getdata013_project

Getting and Cleanning Data (Apr 6 -Mar 3, 2015)

Excerpt from the project's description:

One of the most exciting areas in all of data science right now is wearable computing. 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: link

Here are the data for the project: link

This is a repo submitted for the course's peer assessments, and contains:

CodeBook.md, a code book that describes the variables, the data, and any transformations or work that I performed to clean up the data.

run_analysis.R, R script that does the following:

1. Merges the training set and the test set (with subjects' identifier).

   • UCI HAR Dataset//train//X_train.txt 7352 obs. (windows) of 561 vars (features estimated from signals)

   • UCI HAR Dataset//train//y_train.txt 7352 obs. of 1 var

   • UCI HAR Dataset//test//X_test.txt 2947 obs. of 561 vars

   • UCI HAR Dataset//test//y_test.txt 2947 obs. of 1 var

2. Extracts only the measurements on the mean and standard deviation for each measurement.

   • variables with names contain mean() or std()`

3. Uses descriptive activity names to name the activities in the data set.

   • UCI HAR Dataset//activity_labels.txt

4. Appropriately labels the data set with descriptive variable names.

   • UCI HAR Dataset//featurs.txt

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.