The purpose of this project is to demonstrate your ability to collect, work
with, and clean a data set. The goal is to prepare tidy data that can be used
for later analysis. You will be graded by your peers on a series of yes/no
questions related to the project. You will be required to submit: 1) a tidy
data set as described below, 2) a link to a Github repository with your script
for performing the analysis, and 3) a code book that describes the variables,
the data, and any transformations or work that you performed to clean up the
data called CodeBook.md
. You should also include a README.md
in the repo
with your scripts. This repo explains how all of the scripts work and how they
are connected.
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.
- Creates a second, independent tidy data set with the average of each variable for each activity and each subject.
Good luck!
A code book that describes the variables, the data, and any
transformations or work that you performed to clean up the data called
CodeBook.md
.
You should also include a README.md
in the repo with your
scripts. This repo explains how all of the scripts work and how they are
connected.
- Read data sets and combine them
- Read subjects and combine them
- Read data labels and combine them
- Read features list
- Subset only only std and mean features from list
- Perform same subset on data set
- Rename features to be more readable names
- Read activity list
- Rename activities to be more readable names
- Rename data labels with activity name
- Merge data, subjects, and labels to single data set
- Write data set to file
- Average of measurement for activity and subject
- For each activity in a subject, get the full list of measurements
- Calculate the mean of each of these activities
- Place the means in subsequent columns of the subject/activity row
- Write data set to file