jschelbert / getting-and-cleaning-data-course-project

Getting and cleaning data - Course project

This repository contains all files which are associated with the assignment of week 4 within the Coursera course "Getting and cleaning data". This document explains the files wihin the repository and gives some additional information.

Task

The overall goal of the assignment is to show that the student is able to handle data, describe his approach when manipulating data and obtaining a tidy data set from potential untidy data sources. For this a analysis script was written to obtain a tidy data set from a real world data set on

The script run_analysis.R should achieve the following (taken from the assignment instructions):

1. 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.

Files

The following files are contained in the repository:

• CodeBook.md contains information about the variables, processing steps and final outcome.
• README.md is this very file you are reading at the moment. It (hopefully) explains all files and the background.
• run_analysis.R is the main working horse. This file does the reading of the supplied data and processing to get the tidy data set.
• tidydata.txt is the product of the script run_analysis.R and contains the tidy data set. More precisely it has for every subject and activity the mean of all variables in the original data set that contain "mean" or "std" (standard deviation). You can read the file by tidy_data <- read.table("tidydata.txt").

Prerequisites

In order to run the script, you need the data set extracted to the root of the repository. In addition, you'll also need the following libraries installed, as they are used in the script:

• dplyr
• data.table
• assertthat

Execution of the analysis

Just set the working directory to the root folder of the repository, source the script via source(run_analysis.R) and then run it by tidydata <- run_analysis(). This will also generate the file tidydata.txt.