all 30 subjects.
2. Select only the measurements on the mean and standard deviation for each measurement. Details on how these measurements were defined for selection can be found in CodeBook.md
3. Descriptively label the activities that each subject was engaged in while their mesaurements were being taken by their phone's gyroscope and accelerometer.
4. Give the variable names descriptive labels.
5. Generate a tidy set with the mean of each selected in which the variable is aggregated by mean for each subject and each activity.

The developer of run_analysis.R used
R version 3.0.3 (2014-03-06) -- "Warm Puppy"
Platform: x86_64-apple-darwin10.8.0 (64-bit)

run_analysis.R loads the package, reshape2, so you will need to have downloaded reshape2 prior to running the script.

run_analysis.R, can be run to accomplish each of the goals outlined in the introduction above. It accomplishes the goals in a series of eight steps.

1. It is not assumed that the needed data sets from the UCI machine learning repositiory are in your working directory. Therefore, in the first step, a directory, UCI_HAR_Data, is created to store the data. The data is downloaded, unzipped, and saved to UCI_HAR_Data.
2. Merge the training and test data sets
3. Select only the measurements on the mean and standard deviation for each measurement. Details on how these measurements were defined for selection can be found in CodeBook.md
4. Order merged data by subject ID number and activity label
5. "Melt Data" as described by Hadley Wickham in his paper "Reshaping Data with reshape Package", Journal of Statistical Software, Nov. 2007, Volume 21, Issue 12.
6. Reshape and aggregate data using mean() function
7. Label the data set with descriptive variable names
8. Write tidy data set to .txt file. Space selected as delimiter.

Detailed comments on how each of these steps is carried out can be found in run_analysis.R

This script produces three data frames.

1. mergedSet: This frame is formed when the training and test sets are merged. The dimensions of this set are 10299 x 68
2. fullMelt: This is a tidy data set formed when MergedSet is "melted" using the melt function from the reshape2 package. fullMelt has 3 identifier (ID) variables (subjectID, activityLabel, and variable) and one measured variable. Each row represents one observation of one variable. The dimensions of this set are 679734 X 4
3. TidyData: This data has been aggregated by mean and reshaped. Variable names have been cleaned and rendered more descriptive. The dimensions of this set are 180 x 68.

run_analysis.R writes TidyData to a space delimited .txt file with the command
write.table(TidyData,"TidySamsungGalaxyData.txt",sep = " ",row.names = FALSE)

In addition to including TidySamsungGalaxyData.txt in this respository, it has been uploaded to the Getting and Cleaning Data course website for evaluation. Rather than evaluating its tidiness using the URI provided by the course website, you may want to veiw it in this repository or load as an R data frame using the command
TidyData <- read.table("TidySamsungGalaxyData.txt", header = TRUE)

CodeBook.md is a code book that modifies and updates the code books that were donloaded along with the Samsung Galaxy data sets. Variables and summaries calculated are described in detail. Units, and other relevant information are also provided.