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For Coursera Course Project
--- title: "Human Activity Recognition Using Smartphones" output: html_document --- ## Project Introduction One of the most exciting areas in all of data science right now is wearable computing - see for example [this article](http://www.insideactivitytracking.com/data-science-activity-tracking-and-the-battle-for-the-worlds-top-sports-brand/). 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 UCI [Human Activity Recognition Using Smartphones Data Set](http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones). ## Project Requirement 1. Merges the training and the test sets to create one data set. 2. Extracts only the measurements on the mean and standard deviation for each measurement. 3. Uses descriptive activity names to name the activities in the data set 4. Appropriately labels the data set with descriptive variable names. 5. 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. ## R code R code could be find via [run_analysis.R](run_analysis.R) file. ## Data CodeBook The [codebook](CodeBook.Rmd) available in this repo as well, which is to explain how to finish the Proejct Requirement above-mentioned.
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