This repo contains files necessary for passing the Getting and Cleaning Data module at Coursera.org
The original dataset downloaded from https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip (see full description here) contains data collected from the accelerometers from the Samsung Galaxy S smartphone that were processed and assigned to 6 activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) the test subjects were performing while wearing the smartphone.
The archive contains several files into which the dataset is fragmented. See more info in the file CodeBook.md.
- Merge the training and the test sets to create one data set.
- Extract only the measurements on the mean and standard deviation for each measurement.
- Use descriptive activity names to name the activities in the data set
- Appropriately label the data set with descriptive activity names.
- Create a second, independent tidy data set with the average of each variable for each activity and each subject.
- run_analysis.R: the main script performing the steps listed above
- averages.txt: tidy dataset generated by run_analysis.R
- CodeBook.md: markdown file describing the variables, the data, and any transformations or work that were performed to clean up the data
- README.md [this file]: markdown file with the basic overview of this repo's content and the assignment
[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