Getting and Cleaning Data - Assignment

Summary

This project focuses on obtaining data, using R to clean the data, and finally creating a text file of the tidy data.

Overview

Data Summary

The x variable entries in the train and test data sets are measurements of human activity (such as walking_upstairs, walking_downstains, sitting, etc.). These measurements were recorded using a waist-mounted smartphone from 30 subjects. The y variable entries in the train and test data sets are the activities the subjects performed.

R Script

To obtain the desired output, it requires downloading the following R Project and running the following R script:

• Coursera - R Training.Rproj
• run_analysis.R

Human Activity Recognition Data

The following reports are required to run the script:

• X_test.txt
• Y_test.txt
• subject_test.txt
• X_train.txt
• Y_train.txt
• subject_train.txt

Note: reports should be saved in the "raw" directory within the "getting_and_cleaning_data" directory.

Expected Outputs

Files

The following .txt file is created from running the code:

• tidydata_full.txt

Requirements

Software

Software required to generate necessary outputs for DSD deliverable:

• R v 3.5+
• R Packages
  • dplyr
  • tidyr

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Detailed Process

Preparing for the Script Run

1. Download the Coursera - R Training.zip file and unzip it to any directory on your system.
2. Download the data from the "Getting and Cleaning Data" project summary
3. Unzip the data, placing the extracted file directory into the "raw" directory within the "getting_and_cleaning_data" directory.
4. Open the Coursera - R Training.Rproj project and the run_analysis.R file
5. Ensure required packages above are installed prior to running the script.
6. Run the script.

Code Explanation

Once the data gets read into the environment, the script does the following:

1. Merges train and test data with respect to the variable (x or y) by using rbind command.
2. Subsets the data by extracting only the entries that contain "mean" or "std" by using the grepl command.
3. Renames the columns of the data tables using appropriate labels.
4. Creates tidy data set and writes it to a .txt file in the "finished" directory.