This repository is a Olayinka submission for Getting and Cleaning Data course project. It has the instructions on how to run analysis on Human Activity recognition dataset.
The purpose of this project is to demonstrate the ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. In more details, the following steps are required to complete the assignment: 1) a tidy data set as described below; 2) a link to a Github repository with your script for performing the analysis; 3) a Code book that describes the variables, the data, and any transformations performed to clean up the data called.
The data considered for the Project represent data collected from the accelerometers from the Samsung Galaxy S smartphone.
A full description is available at the web site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones.
The whole data package can be downloaded at the following link: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
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CodeBook.mda code book that describes the variables, the data, and any transformations or work that I performed to clean up the data -
run_analysis.Rperforms the data preparation and then followed by the 5 steps required as described in the course project's definition:- 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.
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ProjectCleanedData.txtis the exported final data after going through all the sequences described above.