# Getting-and-Cleaning-Data This repository stores the code and codebook for the "Getting and Cleaning Data" course project The course project: One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Here are the data for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip You should create one R script called run_analysis.R that does the following. * Merges the training and the test sets to create one data set. * Extracts only the measurements on the mean and standard deviation for each measurement. * Uses descriptive activity names to name the activities in the data set * Appropriately labels the data set with descriptive variable names. * From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject. The run_analysis.R script cleans up the data by: 1. Merging the training dataset namely X_train, y_train, subject_train to their corresponding X_test, y_test, subject_test in the test datasets to create one seamless data set. Thus X_train.txt is merged with X_test.txt y_train.txt is merged with y_test.txt subject_train.txt is merged with subject-test.txt using the rbind() function 2. Extracting the measurements on the mean and standard deviation for each measurement from the features.txt 3. Applying the appropriate descriptive activity names extracted from the activity_labels.txt dataset to the respective activities in the activities dataset. The labels are converted to lower case while underscores and paranthesis are removed The descriptive names are: * walking * walkingupstairs * walkingdownstairs * sitting * standing * laying 4. Labelling the dataset with the appropriate descriptive names and then merging all 3 datasets namely Subject_data, Y_data and X_data into a single data frame. 5. Creating a 2nd, independent tidy data set with the average of each measurement for each activity and each subject. The resultant data is save as dataset_averages using the write.table() function with row.name=FALSE option.