Getting and Cleaning Data Project
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.
Files list:
tidyData.csv data set with the average of each variable for each activity and each subject
README.MD: Explain what the analysis files did
CodeBook.MD : List all the variables and summaries you calculated, along with units, and any other relevant information
run_analysis.R: The script to be run to get the intended results
Solution: Merged the training and the test sets to create one data set. Extracted only the measurements on the mean and standard deviation for each measurement. Used descriptive activity names to name the activities in the data set and appropriately labeled the data set with descriptive variable names. Created a tidy data set, tidyData.csv with the average of each variable for each activity and each subject
Usage
Clone this repository: with git clone https://github.com/michelemostarda/cousera-getting-cleaning.git
Move in repo dir
Load run_analysis.R in your R environment
Run function runAnalysis()
It will
download the dataset archive
unzip it
process it and (re-)generate the tidy.csv file (also included in commit).
For further details please refer the source code itself.