Getting and Cleaning Data Course Project

With this project I try to demonstrates my ability to collect, work with, and clean a data set that can be used for later analysis.

This project contains an R script called run_analysis.R and the CodeBook.md

run_analysis.R

A cleanup script that calculates means per subject's activity of the mean and standard deviation of the Human Activity Recognition Using Smartphones. This dataset should be present in the R file directory.

Once executed, the resulting dataset will be called tidy_data_set.txt.txt It contains one row for each subject/activity pair and columns for subject, activity, and each feature that is a mean or standard deviation of the original dataset.

Steps

• Read the list of subjects. *For both the train and test data sets, produces a temporary data set: Extract the mean and standard deviation features (listed in CodeBook.md in 'Extracted Features').
• Read the list of activities.
• Set the activity labels (not ids) into the values table.
• Set the subject id into the values table.
• Merges the traing and test temporary data sets.
• Set each variable on its own row.
• Metls the entire table, keying on subject/acitivity pairs, applying the mean function to each vector of values in each subject/activity pair. This is the clean dataset.
• Write the clean dataset to disk.

CodeBook.md

The code book describes the variables, the data, and any transformations or work performed to clean up the data

Data References

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 [email protected]