• README.md - this file
• run_analysis.R - R script for cleaning "UCI HAR Dataset"
• codebook.md - codebook that describes dataset that is created for step 5

• Place 'run_analysis.R' script at same level as "UCI HAR Dataset"
• Use setwd() command to set your working directory to script location. (example: setwd("~/R/getdata-012/course_project"))
• source("run_analysis.R")
• "tidy_data" folder will be created, which will contain "step_5_tidy_data.txt"
• "step_5_tidy_data.txt" was the dataset submitted on Coursera for part 1

How the Samsung dataset "UCI HAR Dataset" gets converted to a tidy dataset 
as specified in Step 5 of the course project instructions. 

• 1. Import feature.txt. Extract feature names with only mean() or std() for each measurement
• 2. Import X data. Subset X for mean & std data
• 3. Import subject data
• 4. Import y data
• 5. Import activity labels for y data
• 6. Combine tables together into mean & std tables (data still messy)
• 7. Convert mean & std tables to tidy data
• 8. Merge to combine std data into mean table
• 9. Check for merging errors
• 10. Replace activity_id with descriptive activity name
• 11. Creating second tidy data set, with the average of each variable for each activity and each subject
• 12. Save the tidy data sets to .txt files

Use of this dataset in publications must be acknowledged by referencing the following publication [1]

• [1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012